start-ver=1.4 cd-journal=joma no-vol=82 cd-vols= no-issue=10 article-no= start-page=26-1707 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2026 dt-pub=2026 dt-online= en-article= kn-article= en-subject= kn-subject= en-title=A Bayesian Approach to ADC-to-Dose Conversion in Radiochromic Film Dosimetry kn-title=ベイズ推定を用いたラジオクロミックフィルムのADC値–線量変換法 en-subtitle= kn-subtitle= en-abstract=Purpose: Radiochromic film is widely used for patient-specific intensity-modulated radiation therapy quality assurance (IMRT QA) because of its high spatial resolution. However, calibration for converting pixel values to dose requires irradiation at multiple known dose levels, resulting in a substantial workload. Consequently, in some institutions, calibration is performed only under limited circumstances, such as when the film lot changes. This study aimed to develop and validate a Bayesian inference model that estimates the calibration curve using previously acquired calibration datasets together with the unirradiated pixel value of the target film, thereby improving the efficiency of the calibration process. Methods: A total of 93 calibration datasets acquired using TomoHD were analyzed. A quadratic polynomial regression model was constructed with dose and elapsed time from irradiation to scanning as explanatory variables. Separate models were developed for each scanner orientation (portrait and landscape), and the intercept was estimated using the unirradiated pixel value of the target film. Bayesian inference was performed using Stan, and model convergence was evaluated by trace plots and the Rhat statistic. Model performance was validated using 10 EBT4 films from a different lot by comparing predicted and measured pixel values and evaluating dose errors. Results: All models showed good convergence, with Rhat values below 1.01. For the validation films, the pixel value error was less than 3.6%, and the coefficient of determination (R2) exceeded 0.99. The mean dose error was 2.7±1.3 cGy at 24.0 cGy and 37.5±12.8 cGy at 868.2 cGy. The maximum dose error was 66.6 cGy at 710.6 cGy. Conclusion: The proposed method demonstrated the feasibility of estimating the calibration curve using previously acquired calibration datasets and the unirradiated pixel value of the target film. This approach has the potential to improve the efficiency of radiochromic film calibration. Future studies should apply this method to patient-specific IMRT QA and investigate its impact on dose distribution evaluation. kn-abstract=【目的】ラジオクロミックフィルムは高い空間分解能を有することから，患者別IMRT品質保証（IMRT QA）に広く利用されている．一方，ピクセル値を線量へ変換するためのキャリブレーションには，複数の既知線量で照射したフィルムが必要であり，作業負担が大きいことから，施設によってはフィルムロット変更時などに限定して実施される場合がある．本研究では，過去のキャリブレーションデータセットと対象フィルムの未照射時ピクセル値を用いてキャリブレーション曲線を推定するベイズ推定モデルを構築し，キャリブレーション作業の効率化を目的としてその有用性を検証した．【方法】TomoHDを用いて取得した93組のキャリブレーションデータセットを解析対象とした．線量および照射からスキャンまでの経過時間を説明変数とする二次多項式回帰モデルを構築した．モデルはスキャナの読み取り方向（縦方向・横方向）ごとに作成し，対象フィルムの未照射時ピクセル値を切片項として推定した．ベイズ推定にはStanを用い，トレースプロットおよびRhat統計量により収束性を評価した．更にモデル性能の評価には，異なるロットのEBT4フィルム10枚を用いて予測ピクセル値と実測値を比較するとともに，線量誤差を評価した．【結果】すべてのモデルでRhatは1.01未満を示し，良好な収束性が確認された．検証用フィルムでは，ピクセル値の誤差は3.6%未満，決定係数（R2）は0.99以上であった．また，線量誤差は24 cGyで平均2.7 cGy（SD 1.3），868.2 cGyで平均37.5 cGy（SD 12.8）であり，最大線量誤差は710.6 cGyにおいて66.6 cGyであった．【結語】本手法は，過去のキャリブレーションデータセットと対象フィルムの未照射時ピクセル値を用いてキャリブレーション曲線を推定できる可能性を示した．また，ラジオクロミックフィルムのキャリブレーション作業の効率化に寄与する可能性が示唆された．今後は患者別IMRT QAに適用し，線量分布評価への影響を検証する必要がある． en-copyright= kn-copyright= en-aut-name=TanimotoYuki en-aut-sei=Tanimoto en-aut-mei=Yuki kn-aut-name=谷本 祐樹 kn-aut-sei=谷本 kn-aut-mei=祐樹 aut-affil-num=1 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name=杉本 昂平 kn-aut-sei=杉本 kn-aut-mei= 昂平 aut-affil-num=2 ORCID= en-aut-name=TamoriMasahide en-aut-sei=Tamori en-aut-mei=Masahide kn-aut-name=田盛 雅英 kn-aut-sei=田盛 kn-aut-mei=雅英 aut-affil-num=3 ORCID= en-aut-name=YatsukiMiho en-aut-sei=Yatsuki en-aut-mei=Miho kn-aut-name=八木 美保 kn-aut-sei=八木 kn-aut-mei=美保 aut-affil-num=4 ORCID= en-aut-name=YoshidaShohei en-aut-sei=Yoshida en-aut-mei=Shohei kn-aut-name=吉田 昌平 kn-aut-sei=吉田 kn-aut-mei= 昌平 aut-affil-num=5 ORCID= en-aut-name=SugaharaKazuma en-aut-sei=Sugahara en-aut-mei=Kazuma kn-aut-name=菅原 一真 kn-aut-sei=菅原 kn-aut-mei=一真 aut-affil-num=6 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name=笈田 将皇 kn-aut-sei=笈田 kn-aut-mei=将皇 aut-affil-num=7 ORCID= affil-num=1 en-affil=Department of Radiology, NHO Iwakuni Clinical Center kn-affil=NHO岩国医療センター放射線科 affil-num=2 en-affil=Department of Radiological Technology, Faculty of Health Science and Technology, Kawasaki University of Medical Welfare kn-affil=川崎医療福祉大学医療技術学部診療放射線技術学科 affil-num=3 en-affil=Department of Radiology, NHO Kure Medical Center and Chugoku Cancer Center kn-affil=NHO呉医療センター・中国がんセンター中央放射線センター affil-num=4 en-affil=Department of Radiology, NHO Kure Medical Center and Chugoku Cancer Center kn-affil=NHO呉医療センター・中国がんセンター中央放射線センター affil-num=5 en-affil=Department of Radiology, NHO Kure Medical Center and Chugoku Cancer Center kn-affil=NHO呉医療センター・中国がんセンター中央放射線センター affil-num=6 en-affil=Department of Radiology, NHO Kure Medical Center and Chugoku Cancer Center kn-affil=NHO呉医療センター・中国がんセンター中央放射線センター affil-num=7 en-affil=Faculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil=岡山大学学術研究院ヘルスシステム統合科学学域 en-keyword=radiation therapy kn-keyword=radiation therapy en-keyword=quality assurance kn-keyword=quality assurance en-keyword=patient-specific IMRT QA kn-keyword=patient-specific IMRT QA en-keyword=radiochromic film kn-keyword=radiochromic film en-keyword=Bayesian inference kn-keyword=Bayesian inference END start-ver=1.4 cd-journal=joma no-vol=6 cd-vols= no-issue= article-no= start-page=49 end-page=57 dt-received= dt-revised= dt-accepted= dt-pub-year=2026 dt-pub=20260331 dt-online= en-article= kn-article= en-subject= kn-subject= en-title=Advanced Hospital Training Activities in Fiscal 2025 kn-title=2025 年度における「先進病院実習」の取り組み en-subtitle= kn-subtitle= en-abstract= kn-abstract= en-copyright= kn-copyright= en-aut-name=MORITAMizuki en-aut-sei=MORITA en-aut-mei=Mizuki kn-aut-name=森田瑞樹 kn-aut-sei=森田 kn-aut-mei=瑞樹 aut-affil-num=1 ORCID= en-aut-name=MAGARIMasaki en-aut-sei=MAGARI en-aut-mei=Masaki kn-aut-name=曲正樹 kn-aut-sei=曲 kn-aut-mei=正樹 aut-affil-num=2 ORCID= en-aut-name=WangJin en-aut-sei=Wang en-aut-mei=Jin kn-aut-name=王璡 kn-aut-sei=王 kn-aut-mei=璡 aut-affil-num=3 ORCID= en-aut-name=WATANABEToyohiko en-aut-sei=WATANABE en-aut-mei=Toyohiko kn-aut-name=渡邉豊彦 kn-aut-sei=渡邉 kn-aut-mei=豊彦 aut-affil-num=4 ORCID= en-aut-name=OITAMasataka en-aut-sei=OITA en-aut-mei=Masataka kn-aut-name=笈田将皇 kn-aut-sei=笈田 kn-aut-mei=将皇 aut-affil-num=5 ORCID= en-aut-name=HARADANahoko en-aut-sei=HARADA en-aut-mei=Nahoko kn-aut-name=原田奈穂子 kn-aut-sei=原田 kn-aut-mei=奈穂子 aut-affil-num=6 ORCID= en-aut-name=SHISHIDOKeisuke en-aut-sei=SHISHIDO en-aut-mei=Keisuke kn-aut-name=宍戸圭介 kn-aut-sei=宍戸 kn-aut-mei=圭介 aut-affil-num=7 ORCID= affil-num=1 en-affil=Faculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil=岡山大学学術研究院ヘルスシステム統合科学学域 affil-num=2 en-affil=Faculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil=岡山大学学術研究院ヘルスシステム統合科学学域 affil-num=3 en-affil=Faculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil=岡山大学学術研究院ヘルスシステム統合科学学域 affil-num=4 en-affil=Faculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil=岡山大学学術研究院ヘルスシステム統合科学学域 affil-num=5 en-affil=Faculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil=岡山大学学術研究院ヘルスシステム統合科学学域 affil-num=6 en-affil=Faculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil=岡山大学学術研究院ヘルスシステム統合科学学域 affil-num=7 en-affil=Faculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil=岡山大学学術研究院ヘルスシステム統合科学学域 END start-ver=1.4 cd-journal=joma no-vol=140 cd-vols= no-issue= article-no= start-page=105684 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2025 dt-pub=202512 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Improvements of lateral penumbra at various depth regions in proton pencil beam scanning with a multileaf collimator: Dose verifications and plan comparisons en-subtitle= kn-subtitle= en-abstract= kn-abstract=Purpose: In scanned proton therapy, the current consensus is that the effective range of the collimator’s contribution to lateral penumbra improvement is up to approximately 150 mm depth. We characterized the penumbra variations for scanned proton beams with or without a new type of multileaf collimator (MLC) under various air gaps, depth, and with or without range shifter (RS).
Methods: Eighty-six uniform dose plans were created (38 RS-negative and 48 RS-positive plans) for nine box targets of 60 × 60 × 54 mm3 at 0–280 mm depths in water. They were created with or without MLC, with 50–300 mm air gaps. The penumbra and average doses of MLC-positive and MLC-negative plans at the organs at risk (OAR) region of each box plan were compared. Besides, several plan doses were validated by measurements with penumbra (with an average of 80–20 % dose point widths for both side profiles) differences and 2D gamma analysis.
Results: The MLC-positive plans reduced the penumbra and mean OAR doses by 1.0–5.1 mm and 3.3–13.5 %, respectively, compared to MLC-negative plans even at >150 mm depths. The penumbra differences in measurements were <±1.5 mm for all plans. The mean gamma scores at 2 %/2 mm were 97.9 ± 2.3 % and 97.4 ± 3.1 % for the MLC-negative and MLC-positive plans, respectively.
Conclusions: The MLC-positive beams improved the penumbra and reduced the OAR dose in every depth region and air gap. We have shown that PBS with MLCs can be useful at more than 150 mm regions, depending on the machine. en-copyright= kn-copyright= en-aut-name=TominagaYuki en-aut-sei=Tominaga en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=WakisakaYushi en-aut-sei=Wakisaka en-aut-mei=Yushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KatoTakahiro en-aut-sei=Kato en-aut-mei=Takahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=YasuiKeisuke en-aut-sei=Yasui en-aut-mei=Keisuke kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=KatoRyohei en-aut-sei=Kato en-aut-mei=Ryohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=IchiharaMasaya en-aut-sei=Ichihara en-aut-mei=Masaya kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=TomidaMasashi en-aut-sei=Tomida en-aut-mei=Masashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=SasakiMotoharu en-aut-sei=Sasaki en-aut-mei=Motoharu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=NishioTeiji en-aut-sei=Nishio en-aut-mei=Teiji kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= affil-num=1 en-affil=Department of Radiotherapy, Medical Co. Hakuhokai, Osaka Proton Therapy Clinic kn-affil= affil-num=2 en-affil=Department of Radiotherapy, Medical Co. Hakuhokai, Osaka Proton Therapy Clinic kn-affil= affil-num=3 en-affil=Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University kn-affil= affil-num=4 en-affil=School of Medical Sciences, Fujita Health University kn-affil= affil-num=5 en-affil=Department of Radiation Physics and Technology, Southern Tohoku Proton Therapy Center kn-affil= affil-num=6 en-affil=Medical Physics Laboratory, Division of Health Science, Graduate School of Medicine, The University of Osaka kn-affil= affil-num=7 en-affil=Department of Proton Beam Technology Room, Narita Memorial Proton Center kn-affil= affil-num=8 en-affil=Graduate School of Biomedical Sciences, Tokushima University kn-affil= affil-num=9 en-affil=Faculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=10 en-affil=Medical Physics Laboratory, Division of Health Science, Graduate School of Medicine, The University of Osaka kn-affil= en-keyword=Proton therapy kn-keyword=Proton therapy en-keyword=Pencil beam scanning kn-keyword=Pencil beam scanning en-keyword=Multileaf collimator kn-keyword=Multileaf collimator en-keyword=Lateral penumbra kn-keyword=Lateral penumbra en-keyword=Treatment planning kn-keyword=Treatment planning END start-ver=1.4 cd-journal=joma no-vol=52 cd-vols= no-issue=8 article-no= start-page=e18026 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2025 dt-pub=202508 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Commissioning of respiratory‐gated 4D dynamic dose calculations for various gating widths without spot timestamp in proton pencil beam scanning en-subtitle= kn-subtitle= en-abstract= kn-abstract=Background: Proton pencil beam scanning (PBS) is susceptible to dose degradation because of interplay effects on moving targets. For cases of unacceptable motion, respiratory-gated (RG) irradiation is an effective alternative to free breathing (FB) irradiation. However, the introduction of RG irradiation with larger gate widths (GW) is hindered by interplay effects, which are analogous to those observed with FB irradiation. Accurate estimation of interplay effects can be performed by recording spot timestamps. However, our machine lacks this feature, making it imperative to find an alternative approach. Thus, we developed an RG 4-dimensional dynamic dose (RG-4DDD) system without spot timestamps.
Purpose: This study aimed to investigate the accuracy of calculated doses from the RG-4DDD system for PBS plans with varying breathing curves, amplitudes, and periods for 10%–50% GW.
Methods: RG-4DDDs were reconstructed using in-house developed software that assigned timestamps to individual spots, integrated start times for spills with breathing curves, and utilized deformable registrations for dose accumulation. Three cubic verification plans were created using a heterogeneous phantom. Additionally, typical liver and lung cases were employed for patient plan validation. Single- and multi-field-optimized (SFO and IMPT) plans (ten beams in total) were created for the liver and lung cases in a homogeneous phantom. Lateral profile measurements were obtained under both motion and no-motion conditions using a 2D ionization chamber array (2D-array) and EBT3 Gafchromic films on the CIRS dynamic platform. Breathing curves from the cubic plans were used to assess nine patterns of sine curves, with amplitudes of 5.0–10.0 mm (10.0–20.0 mm target motions) and periods of 3–6 sec. Patient field verifications were conducted using a representative patient curve with an average amplitude of 6.4 mm and period of 3.2 sec. Additional simulations were performed assuming a ± 10% change in assigned timestamps for the dose rate (DR), spot spill (0.08-s), and gate time delay (0.1-s) to evaluate the effect of parameter selection on our 4DDD models. The 4DDDs were compared with measured values using the 2D gamma index and absolute doses over that required for dosing 95% of the target.
Results: The 2D-array measurements showed that average gamma scores for the reference (no motion) and 4DDD plans for all GWs were at least 99.9 ± 0.2% and 98.2 ± 2.4% at 3%/3 mm, respectively. The gamma scores of the 4DDDs in film measurements exceeded 95.4% and 92.9% at 2%/2 mm for the cubic and patient plans, respectively. The 4DDD calculations were acceptable under DR changes of ±10% and both spill and gate time delays of ±0.18 sec. For the 4DDD plan using all GWs for all measurement points, the absolute point differences for all validation plans were within ±5.0% for 99.1% of the points.
Conclusions: The RG-4DDD calculations (less than 50% GW) of the heterogeneous and actual patient plans showed good agreement with measurements for various breathing curves in the amplitudes and periods described above. The proposed system allows us to evaluate actual RG irradiation without requiring the ability to record spot timestamps. en-copyright= kn-copyright= en-aut-name=TominagaYuki en-aut-sei=Tominaga en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=WakisakaYushi en-aut-sei=Wakisaka en-aut-mei=Yushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KatoTakahiro en-aut-sei=Kato en-aut-mei=Takahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=IchiharaMasaya en-aut-sei=Ichihara en-aut-mei=Masaya kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=YasuiKeisuke en-aut-sei=Yasui en-aut-mei=Keisuke kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=SasakiMotoharu en-aut-sei=Sasaki en-aut-mei=Motoharu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=NishioTeiji en-aut-sei=Nishio en-aut-mei=Teiji kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= affil-num=1 en-affil=Department of Radiotherapy, Medical Co. Hakuhokai, Osaka Proton Therapy Clinic kn-affil= affil-num=2 en-affil=Department of Radiotherapy, Medical Co. Hakuhokai, Osaka Proton Therapy Clinic kn-affil= affil-num=3 en-affil=Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University kn-affil= affil-num=4 en-affil=Medical Physics Laboratory, Division of Health Science, Graduate School of Medicine, The University of Osaka kn-affil= affil-num=5 en-affil=School of Medical Sciences, Fujita Health University kn-affil= affil-num=6 en-affil=Graduate School of Biomedical Sciences, Tokushima University kn-affil= affil-num=7 en-affil=Faculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=8 en-affil=Medical Physics Laboratory, Division of Health Science, Graduate School of Medicine, The University of Osaka kn-affil= en-keyword=4D dynamic dose kn-keyword=4D dynamic dose en-keyword=interplay effect kn-keyword=interplay effect en-keyword=pencil beam scanning kn-keyword=pencil beam scanning en-keyword=proton therapy kn-keyword=proton therapy en-keyword=respiratory gating kn-keyword=respiratory gating END start-ver=1.4 cd-journal=joma no-vol=26 cd-vols= no-issue=6 article-no= start-page=e70119 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2025 dt-pub=20250519 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Quantitative quality control of 3D water tank using image analysis en-subtitle= kn-subtitle= en-abstract= kn-abstract=Background and objective: Accurate beam data acquisition using three-dimensional (3D) water tanks is essential for beam commissioning and quality control (QC) in clinical radiation therapy. This study introduces a novel method for quantitative QC of the system, utilizing MV images and webcam videos. The stability of the motor drive speed and the positional accuracy of the fixture were evaluated under two measurement modes: “continuous mode” and “step-by-step mode.”
Methods: A TRUFIX mounting system (PTW Freiburg Inc., Germany) was used to attach the center of the steel ball to its top, ensuring alignment with the water surface of the tank. To assess deviations from the radiation isocenter, MV images were acquired and compared with digitally reconstructed radiographs (DRRs). These evaluations were performed at different speed settings (slow, medium, and fast) using ET CT Body Marker (BRAINLAB Inc., USA) mounted on the drive unit. A webcam was utilized to capture the images, and custom-developed tracking software was employed to analyze deviations in driving speed and positional errors.
Results: The mean error of the radiation isocenter was 0.37 ± 0.09 mm. As the motor drive speed increased, the discrepancy between the set speed and the actual speed observed in the analysis also became larger. In “continuous mode,” the deviation from the displayed value was greater than that observed in “step-by-step mode.”
Conclusion: It is demonstrated that the proposed analysis method can quantitatively evaluate radiation isocenter misalignment, tank setup position deviation, and both the indicated drive speed values and their stability. At higher drive speeds, the “step-by-step mode” showed smaller deviations from the indicated values. en-copyright= kn-copyright= en-aut-name=TanimotoYuki en-aut-sei=Tanimoto en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KoshiKazunobu en-aut-sei=Koshi en-aut-mei=Kazunobu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=HiroshigeAkira en-aut-sei=Hiroshige en-aut-mei=Akira kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=YoshidaShohei en-aut-sei=Yoshida en-aut-mei=Shohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=FujitaYoshiki en-aut-sei=Fujita en-aut-mei=Yoshiki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=NakahiraAtsuki en-aut-sei=Nakahira en-aut-mei=Atsuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=NakanishiDaiki en-aut-sei=Nakanishi en-aut-mei=Daiki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=HondaHirofumi en-aut-sei=Honda en-aut-mei=Hirofumi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= affil-num=1 en-affil=Department of Radiology, NHO Kure Medical Center and Chugoku Cancer Center kn-affil= affil-num=2 en-affil=Department of Radiological Technology, Faculty of Health Science and Technology, Kawasaki University of Medical Welfare kn-affil= affil-num=3 en-affil=Department of Radiology, NHO Fukuyama Medical Center kn-affil= affil-num=4 en-affil=Department of Radiology, NHO Shikoku Cancer Center kn-affil= affil-num=5 en-affil=Department of Radiology, NHO Shikoku Cancer Center kn-affil= affil-num=6 en-affil=Department of Radiology, NHO Shikoku Cancer Center kn-affil= affil-num=7 en-affil=Department of Radiology, NHO Shikoku Cancer Center kn-affil= affil-num=8 en-affil=Division of Radiology, Department of Medical Technology, Kyushu University Hospital kn-affil= affil-num=9 en-affil=Department of Radiological Technology, Ehime University Hospital kn-affil= affil-num=10 en-affil=Department of Healthcare Science, Faculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= en-keyword=3D water tank kn-keyword=3D water tank en-keyword=drive speed stability kn-keyword=drive speed stability en-keyword=quality control kn-keyword=quality control en-keyword=radiation isocenter kn-keyword=radiation isocenter en-keyword=x-ray image analysis kn-keyword=x-ray image analysis END start-ver=1.4 cd-journal=joma no-vol=18 cd-vols= no-issue=1 article-no= start-page=78 end-page=85 dt-received= dt-revised= dt-accepted= dt-pub-year=2024 dt-pub=20241118 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Standardization of radiation therapy quality control system through mutual quality control based on failure mode and effects analysis en-subtitle= kn-subtitle= en-abstract= kn-abstract=The advancement of irradiation technology has increased the demand for quality control of radiation therapy equipment. Consequently, the number of quality control items and required personnel have also increased. However, differences in the proportion of qualified personnel to irradiation techniques have caused bias in quality control systems among institutions. To standardize the quality across institutions, researchers should conduct mutual quality control by analyzing the quality control data of one institution at another institution and comparing the results with those of their own institutions. This study uses failure mode and effects analysis (FMEA) to identify potential risks in 12 radiation therapy institutions, compares the results before and after implementation of mutual quality control, and examines the utility of mutual quality control in risk reduction. Furthermore, a cost-effectiveness factor is introduced into FMEA to evaluate the utility of mutual quality control. en-copyright= kn-copyright= en-aut-name=TanimotoYuki en-aut-sei=Tanimoto en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KoshiKazunobu en-aut-sei=Koshi en-aut-mei=Kazunobu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=IshiwakiKiyoshi en-aut-sei=Ishiwaki en-aut-mei=Kiyoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=HiramatsuFutoshi en-aut-sei=Hiramatsu en-aut-mei=Futoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=SasakiToshihisa en-aut-sei=Sasaki en-aut-mei=Toshihisa kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=IseHiroki en-aut-sei=Ise en-aut-mei=Hiroki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=MiyagawaTakashi en-aut-sei=Miyagawa en-aut-mei=Takashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=MaedaTakeshi en-aut-sei=Maeda en-aut-mei=Takeshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=OkahiraShinsuke en-aut-sei=Okahira en-aut-mei=Shinsuke kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=HamaguchiTakashi en-aut-sei=Hamaguchi en-aut-mei=Takashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=KawaguchiTatsuya en-aut-sei=Kawaguchi en-aut-mei=Tatsuya kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=FunadaNorihiro en-aut-sei=Funada en-aut-mei=Norihiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=YamamotoShuhei en-aut-sei=Yamamoto en-aut-mei=Shuhei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=HiroshigeAkira en-aut-sei=Hiroshige en-aut-mei=Akira kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=MukaiYuki en-aut-sei=Mukai en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= en-aut-name=YoshidaShohei en-aut-sei=Yoshida en-aut-mei=Shohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=17 ORCID= en-aut-name=FujitaYoshiki en-aut-sei=Fujita en-aut-mei=Yoshiki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=18 ORCID= en-aut-name=NakahiraAtsuki en-aut-sei=Nakahira en-aut-mei=Atsuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=19 ORCID= en-aut-name=HondaHirofumi en-aut-sei=Honda en-aut-mei=Hirofumi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=20 ORCID= affil-num=1 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=2 en-affil=Faculty of Interdisciplinary Science and Engineering in Health Systems, Department of Healthcare Science, Okayama University kn-affil= affil-num=3 en-affil=Department of Radiology, NHO Fukuyama Medical Center kn-affil= affil-num=4 en-affil=Department of Radiology, NHO Iwakuni Medical Center kn-affil= affil-num=5 en-affil=Department of Radiology, NHO Hamada Medical Center kn-affil= affil-num=6 en-affil=Department of Radiology, NHO Higashi-Hiroshima Medical Center kn-affil= affil-num=7 en-affil=Department of Radiology, NHO Iwakuni Medical Center kn-affil= affil-num=8 en-affil=Department of Radiology, NHO Kanmon Medical Center kn-affil= affil-num=9 en-affil=Department of Radiology, NHO Kochi National Hospital kn-affil= affil-num=10 en-affil=Department of Radiology, NHO Yamaguchi-Ube Medical Center kn-affil= affil-num=11 en-affil=Department of Radiology, NHO Okayama Medical Center kn-affil= affil-num=12 en-affil=Department of Radiology, NHO Shikoku Medical Center for Children and Adults kn-affil= affil-num=13 en-affil=Department of Radiology, NHO Hamada Medical Center kn-affil= affil-num=14 en-affil=Department of Radiology, NHO Fukuyama Medical Center kn-affil= affil-num=15 en-affil=Department of Radiology, NHO Shikoku Cancer Center kn-affil= affil-num=16 en-affil=Department of Radiology, NHO Shikoku Cancer Center kn-affil= affil-num=17 en-affil=Department of Radiology, NHO Shikoku Cancer Center kn-affil= affil-num=18 en-affil=Department of Radiology, NHO Shikoku Cancer Center kn-affil= affil-num=19 en-affil=Department of Radiology, NHO Shikoku Cancer Center kn-affil= affil-num=20 en-affil=Department of Radiological Technology, Ehime University Hospital kn-affil= en-keyword=Radiation therapy kn-keyword=Radiation therapy en-keyword=Quality control kn-keyword=Quality control en-keyword=Failure mode and effects analysis kn-keyword=Failure mode and effects analysis en-keyword=Cost-effectiveness kn-keyword=Cost-effectiveness END start-ver=1.4 cd-journal=joma no-vol=15 cd-vols= no-issue=6 article-no= start-page=668 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2025 dt-pub=20250310 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Robustness of Machine Learning Predictions for Determining Whether Deep Inspiration Breath-Hold Is Required in Breast Cancer Radiation Therapy en-subtitle= kn-subtitle= en-abstract= kn-abstract=Background/Objectives: Deep inspiration breath-hold (DIBH) is a commonly used technique to reduce the mean heart dose (MHD), which is critical for minimizing late cardiac side effects in breast cancer patients undergoing radiation therapy (RT). Although previous studies have explored the potential of machine learning (ML) to predict which patients might benefit from DIBH, none have rigorously assessed ML model performance across various MHD thresholds and parameter settings. This study aims to evaluate the robustness of ML models in predicting the need for DIBH across different clinical scenarios. Methods: Using data from 207 breast cancer patients treated with RT, we developed and tested ML models at three MHD cut-off values (240, 270, and 300 cGy), considering variations in the number of independent variables (three vs. six) and folds in the cross-validation (three, four, and five). Robustness was defined as achieving high F2 scores and low instability in predictive performance. Results: Our findings indicate that the decision tree (DT) model demonstrated consistently high robustness at 240 and 270 cGy, while the random forest model performed optimally at 300 cGy. At 240 cGy, a threshold critical to minimize late cardiac risks, the DT model exhibited stable predictive power, reducing the risk of overestimating DIBH necessity. Conclusions: These results suggest that the DT model, particularly at lower MHD thresholds, may be the most reliable for clinical applications. By providing a tool for targeted DIBH implementation, this model has the potential to enhance patient-specific treatment planning and improve clinical outcomes in RT. en-copyright= kn-copyright= en-aut-name=Al-HammadWlla E. en-aut-sei=Al-Hammad en-aut-mei=Wlla E. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=Al JamalJamal, Ghaida en-aut-sei=Al Jamal en-aut-mei=Jamal, Ghaida kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=FujikuraMamiko en-aut-sei=Fujikura en-aut-mei=Mamiko kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=KamizakiRyo en-aut-sei=Kamizaki en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=YoshidaSuzuka en-aut-sei=Yoshida en-aut-mei=Suzuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=NakamuraYoshihide en-aut-sei=Nakamura en-aut-mei=Yoshihide kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=BarhamMajd en-aut-sei=Barham en-aut-mei=Majd kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=TekikiNouha en-aut-sei=Tekiki en-aut-mei=Nouha kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=HisatomiMiki en-aut-sei=Hisatomi en-aut-mei=Miki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=AsaumiJunichi en-aut-sei=Asaumi en-aut-mei=Junichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= affil-num=1 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=2 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Department of Oral Medicine and Oral Surgery, Faculty of Dentistry, Jordan University of Science and Technology kn-affil= affil-num=4 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=5 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=6 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=7 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=8 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=9 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=10 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=11 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=12 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=13 en-affil=Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University kn-affil= affil-num=14 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=15 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=16 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= en-keyword=breast cancer kn-keyword=breast cancer en-keyword=radiation therapy kn-keyword=radiation therapy en-keyword=heart dose kn-keyword=heart dose en-keyword=cut-off value kn-keyword=cut-off value en-keyword=machine learning kn-keyword=machine learning en-keyword=robustness kn-keyword=robustness en-keyword=instability kn-keyword=instability en-keyword=F2 score kn-keyword=F2 score en-keyword=deep inspiration breath-hold technique kn-keyword=deep inspiration breath-hold technique en-keyword=computed tomography kn-keyword=computed tomography END start-ver=1.4 cd-journal=joma no-vol=15 cd-vols= no-issue=6 article-no= start-page=790 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2025 dt-pub=20250320 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Improving Diagnostic Performance for Head and Neck Tumors with Simple Diffusion Kurtosis Imaging and Machine Learning Bi-Parameter Analysis en-subtitle= kn-subtitle= en-abstract= kn-abstract=Background/Objectives: Mean kurtosis (MK) values in simple diffusion kurtosis imaging (SDI)-a type of diffusion kurtosis imaging (DKI)-have been reported to be useful in the diagnosis of head and neck malignancies, for which pre-processing with smoothing filters has been reported to improve the diagnostic accuracy. Multi-parameter analysis using DKI in combination with other image types has recently been reported to improve the diagnostic performance. The purpose of this study was to evaluate the usefulness of machine learning (ML)-based multi-parameter analysis using the MK and apparent diffusion coefficient (ADC) values-which can be acquired simultaneously through SDI-for the differential diagnosis of benign and malignant head and neck tumors, which is important for determining the treatment strategy, as well as examining the usefulness of filter pre-processing. Methods: A total of 32 pathologically diagnosed head and neck tumors were included in the study, and a Gaussian filter was used for image pre-processing. MK and ADC values were extracted from pixels within the tumor area and used as explanatory variables. Five ML algorithms were used to create models for the prediction of tumor status (benign or malignant), which were evaluated through ROC analysis. Results: Bi-parameter analysis with gradient boosting achieved the best diagnostic performance, with an AUC of 0.81. Conclusions: The usefulness of bi-parameter analysis with ML methods for the differential diagnosis of benign and malignant head and neck tumors using SDI data were demonstrated. en-copyright= kn-copyright= en-aut-name=YoshidaSuzuka en-aut-sei=Yoshida en-aut-mei=Suzuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=NakamuraYoshihide en-aut-sei=Nakamura en-aut-mei=Yoshihide kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=FukumuraYuka en-aut-sei=Fukumura en-aut-mei=Yuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=NakamitsuYuki en-aut-sei=Nakamitsu en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=Al-HammadWlla E. en-aut-sei=Al-Hammad en-aut-mei=Wlla E. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=ShimizuYudai en-aut-sei=Shimizu en-aut-mei=Yudai kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=BarhamMajd en-aut-sei=Barham en-aut-mei=Majd kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=TekikiNouha en-aut-sei=Tekiki en-aut-mei=Nouha kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=KamaruddinNurul N. en-aut-sei=Kamaruddin en-aut-mei=Nurul N. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=HisatomiMiki en-aut-sei=Hisatomi en-aut-mei=Miki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=YanagiYoshinobu en-aut-sei=Yanagi en-aut-mei=Yoshinobu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= en-aut-name=AsaumiJunichi en-aut-sei=Asaumi en-aut-mei=Junichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=17 ORCID= affil-num=1 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=2 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=4 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=5 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=6 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=7 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=8 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=9 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=10 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=11 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=12 en-affil=Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University kn-affil= affil-num=13 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=14 en-affil=Department of Oral Rehabilitation and Regenerative Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=15 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=16 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=17 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= en-keyword=head and neck tumors kn-keyword=head and neck tumors en-keyword=mean kurtosis kn-keyword=mean kurtosis en-keyword=simple diffusion kurtosis imaging kn-keyword=simple diffusion kurtosis imaging en-keyword=magnetic resonance imaging kn-keyword=magnetic resonance imaging en-keyword=apparent diffusion coefficient value kn-keyword=apparent diffusion coefficient value en-keyword=diffusion kurtosis imaging kn-keyword=diffusion kurtosis imaging en-keyword=machine learning kn-keyword=machine learning en-keyword=bi-parameter analysis kn-keyword=bi-parameter analysis en-keyword=gradient boosting kn-keyword=gradient boosting en-keyword=differential diagnosis of benign and malignant kn-keyword=differential diagnosis of benign and malignant END start-ver=1.4 cd-journal=joma no-vol=16 cd-vols= no-issue=4 article-no= start-page=497 end-page=505 dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=20230915 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Investigation of uncertainty in internal target volume definition for lung stereotactic body radiotherapy en-subtitle= kn-subtitle= en-abstract= kn-abstract=This study evaluated the validity of internal target volumes (ITVs) defined by three- (3DCT) and four-dimensional computed tomography (4DCT), and subsequently compared them with actual movements during treatment. Five patients with upper lobe lung tumors were treated with stereotactic body radiotherapy (SBRT) at 48 Gy in four fractions. Planning 3DCT images were acquired with peak-exhale and peak-inhale breath-holds, and 4DCT images were acquired in the cine mode under free breathing. Cine images were acquired using an electronic portal imaging device during irradiation. Tumor coverage was evaluated based on the manner in which the peak-to-peak breathing amplitude on the planning CT covered the range of tumor motion (± 3 SD) during irradiation in the left–right, anteroposterior, and cranio-caudal (CC) directions. The mean tumor coverage of the 4DCT-based ITV was better than that of the 3DCT-based ITV in the CC direction. The internal margin should be considered when setting the irradiation field for 4DCT. The proposed 4DCT-based ITV can be used as an efficient approach in free-breathing SBRT for upper-lobe tumors of the lung because its coverage is superior to that of 3DCT. en-copyright= kn-copyright= en-aut-name=NakanishiDaiki en-aut-sei=Nakanishi en-aut-mei=Daiki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=FukunagaJun-Ichi en-aut-sei=Fukunaga en-aut-mei=Jun-Ichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=HiroseTaka-Aki en-aut-sei=Hirose en-aut-mei=Taka-Aki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=YoshitakeTadamasa en-aut-sei=Yoshitake en-aut-mei=Tadamasa kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=SasakiMotoharu en-aut-sei=Sasaki en-aut-mei=Motoharu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= affil-num=1 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=3 en-affil=Division of Radiology, Department of Medical Technology, Kyushu University Hospital kn-affil= affil-num=4 en-affil=Division of Radiology, Department of Medical Technology, Kyushu University Hospital kn-affil= affil-num=5 en-affil=Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University kn-affil= affil-num=6 en-affil=Graduate School of Biomedical Sciences, Tokushima University kn-affil= en-keyword=4DCT kn-keyword=4DCT en-keyword=3DCT kn-keyword=3DCT en-keyword=Internal target volume kn-keyword=Internal target volume en-keyword=EPID imaging kn-keyword=EPID imaging en-keyword=Stereotactic body radiotherapy kn-keyword=Stereotactic body radiotherapy en-keyword=Lung cancer kn-keyword=Lung cancer END start-ver=1.4 cd-journal=joma no-vol=13 cd-vols= no-issue=6 article-no= start-page=1783 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2024 dt-pub=20240320 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Enhancing Diagnostic Precision: Evaluation of Preprocessing Filters in Simple Diffusion Kurtosis Imaging for Head and Neck Tumors en-subtitle= kn-subtitle= en-abstract= kn-abstract=Background: Our initial clinical study using simple diffusion kurtosis imaging (SDI), which simultaneously produces a diffusion kurtosis image (DKI) and an apparent diffusion coefficient map, confirmed the usefulness of SDI for tumor diagnosis. However, the obtained DKI had noticeable variability in the mean kurtosis (MK) values, which is inherent to SDI. We aimed to improve this variability in SDI by preprocessing with three different filters (Gaussian [G], median [M], and nonlocal mean) of the diffusion-weighted images used for SDI. Methods: The usefulness of filter parameters for diagnosis was examined in basic and clinical studies involving 13 patients with head and neck tumors. Results: The filter parameters, which did not change the median MK value, but reduced the variability and significantly homogenized the MK values in tumor and normal tissues in both basic and clinical studies, were identified. In the receiver operating characteristic curve analysis for distinguishing tumors from normal tissues using MK values, the area under curve values significantly improved from 0.627 without filters to 0.641 with G (sigma = 0.5) and 0.638 with M (radius = 0.5). Conclusions: Thus, image pretreatment with G and M for SDI was shown to be useful for improving tumor diagnosis in clinical practice. en-copyright= kn-copyright= en-aut-name=NakamitsuYuki en-aut-sei=Nakamitsu en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=ShimizuYudai en-aut-sei=Shimizu en-aut-mei=Yudai kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=YoshimuraYuuki en-aut-sei=Yoshimura en-aut-mei=Yuuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=YoshidaSuzuka en-aut-sei=Yoshida en-aut-mei=Suzuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=NakamuraYoshihide en-aut-sei=Nakamura en-aut-mei=Yoshihide kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=FukumuraYuka en-aut-sei=Fukumura en-aut-mei=Yuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=KamizakiRyo en-aut-sei=Kamizaki en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=Al-HammadWlla E. en-aut-sei=Al-Hammad en-aut-mei=Wlla E. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=BarhamMajd en-aut-sei=Barham en-aut-mei=Majd kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=TekikiNouha en-aut-sei=Tekiki en-aut-mei=Nouha kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= en-aut-name=AsaumiJunichi en-aut-sei=Asaumi en-aut-mei=Junichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=17 ORCID= affil-num=1 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=2 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=4 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=5 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=6 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=7 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=8 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=9 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=10 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=11 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=12 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=13 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=14 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=15 en-affil=Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University kn-affil= affil-num=16 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=17 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= en-keyword=diffusion-weighted image kn-keyword=diffusion-weighted image en-keyword=Gaussian filter kn-keyword=Gaussian filter en-keyword=head and neck tumor kn-keyword=head and neck tumor en-keyword=magnetic resonance imaging kn-keyword=magnetic resonance imaging en-keyword=mean kurtosis kn-keyword=mean kurtosis en-keyword=median filter kn-keyword=median filter en-keyword=nonlocal mean filter kn-keyword=nonlocal mean filter en-keyword=phantom kn-keyword=phantom en-keyword=simple diffusion kurtosis imaging kn-keyword=simple diffusion kurtosis imaging en-keyword=restricted diffusion-weighted image kn-keyword=restricted diffusion-weighted image END start-ver=1.4 cd-journal=joma no-vol=26 cd-vols= no-issue=5 article-no= start-page=536 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=20231002 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Evaluation of the accuracy of heart dose prediction by machine learning for selecting patients not requiring deep inspiration breath‑hold radiotherapy after breast cancer surgery en-subtitle= kn-subtitle= en-abstract= kn-abstract=Increased heart dose during postoperative radiotherapy (RT) for left‑sided breast cancer (BC) can cause cardiac injury, which can decrease patient survival. The deep inspiration breath‑hold technique (DIBH) is becoming increasingly common for reducing the mean heart dose (MHD) in patients with left‑sided BC. However, treatment planning and DIBH for RT are laborious, time‑consuming and costly for patients and RT staff. In addition, the proportion of patients with left BC with low MHD is considerably higher among Asian women, mainly due to their smaller breast volume compared with that in Western countries. The present study aimed to determine the optimal machine learning (ML) model for predicting the MHD after RT to pre‑select patients with low MHD who will not require DIBH prior to RT planning. In total, 562 patients with BC who received postoperative RT were randomly divided into the trainval (n=449) and external (n=113) test datasets for ML using Python (version 3.8). Imbalanced data were corrected using synthetic minority oversampling with Gaussian noise. Specifically, right‑left, tumor site, chest wall thickness, irradiation method, body mass index and separation were the six explanatory variables used for ML, with four supervised ML algorithms used. Using the optimal value of hyperparameter tuning with root mean squared error (RMSE) as an indicator for the internal test data, the model yielding the best F2 score evaluation was selected for final validation using the external test data. The predictive ability of MHD for true MHD after RT was the highest among all algorithms for the deep neural network, with a RMSE of 77.4, F2 score of 0.80 and area under the curve‑receiver operating characteristic of 0.88, for a cut‑off value of 300 cGy. The present study suggested that ML can be used to pre‑select female Asian patients with low MHD who do not require DIBH for the postoperative RT of BC. en-copyright= kn-copyright= en-aut-name=KamizakiRyo en-aut-sei=Kamizaki en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=Al‑HammadWlla en-aut-sei=Al‑Hammad en-aut-mei=Wlla kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=TekikiNouha en-aut-sei=Tekiki en-aut-mei=Nouha kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=IshizakaHinata en-aut-sei=Ishizaka en-aut-mei=Hinata kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=BarhamMajd en-aut-sei=Barham en-aut-mei=Majd kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=NakamitsuYuki en-aut-sei=Nakamitsu en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=HiranoMasaki en-aut-sei=Hirano en-aut-mei=Masaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=MutoYuki en-aut-sei=Muto en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=IharaHiroki en-aut-sei=Ihara en-aut-mei=Hiroki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=SugiyamaSoichi en-aut-sei=Sugiyama en-aut-mei=Soichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= affil-num=1 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=2 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=4 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=5 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=6 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=7 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=8 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=9 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=10 en-affil=Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An‑Najah National University kn-affil= affil-num=11 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=12 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=13 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=14 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=15 en-affil=Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=16 en-affil=Department of Proton Beam Therapy, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= en-keyword=BC kn-keyword=BC en-keyword=RT kn-keyword=RT en-keyword=heart dose kn-keyword=heart dose en-keyword=ML kn-keyword=ML en-keyword=DNN kn-keyword=DNN en-keyword=DIBH kn-keyword=DIBH END start-ver=1.4 cd-journal=joma no-vol=9 cd-vols= no-issue=8 article-no= start-page=e19038 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=202308 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Bayesian statistical modeling to predict observer-specific optimal windowing parameters in magnetic resonance imaging en-subtitle= kn-subtitle= en-abstract= kn-abstract=Magnetic resonance (MR) images require a process known as windowing for optimizing the display conditions. However, the conventional windowing process often fails to achieve the preferred display conditions for observers due to various factors. This study proposes a novel framework for predicting the preferred windowing parameters for each observer using Bayesian statistical modeling. MR images obtained from 1000 patients were divided into training and test sets at a 7:3 ratio. The image intensity and windowing parameters were standardized using previously reported methods. Bayesian statistical modeling was utilized to predict the windowing parameters preferred by three MR imaging (MRI) operators. The performance of the proposed framework was evaluated by assessing the mean relative error (MRE), mean absolute error (MAE), and Pearson's correlation coefficient (ρ) of the test set. In addition, the naive method, which presumes that the average value of the windowing parameters for each acquisition sequence and body region in the training set is optimal, was also used for comparison. Three MRI operators and three radiologists conducted visual assessments. The mean MRE, MAE, and ρ values for the window level and width (WL/WW) in the proposed framework were 12.6 and 13.9, 42.9 and 85.4, and 0.98 and 0.98, respectively. These results outperformed those obtained using the naive method. The visual assessments revealed no significant differences between the original and predicted display conditions, indicating that the proposed framework accurately predicts individualized windowing parameters with the additional advantages of robustness and ease of use. Thus, the proposed framework can effectively predict the windowing parameters preferred by each observer. en-copyright= kn-copyright= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= affil-num=1 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=2 en-affil=Faculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=3 en-affil=Department of Radiological Technology, Faculty of Health Sciences, Okayama University kn-affil= en-keyword=MR image kn-keyword=MR image en-keyword=Image intensity standardization kn-keyword=Image intensity standardization en-keyword=Windowing kn-keyword=Windowing en-keyword=Prediction kn-keyword=Prediction en-keyword=Bayesian statistical modeling kn-keyword=Bayesian statistical modeling END start-ver=1.4 cd-journal=joma no-vol=30 cd-vols= no-issue=8 article-no= start-page=7412 end-page=7424 dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=20230804 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Mean Heart Dose Prediction Using Parameters of Single-Slice Computed Tomography and Body Mass Index: Machine Learning Approach for Radiotherapy of Left-Sided Breast Cancer of Asian Patients en-subtitle= kn-subtitle= en-abstract= kn-abstract=Deep inspiration breath-hold (DIBH) is an excellent technique to reduce the incidental radiation received by the heart during radiotherapy in patients with breast cancer. However, DIBH is costly and time-consuming for patients and radiotherapy staff. In Asian countries, the use of DIBH is restricted due to the limited number of patients with a high mean heart dose (MHD) and the shortage of radiotherapy personnel and equipment compared to that in the USA. This study aimed to develop, evaluate, and compare the performance of ten machine learning algorithms for predicting MHD using a patient's body mass index and single-slice CT parameters to identify patients who may not require DIBH. Machine learning models were built and tested using a dataset containing 207 patients with left-sided breast cancer who were treated with field-in-field radiotherapy with free breathing. The average MHD was 251 cGy. Stratified repeated four-fold cross-validation was used to build models using 165 training data. The models were compared internally using their average performance metrics: F2 score, AUC, recall, accuracy, Cohen's kappa, and Matthews correlation coefficient. The final performance evaluation for each model was further externally analyzed using 42 unseen test data. The performance of each model was evaluated as a binary classifier by setting the cut-off value of MHD & GE; 300 cGy. The deep neural network (DNN) achieved the highest F2 score (78.9%). Most models successfully classified all patients with high MHD as true positive. This study indicates that the ten models, especially the DNN, might have the potential to identify patients who may not require DIBH. en-copyright= kn-copyright= en-aut-name=Al-HammadWlla E. en-aut-sei=Al-Hammad en-aut-mei=Wlla E. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KamizakiRyo en-aut-sei=Kamizaki en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=TekikiNouha en-aut-sei=Tekiki en-aut-mei=Nouha kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=IshizakaHinata en-aut-sei=Ishizaka en-aut-mei=Hinata kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=BarhamMajd en-aut-sei=Barham en-aut-mei=Majd kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=ShimizuYudai en-aut-sei=Shimizu en-aut-mei=Yudai kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=NakamitsuYuki en-aut-sei=Nakamitsu en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=AsaumiJunichi en-aut-sei=Asaumi en-aut-mei=Junichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= affil-num=1 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=2 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=4 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=5 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=6 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=7 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=8 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=9 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=10 en-affil=Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University kn-affil= affil-num=11 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=12 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=13 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=14 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= en-keyword=breast cancer kn-keyword=breast cancer en-keyword=radiotherapy kn-keyword=radiotherapy en-keyword=heart dose kn-keyword=heart dose en-keyword=machine learning kn-keyword=machine learning en-keyword=deep neural network kn-keyword=deep neural network en-keyword=deep inspiration breath-hold technique kn-keyword=deep inspiration breath-hold technique en-keyword=computed tomography kn-keyword=computed tomography END start-ver=1.4 cd-journal=joma no-vol=77 cd-vols= no-issue=3 article-no= start-page=273 end-page=280 dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=202306 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Usefulness of Simple Diffusion Kurtosis Imaging for Head and Neck Tumors: An Early Clinical Study en-subtitle= kn-subtitle= en-abstract= kn-abstract=Diffusion kurtosis (DK) imaging (DKI), a type of restricted diffusion-weighted imaging, has been reported to be useful for tumor diagnoses in clinical studies. We developed a software program to simultaneously create DK images with apparent diffusion coefficient (ADC) maps and conducted an initial clinical study. Multi-shot echo-planar diffusion-weighted images were obtained at b-values of 0, 400, and 800 sec/mm2 for simple DKI, and DK images were created simultaneously with the ADC map. The usefulness of the DK image and ADC map was evaluated using a pixel analysis of all pixels and a median analysis of the pixels of each case. Tumor and normal tissues differed significantly in both pixel and median analyses. In the pixel analysis, the area under the curve was 0.64 for the mean kurtosis (MK) value and 0.77 for the ADC value. In the median analysis, the MK value was 0.74, and the ADC value was 0.75. The MK and ADC values correlated moderately in the pixel analysis and strongly in the median analysis. Our simple DKI system created DK images simultaneously with ADC maps, and the obtained MK and ADC values were useful for differentiating head and neck tumors from normal tissue. en-copyright= kn-copyright= en-aut-name=ShimizuYudai en-aut-sei=Shimizu en-aut-mei=Yudai kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=NakamitsuYuki en-aut-sei=Nakamitsu en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=Al-HammadWlla E. en-aut-sei=Al-Hammad en-aut-mei=Wlla E. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=YoshidaSuzuka en-aut-sei=Yoshida en-aut-mei=Suzuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=FukumuraYuka en-aut-sei=Fukumura en-aut-mei=Yuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=NakamuraYoshihide en-aut-sei=Nakamura en-aut-mei=Yoshihide kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=KamizakiRyo en-aut-sei=Kamizaki en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=ImajohSatoshi en-aut-sei=Imajoh en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=BamgboseBabatunde O. en-aut-sei=Bamgbose en-aut-mei=Babatunde O. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=YanagiYoshinobu en-aut-sei=Yanagi en-aut-mei=Yoshinobu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= en-aut-name=AsaumiJunichi en-aut-sei=Asaumi en-aut-mei=Junichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=17 ORCID= affil-num=1 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=2 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=4 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=5 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=6 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=7 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=8 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=9 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=10 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=11 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=12 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=13 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=14 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=15 en-affil=Department of Oral Diagnostic Sciences, Faculty of Dentistry, Bayero University kn-affil= affil-num=16 en-affil=Department of Dental Informatics, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=17 en-affil=Department of Oral and Maxillofacial Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= en-keyword=simple diffusion kurtosis imaging kn-keyword=simple diffusion kurtosis imaging en-keyword=mean kurtosis kn-keyword=mean kurtosis en-keyword=clinical trial kn-keyword=clinical trial en-keyword=head and neck tumor kn-keyword=head and neck tumor en-keyword=magnetic resonance imaging kn-keyword=magnetic resonance imaging END start-ver=1.4 cd-journal=joma no-vol=25 cd-vols= no-issue=3 article-no= start-page=109 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=2023124 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Quantitative evaluation of the reduction of distortion and metallic artifacts in magnetic resonance images using the multiacquisition variable‑resonance image combination selective sequence en-subtitle= kn-subtitle= en-abstract= kn-abstract=Magnetic resonance imaging (MRI) is superior to computed tomography (CT) in determining changes in tissue structure, such as those observed following inflammation and infection. However, when metal implants or other metal objects are present, MRI exhibits more distortion and artifacts compared with CT, which hinders the accurate measurement of the implants. A limited number of reports have examined whether the novel MRI sequence, multiacquisition variable-resonance image combination selective (MAVRIC SL), can accurately measure metal implants without distortion. Therefore, the present study aimed to demonstrate whether MAVRIC SL could accurately measure metal implants without distortion and whether the area around the metal implants could be well delineated without artifacts. An agar phantom containing a titanium alloy lumbar implant was used for the present study and was imaged using a 3.0 T MRI machine. A total of three imaging sequences, namely MAVRIC SL, CUBE and magnetic image compilation (MAGiC), were applied and the results were compared. Distortion was evaluated by measuring the screw diameter and distance between the screws multiple times in the phase and frequency directions by two different investigators. The artifact region around the implant was examined using a quantitative method following standardization of the phantom signal values. It was revealed that MAVRIC SL was a superior sequence compared with CUBE and MAGiC, as there was significantly less distortion, a lack of bias between the two different investigators and significantly reduced artifact regions. These results suggested the possibility of utilizing MAVRIC SL for follow-up to observe metal implant insertions. en-copyright= kn-copyright= en-aut-name=HiranoMasaki en-aut-sei=Hirano en-aut-mei=Masaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=MutoYuki en-aut-sei=Muto en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=FujiwaraYuta en-aut-sei=Fujiwara en-aut-mei=Yuta kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=SasakiTomoaki en-aut-sei=Sasaki en-aut-mei=Tomoaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=KamizakiRyo en-aut-sei=Kamizaki en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=ImajohSatoshi en-aut-sei=Imajoh en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=E. Al-HammadWlla en-aut-sei=E. Al-Hammad en-aut-mei=Wlla kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=NakamitsuYuki en-aut-sei=Nakamitsu en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=ShimizuYudai en-aut-sei=Shimizu en-aut-mei=Yudai kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=O. BamgboseBabatunde en-aut-sei=O. Bamgbose en-aut-mei=Babatunde kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= affil-num=1 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=2 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=4 en-affil=Division of Clinical Radiology Service, Okayama Central Hospital kn-affil= affil-num=5 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=6 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=7 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=8 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=9 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=10 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=11 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700‑8558, Japan kn-affil= affil-num=12 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=13 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University, Okayama, 770‑8558, Japan kn-affil= affil-num=14 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=15 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=16 en-affil=Department of Oral Diagnostic Sciences, Faculty of Dentistry, Bayero University kn-affil= en-keyword=MAVRIC SL kn-keyword=MAVRIC SL en-keyword=metal artifacts kn-keyword=metal artifacts en-keyword=implant kn-keyword=implant en-keyword=phantom kn-keyword=phantom en-keyword=MRI kn-keyword=MRI END start-ver=1.4 cd-journal=joma no-vol=23 cd-vols= no-issue= article-no= start-page=e13817 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2022 dt-pub=20221124 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Validation of pencil beam scanning proton therapy with multi-leaf collimator calculated by a commercial Monte Carlo dose engine en-subtitle= kn-subtitle= en-abstract= kn-abstract=This study aimed to evaluate the clinical beam commissioning results and lateral penumbra characteristics of our new pencil beam scanning (PBS) proton therapy using a multi-leaf collimator (MLC) calculated by use of a commercial Monte Carlo dose engine. Eighteen collimated uniform dose plans for cubic targets were optimized by the RayStation 9A treatment planning system (TPS), varying scan area, modulation widths, measurement depths, and collimator angles. To test the patient-specific measurements, we also created and verified five clinically realistic PBS plans with the MLC, such as the liver, prostate, base-of-skull, C-shape, and head-and-neck. The verification measurements consist of the depth dose (DD), lateral profile (LP), and absolute dose (AD). We compared the LPs and ADs between the calculation and measurements. For the cubic plans, the gamma index pass rates (gamma-passing) were on average 96.5% +/- 4.0% at 3%/3 mm for the DD and 95.2% +/- 7.6% at 2%/2 mm for the LP. In several LP measurements less than 75 mm depths, the gamma-passing deteriorated (increased the measured doses) by less than 90% with the scattering such as the MLC edge and range shifter. The deteriorated gamma-passing was satisfied by more than 90% at 2%/2 mm using uncollimated beams instead of collimated beams except for three planes. The AD differences and the lateral penumbra width (80%-20% distance) were within +/- 1.9% and +/- 1.1 mm, respectively. For the clinical plan measurements, the gamma-passing of LP at 2%/2 mm and the AD differences were 97.7% +/- 4.2% on average and within +/- 1.8%, respectively. The measurements were in good agreement with the calculations of both the cubic and clinical plans inserted in the MLC except for LPs less than 75 mm regions of some cubic and clinical plans. The calculation errors in collimated beams can be mitigated by substituting uncollimated beams. en-copyright= kn-copyright= en-aut-name=TominagaYuki en-aut-sei=Tominaga en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=SakuraiYusuke en-aut-sei=Sakurai en-aut-mei=Yusuke kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=MiyataJunya en-aut-sei=Miyata en-aut-mei=Junya kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=HaradaShuichi en-aut-sei=Harada en-aut-mei=Shuichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=AkagiTakashi en-aut-sei=Akagi en-aut-mei=Takashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= affil-num=1 en-affil=Division of Radiological Technology, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=2 en-affil=Department of Radiotherapy, Medical Co. Hakuhokai, Osaka Proton Therapy Clinic kn-affil= affil-num=3 en-affil=Division of Radiological Technology, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=4 en-affil=Hyogo Ion Beam Medical Support kn-affil= affil-num=5 en-affil=Hyogo Ion Beam Medical Support kn-affil= affil-num=6 en-affil=Division of Radiological Technology, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= en-keyword=commissioning kn-keyword=commissioning en-keyword=lateral penumbra kn-keyword=lateral penumbra en-keyword=multi-leaf collimator kn-keyword=multi-leaf collimator en-keyword=pencil beam scanning kn-keyword=pencil beam scanning en-keyword=proton therapy kn-keyword=proton therapy END start-ver=1.4 cd-journal=joma no-vol=74 cd-vols= no-issue=5 article-no= start-page=415 end-page=422 dt-received= dt-revised= dt-accepted= dt-pub-year=2020 dt-pub=202010 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Oblique Surface Dose Calculation in High-Energy X-ray Therapy en-subtitle= kn-subtitle= en-abstract= kn-abstract=During radiation therapy, incident radiation oblique to the skin surface is high and may cause severe skin damage. Understanding the dose of radiation absorbed by the skin is important for predicting skin damage due to radiation. In this study, we used a high-energy (4 MV) X-ray system and an optically stimulated luminescence dosimeter (OSLD) that was developed for personal exposure dosimetry. We determined the dose variation and angular dependence, which are the characteristics of a small OSLD required to derive the calculation formula for the oblique surface dose. The dose variation was determined using the coefficient of variation. The maximum coefficient of variation for 66 small-field OSLDs was 1.71%. The angular dependence, obtained from the dose ratio of the dosimeter in the vertical direction, had a maximum value of 1.37. We derived a new equation in which the oblique surface dose can be calculated within the error range of −7.7-5.1%. en-copyright= kn-copyright= en-aut-name=NarihiroNaomasa en-aut-sei=Narihiro en-aut-mei=Naomasa kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=TakedaYoshihiro en-aut-sei=Takeda en-aut-mei=Yoshihiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= affil-num=1 en-affil=Graduate School of Health Sciences, Department of Radiological Technology, Okayama University kn-affil= affil-num=2 en-affil=Department of Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=3 en-affil=Graduate School of Health Sciences, Department of Radiological Technology, Okayama University kn-affil= en-keyword=optically stimulated luminescent dosimeter kn-keyword=optically stimulated luminescent dosimeter en-keyword=radiotherapy kn-keyword=radiotherapy en-keyword=oblique surface dose kn-keyword=oblique surface dose en-keyword=high-energy X-ray therapy kn-keyword=high-energy X-ray therapy en-keyword=angular dependence kn-keyword=angular dependence END start-ver=1.4 cd-journal=joma no-vol=21 cd-vols= no-issue=2 article-no= start-page=89 end-page=97 dt-received= dt-revised= dt-accepted= dt-pub-year=2020 dt-pub=20200120 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Impact of patient positioning uncertainty in noncoplanar intracranial stereotactic radiotherapy en-subtitle= kn-subtitle= en-abstract= kn-abstract=The aim of this study is to evaluate the patient positioning uncertainty in noncoplanar stereotactic radiosurgery or stereotactic radiotherapy (SRS/SRT) for intracranial lesions with the frameless 6D ExacTrac system. In all, 28 patients treated with SRS/SRT of 70 treatment plans at our institution were evaluated in this study. Two X-ray images with the frameless 6D ExacTrac system were first acquired to correct (XC) and verify (XV) the patient position at a couch angle of 0o. Subsequently, the XC and XV images were also acquired at each planned couch angle for using noncoplanar beams to detect position errors caused by rotating a couch. The translational XC and XV shift values at each couch angle were calculated for each plan. The percentages of the translational XC shift values within 1.0 mm for each planned couch angle for using noncoplanar beams were 77.86%, 72.26%, and 98.47% for the lateral, longitudinal, and vertical directions, respectively. Those within 2.0 mm were 98.22%, 97.96%, and 99.75% for the lateral, longitudinal, and vertical directions, respectively. The maximum absolute values of the translational XC shifts among all planned couch angles for using noncoplanar beams were 2.69, 2.45, and 2.17 mm for the lateral, longitudinal, and vertical directions, respectively. The overall absolute values of the translational XV shifts were less than 1.0 mm for all directions except for one case in the longitudinal direction. The patient position errors were detected after couch rotation for using noncoplanar beams, and they exceeded a planning target volume (PTV) margin of 1.0-2.0 mm used commonly in SRS/SRT treatment. These errors need to be corrected at each planned couch angle, or the PTV margin should be enlarged. en-copyright= kn-copyright= en-aut-name=TanakaYoshihiro en-aut-sei=Tanaka en-aut-mei=Yoshihiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=InomataShinichiro en-aut-sei=Inomata en-aut-mei=Shinichiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=FuseToshiaki en-aut-sei=Fuse en-aut-mei=Toshiaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=AkinoYuichi en-aut-sei=Akino en-aut-mei=Yuichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=ShimomuraKohei en-aut-sei=Shimomura en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= affil-num=1 en-affil=Department of Radiation Therapy,Japanese Red Cross Society Kyoto Daiichi Hospital kn-affil= affil-num=2 en-affil=Department of Healthcare Sciences,Graduate School of Interdisciplinary Scienceand Engineering in Health Systems,Okayama University kn-affil= affil-num=3 en-affil=Department of Radiation Therapy,Japanese Red Cross Society Kyoto Daiichi Hospital kn-affil= affil-num=4 en-affil=Department of Radiation Therapy,Japanese Red Cross Society Kyoto Daiichi Hospital kn-affil= affil-num=5 en-affil=Oncology Center, Osaka University Hospital kn-affil= affil-num=6 en-affil=Kyoto College of Medical Science kn-affil= en-keyword=IGRT kn-keyword=IGRT en-keyword=noncoplanar radiotherapy kn-keyword=noncoplanar radiotherapy en-keyword=patient positioning uncertainty kn-keyword=patient positioning uncertainty en-keyword=SRS kn-keyword=SRS en-keyword=SRT kn-keyword=SRT END