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