Nature PortfolioActa Medica Okayama2045-23221412024Training high-performance deep learning classifier for diagnosis in oral cytology using diverse annotations17591ENShintaroSukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityFutaTanakaDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityKeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityTakeshiHaraDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityTakanagaOchiaiDivision of Oral Pathogenesis and Disease Control, Department of Oral Pathology, Asahi University School of DentistryKatsumitsuShimadaDepartment of Oral Pathology, Graduate School of Oral Medicine, Matsumoto Dental UniversityYutaInoueDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityYoshihiroTakiDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityFumiNakaiDepartment of Oral and Maxillofacial Surgery, Kagawa University Faculty of MedicineYasuhiroNakaiDepartment of Oral and Maxillofacial Surgery, Kagawa University Faculty of MedicineTakanoriIshihamaDepartment of Oral and Maxillofacial Surgery, Kagawa University Faculty of MedicineRyoMiyazakiStony Brook Cancer Center, Stony Brook UniversitySatoshiMurakamiDepartment of Oral Pathology, Graduate School of Oral Medicine, Matsumoto Dental UniversityHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityMinoruMiyakeDepartment of Oral and Maxillofacial Surgery, Kagawa University Faculty of MedicineThe uncertainty of true labels in medical images hinders diagnosis owing to the variability across professionals when applying deep learning models. We used deep learning to obtain an optimal convolutional neural network (CNN) by adequately annotating data for oral exfoliative cytology considering labels from multiple oral pathologists. Six whole-slide images were processed using QuPath for segmenting them into tiles. The images were labeled by three oral pathologists, resulting in 14,535 images with the corresponding pathologists' annotations. Data from three pathologists who provided the same diagnosis were labeled as ground truth (GT) and used for testing. We investigated six models trained using the annotations of (1) pathologist A, (2) pathologist B, (3) pathologist C, (4) GT, (5) majority voting, and (6) a probabilistic model. We divided the test by cross-validation per slide dataset and examined the classification performance of the CNN with a ResNet50 baseline. Statistical evaluation was performed repeatedly and independently using every slide 10 times as test data. For the area under the curve, three cases showed the highest values (0.861, 0.955, and 0.991) for the probabilistic model. Regarding accuracy, two cases showed the highest values (0.988 and 0.967). For the models using the pathologists and GT annotations, many slides showed very low accuracy and large variations across tests. Hence, the classifier trained with probabilistic labels provided the optimal CNN for oral exfoliative cytology considering diagnoses from multiple pathologists. These results may lead to trusted medical artificial intelligence solutions that reflect diverse diagnoses of various professionals.No potential conflict of interest relevant to this article was reported.Spandidos PublicationsActa Medica Okayama1792-10742452022Investigation of bone invasion and underlying mechanisms of oral cancer using a cell line‑derived xenograft model382ENQiushengShanDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesHarukaOmoriDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesHotakaKawaiDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesMay WathoneOoDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesShintaroSukegawaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesMasaeFujiiDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesYasunoriInadaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesShoSanoDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesKeisukeNakanoDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesThe cancer stroma regulates bone invasion in oral squamous cell carcinoma (OSCC). However, data on normal stroma are limited. In the present study, the effects of gingival and periodontal ligament tissue‑derived stromal cells (G‑SCs and P‑SCs, respectively) and human dermal fibroblasts (HDFs) on bone resorption and osteoclast activation were assessed using hematoxylin and eosin and tartrate‑resistant acid phosphatase staining in a cell line‑derived xenograft model. The results demonstrated that G‑SCs promoted bone invasion and osteoclast activation and inhibited osteoclast proliferation following crosstalk with the human OSCC HSC‑3 cell line, whereas P‑SCs inhibited bone resorption and promoted osteoclast proliferation in vitro but had a minimal effect on osteoclast activation both in vitro and in vivo following crosstalk with HSC‑3 cells. Furthermore, the effects of G‑SCs, P‑SCs and HDFs on protein expression levels of matrix metalloproteinase (MMP)‑9, membrane type 1 MMP (MT1‑MMP), Snail, parathyroid hormone‑related peptide (PTHrP) and receptor activator of NF‑ƒÈB ligand (RANKL) in HSC‑3 cells in OSCC bone invasion regions were assessed using immunohistochemistry. The results demonstrated that G‑SCs had a more prominent effect on the expression of MMP‑9, MT1‑MMP, Snail, PTHrP, and RANKL, whereas P‑SCs only promoted RANKL and PTHrP expression and exerted a minimal effect on MMP‑9, MT1‑MMP and Snail expression. The potential genes underlying the differential effects of G‑SCs and P‑SCs on bone invasion in OSCC were evaluated using a microarray, which indicated that cyclin‑dependent kinase 1, insulin, aurora kinase A, cyclin B1 and DNA topoisomerase II alpha underlaid these differential effects. Therefore, these results demonstrated that G‑SCs promoted bone invasion in OSCC by activating osteoclasts on the bone surface, whereas P‑SCs exerted an inhibitory effect. These findings could indicate a potential regulatory mechanism for bone invasion in OSCC.No potential conflict of interest relevant to this article was reported.Spandidos PublicationsActa Medica Okayama1021-335X4742022Significance of cancer stroma for bone destruction in oral squamous cell carcinoma using different cancer stroma subtypes81ENQiushengShanDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesHotakaKawaiDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesMay WathoneOoDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesYasunoriInadaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesShintaroSukegawaDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalShigekoFushimiDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesKeisukeNakanoDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesStromal cells in the tumor microenvironment (TME) can regulate the progression of numerous types of cancer; however, the bone invasion of oral squamous cell carcinoma (OSCC) has been poorly investigated. In the present study, the effect of verrucous SCC‑associated stromal cells (VSCC‑SCs), SCC‑associated stromal cells (SCC‑SCs) and human dermal fibroblasts on bone resorption and the activation of HSC‑3 osteoclasts in vivo were examined by hematoxylin and eosin, AE1/3 (pan‑cytokeratin) and tartrate‑resistant acid phosphatase staining. In addition, the expression levels of matrix metalloproteinase (MMP)9, membrane‑type 1 MMP (MT1‑MMP), Snail, receptor activator of NF‑ƒÈB ligand (RANKL) and parathyroid hormone‑related peptide (PTHrP) in the bone invasion regions of HSC‑3 cells were examined by immunohistochemistry. The results suggested that both SCC‑SCs and VSCC‑SCs promoted bone resorption, the activation of osteoclasts, and the expression levels of MMP9, MT1‑MMP, Snail, RANKL and PTHrP. However, SCC‑SCs had a more prominent effect compared with VSCC‑SCs. Finally, microarray data were used to predict potential genes underlying the differential effects of VSCC‑SCs and SCC‑SCs on bone invasion in OSCC. The results revealed that IL1B, ICAM1, FOS, CXCL12, INS and NGF may underlie these differential effects. In conclusion, both VSCC‑SCs and SCC‑SCs may promote bone invasion in OSCC by enhancing the expression levels of RANKL in cancer and stromal cells mediated by PTHrP; however, SCC‑SCs had a more prominent effect. These findings may represent a potential regulatory mechanism underlying the bone invasion of OSCC.No potential conflict of interest relevant to this article was reported.Nature PortfolioActa Medica Okayama2045-23221312023Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists11676ENShintaroSukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversitySawakoOnoDepartment of Pathology, Kagawa Prefectural Central HospitalFutaTanakaDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityYutaInoueDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityTakeshiHaraDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityKazumasaYoshiiDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityKeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHotakaKawaiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityShimadaKatsumitsuDepartment of Oral Pathology, Graduate School of Oral Medicine, Matsumoto Dental UniversityFumiNakaiDepartment of Oral and Maxillofacial Surgery, Kagawa University Faculty of MedicineYasuhiroNakaiDepartment of Oral and Maxillofacial Surgery, Kagawa University Faculty of MedicineRyoMiyazakiDepartment of Oral and Maxillofacial Surgery, Kagawa University Faculty of MedicineSatoshiMurakamiDepartment of Oral Pathology, Graduate School of Oral Medicine, Matsumoto Dental UniversityHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityMinoruMiyakeDepartment of Oral and Maxillofacial Surgery, Kagawa University Faculty of MedicineThe study aims to identify histological classifiers from histopathological images of oral squamous cell carcinoma using convolutional neural network (CNN) deep learning models and shows how the results can improve diagnosis. Histopathological samples of oral squamous cell carcinoma were prepared by oral pathologists. Images were divided into tiles on a virtual slide, and labels (squamous cell carcinoma, normal, and others) were applied. VGG16 and ResNet50 with the optimizers stochastic gradient descent with momentum and spectral angle mapper (SAM) were used, with and without a learning rate scheduler. The conditions for achieving good CNN performances were identified by examining performance metrics. We used ROCAUC to statistically evaluate diagnostic performance improvement of six oral pathologists using the results from the selected CNN model for assisted diagnosis. VGG16 with SAM showed the best performance, with accuracy = 0.8622 and AUC = 0.9602. The diagnostic performances of the oral pathologists statistically significantly improved when the diagnostic results of the deep learning model were used as supplementary diagnoses (p-value = 0.031). By considering the learning results of deep learning model classifiers, the diagnostic accuracy of pathologists can be improved. This study contributes to the application of highly reliable deep learning models for oral pathological diagnosis.No potential conflict of interest relevant to this article was reported.Public Library ScienceActa Medica Okayama1932-62031772022Is attention branch network effective in classifying dental implants from panoramic radiograph images by deep learning?e0269016ENShintaroSukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKazumasaYoshiiDepartment of Intelligence Science and Engineering, Graduate School of Natural Science and Technology, Gifu UniversityTakeshiHaraDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityFutaTanakaDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityKatsusukeYamashitaPolytechnic Center KagawaTutaroKagayaDepartment of Intelligence Science and Engineering, Graduate School of Natural Science and Technology, Gifu UniversityKeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHotakaKawaiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityYoshihikoFurukiDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalAttention mechanism, which is a means of determining which part of the forced data is emphasized, has attracted attention in various fields of deep learning in recent years. The purpose of this study was to evaluate the performance of the attention branch network (ABN) for implant classification using convolutional neural networks (CNNs). The data consisted of 10191 dental implant images from 13 implant brands that cropped the site, including dental implants as pretreatment, from digital panoramic radiographs of patients who underwent surgery at Kagawa Prefectural Central Hospital between 2005 and 2021. ResNet 18, 50, and 152 were evaluated as CNN models that were compared with and without the ABN. We used accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristics curve as performance metrics. We also performed statistical and effect size evaluations of the 30-time performance metrics of the simple CNNs and the ABN model. ResNet18 with ABN significantly improved the dental implant classification performance for all the performance metrics. Effect sizes were equivalent to "Huge" for all performance metrics. In contrast, the classification performance of ResNet50 and 152 deteriorated by adding the attention mechanism. ResNet18 showed considerably high compatibility with the ABN model in dental implant classification (AUC = 0.9993) despite the small number of parameters. The limitation of this study is that only ResNet was verified as a CNN; further studies are required for other CNN models.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama2227-905910112022SOD3 Expression in Tumor Stroma Provides the Tumor Vessel Maturity in Oral Squamous Cell Carcinoma2729ENMay WathoneOoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHotakaKawaiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHtoo ShweEainDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityYaminSoeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityShoSanouDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityQiushengShanDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityYasunoriInadaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityMasaeFujiiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityYokoFukuharaDepartment of Oral Morphology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University,ZiyiWangDepartment of Molecular Biology and Biochemistry, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityShintaroSukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityMitsuakiOnoDepartment of Molecular Biology and Biochemistry, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityTumor angiogenesis is one of the hallmarks of solid tumor development. The progressive tumor cells produce the angiogenic factors and promote tumor angiogenesis. However, how the tumor stromal cells influence tumor vascularization is still unclear. In the present study, we evaluated the effects of oral squamous cell carcinoma (OSCC) stromal cells on tumor vascularization. The tumor stromal cells were isolated from two OSCC patients with different subtypes: low invasive verrucous squamous carcinoma (VSCC) and highly invasive squamous cell carcinoma (SCC) and co-xenografted with the human OSCC cell line (HSC-2) on nude mice. In comparison, the CD34+ vessels in HSC-2+VSCC were larger than in HSC-2+SCC. Interestingly, the vessels in the HSC-2+VSCC expressed vascular endothelial cadherin (VE-cadherin), indicating well-formed vascularization. Our microarray data revealed that the expression of extracellular superoxide dismutase, SOD3 mRNA is higher in VSCC stromal cells than in SCC stromal cells. Moreover, we observed that SOD3 colocalized with VE-cadherin on endothelial cells of low invasive stroma xenograft. These data suggested that SOD3 expression in stromal cells may potentially regulate tumor vascularization in OSCC. Thus, our study suggests the potential interest in SOD3-related vascular integrity for a better OSCC therapeutic strategy.No potential conflict of interest relevant to this article was reported.Nature PortfolioActa Medica Okayama2045-23221212022Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography16925ENShintaroSukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityFutaTanakaDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University TakeshiHaraDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University KazumasaYoshiiDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University KatsusukeYamashitaPolytechnic Center KagawaKeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHotakaKawaiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityYoshihikoFurukiDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalIn this study, the accuracy of the positional relationship of the contact between the inferior alveolar canal and mandibular third molar was evaluated using deep learning. In contact analysis, we investigated the diagnostic performance of the presence or absence of contact between the mandibular third molar and inferior alveolar canal. We also evaluated the diagnostic performance of bone continuity diagnosed based on computed tomography as a continuity analysis. A dataset of 1279 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014-2021) was used for the validation. The deep learning models were ResNet50 and ResNet50v2, with stochastic gradient descent and sharpness-aware minimization (SAM) as optimizers. The performance metrics were accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). The results indicated that ResNet50v2 using SAM performed excellently in the contact and continuity analyses. The accuracy and AUC were 0.860 and 0.890 for the contact analyses and 0.766 and 0.843 for the continuity analyses. In the contact analysis, SAM and the deep learning model performed effectively. However, in the continuity analysis, none of the deep learning models demonstrated significant classification performance.No potential conflict of interest relevant to this article was reported.Nature PortfolioActa Medica Okayama2045-23221212022Effective deep learning for oral exfoliative cytology classification13281ENShintaroSukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityFutaTanakaDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityKeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityTakeshiHaraDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityKazumasaYoshiiDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityKatsusukeYamashitaPolytechnic Center KagawaSawakoOnoDepartment of Pathology, Kagawa Prefectural Central HospitalKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHotakaKawaiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityYoshihikoFurukiDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalThe use of sharpness aware minimization (SAM) as an optimizer that achieves high performance for convolutional neural networks (CNNs) is attracting attention in various fields of deep learning. We used deep learning to perform classification diagnosis in oral exfoliative cytology and to analyze performance, using SAM as an optimization algorithm to improve classification accuracy. The whole image of the oral exfoliation cytology slide was cut into tiles and labeled by an oral pathologist. CNN was VGG16, and stochastic gradient descent (SGD) and SAM were used as optimizers. Each was analyzed with and without a learning rate scheduler in 300 epochs. The performance metrics used were accuracy, precision, recall, specificity, F1 score, AUC, and statistical and effect size. All optimizers performed better with the rate scheduler. In particular, the SAM effect size had high accuracy (11.2) and AUC (11.0). SAM had the best performance of all models with a learning rate scheduler. (AUC = 0.9328) SAM tended to suppress overfitting compared to SGD. In oral exfoliation cytology classification, CNNs using SAM rate scheduler showed the highest classification performance. These results suggest that SAM can play an important role in primary screening of the oral cytological diagnostic environment.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama2227-90321072022Incidence and Risk of Anti-Resorptive Agent-Related Osteonecrosis of the Jaw after Tooth Extraction: A Retrospective Study1332ENRiekoShimizuDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalShintaroSukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityYukaSukegawaDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalKazuakiHasegawaDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalSawakoOnoDepartment of Pathology, Kagawa Prefectural Central HospitalTomoyaNakamuraDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalAiFujimuraDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalAyakaFujisawaDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalKeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHotakaKawaiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityYoshihikoFurukiDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalBone-modifying agents (BMA) such as bisphosphonates and denosumab are frequently used for the treatment of bone metastases, osteoporosis, and multiple myeloma. BMA may lead to anti-resorptive agent-related osteonecrosis of the jaw (ARONJ). This study aimed to clarify the risk factors for and probabilities of developing ARONJ after tooth extraction in patients undergoing BMA therapy. In this study, the records of 505 target sites of 302 patients undergoing BMA who presented with mandibular fractures at the Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, from March 2014 to January 2022, were retrospectively analyzed for the onset of ARONJ after tooth extraction. The following variables were investigated as attributes: anatomy, health status, and dental treatment. The correlation coefficient was calculated for the success or failure of endodontic surgery for each variable, the odds ratio was calculated for the upper variable, and the factors related to the onset of ARONJ were identified. The incidence rate of ARONJ was found to be 3.2%. Hypoparathyroidism was an important factor associated with ARONJ development. Thus, systemic factors are more strongly related to the onset of ARONJ after tooth extraction than local factors.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama2227-90321072022The Effectiveness of Pre-Operative Screening Tests in Determining Viral Infections in Patients Undergoing Oral and Maxillofacial Surgery1348ENShintaroSukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityYukaSukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKazuakiHasegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversitySawakoOnoDepartment of Pathology, Kagawa Prefectural Central HospitalTomoyaNakamura Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalAiFujimura Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalAyakaFujisawa Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalKeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHotakaKawaiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityYumikaMukainakaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityYoshihikoFurukiDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalWe analyzed the rate of patients with hepatitis B virus (HBV), hepatitis C virus (HCV), or human immunodeficiency virus (HIV) infection diagnosed by pre-operative screening and estimated its cost. We retrospectively analyzed patients who underwent elective surgery at our maxillofacial surgery department between April 2014 and March 2022. We compared the number of patients with each infection identified by pre-operative screening and a pre-operative questionnaire. We also compared the prevalence of infections with varying age, sex, and oral diseases, and calculated the cost of screening per positive result. The prevalence of HBV, HCV, and HIV was 0.39% (62/15,842), 0.76% (153/15,839), and 0.07% (10/12,745), respectively. The self-reported rates were as follows: HBV, 63.4% (26/41); HCV, 50.4% (62/123); HIV, 87.5% (7/8). Differences in sex were statistically significant for all infectious diseases; age significantly affected HBV and HCV rates. There was no association between the odds ratio of oral disease and viral infections. The cost per positive result was $1873.8, $905.8, and $11,895.3 for HBV, HCV, and HIV, respectively. Although self-assessment using questionnaires is partially effective, it has inadequate screening accuracy. Formulating an auxiliary diagnosis of infectious diseases with oral diseases was challenging. The cost determined was useful for hepatitis, but not HIV.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama2227-90321052022Impact of System and Diagnostic Errors on Medical Litigation Outcomes: Machine Learning-Based Prediction Models892ENNorioYamamotoDepartment of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityShintaroSukegawaDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalTakashiWatariGeneral Medicine Center, Shimane University HospitalNo prediction models using use conventional logistic models and machine learning exist for medical litigation outcomes involving medical doctors. Using a logistic model and three machine learning models, such as decision tree, random forest, and light-gradient boosting machine (LightGBM), we evaluated the prediction ability for litigation outcomes among medical litigation in Japan. The prediction model with LightGBM had a good predictive ability, with an area under the curve of 0.894 (95% CI; 0.893-0.895) in all patients' data. When evaluating the feature importance using the SHApley Additive exPlanation (SHAP) value, the system error was the most significant predictive factor in all clinical settings for medical doctors' loss in lawsuits. The other predictive factors were diagnostic error in outpatient settings, facility size in inpatients, and procedures or surgery settings. Our prediction model is useful for estimating medical litigation outcomes.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama1996-19441592022Prognostic Factors in Endodontic Surgery Using an Endoscope: A 1 Year Retrospective Cohort Study3353ENShintaroSukegawaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesRiekoShimizuDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalYukaSukegawaDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalKazuakiHasegawaDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalSawakoOnoDepartment of Pathology, Kagawa Prefectural Central HospitalAiFujimuraDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalIzumiYamamotoDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalKeisukeNakanoDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesHotakaKawaiDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesYoshihikoFurukiDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalThis retrospective study clarified the success rate of endoscopic endodontic surgeries and identified predictors accounting for successful surgeries. In this retrospective study, 242 patients (90 males, 152 females) who underwent endoscopic endodontic surgery at a single general hospital and were diagnosed through follow-up one year later were included. Risk factors were categorized into attributes, general health, anatomy, and surgery. Then, the correlation coefficient was calculated for the success or failure of endodontic surgery for each variable, the odds ratio was calculated for the upper variable, and factors related to the surgical prognosis factor were identified. The success rate of endodontic surgery was 95.3%, showing that it was a highly predictable treatment. The top three correlation coefficients were post, age, and perilesional sclerotic signs. Among them, the presence of posts was the highest, compared with the odds ratio, which was 9.592. This retrospective study revealed the success rate and risk factors accounting for endoscopic endodontic surgeries. Among the selected clinical variables, the presence of posts was the most decisive risk factor determining the success of endodontic surgeries.No potential conflict of interest relevant to this article was reported.Nature PortfolioActa Medica Okayama2045-23221212022Identification of osteoporosis using ensemble deep learning model with panoramic radiographs and clinical covariates6088ENShintaroSukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityAiFujimuraDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalAkiraTaguchiDepartment of Oral and Maxillofacial Radiology, School of Dentistry, Matsumoto Dental UniversityNorioYamamotoDepartment of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityAkiraKitamuraSearch Space Inc.RyosukeGotoSearch Space Inc.KeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHotakaKawaiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityYoshihikoFurukiDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalOsteoporosis is becoming a global health issue due to increased life expectancy. However, it is difficult to detect in its early stages owing to a lack of discernible symptoms. Hence, screening for osteoporosis with widely used dental panoramic radiographs would be very cost-effective and useful. In this study, we investigate the use of deep learning to classify osteoporosis from dental panoramic radiographs. In addition, the effect of adding clinical covariate data to the radiographic images on the identification performance was assessed. For objective labeling, a dataset containing 778 images was collected from patients who underwent both skeletal-bone-mineral density measurement and dental panoramic radiography at a single general hospital between 2014 and 2020. Osteoporosis was assessed from the dental panoramic radiographs using convolutional neural network (CNN) models, including EfficientNet-b0, -b3, and -b7 and ResNet-18, -50, and -152. An ensemble model was also constructed with clinical covariates added to each CNN. The ensemble model exhibited improved performance on all metrics for all CNNs, especially accuracy and AUC. The results show that deep learning using CNN can accurately classify osteoporosis from dental panoramic radiographs. Furthermore, it was shown that the accuracy can be improved using an ensemble model with patient covariates.No potential conflict of interest relevant to this article was reported.Nature PortfolioActa Medica Okayama2045-23221212022Evaluation of multi-task learning in deep learning-based positioning classification of mandibular third molars684ENShintaroSukegawaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesTamamoMatsuyamaDepartment of Molecular Oral Medicine and Maxillofacial Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima UniversityFutaTanakaDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityTakeshiHaraDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityKazumasaYoshiiDepartment of Intelligence Science and Engineering, Graduate School of Natural Science and Technology, Gifu UniversityKatsusukeYamashitaPolytechnic Center KagawaKeisukeNakanoDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesHotakaKawaiDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesYoshihikoFurukiDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalPell and Gregory, and Winter's classifications are frequently implemented to classify the mandibular third molars and are crucial for safe tooth extraction. This study aimed to evaluate the classification accuracy of convolutional neural network (CNN) deep learning models using cropped panoramic radiographs based on these classifications. We compared the diagnostic accuracy of single-task and multi-task learning after labeling 1330 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014-2021). The mandibular third molar classifications were analyzed using a VGG 16 model of a CNN. We statistically evaluated performance metrics [accuracy, precision, recall, F1 score, and area under the curve (AUC)] for each prediction. We found that single-task learning was superior to multi-task learning (all p < 0.05) for all metrics, with large effect sizes and low p-values. Recall and F1 scores for position classification showed medium effect sizes in single and multi-task learning. To our knowledge, this is the first deep learning study to examine single-task and multi-task learning for the classification of mandibular third molars. Our results demonstrated the efficacy of implementing Pell and Gregory, and Winter's classifications for specific respective tasks.No potential conflict of interest relevant to this article was reported.American Society for Clinical InvestigationActa Medica Okayama2379-3708712022Resident stroma-secreted chemokine CCL2 governs myeloid-derived suppressor cells in the tumor microenvironmente148960ENMay WathoneOoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHotakaKawaiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityShutaTomidaCenter for Comprehensive Genomic Medicine, Okayama University HospitalTakanoriEguchiDepartment of Dental Pharmacology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKishoOnoDepartment of Oral and Maxillofacial Surgery, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityQiushengShanDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityToshiakiOharaDepartment of Pathology and Experimental Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversitySaoriYoshidaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHarukaOmoriDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityShintaroSukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKuniakiOkamotoDepartment of Dental Pharmacology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityAkiraSasakiDepartment of Oral and Maxillofacial Surgery, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityAccumulating evidence has shown that cancer stroma and BM-derived cells (BMDCs) in the tumor microenvironment (TME) play vital roles in tumor progression. However, the mechanism by which oral cancer stroma recruits any particular subset of BMDCs remains largely unknown. Here, we sought to identify the subset of BMDCs that is recruited by cancer stroma. We established a sequential transplantation model in BALB/c nude mice, including (a) BM transplantation of GFP-expressing cells and (b) coxenografting of patient-derived stroma (PDS; 2 cases, designated PDS1 and PDS2) with oral cancer cells (HSC-2). As controls, xenografting was performed with HSC-2 alone or in combination with normal human dermal fibroblasts (HDF). PDS1, PDS2, and HDF all promoted BMDC migration in vitro and recruitment in vivo. Multicolor immunofluorescence revealed that the PDS coxenografts recruited Arginase-1(+)CD11b(+)GR1(+)GFP(+) cells, which are myeloid-derived suppressor cells (MDSCs), to the TME, whereas the HDF coxenograft did not. Screening using microarrays revealed that PDS1 and PDS2 expressed CCL2 mRNA (encoding C-C motif chemokine ligand 2) at higher levels than did HDF. Indeed, PDS xenografts contained significantly higher proportions of CCL2(+) stromal cells and CCR2(+)Arginase-1(+)CD11b(+)GR1(+) MDSCs (as receiver cells) than the HDF coxenograft. Consistently, a CCL2 synthesis inhibitor and a CCR2 antagonist significantly inhibited the PDS-driven migration of BM cells in vitro. Furthermore, i.p. injection of the CCR2 antagonist to the PDS xenograft models significantly reduced the CCR2(+)Arginase-1(+)CD11b(+)GR1(+) MDSC infiltration to the TME. In conclusion, oral cancer stroma-secreted CCL2 is a key signal for recruiting CCR2(+) MDSCs from BM to the TME.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama2072-66941412021Cancer-Associated Stromal Cells Promote the Contribution of MMP2-Positive Bone Marrow-Derived Cells to Oral Squamous Cell Carcinoma Invasion137ENMay WathoneOoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHotakaKawaiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityQiushengShanDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHtoo ShweEainDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityShintaroSukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversitySimple Summary Based on its invasiveness, oral squamous cell carcinoma (OSCC) shows two different subtypes: less-invasive verrucous squamous carcinoma (VSCC) or highly invasive squamous cell carcinoma (SCC). The stromal component influences OSCC progression and invasion. On the other hand, bone marrow-derived cells (BMDCs) are recruited into tumors and involved in tumor development. We hypothesized that stromal factors might also affect the relation of BMDCs and tumor invasion. We established the OSCC models transplanted with stromal cells from VSCC and SCC, and we compared the potential stromal factors of VSCC and SCC for the involvement of BMDCs in tumor invasion. Our study showed that stromal factors IL6 and IL1B might promote the contribution of MMP-2 positive BMDCs to OSCC invasion. Tumor stromal components contribute to tumor development and invasion. However, the role of stromal cells in the contribution of bone marrow-derived cells (BMDCs) in oral squamous cell carcinoma (OSCC) invasion is unclear. In the present study, we created two different invasive OSCC patient-derived stroma xenografts (PDSXs) and analyzed and compared the effects of stromal cells on the relation of BMDCs and tumor invasion. We isolated stromal cells from two OSCC patients: less invasive verrucous OSCC (VSCC) and highly invasive conventional OSCC (SCC) and co-xenografted with the OSCC cell line (HSC-2) on green fluorescent protein (GFP)-positive bone marrow (BM) cells transplanted mice. We traced the GFP-positive BM cells by immunohistochemistry (IHC) and detected matrix metalloproteinase 2 (MMP2) expression on BM cells by double fluorescent IHC. The results indicated that the SCC-PDSX promotes MMP2-positive BMDCs recruitment to the invasive front line of the tumor. Furthermore, microarray analysis revealed that the expressions of interleukin 6; IL-6 mRNA and interleukin 1 beta; IL1B mRNA were higher in SCC stromal cells than in VSCC stromal cells. Thus, our study first reports that IL-6 and IL1B might be the potential stromal factors promoting the contribution of MMP2-positive BMDCs to OSCC invasion.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama1010-660X5782021Effect of Patient Clinical Variables in Osteoporosis Classification Using Hip X-rays in Deep Learning Analysis846ENNorioYamamotoDepartment of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityShintaroSukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKazutakaYamashitaDepartment of Orthopedic Surgery, Kagawa Prefectural Central HospitalMasakiManabeDepartment of Radiation Technology, Kagawa Prefectural Central HospitalKeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHotakaKawaiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityToshifumiOzakiDepartment of Orthopaedic Surgery, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKeisukeKawasakiDepartment of Orthopedic Surgery, Kagawa Prefectural Central HospitalHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityYoshihikoFurukiDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalTakashiYorifujiDepartment of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityBackground and Objectives: A few deep learning studies have reported that combining image features with patient variables enhanced identification accuracy compared with image-only models. However, previous studies have not statistically reported the additional effect of patient variables on the image-only models. This study aimed to statistically evaluate the osteoporosis identification ability of deep learning by combining hip radiographs with patient variables. Materials andMethods: We collected a dataset containing 1699 images from patients who underwent skeletal-bone-mineral density measurements and hip radiography at a general hospital from 2014 to 2021. Osteoporosis was assessed from hip radiographs using convolutional neural network (CNN) models (ResNet18, 34, 50, 101, and 152). We also investigated ensemble models with patient clinical variables added to each CNN. Accuracy, precision, recall, specificity, F1 score, and area under the curve (AUC) were calculated as performance metrics. Furthermore, we statistically compared the accuracy of the image-only model with that of an ensemble model that included images plus patient factors, including effect size for each performance metric. Results: All metrics were improved in the ResNet34 ensemble model compared with the image-only model. The AUC score in the ensemble model was significantly improved compared with the image-only model (difference 0.004; 95% CI 0.002-0.0007; p = 0.0004, effect size: 0.871). Conclusions: This study revealed the additional effect of patient variables in identification of osteoporosis using deep CNNs with hip radiographs. Our results provided evidence that the patient variables had additive synergistic effects on the image in osteoporosis identification.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama2571-841X412021A Case Report of Primordial Odontogenic Tumor That Required Distinction from a Dentigerous Cyst4ENSawakoOnoDepartment of Pathology, Okayama University HospitalHotakaKawaiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityShintaroSukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityTadashiYoshinoDepartment of Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityPrimordial odontogenic tumor (POT) is a rare odontogenic tumor characterized by a variably cellular loose fibrous tissue with areas similar to the dental papilla and covered by cuboidal to columnar epithelium. We herein report a case of POT in a 14-year-old boy. Computed tomography (CT) exhibited a round cavity with a defined cortical border circumscribing the tooth of the second molar. However, the gross finding was a solid mass, not a cyst. Histologically, the tumor consisted of dental papillalike myxoid connective tissue covered by columnar epithelium. Therefore, although the clinical diagnosis was dentigerous cyst (DC), we diagnosed POT based on histologic findings. Clinical findings of POT resemble DC, but the clinical behavior of POT is different to DC, such as cortical expansion and root resorption of teeth. Therefore, histological differentiation of POT from DC is critical for accurate diagnosis.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama2072-669413142021The Origin of Stroma Influences the Biological Characteristics of Oral Squamous Cell Carcinoma3491ENHarukaOmoriDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical ScienceQiushengShanDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical ScienceKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical ScienceKeisukeNakanoDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical ScienceHotakaKawaiDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical ScienceShintaroSukegawaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical ScienceHidetsuguTsujigiwaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical ScienceHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical ScienceSimple Summary Normal stromal cells play a significant role in the progression of cancers but are poorly investigated in oral squamous cell carcinoma (OSCC). In this study, we found that stromal cells derived from the gingival and periodontal ligament tissues could inhibit differentiation and promote the proliferation, invasion, and migration of OSCC both in vitro and in vivo. Furthermore, microarray data suggested that genes, such as CDK1, BUB1B, TOP2A, DLGAP5, BUB1, and CCNB2, probably play a role in influencing the different effects of gingival stromal tissue cells (G-SCs) and periodontal ligament stromal cells (P-SCs) on the progression of OSCC. Therefore, both G-SCs and P-SCs could promote the progression of OSCC, which could be a potential regulatory mechanism in the progression of OSCC. Normal stromal cells surrounding the tumor parenchyma, such as the extracellular matrix (ECM), normal fibroblasts, mesenchymal stromal cells, and osteoblasts, play a significant role in the progression of cancers. However, the role of gingival and periodontal ligament tissue-derived stromal cells in OSCC progression is unclear. In this study, the effect of G-SCs and P-SCs on the differentiation, proliferation, invasion, and migration of OSCC cells in vitro was examined by Giemsa staining, Immunofluorescence (IF), (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) (MTS), invasion, and migration assays. Furthermore, the effect of G-SCs and P-SCs on the differentiation, proliferation, and bone invasion by OSCC cells in vivo was examined by hematoxylin-eosin (HE) staining, immunohistochemistry (IHC), and tartrate-resistant acid phosphatase (TRAP) staining, respectively. Finally, microarray data and bioinformatics analyses identified potential genes that caused the different effects of G-SCs and P-SCs on OSCC progression. The results showed that both G-SCs and P-SCs inhibited the differentiation and promoted the proliferation, invasion, and migration of OSCC in vitro and in vivo. In addition, genes, including CDK1, BUB1B, TOP2A, DLGAP5, BUB1, and CCNB2, are probably involved in causing the different effects of G-SCs and P-SCs on OSCC progression. Therefore, as a potential regulatory mechanism, both G-SCs and P-SCs can promote OSCC progression.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama2218-273X1162021Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images815ENShintaroSukegawaDentistry and Pharmaceutical Sciences, Department of Oral Pathology and Medicine, Okayama University Graduate School of MedicineKazumasaYoshiiDepartment of Intelligence Science and Engineering, Graduate School of Natural Science and Technology, Gifu UniversityTakeshiHaraDepartment of Intelligence Science and Engineering, Graduate School of Natural Science and Technology, Gifu UniversityTamamoMatsuyamaDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalKatsusukeYamashitaPolytechnic Center KagawaKeisukeNakanoDentistry and Pharmaceutical Sciences, Department of Oral Pathology and Medicine, Okayama University Graduate School of MedicineKiyofumiTakabatakeDentistry and Pharmaceutical Sciences, Department of Oral Pathology and Medicine, Okayama University Graduate School of MedicineHotakaKawaiDentistry and Pharmaceutical Sciences, Department of Oral Pathology and Medicine, Okayama University Graduate School of MedicineHitoshiNagatsukaDentistry and Pharmaceutical Sciences, Department of Oral Pathology and Medicine, Okayama University Graduate School of MedicineYoshihikoFurukiDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalIt is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images. For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages were obtained from the digital panoramic radiographs of patients who underwent procedures at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2020. Five deep convolutional neural network (CNN) models (ResNet18, 34, 50, 101 and 152) were evaluated. The accuracy, precision, recall, specificity, F1 score, and area under the curve score were calculated for each CNN. We also compared the multi-task and single-task accuracies of brand classification and implant treatment stage classification. Our analysis revealed that the larger the number of parameters and the deeper the network, the better the performance for both classifications. Multi-tasking significantly improved brand classification on all performance indicators, except recall, and significantly improved all metrics in treatment phase classification. Using CNNs conferred high validity in the classification of dental implant brands and treatment stages. Furthermore, multi-task learning facilitated analysis accuracy.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama1996-194414122021Biological Effects of Bioresorbable Materials in Alveolar Ridge Augmentation: Comparison of Early and Slow Resorbing Osteosynthesis Materials3286ENHotakaKawaiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityShintaroSukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversitySawakoOnoDepartment of Pathology, Kagawa Prefectural Central HospitalHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityYoshihikoFurukiDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalThe purpose of this study was to investigate the bone healing properties and histological environment of a u-HA/PLLA/PGA (u-HA-uncalcined and unsintered hydroxyapatite, PLLA-Poly L-lactic acid, PGA-polyglycolic acid) composite device in humans, and to understand the histological dynamics of using this device for maxillofacial treatments. Twenty-one subjects underwent pre-implant maxillary alveolar ridge augmentation with mandibular cortical bone blocks using u-HA/PLLA or u-HA/PLLA/PGA screws for fixation. Six months later, specimens of these screws and their adjacent tissue were retrieved. A histological and immunohistochemical evaluation of these samples was performed using collagen 1a, ALP (alkaline phosphatase), and osteocalcin. We observed that alveolar bone augmentation was successful for all of the subjects. Upon histological evaluation, the u-HA/PLLA screws had merged with the bone components, and the bone was directly connected to the biomaterial. In contrast, direct bone connection was not observed for the u-HA/PLLA/PGA screw. Immunohistological findings showed that in the u-HA/PLLA group, collagen 1a was positive for fibers that penetrated vertically into the bone. Alkaline phosphatase was positive only in the u-HA/PLLA stroma, and the stroma was negative for osteocalcin. In this study, u-HA/PLLA showed a greater bioactive bone conductivity than u-HA/PLLA/PGA and a higher biocompatibility for direct bone attachment. Furthermore, u-HA/PLLA was shown to have the potential for bone formation in the stroma.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama1996-194414122021Preparation of Absorption-Resistant Hard Tissue Using Dental Pulp-Derived Cells and Honeycomb Tricalcium Phosphate3409ENKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityKeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityHotakaKawaiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityYasunoriInadaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityShintaroSukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityQiushengShanDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityShigekoFushimiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityHidetsuguTsujigiwaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityIn recent years, there has been increasing interest in the treatment of bone defects using undifferentiated mesenchymal stem cells (MSCs) in vivo. Recently, dental pulp has been proposed as a promising source of pluripotent mesenchymal stem cells (MSCs), which can be used in various clinical applications. Dentin is the hard tissue that makes up teeth, and has the same composition and strength as bone. However, unlike bone, dentin is usually not remodeled under physiological conditions. Here, we generated odontoblast-like cells from mouse dental pulp stem cells and combined them with honeycomb tricalcium phosphate (TCP) with a 300 mu m hole to create bone-like tissue under the skin of mice. The bone-like hard tissue produced in this study was different from bone tissue, i.e., was not resorbed by osteoclasts and was less easily absorbed than the bone tissue. It has been suggested that hard tissue-forming cells induced from dental pulp do not have the ability to induce osteoclast differentiation. Therefore, the newly created bone-like hard tissue has high potential for absorption-resistant hard tissue repair and regeneration procedures.No potential conflict of interest relevant to this article was reported.Ivyspring InternationalActa Medica Okayama1449-19071882021Potential role of myeloid-derived suppressor cells in transition from reaction to repair phase of bone healing process18241830ENHotakaKawaiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityMay WathoneOoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHidetsuguTsujigiwaDepartment of Life Science, Faculty of Science, Okayama University of ScienceKeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityShintaro SukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityMyeloid-derived suppressor cells (MDSCs) are a heterogeneous population of immature myeloid cells with immunosuppressive functions; these cells play a key role in infection, immunization, chronic inflammation, and cancer. Recent studies have reported that immunosuppression plays an important role in the healing process of tissues and that Treg play an important role in fracture healing. MDSCs suppress active T cell proliferation and reduce the severity of arthritis in mice and humans. Together, these findings suggest that MDSCs play a role in bone biotransformation. In the present study, we examined the role of MDSCs in the bone healing process by creating a bone injury at the tibial epiphysis in mice. MDSCs were identified by CD11b and GR1 immunohistochemistry and their role in new bone formation was observed by detection of Runx2 and osteocalcin expression. Significant numbers of MDSCs were observed in transitional areas from the reactionary to repair stages. Interestingly, MDSCs exhibited Runx2 and osteocalcin expression in the transitional area but not in the reactionary area. And at the same area, cllagene-1 and ALP expression level increased in osteoblast progenitor cells. These data is suggesting that MDSCs emerge to suppress inflammation and support new bone formation. Here, we report, for the first time (to our knowledge), the role of MDSCs in the initiation of bone formation. MDSC appeared at the transition from inflammation to bone making and regulates bone healing by suppressing inflammation.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama1996-194413222020Geometrical Structure of Honeycomb TCP to Control Dental Pulp-Derived Cell Differentiation5155ENKiyofumiTakabatakeDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityHidetsuguTsujigiwaDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityKeisukeNakanoDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityYasunoriInadaDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityShanQiushengDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityHotakaKawaiDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityShintaro SukegawaDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityShigekoFushimiDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityHitoshiNagatsukaDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityRecently, dental pulp has been attracting attention as a promising source of multipotent mesenchymal stem cells (MSCs) for various clinical applications of regeneration fields. To date, we have succeeded in establishing rat dental pulp-derived cells showing the characteristics of odontoblasts under in vitro conditions. We named them Tooth matrix-forming, GFP rat-derived Cells (TGC). However, though TGC form massive dentin-like hard tissues under in vivo conditions, this does not lead to the induction of polar odontoblasts. Focusing on the importance of the geometrical structure of an artificial biomaterial to induce cell differentiation and hard tissue formation, we previously have succeeded in developing a new biomaterial, honeycomb tricalcium phosphate (TCP) scaffold with through-holes of various diameters. In this study, to induce polar odontoblasts, TGC were induced to form odontoblasts using honeycomb TCP that had various hole diameters (75, 300, and 500 mu m) as a scaffold. The results showed that honeycomb TCP with 300-mu m hole diameters (300TCP) differentiated TGC into polar odontoblasts that were DSP positive. Therefore, our study indicates that 300TCP is an appropriate artificial biomaterial for dentin regeneration.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama2218-273X10112020Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates1534ENNorioYamamotoDepartment of Orthopaedic Surgery, Kagawa Prefectural Central HospitalShintaro SukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityAkiraKitamuraSearch Space Inc.RyosukeGotoSearch Space Inc.TomoyukiNodaDepartment of Musculoskeletal Traumatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesKeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHotakaKawaiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKeisukeKawasakiDepartment of Orthopaedic Surgery, Kagawa Prefectural Central HospitalYoshihikoFurukiDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalToshifumiOzakiDepartment of Orthopaedic Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesThis study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal bone mineral density measurement and hip radiography at a single general hospital between 2014 and 2019. Osteoporosis was assessed from the hip radiographs using five convolutional neural network (CNN) models. We also investigated ensemble models with clinical covariates added to each CNN. The accuracy, precision, recall, specificity, negative predictive value (npv), F1 score, and area under the curve (AUC) score were calculated for each network. In the evaluation of the five CNN models using only hip radiographs, GoogleNet and EfficientNet b3 exhibited the best accuracy, precision, and specificity. Among the five ensemble models, EfficientNet b3 exhibited the best accuracy, recall, npv, F1 score, and AUC score when patient variables were included. The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama1422-006721202020Impact of the Stroma on the Biological Characteristics of the Parenchyma in Oral Squamous Cell Carcinoma7714ENKiyofumiTakabatakeDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityHotakaKawaiDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityHarukaOmoriDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityShanQiushengDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityMay WathoneOoDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityShintaro SukegawaDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityKeisukeNakanoDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityHidetsuguTsujigiwaDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversityHitoshiNagatsukaDepartment of Oral Pathology and Medicine Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama UniversitySolid tumors consist of the tumor parenchyma and stroma. The standard concept of oncology is that the tumor parenchyma regulates the tumor stroma and promotes tumor progression, and that the tumor parenchyma represents the tumor itself and defines the biological characteristics of the tumor tissue. Thus, the tumor stroma plays a pivotal role in assisting tumor parenchymal growth and invasiveness and is regarded as a supporter of the tumor parenchyma. The tumor parenchyma and stroma interact with each other. However, the influence of the stroma on the parenchyma is not clear. Therefore, in this study, we investigated the effect of the stroma on the parenchyma in oral squamous cell carcinoma (OSCC). We isolated tumor stroma from two types of OSCCs with different invasiveness (endophytic type OSCC (ED-st) and exophytic type OSCC (EX-st)) and examined the effect of the stroma on the parenchyma in terms of proliferation, invasion, and morphology by co-culturing and co-transplanting the OSCC cell line (HSC-2) with the two types of stroma. Both types of stroma were partially positive for alpha-smooth muscle actin. The tumor stroma increased the proliferation and invasion of tumor cells and altered the morphology of tumor cells in vitro and in vivo. ED-st exerted a greater effect on the tumor parenchyma in proliferation and invasion than EX-st. Morphological analysis showed that ED-st changed the morphology of HSC-2 cells to the invasive type of OSCC, and EX-st altered the morphology of HSC-2 cells to verrucous OSCC. This study suggests that the tumor stroma influences the biological characteristics of the parenchyma and that the origin of the stroma is strongly associated with the biological characteristics of the tumor.No potential conflict of interest relevant to this article was reported.BMCActa Medica Okayama1746-15961512020Clinical study on primary screening of oral cancer and precancerous lesions by oral cytology107ENShintaro SukegawaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesSawakoOnoDepartment of Pathology, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical SciencesKeisukeNakanoDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesHotakaKawaiDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesYoshihikoFurukiDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalBackground This study was conducted to compare the histological diagnostic accuracy of conventional oral-based cytology and liquid-based cytology (LBC) methods. Methods Histological diagnoses of 251 cases were classified as negative (no malignancy lesion, inflammation, or mild/moderate dysplasia) and positive [severe dysplasia/carcinoma in situ (CIS) and squamous cell carcinoma (SCC)]. Cytological diagnoses were classified as negative for intraepithelial lesion or malignancy (NILM), oral low-grade squamous intraepithelial lesion (OLSIL), oral high-grade squamous intraepithelial lesion (OHSIL), or SCC. Cytological diagnostic results were compared with histology results. Results Of NILM cytology cases, the most frequent case was negative [LBCn = 50 (90.9%), conventionaln = 22 (95.7%)]. Among OLSIL cytodiagnoses, the most common was negative (LBCn = 34; 75.6%, conventionaln = 14; 70.0%). Among OHSIL cytodiagnoses (LBCn = 51, conventionaln = 23), SCC was the most frequent (LBCn = 31; 60.8%, conventionaln = 7; 30.4%). Negative cases were common (LBCn = 13; 25.5%, conventionaln = 14; 60.9%). Among SCC cytodiagnoses SCC was the most common (LBCn = 16; 88.9%, conventionaln = 14; 87.5%). Regarding the diagnostic results of cytology, assuming OHSIL and SCC as cytologically positive, the LBC method/conventional method showed a sensitivity of 79.4%/76.7%, specificity of 85.1%/69.2%, false-positive rate of 14.9%/30.7%, and false-negative rate of 20.6%/23.3%. Conclusions LBC method was superior to conventional cytodiagnosis methods. It was especially superior for OLSIL and OHSIL. Because of the false-positive and false-negative cytodiagnoses, it is necessary to make a comprehensive diagnosis considering the clinical findings.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama2218-273X1072020Deep Neural Networks for Dental Implant System Classification984ENShintaro SukegawaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesKazumasaYoshiiDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityTakeshiHaraDepartment of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu UniversityKatsusukeYamashitaPolytechnic Center KagawaKeisukeNakanoDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesNorioYamamotoDepartment of Orthopaedic Surgery, Kagawa Prefectural Central HospitalHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesYoshihikoFurukiIn this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama12222019Advantage of Alveolar Ridge Augmentation with Bioactive/Bioresorbable Screws Made of Composites of Unsintered Hydroxyapatite and Poly-L-lactide3681ENShintaro SukegawaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityHotakaKawaiDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKeisukeNakanoDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityTakahiroKannoDepartment of Oral and Maxillofacial Surgery, Shimane University Faculty of MedicineHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama UniversityYoshihikoFurukiDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalWe studied human bone healing characteristics and the histological osteogenic environment by using devices made of a composite of uncalcined and unsintered hydroxyapatite (u-HA) and poly-L-lactide (PLLA). In eight cases of fixation, we used u-HA/PLLA screws for maxillary alveolar ridge augmentation, for which mandibular cortical bone block was used in preimplantation surgery. Five appropriate samples with screws were evaluated histologically and immunohistochemically for runt-related transcription factor 2 (RUNX2), transcription factor Sp7 (Osterix), and leptin receptor (LepR). In all cases, histological evaluation revealed that bone components had completely surrounded the u-HA/PLLA screws, and the bone was connected directly to the biomaterial. Inflammatory cells did not invade the space between the bone and the u-HA/PLLA screw. Immunohistochemical evaluation revealed that many cells were positive for RUNX2 or Osterix, which are markers for osteoblast and osteoprogenitor cells, in the tissues surrounding u-HA/PLLA. In addition, many bone marrow-derived mesenchymal stem cells were notably positive for both LepR and RUNX2. The u-HA/PLLA material showed excellent bioactive osteoconductivity and a highly biocompatibility with bone directly attached. In addition, our findings suggest that many bone marrow-derived mesenchymal stem cells and mature osteoblast are present in the osteogenic environment created with u-HA/PLLA screws and that this environment is suitable for osteogenesis.No potential conflict of interest relevant to this article was reported.MDPIActa Medica Okayama1996-19441292019Biomechanical Loading Comparison between Titanium and Unsintered Hydroxyapatite/Poly-L-Lactide Plate System for Fixation of Mandibular Subcondylar Fractures1557ENShintaroSukegawaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesTakahiroKannoDepartment of Oral and Maxillofacial Surgery, Shimane University Faculty of MedicineNorioYamamotoDepartment of Orthopaedic Surgery, Kagawa Prefectural Central HospitalKeisukeNakanoDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesKiyofumiTakabatakeDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesHotakaKawaiDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesHitoshiNagatsukaDepartment of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical SciencesYoshihikoFurukiDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central HospitalOsteosynthesis absorbable materials made of uncalcined and unsintered hydroxyapatite (u-HA) particles, poly-l-lactide (PLLA), and u-HA/PLLA are bioresorbable, and these plate systems have feasible bioactive osteoconductive capacities. However, their strength and stability for fixation in mandibular subcondylar fractures remain unclear. This in vitro study aimed to assess the biomechanical strength of u-HA/PLLA bioresorbable plate systems after internal fixation of mandibular subcondylar fractures. Tensile and shear strength were measured for each u-HA/PLLA and titanium plate system. To evaluate biomechanical behavior, 20 hemimandible replicas were divided into 10 groups, each comprising a titanium plate and a bioresorbable plate. A linear load was applied anteroposteriorly and lateromedially to each group to simulate the muscular forces in mandibular condylar fractures. All samples were analyzed for each displacement load and the displacement obtained by the maximum load. Tensile and shear strength of the u-HA/PLLA plate were each approximately 45% of those of the titanium plates. Mechanical resistance was worst in the u-HA/PLLA plate initially loaded anteroposteriorly. Titanium plates showed the best mechanical resistance during lateromedial loading. Notably, both plates showed similar resistance when a lateromedially load was applied. In the biomechanical evaluation of mandibular condylar fracture treatment, the u-HA/PLLA plates had sufficiently high resistance in the two-plate fixation method.No potential conflict of interest relevant to this article was reported.