MDPI Acta Medica Okayama 2075-4418 15 6 2025 Robustness of Machine Learning Predictions for Determining Whether Deep Inspiration Breath-Hold Is Required in Breast Cancer Radiation Therapy 668 EN Wlla E. Al-Hammad Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Masahiro Kuroda Radiological Technology, Graduate School of Health Sciences, Okayama University Jamal, Ghaida Al Jamal Department of Oral Medicine and Oral Surgery, Faculty of Dentistry, Jordan University of Science and Technology Mamiko Fujikura Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Ryo Kamizaki Radiological Technology, Graduate School of Health Sciences, Okayama University Kazuhiro Kuroda Radiological Technology, Graduate School of Health Sciences, Okayama University Suzuka Yoshida Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Yoshihide Nakamura Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Masataka Oita Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University Yoshinori Tanabe Radiological Technology, Graduate School of Health Sciences, Okayama University Kohei Sugimoto Radiological Technology, Graduate School of Health Sciences, Okayama University Irfan Sugianto Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University Majd Barham Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University Nouha Tekiki Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Miki Hisatomi Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Junichi Asaumi Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University 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. No potential conflict of interest relevant to this article was reported.

MDPI Acta Medica Okayama 2075-4418 15 6 2025 Improving Diagnostic Performance for Head and Neck Tumors with Simple Diffusion Kurtosis Imaging and Machine Learning Bi-Parameter Analysis 790 EN Suzuka Yoshida Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Masahiro Kuroda Radiological Technology, Graduate School of Health Sciences, Okayama University Yoshihide Nakamura Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Yuka Fukumura Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Yuki Nakamitsu Radiological Technology, Graduate School of Health Sciences, Okayama University Wlla E. Al-Hammad Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Kazuhiro Kuroda Radiological Technology, Graduate School of Health Sciences, Okayama University Yudai Shimizu Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Yoshinori Tanabe Radiological Technology, Graduate School of Health Sciences, Okayama University Masataka Oita Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University Irfan Sugianto Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University Majd Barham Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University Nouha Tekiki Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Nurul N. Kamaruddin Department of Oral Rehabilitation and Regenerative Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Miki Hisatomi Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Yoshinobu Yanagi Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University Junichi Asaumi Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University 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. No potential conflict of interest relevant to this article was reported.