スギモト　コウヘイ   杉本　昂平    所属   川崎医療福祉大学  医療技術学部 診療放射線技術学科    職種   助教
論文種別	原著
言語種別	英語
査読の有無	査読あり
表題	Mean Heart Dose Prediction Using Parameters of Single-Slice Computed Tomography and Body Mass Index: Machine Learning Approach for Radiotherapy of Left-Sided Breast Cancer of Asian Patients.
掲載誌名	正式名：Current oncology (Toronto, Ont.) 略　 称：Curr Oncol ISSNコード：17187729/11980052
掲載区分	国外
巻・号・頁	30(8),pp.7412-7424
著者・共著者	Wlla E Al-Hammad, Masahiro Kuroda, Ryo Kamizaki, Nouha Tekiki, Hinata Ishizaka, Kazuhiro Kuroda, Kohei Sugimoto, Masataka Oita, Yoshinori Tanabe, Majd Barham, Irfan Sugianto, Yudai Shimizu, Yuki Nakamitsu, Junichi Asaumi
発行年月	2023/08
概要	Deep inspiration breath-hold (DIBH) is an excellent technique to reduce the incidental radiation received by the heart during radiotherapy in patients with breast cancer. However, DIBH is costly and time-consuming for patients and radiotherapy staff. In Asian countries, the use of DIBH is restricted due to the limited number of patients with a high mean heart dose (MHD) and the shortage of radiotherapy personnel and equipment compared to that in the USA. This study aimed to develop, evaluate, and compare the performance of ten machine learning algorithms for predicting MHD using a patient's body mass index and single-slice CT parameters to identify patients who may not require DIBH. Machine learning models were built and tested using a dataset containing 207 patients with left-sided breast cancer who were treated with field-in-field radiotherapy with free breathing. The average MHD was 251 cGy. Stratified repeated four-fold cross-validation was used to build models using 165 training data. The models were compared internally using their average performance metrics: F2 score, AUC, recall, accuracy, Cohen's kappa, and Matthews correlation coefficient. The final performance evaluation for each model was further externally analyzed using 42 unseen test data. The performance of each model was evaluated as a binary classifier by setting the cut-off value of MHD ≥ 300 cGy. The deep neural network (DNN) achieved the highest F2 score (78.9%). Most models successfully classified all patients with high MHD as true positive. This study indicates that the ten models, especially the DNN, might have the potential to identify patients who may not require DIBH.
DOI	10.3390/curroncol30080537
PMID	37623018