{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T19:10:37Z","timestamp":1787166637328,"version":"build-2736575974"},"reference-count":32,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Current Oncology"],"abstract":"<jats:p>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\u2019s 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\u2019s 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 \u2265 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.<\/jats:p>","DOI":"10.3390\/curroncol30080537","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T09:28:34Z","timestamp":1691141314000},"page":"7412-7424","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Mean Heart Dose Prediction Using Parameters of Single-Slice Computed Tomography and Body Mass Index: Machine Learning Approach for Radiotherapy of Left-Sided Breast Cancer of Asian Patients"],"prefix":"10.3390","volume":"30","author":[{"given":"Wlla E.","family":"Al-Hammad","sequence":"first","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan"},{"name":"Department of Oral Medicine and Oral Surgery, Faculty of Dentistry, Jordan University of Science and Technology, Irbid 22110, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6532-2575","authenticated-orcid":false,"given":"Masahiro","family":"Kuroda","sequence":"additional","affiliation":[{"name":"Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryo","family":"Kamizaki","sequence":"additional","affiliation":[{"name":"Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nouha","family":"Tekiki","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hinata","family":"Ishizaka","sequence":"additional","affiliation":[{"name":"Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuhiro","family":"Kuroda","sequence":"additional","affiliation":[{"name":"Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan"},{"name":"Department of Health and Welfare Science, Graduate School of Health and Welfare Science, Okayama Prefectural University, Okayama 719-1197, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kohei","family":"Sugimoto","sequence":"additional","affiliation":[{"name":"Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masataka","family":"Oita","sequence":"additional","affiliation":[{"name":"Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University, Okayama 770-8558, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7259-3317","authenticated-orcid":false,"given":"Yoshinori","family":"Tanabe","sequence":"additional","affiliation":[{"name":"Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Majd","family":"Barham","sequence":"additional","affiliation":[{"name":"Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University, Nablus 44839, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irfan","family":"Sugianto","sequence":"additional","affiliation":[{"name":"Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University, Sulawesi 90245, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yudai","family":"Shimizu","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuki","family":"Nakamitsu","sequence":"additional","affiliation":[{"name":"Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700-8558, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1380-5639","authenticated-orcid":false,"given":"Junichi","family":"Asaumi","sequence":"additional","affiliation":[{"name":"Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1016\/S0140-6736(11)61629-2","article-title":"Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: Meta-analysis of individual patient data for 10,801 women in 17 randomised 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