カナイ　タカユキ   金井　貴幸    所属   医学部 医学科（東京女子医科大学病院）    職種   講師
論文種別	原著
言語種別	英語
査読の有無	査読あり
表題	Prediction of radiation pneumonitis with machine learning using 4D-CT based dose-function features.
掲載誌名	正式名：Journal of radiation research 略　 称：J Radiat Res ISSNコード：13499157/04493060
掲載区分	国内
巻・号・頁	63(1),pp.71-79
著者・共著者	Katsuta Yoshiyuki, Kadoya Noriyuki, Mouri Shina, Tanaka Shohei, Kanai Takayuki, Takeda Kazuya, Yamamoto Takaya, Ito Kengo, Kajikawa Tomohiro, Nakajima Yujiro, Jingu Keiichi
発行年月	2022/01
概要	In this article, we highlight the fundamental importance of the simultaneous use of dose-volume histogram (DVH) and dose-function histogram (DFH) features based on functional images calculated from 4-dimensional computed tomography (4D-CT) and deformable image registration (DIR) in developing a multivariate radiation pneumonitis (RP) prediction model. The patient characteristics, DVH features and DFH features were calculated from functional images by Hounsfield unit (HU) and Jacobian metrics, for an RP grade ≥ 2 multivariate prediction models were computed from 85 non-small cell lung cancer patients. The prediction model is developed using machine learning via a kernel-based support vector machine (SVM) machine. In the patient cohort, 21 of the 85 patients (24.7%) presented with RP grade ≥ 2. The median area under curve (AUC) was 0.58 for the generated 50 prediction models with patient clinical features and DVH features. When HU metric and Jacobian metric DFH features were added, the AUC improved to 0.73 and 0.68, respectively. We conclude that predictive RP models that incorporate DFH features were successfully developed via kernel-based SVM. These results demonstrate that effectiveness of the simultaneous use of DVH features and DFH features calculated from 4D-CT and DIR on functional image-guided radiotherapy.
DOI	10.1093/jrr/rrab097
PMID	34718683