Results: The final prediction model included 20 radiomics features and eight clinical and dosimetric characteristics. The five parameter -tuned learners and the other four learners (i.e., logistic regression (LR), K-nearest neighbors (KNN), linear discriminant analysis (LDA), Bagging) whose parameters cannot be tuned, all as the primary week learners, were fed into the subsequent meta-learners for training and learning the final prediction model. To achieve the best prediction performance, a Bayesian optimization based multi-parameter tuning technology was adopted for the AdaBoost, random forest (RF), decision tree (DT), gradient boosting (GB) and extra tree (XTree) five machine learning models. A total of 4309 radiomics features extracted from these six ROIs, as well as clinical and dosimetric characteristics, were used to train and validate the prediction model using nine mainstream deep machine learning algorithms and three stacking classifiers (i.e., meta-learners). Six regions of interest (ROIs) were delineated based on three PTV dose -gradient-related and three skin dose-gradient-related parameters (i.e., isodose). Materials and methods: The study retrospectively included 214 patients with breast cancer who received radiotherapy after breast surgeries. Purpose: In this study, we aimed to develop a novel Bayesian optimization based multi-stacking deep learning platform for the prediction of radiation-induced dermatitis (grade ≥ two) (RD 2+) before radiotherapy, by using multi-region dose-gradient-related radiomics features extracted from pre-treatment planning four-dimensional computed tomography (4D-CT) images, as well as clinical and dosimetric characteristics of breast cancer patients who underwent radiotherapy. 5Laboratory of Medical Imaging and Translational Medicine, Hangzhou Cancer Hospital Affiliated to Zhejiang University School of Medicine, Hangzhou, China.4Department of Radiotherapy, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou, China.3Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China. 2Department of Tumor Radiotherapy, Hangzhou Cancer Hospital, Hangzhou, China.1Department of Tumor Radiotherapy, The First People’s Hospital of Fuyang Hangzhou, Hangzhou, China.Kuan Wu 1*, Xiaoyan Miu 1, Hui Wang 2 and Xiadong Li 2,3,4,5*†
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