Prediction of radiation-induced acute skin toxicity in breast cancer patients using data encapsulation screening and dose-gradient-based multi-region radiomics technique: A multicenter study.

Feng, Huichun; Wang, Hui; Xu, Lixia; Ren, Yao; Ni, Qianxi; Yang, Zhen; Ma, Shenglin; Deng, Qinghua; Chen, Xueqin; Xia, Bing; Kuang, Yu; Li, Xiadong

Feng, Huichun; Wang, Hui; Xu, Lixia; Ren, Yao; Ni, Qianxi; Yang, Zhen; Ma, Shenglin; Deng, Qinghua; Chen, Xueqin; Xia, Bing; Kuang, Yu; Li, Xiadong.

Afiliación

Feng H; Medical Imaging and Translational Medicine Laboratory, Hangzhou Cancer Center, Hangzhou, China.
Wang H; Patient follow-up center, Hangzhou Cancer Hospital, Hangzhou, China.
Xu L; Medical Imaging and Translational Medicine Laboratory, Hangzhou Cancer Center, Hangzhou, China.
Ren Y; Department of Radiotherapy, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Ni Q; Medical Imaging and Translational Medicine Laboratory, Hangzhou Cancer Center, Hangzhou, China.
Yang Z; Department of Radiotherapy, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Ma S; Medical Imaging and Translational Medicine Laboratory, Hangzhou Cancer Center, Hangzhou, China.
Deng Q; Department of Radiotherapy, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Chen X; Department of Radiology, Hunan Cancer Hospital, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
Xia B; Department of Radiotherapy, Xiangya Hospital Central South University, Changsha, China.
Kuang Y; Medical Imaging and Translational Medicine Laboratory, Hangzhou Cancer Center, Hangzhou, China.
Li X; Medical Oncology, Xiaoshan Hospital Affiliated to Hangzhou Normal University, Hangzhou, China.

Front Oncol ; 12: 1017435, 2022.

Article en En | MEDLINE | ID: mdl-36439515

Palabras clave

Breast cancer; gradient boosting decision tree; machine learning; radiation therapy; radiation-induced skin toxicity; radiomics

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Aspecto: Patient_preference Idioma: En Revista: Front Oncol Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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