A Bayesian optimization tunning integrated multi-stacking classifier framework for the prediction of radiodermatitis from 4D-CT of patients underwent breast cancer radiotherapy

A Bayesian optimization tunning integrated multi-stacking classifier framework for the prediction of radiodermatitis from 4D-CT of patients underwent breast cancer radiotherapy

PurposeIn 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-treatmen...

Full description

Bibliographic Details
Main Authors: Kuan Wu, Xiaoyan Miu, Hui Wang, Xiadong Li
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-06-01
Series:Frontiers in Oncology
Subjects:
breast cancer
radiation therapy
radiation-induced dermatitis
radiomics
stackingclassifier
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2023.1152020/full

Internet

https://www.frontiersin.org/articles/10.3389/fonc.2023.1152020/full

Similar Items