Hybrid deep learning enables multi-institutional delineation of active bone marrow for gynecologic radiotherapy

Hybrid deep learning enables multi-institutional delineation of active bone marrow for gynecologic radiotherapy

Background and purpose: Pelvic radiotherapy for gynecologic cancer inevitably irradiates sensitive areas like iliac bones, lumbar vertebrae, and sacrum. Using 18F-FDG PET/CT as a reference, we developed a deep learning method to detect hematopoietic active bone marrow (ABM) on CT in gynecologic canc...

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Bibliographic Details
Main Authors: Zhe Zhang, Xiao Lu, Sicheng He, Tao Huang, Shaobin Wang, Mingjun Lu, Xiaomin Zhang, Zhibo Tan, John Moraros, Lei Zhang, Xin Li, Zhan Li, Zihao Deng, Yimeng Zhang, Mengjie Dong, Shuihua Wang, Yajie Liu
Format: Article
Language:English
Published: Elsevier 2025-07-01
Series:Physics and Imaging in Radiation Oncology
Subjects:
Active bone marrow
Gynecologic radiotherapy
Hybrid deep learning segmentation
Multi-institutional study
Hematologic toxicity
Online Access:http://www.sciencedirect.com/science/article/pii/S2405631625001289
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