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

ABSTRACT: 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 cancer patients and assess clinical benefits. We analyzed 319 patients from five institutions retrospectively. ABM was divided into three 18F-FDG PET/CT-defined subregions: active iliac bone marrow (A_IBM), active sacral bone marrow (A_SBM), and active lumbar vertebrae bone marrow (A_LVBM), defined as areas with standardized uptake values exceeding subregional means. Six deep learning models were trained: hybrid nnU-Net, U-Net, V-Net, ResU-Net, nnU-Net, and UNETR. The hybrid nnU-Net approach integrated nnU-Net predictions

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC12361777 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature