Mason J Belue, Stephanie A Harmon, Dong Yang, Julie Y An, Sonia Gaur, Yan Mee Law, Evrim Turkbey, Ziyue Xu, Jesse Tetreault, Nathan S Lay, Enis C Yilmaz, Tim E Phelps, Benjamin Simon, Liza Lindenberg, Esther Mena, Peter A Pinto, Ulas Bagci, Bradford J Wood, Deborah E Citrin, William L Dahut, Ravi A Madan, James L Gulley, Daguang Xu, Peter L Choyke, Baris Turkbey
RATIONALE AND OBJECTIVES: Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs. metastatic lesion classification on staging CTs and to compare its performance with radiologists. MATERIALS AND METHODS: This retrospective study developed two AI models using 297 staging CT scans (81 metastatic) with 4601 benign and 1911 metastatic lesions in PCa patients...
January 22, 2024: Academic Radiology