Early Identification and Management of Biochemical Recurrence Following Radical Prostatectomy.
Authors
Affiliations (6)
Affiliations (6)
- Department of Urology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
- Department of Ultrasound, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
- Department of Urology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
- Department of Pathology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China. [email protected].
- Department of Urology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. [email protected].
- Department of Urology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China. [email protected].
Abstract
Biochemical recurrence (BCR) is a common clinical event after radical prostatectomy (RP). Accurate risk stratification and timely individualized treatment are still major problems in clinical practice and research. Current clinical guidelines and key original studies describe several main components of BCR management after RP, including how BCR is defined, which factors affect prognosis, how imaging is used for evaluation, and which treatment options are available. These components help guide clinical decisions at different stages of care. Recent work has focused on the use of artificial intelligence (AI). Multimodal AI models that integrate clinical features, MRI-based radiomics, and whole-slide image pathology features demonstrate superior performance compared with single-modal approaches in predicting BCR after RP.