Artificial intelligence-assisted enhancement of prostate volume metrics in the diagnosis of clinically significant prostate cancer: a retrospective analysis

Yan Zhang; Shengyun He; Jie Chen; Guiqin Liu; Yuansheng Luo; Yang Song; Yan Zhou; Yinjie Zhu; Xiaobin Wei; Guangyu Wu

doi:10.21037/qims-24-1015

Artificial intelligence-assisted enhancement of prostate volume metrics in the diagnosis of clinically significant prostate cancer: a retrospective analysis

Quant Imaging Med Surg. 2025 Feb 1;15(2):1641-1652. doi: 10.21037/qims-24-1015. Epub 2025 Jan 16.

Authors

Yan Zhang^#¹, Shengyun He^#¹, Jie Chen^#², Guiqin Liu¹, Yuansheng Luo¹, Yang Song³, Yan Zhou¹, Yinjie Zhu⁴, Xiaobin Wei¹, Guangyu Wu¹

Affiliations

¹ Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
² Huangpu Branch, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
³ Magnetic Resonance Research Collaboration Team, Siemens Healthineers Ltd., Shanghai, China.
⁴ Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.

^# Contributed equally.

Abstract

Background: Although artificial intelligence (AI) algorithms provide reliable prostate volume (PV) measurements across various magnetic resonance imaging devices, their impact on prostate cancer risk stratification for patients with a Gleason score of 6 remains unclear. This study aimed to evaluate the benefits of integrating AI-derived PV and transitional or peripheral zone volume (TZV/PZV) measurements with clinical factors to improve prostate-cancer risk stratification.

Methods: Our retrospective cohort included 560 patients with biopsy-confirmed Gleason score 6, stratified based on the outcome of radical prostatectomy as clinically significant prostate cancer (csPC) and clinically insignificant prostate cancer (insPC). We used AI methods for accurate PV and TZV/PZV estimation based on the origin virtual net (Vnet) and with cascade (coarse and fine) Vnet, the best-performing volume segmentation network in the subsequent analysis. We then developed predictive models incorporating clinical factors including age, prostate serum antigen levels, Prostate Imaging Reporting and Data System, positivity in transitional-zone or peripheral-zone biopsy, number of positive cores (foundational model), and novel models integrating PV (model 1) and TZV/PZV (model 2). The efficacy of these models was assessed by the receiver operating characteristic area under the curve (AUC).

Results: For prostate segmentation, the fine cascade Vnet performed best with a Dice similarity coefficient of 0.93 for the whole prostate, 0.82 for the transitional zone, and 0.85 for the peripheral zone. The comparative discriminative power of the three models between csPC and insPC was assessed using the test dataset, indicating an AUC of 0.698 for the foundational model, 0.712 for model 1, and 0.730 for model 2. Model 2 significantly outperformed model 1 (P=0.045) and the foundational model (P=0.005) in distinguishing between csPC and insPC. Model 1 also showed statistically significant improvement over the foundational model (P=0.023).

Conclusions: Incorporating AI-driven PV and TZV/PZV measurements with clinical parameters improves prostate-cancer risk stratification.

Keywords: Artificial intelligence (AI); clinically significant prostate cancer (csPC); prostate volume (PV); transitional/peripheral zone volume (TZV/PZV).