Artificial intelligence-derived transition zone PSA density as a triage tool to reduce unnecessary prostate systematic biopsies in MRI-negative men.
Authors
Affiliations (12)
Affiliations (12)
- Department of Urology, Peking University First Hospital, Beijing, China.
- Institute of Urology, Peking University, Beijing, China.
- National Urological Cancer Center, Beijing, China.
- Department of Radiology, Peking University First Hospital, Beijing, China.
- Department of Biostatistics, Peking University First Hospital, Beijing, China.
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China.
- Department of Urology, Peking University First Hospital, Beijing, China. [email protected].
- Institute of Urology, Peking University, Beijing, China. [email protected].
- National Urological Cancer Center, Beijing, China. [email protected].
- Department of Urology, Peking University First Hospital, Beijing, China. [email protected].
- Institute of Urology, Peking University, Beijing, China. [email protected].
- National Urological Cancer Center, Beijing, China. [email protected].
Abstract
The study aimed to assess the predictive performance of transition zone PSA density (TZ-PSAD) compared to conventional PSA density (PSAD) in detecting clinically significant prostate cancer (csPCa) among patients with negative pre-biopsy MRI findings. The study included 606 patients with negative MRI findings who subsequently underwent transrectal ultrasound-guided systematic biopsy. AI software automatically measured prostate and zonal volumes, from which PSAD and TZ-PSAD (total PSA/transition zone volume) were calculated. Diagnostic performances were evaluated using ROC curve analysis, risk stratification was applied to select patients needing biopsy, and independent predictors of imaging-invisible csPCa were determined through univariate and multivariate analyses. 51 patients (8.4%) were diagnosed with csPCa. TZ-PSAD demonstrated significant superior discriminative ability (AUC = 0.718) compared to PSAD (AUC = 0.686; p = 0.019). Patients with TZ-PSAD ≥ 0.35 ng/mL/cc had a csPCa detection rate of 20.1%, while those below this threshold had a rate of 4.1%. The optimal TZ-PSAD threshold of 0.35 ng/mL/cc showed superior performance than the guideline-recommended PSAD threshold of 0.2 ng/mL/cc. Multivariate analysis identified TZ-PSAD as a strong independent predictor of imaging-invisible csPCa. TZ-PSAD outperforms conventional PSAD in predicting csPCa among men with negative MRI, offering a valuable tool for risk stratification. This facilitates individualized risk assessment, potentially reducing unnecessary biopsies and optimizing patient management. Our AI system delivers accurate and reproducible prostate zone segmentation, while TZ-PSAD derived from AI outperforms conventional PSAD in detecting csPCa in MRI-negative patients and serves as an effective triage tool to optimize biopsy decision-making and reduce unnecessary systematic biopsies. Our AI system enables accurate and reproducible segmentation and measurement of prostate zones. TZ-PSAD demonstrates significantly superior diagnostic performance over conventional PSAD for identifying men with a negative MRI who will have csPCa on a systematic biopsy. TZ-PSAD represents an effective triage metric to reduce unwarranted systematic biopsies in MRI-negative patients.