Work

Automated voxel-based prostate volumetry improves PSA density–based detection of clinically significant prostate cancer in the PSA gray zone: comparison with manual segmentation and the ellipsoid formula

Year: 2026

Type: article

Abstract: Purpose To investigate whether voxel-based prostate volume can improve the diagnostic performance of prostate-specific antigen density (PSAD) for detecting clinically significant prostate cancer (csPCa) in patients within the prostate-specific antigen (PSA) gray zone (4–10 ng/mL), and to compare it with conventional formula-based estimation. Methods In this retrospective study, 489 patients with PSA levels of 4–10 ng/mL who underwent prostate MRI were included. Prostate volumes were calculated using three methods ellipsoid formula ( PV formula ), manual voxel-based segmentation ( PV mask ), and automated voxel-based segmentation ( PV auto_seg ) based on nnU-Net. Agreement between volumetric measurements was assessed using Bland–Altman analysis, and PSAD was calculated according to three prostate volume estimations. Diagnostic performance for the detection of csPCa was evaluated using receiver operating characteristic (ROC) analysis. Results PV formula systematically overestimated prostate volume compared with PV mask , with mean volumes of 58.8 ± 28.1 mL and 54.5 ± 26.6 mL (bias 4.4 mL); whereas PV auto_seg (55.8 ± 20.8 mL) showed close agreement with PV mask and reduced bias of 1.4 mL. In ROC analysis, voxel-based PSAD mask (AUC 0.762, 95% CI 0.717–0.808) outperformed the formula-based PSAD formula (AUC 0.719, 95% CI 0.671–0.767; P = 0.01). PSAD auto_seg calculated from PV auto_seg yielded an AUC similar (0.754, 95% CI 0.707–0.800) to that of with PSAD mask , and also outperformed PSAD formula ( P = 0.02). Conclusion Voxel-based prostate volumetry provides more accurate and consistent volume estimation than the conventional ellipsoid formula, leading to improved PSAD performance for detecting csPCa in the PSA gray zone. Moreover, automated segmentation enables reliable and scalable PSAD calculation with diagnostic performance comparable to manual delineation, supporting its potential for clinical implementation in risk stratification of PCa.

Source: Frontiers in Digital Health

DOI: 10.3389/fdgth.2026.1897681

Authors: Jing Han, Yi Qiu, Houdong Liu, Jing Wen

Institution: Nanjing University

Language: English

Topic: Prostate Cancer Diagnosis and Treatment

Subfield: Urology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,077