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.
- 1Department of Radiology, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, China.
- 2Suzhou Research Center of Medical School, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, China.
- 3Department of Medical Imaging, Jiangsu Medical College, Yancheng, China.
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 (PVformula ), manual voxel-based segmentation (PVmask ), and automated voxel-based segmentation (PVauto_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
PVformula systematically overestimated prostate volume compared with PVmask , with mean volumes of 58.8 ± 28.1 mL and 54.5 ± 26.6 mL (bias 4.4 mL); whereas PVauto_seg (55.8 ± 20.8 mL) showed close agreement with PVmask and reduced bias of 1.4 mL. In ROC analysis, voxel-based PSADmask (AUC 0.762, 95% CI 0.717-0.808) outperformed the formula-based PSADformula (AUC 0.719, 95% CI 0.671-0.767; P = 0.01). PSADauto_seg calculated from PVauto_seg yielded an AUC similar (0.754, 95% CI 0.707-0.800) to that of with PSADmask , and also outperformed PSADformula (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.
Related
- Prospective Comparison of Cognitive MRI-Targeted Biopsy Under Micro-Ultrasound Guidance Versus MRI/Ultrasound Fusion-Guided Transperineal Biopsy for Clinically Significant Prostate Cancer Detection
- Comparison of AI-enabled cardiac chambers volumetry based on non-gated chest CT scans with echocardiography: An AI-CVD study within the Multi-Ethnic Study of Atherosclerosis (MESA)
- Characterization and Antimicrobial Susceptibility Patterns of Clinically Significant Non-fermenting Gram-Negative Bacilli Isolated From Clinical Specimens
- Transcatheter atrial septal defect closure in infants weighing 3 to 6 kg with clinically significant bronchopulmonary dysplasia
- Validation of the discriminatory performance of the iPREFACE score for clinically significant fetal acidemia: an outcome-enriched nested case–control study
BibTeX
@article{han2026automated,
title = {{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.}},
author = {Jing Han and Yi Qiu and Houdong Liu and Jing Wen},
journal = {Frontiers in digital health},
year = {2026},
month = oct,
doi = {10.3389/fdgth.2026.1897681},
url = {https://europepmc.org/article/MED/42910260},
note = {CC BY},
}
RIS
TY - JOUR
TI - 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.
AU - Jing Han
AU - Yi Qiu
AU - Houdong Liu
AU - Jing Wen
PY - 2026
DA - 2026/10/07
JO - Frontiers in digital health
DO - 10.3389/fdgth.2026.1897681
UR - https://europepmc.org/article/MED/42910260
AB - 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 (PVformula ), manual voxel-based segmentation (PVmask ), and automated voxel-based segmentation (PVauto_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 PVformula systematically overestimated prostate volume compared with PVmask , with mean volumes of 58.8 ± 28.1 mL and 54.5 ± 26.6 mL (bias 4.4 mL); whereas PVauto_seg (55.8 ± 20.8 mL) showed close agreement with PVmask and reduced bias of 1.4 mL. In ROC analysis, voxel-based PSADmask (AUC 0.762, 95% CI 0.717-0.808) outperformed the formula-based PSADformula (AUC 0.719, 95% CI 0.671-0.767; P = 0.01). PSADauto_seg calculated from PVauto_seg yielded an AUC similar (0.754, 95% CI 0.707-0.800) to that of with PSADmask , and also outperformed PSADformula (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.
SN - 2673-253X
ER -