{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:51:41Z","timestamp":1791395501135,"version":"4.3.3"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"\n Purpose<\/jats:title>\n 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\u201310\u2005ng\/mL), and to compare it with conventional formula-based estimation.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n In this retrospective study, 489 patients with PSA levels of 4\u201310\u2005ng\/mL who underwent prostate MRI were included. Prostate volumes were calculated using three methods ellipsoid formula (\n \n PV\n formula<\/jats:sub>\n <\/jats:italic>\n ), manual voxel-based segmentation (\n \n PV\n mask<\/jats:sub>\n <\/jats:italic>\n ), and automated voxel-based segmentation (\n \n PV\n auto_seg<\/jats:sub>\n <\/jats:italic>\n ) based on nnU-Net. Agreement between volumetric measurements was assessed using Bland\u2013Altman 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.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n \n PV\n formula<\/jats:sub>\n <\/jats:italic>\n systematically overestimated prostate volume compared with\n \n PV\n mask<\/jats:sub>\n <\/jats:italic>\n , with mean volumes of 58.8\u2009\u00b1\u200928.1\u2005mL and 54.5\u2009\u00b1\u200926.6\u2005mL (bias 4.4\u2005mL); whereas\n \n PV\n auto_seg<\/jats:sub>\n <\/jats:italic>\n (55.8\u2009\u00b1\u200920.8\u2005mL) showed close agreement with\n \n PV\n mask<\/jats:sub>\n <\/jats:italic>\n and reduced bias of 1.4\u2005mL. In ROC analysis, voxel-based\n \n PSAD\n mask<\/jats:sub>\n <\/jats:italic>\n (AUC 0.762, 95% CI 0.717\u20130.808) outperformed the formula-based\n \n PSAD\n formula<\/jats:sub>\n <\/jats:italic>\n (AUC 0.719, 95% CI 0.671\u20130.767;\n P<\/jats:italic>\n \u2009=\u20090.01).\n \n PSAD\n auto_seg<\/jats:sub>\n <\/jats:italic>\n calculated from\n \n PV\n auto_seg<\/jats:sub>\n <\/jats:italic>\n yielded an AUC similar (0.754, 95% CI 0.707\u20130.800) to that of with\n \n PSAD\n mask<\/jats:sub>\n <\/jats:italic>\n , and also outperformed\n \n PSAD\n formula<\/jats:sub>\n <\/jats:italic>\n (\n P<\/jats:italic>\n \u2009=\u20090.02).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1897681","type":"journal-article","created":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:00:15Z","timestamp":1791392415000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated voxel-based prostate volumetry improves PSA density\u2013based detection of clinically significant prostate cancer in the PSA gray zone: comparison with manual segmentation and the ellipsoid formula"],"prefix":"10.3389","volume":"8","author":[{"given":"Jing","family":"Han","sequence":"first","affiliation":[{"name":"Department of Radiology, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University","place":["Suzhou, China"]},{"name":"Suzhou Research Center of Medical School, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University","place":["Suzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University","place":["Suzhou, China"]},{"name":"Suzhou Research Center of Medical School, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University","place":["Suzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Houdong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Jiangsu Medical College","place":["Yancheng, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Wen","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Jiangsu Medical College","place":["Yancheng, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,10,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"229","DOI":"10.3322\/caac.21834","article-title":"Global cancer statistics 2022: gLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J Clin"},{"key":"B2","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.eururo.2024.03.027","article-title":"EAU-EANM-ESTRO-ESUR-ISUP-SIOG guidelines on prostate cancer-2024 update. 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