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Comparative 3D V-Net and U-Net models were developed across 7 MRI sequences (T2-weighted, diffusion-weighted, apparent diffusion coefficient maps, unenhanced T1-weighted, and enhanced corticomedullary, nephrographic, and excretory phases images). A total of 14 models were developed, and 7 pairwise comparisons were performed between the 3D V-Net and U-Net models. Segmentation performance was evaluated using Dice similarity coefficient (DSC) and Hausdorff distance (HD), with subgroup analysis of small cCRMs (\u226440\u00a0mm).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the test set, the excretory-phase V-Net (EPV-Net model) showed the highest DSC, and perform better than the corresponding U-Net (EPU-Net model) across all cCRMs (DSC: 0.74\u2009\u00b1\u20090.05 vs 0.70\u2009\u00b1\u20090.06, P\u2009&amp;lt;\u2009.001; HD: 27.41\u2009\u00b1\u20097.44\u2009mm vs 39.18\u2009\u00b1\u200911.07\u2009mm, P\u2009&amp;lt;\u2009.001) and the 35 small cCRMs subgroup (DSC: 0.74\u2009\u00b1\u20090.05 vs 0.70\u2009\u00b1\u20090.06, P\u2009&amp;lt;\u2009.001; HD: 27.48\u2009mm \u00b1 6.32 vs 38.72\u2009\u00b1\u200910.69\u2009mm, P\u2009&amp;lt;\u2009.001).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The 3D EPV-Net model demonstrated good segmentation accuracy, even for small lesions, supporting its clinical utility for cCRMs evaluation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Advances in knowledge<\/jats:title>\n                    <jats:p>This automated approach may streamline workflow compared to manual segmentation in cCRMs assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bjr\/tqag027","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T05:20:37Z","timestamp":1770355237000},"page":"812-822","source":"Crossref","is-referenced-by-count":0,"title":["Automated segmentation of complicated cystic renal masses using 3D V-Net convolutional neural network on 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100853,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8755-4257","authenticated-orcid":false,"given":"Xu","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Radiology, First Medical Center of Chinese PLA General Hospital , Beijing 100853,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7917-1173","authenticated-orcid":false,"given":"Lin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Innovative Medical Research, Chinese PLA General Hospital, Hospital Management Institute , Beijing 100853,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5197-871X","authenticated-orcid":false,"given":"Huiping","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Radiology, First Medical Center of Chinese PLA General Hospital , 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