{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T05:40:32Z","timestamp":1792561232280,"version":"4.5.0"},"reference-count":23,"publisher":"Ovid Technologies (Wolters Kluwer Health)","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:p>\n                    This retrospective study developed and validated a T2-weighted imaging (T2WI)-based radiomics model for preoperative prediction of apical positive surgical margins (PSMs) in patients undergoing radical prostatectomy for prostate cancer. Overall, 235 patients (74 with apical PSMs and 161 without apical PSMs) were randomly divided into a training set (\n                    <jats:italic toggle=\"yes\">n<\/jats:italic>\n                    = 164; 69.8%) and an independent test set (\n                    <jats:italic toggle=\"yes\">n<\/jats:italic>\n                    = 71; 30.2%). Eligible patients had preoperative multiparametric magnetic resonance imaging within 6 weeks before surgery, an index lesion involving or abutting the prostatic apex, definitive apical margin status, and standard radical prostatectomy without nerve-sparing. Patients with inadequate imaging for apical assessment or region-of-interest delineation, prior transurethral resection of the prostate, or neoadjuvant androgen-deprivation therapy were excluded. A total of 1409 radiomic features were extracted from T2WI. After normalization and removal of highly correlated features (|\n                    <jats:italic toggle=\"yes\">r<\/jats:italic>\n                    | &gt; 0.99), six optimal features were selected by recursive feature elimination. A gradient boosting machine model was constructed, and performance was assessed using the area under the receiver operating characteristic curve (AUC), Brier score, and decision curve analysis. The apical PSM rate was 31.5%. In the test set, the model achieved an AUC of 0.874, Brier score of 0.120, accuracy of 87.3%, sensitivity of 81.8%, and specificity of 89.8%. Skewness was the most important feature, contributing 34.8% to the model. Apical PSMs were significantly associated with extraprostatic extension (\n                    <jats:italic toggle=\"yes\">P<\/jats:italic>\n                    = 0.001), and the tumor-margin model provided the greatest net clinical benefit. T2WI-based radiomics may support individualized preoperative risk assessment and surgical planning for prostate cancer.\n                  <\/jats:p>","DOI":"10.4103\/aja202625","type":"journal-article","created":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T05:00:10Z","timestamp":1792558810000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Preoperative apical margin risk estimation from T2-weighted MRI radiomics in prostate cancer with MRI-visible apical tumors: toward multidisciplinary decision support"],"prefix":"10.4103","author":[{"given":"Yi","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Radiology, Peking University First Hospital, Beijing 100034, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Ze","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Peking University First Hospital, Beijing 100034, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shui","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Aerospace Center Hospital, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xian-Shu","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Peking University First Hospital, Beijing 100034, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming-Wei","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Peking University First Hospital, Beijing 100034, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,10,20]]},"reference":[{"key":"R1-20261021","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1056\/NEJMoa2214122","article-title":"Fifteen-year outcomes after monitoring, surgery, or radiotherapy for prostate 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detection and Gleason score prediction using T2WI: a diagnostic multi-center study","volume":"25","author":"Jin","year":"2025","journal-title":"BMC Cancer"},{"key":"R11-20261021","doi-asserted-by":"crossref","first-page":"4898","DOI":"10.1007\/s00330-020-07569-z","article-title":"Preoperative MRI-based estimation of risk for positive resection margin after radical prostatectomy in patients with prostate cancer: development and validation of a simple scoring system","volume":"31","author":"Park","year":"2021","journal-title":"Eur Radiol"},{"key":"R12-20261021","doi-asserted-by":"crossref","first-page":"1491848","DOI":"10.3389\/fonc.2025.1491848","article-title":"An MRI radiomics model for predicting a prostate-specific antigen response following abiraterone treatment in patients with metastatic castration-resistant prostate cancer","volume":"15","author":"Wu","year":"2025","journal-title":"Front Oncol"},{"key":"R13-20261021","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1007\/s00330-023-10427-3","article-title":"Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis","volume":"34","author":"Ponsiglione","year":"2024","journal-title":"Eur Radiol"},{"key":"R14-20261021","doi-asserted-by":"crossref","first-page":"e231319","DOI":"10.1148\/radiol.231319","article-title":"The image biomarker standardization initiative: standardized convolutional filters for reproducible radiomics and enhanced clinical insights","volume":"310","author":"Whybra","year":"2024","journal-title":"Radiology"},{"key":"R16-20261021","first-page":"10","article-title":"Cancer statistics, 2025","volume":"75","author":"Siegel","year":"2025","journal-title":"CA Cancer J Clin"},{"key":"R17-20261021","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.21037\/tau-20-1447","article-title":"Positive association between preoperative lymphocyte-to-monocyte ratio and risk of the status of positive surgical margins by prostate cancer: results in 497 consecutive patients treated only by radical prostatectomy","volume":"10","author":"Zhou","year":"2021","journal-title":"Transl Androl Urol"},{"key":"R18-20261021","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1038\/s41391-023-00654-6","article-title":"Length of positive surgical margins after radical prostatectomy: does size matter? A systematic review and meta-analysis","volume":"26","author":"John","year":"2023","journal-title":"Prostate Cancer Prostatic Dis"},{"key":"R19-20261021","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1186\/s40644-021-00414-6","article-title":"MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins","volume":"21","author":"He","year":"2021","journal-title":"Cancer Imaging"},{"key":"R20-20261021","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1007\/s11701-021-01333-1","article-title":"The detection rate of apical tumour involvement on preoperative MRI and its impact on clinical outcomes in patients with localized prostate cancer","volume":"16","author":"Veerman","year":"2022","journal-title":"J Robot Surg"},{"key":"R21-20261021","doi-asserted-by":"crossref","first-page":"102240","DOI":"10.1016\/j.clgc.2024.102240","article-title":"Detection of apical cancer with novel imaging modalities to predict apical margin positivity in robotic assisted radical prostatectomy","volume":"22","author":"Wagaskar","year":"2024","journal-title":"Clin Genitourin Cancer"},{"key":"R22-20261021","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/s40644-021-00423-5","article-title":"Differentiating prostate cancer from benign prostatic hyperplasia using whole-lesion histogram and texture analysis of diffusion- and T2-weighted imaging","volume":"21","author":"Xing","year":"2021","journal-title":"Cancer Imaging"},{"key":"R23-20261021","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.21037\/tcr-21-2271","article-title":"Texture analysis based on PI-RADS 4\/5-scored magnetic resonance images combined with machine learning to distinguish benign lesions from prostate cancer","volume":"11","author":"Ma","year":"2022","journal-title":"Transl Cancer Res"},{"key":"R24-20261021","doi-asserted-by":"crossref","first-page":"3255","DOI":"10.21037\/qims-22-1068","article-title":"Development and acceptability validation of a deep learning-based tool for whole-prostate segmentation on multiparametric MRI: a multicenter study","volume":"13","author":"Xu","year":"2023","journal-title":"Quant Imaging Med Surg"}],"container-title":["Asian Journal of Andrology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/aja202625","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T05:00:13Z","timestamp":1792558813000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/aja202625"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,20]]},"references-count":23,"URL":"https:\/\/doi.org\/10.4103\/aja202625","relation":{},"ISSN":["1008-682X","1745-7262"],"issn-type":[{"value":"1008-682X","type":"print"},{"value":"1745-7262","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,20]]},"assertion":[{"value":"2026-01-11","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-10","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}