{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T09:39:25Z","timestamp":1787996365211,"version":"build-2784847793"},"reference-count":39,"publisher":"Elsevier BV","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"name":"First Hospital of Jiaxing"}],"content-domain":{"domain":["academicradiology.org","clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Academic Radiology"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1016\/j.acra.2025.05.062","type":"journal-article","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T23:24:26Z","timestamp":1750375466000},"page":"6729-6738","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":5,"title":["Radiomics for Detection of Prostate Cancer in PI-RADS 3 Lesions: A Systematic Review and Meta-analysis"],"prefix":"10.1016","volume":"32","author":[{"given":"Qian","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanxian","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianzhi","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.acra.2025.05.062_bib1","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.eururo.2024.11.013","article-title":"Recent patterns and trends in global prostate cancer incidence and mortality: an update","volume":"87","author":"Schafer","year":"2025","journal-title":"Eur Urol"},{"key":"10.1016\/j.acra.2025.05.062_bib2","first-page":"229","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":"10.1016\/j.acra.2025.05.062_bib3","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.1016\/S0140-6736(24)00651-2","article-title":"The Lancet Commission on prostate cancer: planning for the surge in cases","volume":"403","author":"James","year":"2024","journal-title":"Lancet"},{"key":"10.1016\/j.acra.2025.05.062_bib4","first-page":"326","article-title":"Multiparametric prostate magnetic resonance imaging in the evaluation of prostate cancer","volume":"66","author":"Turkbey","year":"2016","journal-title":"CA Cancer J Clin"},{"key":"10.1016\/j.acra.2025.05.062_bib5","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1007\/s00330-011-2377-y","article-title":"ESUR prostate MR guidelines 2012","volume":"22","author":"Barentsz","year":"2012","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2025.05.062_bib6","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.eururo.2019.02.033","article-title":"Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2","volume":"76","author":"Turkbey","year":"2019","journal-title":"Eur Urol"},{"key":"10.1016\/j.acra.2025.05.062_bib7","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.eururo.2015.08.052","article-title":"PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2","volume":"69","author":"Weinreb","year":"2016","journal-title":"Eur Urol"},{"key":"10.1016\/j.acra.2025.05.062_bib8","doi-asserted-by":"crossref","first-page":"165","DOI":"10.3233\/CH-189407","article-title":"Comparison of PIRADS 3 lesions with histopathological findings after MRI-fusion targeted biopsy of the prostate in a real world-setting","volume":"71","author":"Schlenker","year":"2019","journal-title":"Clin Hemorheol Microcirc"},{"key":"10.1016\/j.acra.2025.05.062_bib9","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1038\/s41391-021-00417-1","article-title":"Cancer detection rates of the PI-RADSv2.1 assessment categories: systematic review and meta-analysis on lesion level and patient level","volume":"25","author":"Oerther","year":"2022","journal-title":"Prostate Cancer Prostatic Dis"},{"key":"10.1016\/j.acra.2025.05.062_bib10","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","article-title":"Radiomics: the bridge between medical imaging and personalized medicine","volume":"14","author":"Lambin","year":"2017","journal-title":"Nat Rev Clin Oncol"},{"key":"10.1016\/j.acra.2025.05.062_bib11","doi-asserted-by":"crossref","first-page":"8027","DOI":"10.7150\/thno.61207","article-title":"Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies","volume":"11","author":"Spohn","year":"2021","journal-title":"Theranostics"},{"key":"10.1016\/j.acra.2025.05.062_bib12","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"Bmj"},{"key":"10.1016\/j.acra.2025.05.062_bib13","doi-asserted-by":"crossref","DOI":"10.3390\/cancers15133438","article-title":"An apparent diffusion coefficient-based machine learning model can improve prostate cancer detection in the grey area of the prostate imaging reporting and data system category 3: a single-centre experience","volume":"15","author":"Gaudiano","year":"2023","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.acra.2025.05.062_bib14","doi-asserted-by":"crossref","first-page":"2412","DOI":"10.1016\/j.acra.2023.12.042","article-title":"Deep learning based on ResNet-18 for classification of prostate imaging-reporting and data system category 3 Lesions","volume":"31","author":"Kang","year":"2024","journal-title":"Acad Radiol"},{"key":"10.1016\/j.acra.2025.05.062_bib15","article-title":"Development and validation of a radiomics nomogram for predicting clinically significant prostate cancer in PI-RADS 3 lesions","volume":"11","author":"Li","year":"2021","journal-title":"Front Oncol"},{"key":"10.1016\/j.acra.2025.05.062_bib16","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1038\/s41598-020-80749-5","article-title":"Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions","volume":"11","author":"Brancato","year":"2021","journal-title":"Sci Rep"},{"key":"10.1016\/j.acra.2025.05.062_bib17","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1186\/s12880-023-01002-9","article-title":"Machine learning-based radiomics model to predict benign and malignant PI-RADS v2.1 category 3 lesions: a retrospective multi-center study","volume":"23","author":"Jin","year":"2023","journal-title":"BMC Med Imaging"},{"key":"10.1016\/j.acra.2025.05.062_bib18","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1002\/jmri.27692","article-title":"Magnetic resonance imaging radiomics-based machine learning prediction of clinically significant prostate cancer in equivocal PI-RADS 3 lesions","volume":"54","author":"Hectors","year":"2021","journal-title":"J Magn Reson Imaging"},{"key":"10.1016\/j.acra.2025.05.062_bib19","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2023.1247682","article-title":"Magnetic resonance imaging radiomics-based prediction of clinically significant prostate cancer in equivocal PI-RADS 3 lesions in the transitional zone","volume":"13","author":"Zhao","year":"2023","journal-title":"Front Oncol"},{"key":"10.1016\/j.acra.2025.05.062_bib20","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1067\/j.cpradiol.2019.10.009","article-title":"PI-RADS 3 lesions: role of prostate mri texture analysis in the identification of prostate cancer","volume":"50","author":"Giambelluca","year":"2021","journal-title":"Curr Probl Diagn Radiol"},{"key":"10.1016\/j.acra.2025.05.062_bib21","doi-asserted-by":"crossref","DOI":"10.3390\/jcm11216304","article-title":"Radiomics in PI-RADS 3 Multiparametric MRI for prostate cancer identification: literature models re-implementation and proposal of a clinical-radiological model","volume":"11","author":"Corsi","year":"2022","journal-title":"J Clin Med"},{"key":"10.1016\/j.acra.2025.05.062_bib22","doi-asserted-by":"crossref","first-page":"4223","DOI":"10.1007\/s00261-020-02678-1","article-title":"A radiomics machine learning-based redefining score robustly identifies clinically significant prostate cancer in equivocal PI-RADS score 3 lesions","volume":"45","author":"Hou","year":"2020","journal-title":"Abdom Radiol (NY)"},{"key":"10.1016\/j.acra.2025.05.062_bib23","article-title":"Utility of clinical-radiomic model to identify clinically significant prostate cancer in biparametric MRI PI-RADS V2.1 Category 3 lesions","volume":"12","author":"Jin","year":"2022","journal-title":"Front Oncol"},{"key":"10.1016\/j.acra.2025.05.062_bib24","doi-asserted-by":"crossref","first-page":"5647","DOI":"10.1007\/s00261-021-03235-0","article-title":"Utility of machine learning of apparent diffusion coefficient (ADC) and T2-weighted (T2W) radiomic features in PI-RADS version 2.1 category 3 lesions to predict prostate cancer diagnosis","volume":"46","author":"Lim","year":"2021","journal-title":"Abdom Radiol ((NY))"},{"key":"10.1016\/j.acra.2025.05.062_bib25","doi-asserted-by":"crossref","DOI":"10.2196\/22394","article-title":"Radiomic and genomic machine learning method performance for prostate cancer diagnosis: systematic literature review","volume":"23","author":"Castaldo","year":"2021","journal-title":"J Med Internet Res"},{"key":"10.1016\/j.acra.2025.05.062_bib26","article-title":"Considering predictive factors in the diagnosis of clinically significant prostate cancer in patients with PI-RADS 3 lesions","volume":"11","author":"Natale","year":"2021","journal-title":"Life (Basel)"},{"key":"10.1016\/j.acra.2025.05.062_bib27","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1148\/radiol.212692","article-title":"Contribution of dynamic contrast-enhanced and diffusion MRI to PI-RADS for detecting clinically significant prostate cancer","volume":"306","author":"Tavakoli","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2025.05.062_bib28","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.2214\/AJR.17.18008","article-title":"Risk stratification among men with prostate imaging reporting and data system version 2 category 3 transition zone lesions: is biopsy always necessary?","volume":"209","author":"Felker","year":"2017","journal-title":"AJR Am J Roentgenol"},{"key":"10.1016\/j.acra.2025.05.062_bib29","article-title":"Diagnostic ability of dynamic contrast-enhanced magnetic resonance imaging for prostate cancer and clinically significant prostate cancer in equivocal lesions: a systematic review and meta-analysis","volume":"11","author":"Zeng","year":"2021","journal-title":"Front Oncol"},{"key":"10.1016\/j.acra.2025.05.062_bib30","doi-asserted-by":"crossref","DOI":"10.1007\/s00261-025-04914-y","article-title":"Predicting clinically significant prostate cancer in PI-RADS 3 lesions using MRI-based radiomics: a literature review of methodological variations and performance","author":"Serrano","year":"2025","journal-title":"Abdom Radiol ((NY))"},{"key":"10.1016\/j.acra.2025.05.062_bib31","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s00330-019-06381-8","article-title":"Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation","volume":"30","author":"Yamashita","year":"2020","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2025.05.062_bib32","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1016\/j.ijrobp.2018.05.053","article-title":"Repeatability and reproducibility of radiomic features: a systematic review","volume":"102","author":"Traverso","year":"2018","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.acra.2025.05.062_bib33","doi-asserted-by":"crossref","first-page":"10036","DOI":"10.1109\/TPAMI.2024.3432326","article-title":"Rethinking self-supervised semantic segmentation: achieving end-to-end segmentation","volume":"46","author":"Liu","year":"2024","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10.1016\/j.acra.2025.05.062_bib34","doi-asserted-by":"crossref","first-page":"5845","DOI":"10.1007\/s10462-022-10152-1","article-title":"Modality specific U-Net variants for biomedical image segmentation: a survey","volume":"55","author":"Punn","year":"2022","journal-title":"Artif Intell Rev"},{"key":"10.1016\/j.acra.2025.05.062_bib35","doi-asserted-by":"crossref","first-page":"10076","DOI":"10.1109\/TPAMI.2024.3435571","article-title":"Medical image segmentation review: the success of U-Net","volume":"46","author":"Azad","year":"2024","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10.1016\/j.acra.2025.05.062_bib36","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1148\/radiol.2018181432","article-title":"Automated abdominal segmentation of CT scans for body composition analysis using deep learning","volume":"290","author":"Weston","year":"2019","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2025.05.062_bib37","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.103629","article-title":"Deep learning-based segmentation of breast masses in dedicated breast CT imaging: Radiomic feature stability between radiologists and artificial intelligence","volume":"118","author":"Caballo","year":"2020","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.acra.2025.05.062_bib38","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1148\/radiol.2019181960","article-title":"Deep learning-based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses","volume":"292","author":"Choe","year":"2019","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2025.05.062_bib39","doi-asserted-by":"crossref","DOI":"10.1016\/j.acra.2025.03.039","article-title":"External validation of an artificial intelligence algorithm using biparametric MRI and its simulated integration with conventional PI-RADS for prostate cancer detection","author":"Belue","year":"2025","journal-title":"Acad Radiol"}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633225005392?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633225005392?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T16:20:35Z","timestamp":1763742035000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1076633225005392"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":39,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["S1076633225005392"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2025.05.062","relation":{},"ISSN":["1076-6332"],"issn-type":[{"value":"1076-6332","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Radiomics for Detection of Prostate Cancer in PI-RADS 3 Lesions: A Systematic Review and Meta-analysis","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2025.05.062","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Association of Academic Radiology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}