{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T05:18:57Z","timestamp":1791263937964,"version":"4.1.0"},"reference-count":55,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) techniques, particularly those using machine learning and deep learning to analyze multimodal imaging data, have shown considerable promise in enhancing preoperative prediction of extraprostatic extension (EPE) in prostate cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This meta-analysis compares the diagnostic performance of AI-enabled imaging techniques with that of radiologists for predicting preoperative EPE in prostate cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a systematic literature search in PubMed, Embase, and Web of Science up to September 2025, following PRISMA-DTA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis of Diagnostic Test Accuracy) guidelines. Studies applying AI techniques to predict EPE using multiparametric magnetic resonance imaging (mpMRI) and prostate-specific membrane antigen positron emission tomography (PSMA PET) imaging were included. Sensitivity, specificity, and area under the curve (AUC) for both internal and external validation sets were extracted and pooled using a bivariate random effects model. Study quality was assessed using the modified Quality Assessment of Diagnostic Performance Studies (QUADAS-2) tool.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 21 studies were included in the analysis. For internal validation sets in patient-based analyses, mpMRI-based AI demonstrated a pooled sensitivity of 0.77 (95% CI 0.71-0.82), specificity of 0.71 (95% CI 0.64-0.78), and AUC of 0.81 (95% CI 0.77-0.84). In external validation, mpMRI-based AI achieved a sensitivity of 0.66 (95% CI 0.43-0.84), specificity of 0.80 (95% CI 0.64-0.90), and AUC of 0.80 (95% CI 0.77-0.84). In comparison, radiologists achieved a pooled sensitivity of 0.69 (95% CI 0.60-0.76), specificity of 0.73 (95% CI 0.66-0.78), and AUC of 0.77 (95% CI 0.73-0.80). Statistical comparisons between mpMRI-based AI and radiologists showed no significant difference in sensitivity (\n                      <jats:italic>Z<\/jats:italic>\n                      =1.61;\n                      <jats:italic>P<\/jats:italic>\n                      =.10), specificity (\n                      <jats:italic>Z<\/jats:italic>\n                      =0.43;\n                      <jats:italic>P<\/jats:italic>\n                      =.67)\n                      <jats:italic>.<\/jats:italic>\n                      Conversely, the AUC of mpMRI-based AI was significantly higher than that of PSMA PET-based (\n                      <jats:italic>Z<\/jats:italic>\n                      =2.77;\n                      <jats:italic>P=<\/jats:italic>\n                      .01)\n                      <jats:italic>.<\/jats:italic>\n                      PSMA PET-based AI showed moderate performance with sensitivity of 0.73 (95% CI 0.65-0.80), specificity of 0.61 (95% CI 0.30-0.85), and AUC of 0.74 (95% CI 0.70-0.77) in internal validation, and in external validation, it demonstrated sensitivity of 0.77 (95% CI 0.57-0.89) and specificity of 0.50 (95% CI 0.22-0.78), demonstrating no significant advantage over radiologists.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>mpMRI-based AI demonstrated improved diagnostic performance for preoperative prediction of EPE in prostate cancer compared to conventional radiological assessment, achieving higher AUC. However, PSMA PET-based AI models currently offer no significant advantage over either mpMRI-based AI or radiologists. Limitations include the retrospective design and high heterogeneity, which may introduce bias and affect generalizability. Larger, more diverse cohorts are essential for confirming these findings and optimizing the integration of AI in clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/80981","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T19:41:29Z","timestamp":1765309289000},"page":"e80981-e80981","source":"Crossref","is-referenced-by-count":4,"title":["Artificial Intelligence\u2013Enabled Imaging for Predicting Preoperative Extraprostatic Extension in Prostate Cancer: Systematic Review and Meta-Analysis"],"prefix":"10.2196","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5128-9734","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6835-8183","authenticated-orcid":false,"given":"Yiting","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7247-4891","authenticated-orcid":false,"given":"Xi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2553-4559","authenticated-orcid":false,"given":"Haowen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7430-8583","authenticated-orcid":false,"given":"Jia","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,12,9]]},"reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.811044","article-title":"Prostate cancer incidence and mortality: global status and temporal trends in 89 countries from 2000 to 2019","volume":"10","author":"Wang","journal-title":"Front Public Health"},{"issue":"6","key":"R2","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1016\/j.euo.2021.09.006","article-title":"Epidemiology and prevention of prostate cancer","volume":"4","author":"Gandaglia","journal-title":"Eur Urol Oncol"},{"issue":"2","key":"R3","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1097\/PAS.0000000000000530","article-title":"The 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma: definition of grading patterns and proposal for a new grading system","volume":"40","author":"Epstein","journal-title":"Am J Surg Pathol"},{"issue":"1","key":"R4","doi-asserted-by":"publisher","DOI":"10.1186\/s13244-023-01486-7","article-title":"Diagnostic performance of prediction models for extraprostatic extension in prostate cancer: a systematic review and meta-analysis","volume":"14","author":"Zhu","journal-title":"Insights Imaging"},{"issue":"12","key":"R5","doi-asserted-by":"publisher","first-page":"4040","DOI":"10.1007\/s00261-020-02555-x","article-title":"Extraprostatic extension in prostate cancer: primer for radiologists","volume":"45","author":"Shieh","journal-title":"Abdom Radiol (NY)"},{"issue":"8","key":"R6","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e29602","article-title":"An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: preliminary findings","volume":"10","author":"Hiremath","journal-title":"Heliyon"},{"issue":"22","key":"R7","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15225452","article-title":"The development and external validation of artificial intelligence-driven MRI-based models to improve prediction of lesion-specific extraprostatic extension in patients with prostate cancer","volume":"15","author":"van den Berg","journal-title":"Cancers (Basel)"},{"issue":"1154","key":"R8","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1093\/bjr\/tqad038","article-title":"Multimodal radiomics based on 18F-prostate-specific membrane antigen-1007 PET\/CT and multiparametric MRI for prostate cancer extracapsular extension prediction","volume":"97","author":"Pan","journal-title":"Br J Radiol"},{"issue":"2","key":"R9","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.eururo.2015.07.029","article-title":"Accuracy of magnetic resonance imaging for local staging of prostate cancer: a diagnostic meta-analysis","volume":"70","author":"de Rooij","journal-title":"Eur Urol"},{"issue":"8","key":"R10","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0237587","article-title":"FeAture Explorer (FAE): a tool for developing and comparing radiomics models","volume":"15","author":"Song","journal-title":"PLoS ONE"},{"issue":"2","key":"R11","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1007\/s00259-014-2961-x","article-title":"FDG PET\/CT: EANM procedure guidelines for tumour imaging: version 2.0","volume":"42","author":"Boellaard","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"5","key":"R12","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1007\/s11547-024-01810-1","article-title":"MRI-based radiomics for prediction of extraprostatic extension of prostate cancer: a systematic review and meta-analysis","volume":"129","author":"Wen","journal-title":"Radiol Med"},{"key":"R13","doi-asserted-by":"publisher","DOI":"10.1177\/17562872221128791","article-title":"A review of artificial intelligence in prostate cancer detection on imaging","volume":"14","author":"Bhattacharya","journal-title":"Ther Adv Urol"},{"issue":"13","key":"R14","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13133318","article-title":"Artificial intelligence compared to radiologists for the initial diagnosis of prostate cancer on magnetic resonance imaging: a systematic review and recommendations for future studies","volume":"13","author":"Syer","journal-title":"Cancers (Basel)"},{"issue":"6","key":"R15","doi-asserted-by":"publisher","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","journal-title":"Eur Radiol"},{"issue":"3","key":"R16","doi-asserted-by":"publisher","DOI":"10.1200\/EDBK_438516","article-title":"Applications of artificial intelligence in prostate cancer care: a path to enhanced efficiency and outcomes","volume":"44","author":"Riaz","journal-title":"Am Soc Clin Oncol Educ Book"},{"key":"R17","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m2632","article-title":"Preferred Reporting Items for Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA): explanation, elaboration, and checklist","volume":"370","author":"Salameh","journal-title":"BMJ"},{"issue":"8","key":"R18","doi-asserted-by":"publisher","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","article-title":"QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies","volume":"155","author":"Whiting","journal-title":"Ann Intern Med"},{"issue":"1","key":"R19","doi-asserted-by":"publisher","first-page":"51","DOI":"10.7326\/M18-1376","article-title":"PROBAST: a tool to assess the risk of bias and applicability of prediction model studies","volume":"170","author":"Wolff","journal-title":"Ann Intern Med"},{"issue":"7","key":"R20","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1016\/j.jclinepi.2014.01.006","article-title":"Applying Grading of Recommendations Assessment, Development and Evaluation (GRADE) to diagnostic tests was challenging but doable","volume":"67","author":"Gopalakrishna","journal-title":"J Clin Epidemiol"},{"key":"R21","unstructured":"Taova S . GetData digitizing program code: description, testing, training. Presented at: Consultants\u2019 Meeting on Benchmarking of Digitization Software; Nov 12-14, 2012; Vienna, Austria. URL: https:\/\/inis.iaea.org\/records\/d4sbw-hkz28 [Accessed 03-12-2025]"},{"issue":"5","key":"R22","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1177\/0272989X08319957","article-title":"Bivariate random effects meta-analysis of ROC curves","volume":"28","author":"Arends","journal-title":"Med Decis Making"},{"issue":"9","key":"R23","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.1002\/sim.1099","article-title":"Properties of the summary receiver operating characteristic (SROC) curve for diagnostic test data","volume":"21","author":"Walter","journal-title":"Stat Med"},{"issue":"11","key":"R24","doi-asserted-by":"publisher","first-page":"1539","DOI":"10.1002\/sim.1186","article-title":"Quantifying heterogeneity in a meta-analysis","volume":"21","author":"Higgins","journal-title":"Stat Med"},{"issue":"11","key":"R25","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1002\/sim.1187","article-title":"How should meta-regression analyses be undertaken and interpreted?","volume":"21","author":"Thompson","journal-title":"Stat Med"},{"issue":"7109","key":"R26","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1136\/bmj.315.7109.629","article-title":"Bias in meta-analysis detected by a simple, graphical test","volume":"315","author":"Egger","journal-title":"BMJ"},{"issue":"12","key":"R27","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14122821","article-title":"Computational detection of extraprostatic extension of prostate cancer on multiparametric MRI using deep learning","volume":"14","author":"Moroianu","journal-title":"Cancers (Basel)"},{"issue":"11","key":"R28","doi-asserted-by":"publisher","first-page":"1570","DOI":"10.1177\/0284185120905066","article-title":"Magnetic resonance radiomics for prediction of extraprostatic extension in non-favorable intermediate- and high-risk prostate cancer patients","volume":"61","author":"Losneg\u00e5rd","journal-title":"Acta Radiol"},{"key":"R29","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2024.108528","article-title":"Investigation of radiomic features on MRI images to identify extraprostatic extension in prostate cancer","volume":"259","author":"Gumus","journal-title":"Comput Methods Programs Biomed"},{"issue":"10","key":"R30","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1002\/bco2.421","article-title":"Extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithm","volume":"5","author":"Priester","journal-title":"BJUI Compass"},{"issue":"1","key":"R31","doi-asserted-by":"publisher","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","journal-title":"Cancer Imaging"},{"issue":"12","key":"R32","doi-asserted-by":"publisher","first-page":"3805","DOI":"10.1007\/s00259-021-05381-5","article-title":"Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study","volume":"48","author":"Hou","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"1","key":"R33","doi-asserted-by":"publisher","DOI":"10.1186\/s13244-024-01876-5","article-title":"Preoperative detection of extraprostatic tumor extension in patients with primary prostate cancer utilizing [Ga]Ga-PSMA-11 PET\/MRI","volume":"15","author":"Spielvogel","journal-title":"Insights Imaging"},{"issue":"10","key":"R34","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1007\/s11547-022-01541-1","article-title":"Machine learning-based radiomics for multiple primary prostate cancer biological characteristics prediction with F-PSMA-1007 PET: comparison among different volume segmentation thresholds","volume":"127","author":"Yao","journal-title":"Radiol Med"},{"issue":"7","key":"R35","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.21037\/tcr-22-2750","article-title":"Value of a combined magnetic resonance imaging-based radiomics-clinical model for predicting extracapsular extension in prostate cancer: a preliminary study","volume":"12","author":"Yang","journal-title":"Transl Cancer Res"},{"issue":"940","key":"R36","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.00940","article-title":"Radiomics based on multiparametric magnetic resonance imaging to predict extraprostatic extension of prostate cancer","volume":"10","author":"Xu","journal-title":"Front Oncol"},{"issue":"1","key":"R37","doi-asserted-by":"publisher","first-page":"234","DOI":"10.4143\/crt.2020.1221","article-title":"Prediction of pathologic findings with MRI-based clinical staging using the Bayesian network modeling in prostate cancer: a radiation oncologist perspective","volume":"54","author":"Wee","journal-title":"Cancer Res Treat"},{"issue":"10","key":"R38","doi-asserted-by":"publisher","first-page":"4096","DOI":"10.1016\/j.acra.2024.04.011","article-title":"Automated detection and grading of extraprostatic extension of prostate cancer at MRI via cascaded deep learning and random forest classification","volume":"31","author":"Simon","journal-title":"Acad Radiol"},{"issue":"3","key":"R39","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/s11307-019-01405-7","article-title":"Preoperative prediction of extracapsular extension: radiomics signature based on magnetic resonance imaging to stage prostate cancer","volume":"22","author":"Ma","journal-title":"Mol Imaging Biol"},{"issue":"11","key":"R40","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0293672","article-title":"Optimization and validation of 18F-DCFPyL PET radiomics-based machine learning models in intermediate- to high-risk primary prostate cancer","volume":"18","author":"Luining","journal-title":"PLoS ONE"},{"key":"R41","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.839621","article-title":"Multiparametric MRI and machine learning based radiomic models for preoperative prediction of multiple biological characteristics in prostate cancer","volume":"12","author":"Fan","journal-title":"Front Oncol"},{"issue":"4","key":"R42","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11040594","article-title":"Advanced imaging analysis in prostate MRI: building a radiomic signature to predict tumor aggressiveness","volume":"11","author":"Damascelli","journal-title":"Diagnostics (Basel)"},{"issue":"2","key":"R43","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1007\/s00259-020-04971-z","article-title":"Machine learning-based analysis of [F]DCFPyL PET radiomics for risk stratification in primary prostate cancer","volume":"48","author":"Cysouw","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"4","key":"R44","doi-asserted-by":"publisher","first-page":"1222","DOI":"10.1002\/jmri.27678","article-title":"Multiparametric magnetic resonance imaging-based peritumoral radiomics for preoperative prediction of the presence of extracapsular extension with prostate cancer","volume":"54","author":"Bai","journal-title":"J Magn Reson Imaging"},{"issue":"6","key":"R45","doi-asserted-by":"publisher","first-page":"1914","DOI":"10.1002\/jmri.26777","article-title":"MRI-based radiomics signature for the preoperative prediction of extracapsular extension of prostate cancer","volume":"50","author":"Ma","journal-title":"J Magn Reson Imaging"},{"issue":"10","key":"R46","doi-asserted-by":"publisher","first-page":"7575","DOI":"10.1007\/s00330-021-07856-3","article-title":"MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study","volume":"31","author":"Cuocolo","journal-title":"Eur Radiol"},{"issue":"4","key":"R47","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1002\/jmri.28608","article-title":"Deep\u2010learning models for detection and localization of visible clinically significant prostate cancer on multi\u2010parametric MRI","volume":"58","author":"Sun","journal-title":"Magn Reson Imaging"},{"issue":"7","key":"R48","doi-asserted-by":"publisher","first-page":"2379","DOI":"10.1007\/s00261-023-03924-y","article-title":"MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review","volume":"48","author":"Calimano-Ramirez","journal-title":"Abdom Radiol (NY)"},{"issue":"22","key":"R49","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14222576","article-title":"Systematic review of AI-assisted MRI in prostate cancer diagnosis: enhancing accuracy through second opinion tools","volume":"14","author":"Alqahtani","journal-title":"Diagnostics (Basel)"},{"issue":"1","key":"R50","doi-asserted-by":"publisher","first-page":"4261","DOI":"10.1038\/s41598-025-88297-6","article-title":"Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques","volume":"15","author":"Esteban","journal-title":"Sci Rep"},{"issue":"10","key":"R51","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.1016\/j.acra.2018.12.025","article-title":"Detection of extraprostatic extension of cancer on biparametric MRI combining texture analysis and machine learning: preliminary results","volume":"26","author":"Stanzione","journal-title":"Acad Radiol"},{"issue":"1","key":"R52","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1186\/s13244-024-01660-5","article-title":"A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports","volume":"15","author":"Jorg","journal-title":"Insights Imaging"},{"issue":"8","key":"R53","doi-asserted-by":"publisher","first-page":"2891","DOI":"10.1007\/s00261-024-04468-5","article-title":"Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI","volume":"49","author":"Lin","journal-title":"Abdom Radiol"},{"issue":"8056","key":"R54","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1038\/s41586-025-08675-y","article-title":"Multimodal generative AI for medical image interpretation","volume":"639","author":"Rao","journal-title":"Nature New Biol"},{"issue":"6","key":"R55","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1097\/CM9.0000000000003489","article-title":"Artificial intelligence in medical imaging: from task-specific models to large-scale foundation models","volume":"138","author":"Bian","journal-title":"Chin Med J (Engl)"}],"container-title":["Journal of Medical Internet Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jmir.org\/2025\/1\/e80981","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:14:38Z","timestamp":1782843278000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jmir.org\/2025\/1\/e80981"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,9]]},"references-count":55,"URL":"https:\/\/doi.org\/10.2196\/80981","relation":{},"ISSN":["1438-8871"],"issn-type":[{"value":"1438-8871","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,9]]},"article-number":"v27i6e80981"}}