{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:44:04Z","timestamp":1785545044661,"version":"3.56.0"},"reference-count":91,"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) has emerged as a promising tool for prostate cancer (PCa) risk stratification and outcome prediction. However, current studies often lack multicenter external validation, have limited sample sizes, present significant intermodel variability, and face overfitting concerns.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to comprehensively evaluate the diagnostic performance of magnetic resonance imaging (MRI)\u2013based AI models in predicting biochemical recurrence (BCR) of PCa.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Systematic searches were conducted in the PubMed, Embase, Web of Science, and Cochrane Library databases up to January 13, 2026. Studies were included that involved participants diagnosed with PCa, used MRI-based AI for predicting BCR, and had clearly defined reference standards. The quality of the included studies was assessed using the PROBAST+AI tool. A bivariate random effects model was used to pool sensitivity, specificity, and area under the curve (AUC) statistics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 28 studies were included, with 2623 patients in internal validation and 1134 patients in external validation. Diagnostic contingency tables were reconstructed from published performance metrics for most studies, while others were extracted from receiver operating characteristic curves due to the lack of direct reporting. In the internal validation set, pooled sensitivity was 0.80 (95% CI 0.73\u20100.86; prediction interval [PI] 0.48\u20100.99), specificity was 0.83 (95% CI 0.77\u20100.89; PI 0.49\u20101.00), and AUC was 0.86 (95% CI 0.83\u20100.89; PI 0.74\u20100.99). In the external validation set, pooled sensitivity was 0.82 (95% CI 0.72\u20100.91; PI 0.54\u20100.99), specificity was 0.83 (95% CI 0.71\u20100.92; PI 0.49\u20101.00), and AUC was 0.84 (95% CI 0.79\u20100.90; PI 0.70\u20100.98). No statistically significant differences were observed between internal and external validation in sensitivity (\n                      <jats:italic>P<\/jats:italic>\n                      =.73), specificity (\n                      <jats:italic>P<\/jats:italic>\n                      &gt;.99), AUC (\n                      <jats:italic>P<\/jats:italic>\n                      =.53), or diagnostic odds ratio (\n                      <jats:italic>P<\/jats:italic>\n                      =.98). Medical Net and Extreme Gradient Boosting achieved the highest sensitivity and AUC, whereas multiple kernel learning and support vector machine had the highest specificity. Subgroup and meta-regression analyses suggested that AI method, model type, timing of MRI acquisition, and treatment modality may contribute to heterogeneity.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This meta-analysis innovatively realizes the quantitative direct comparison of MRI-based AI model performance between internal and external validation cohorts for PCa BCR prediction. It comprehensively evaluates AI performance across diverse PCa treatment modalities and integrates machine learning and deep learning approaches. For the field, it identifies key sources of performance heterogeneity (eg, MRI acquisition timing and treatment modality) and quantifies the sensitivity-specificity trade-off in integrated radiomic-clinical models, advancing the systematic understanding of MRI-based AI for BCR prediction. In real-world practice, it provides actionable guidance to prioritize pretreatment MRI for AI model development and clinical BCR assessment and underscores the urgent need for standardized imaging protocols and prospective multicenter studies, laying a foundation for the safe clinical translation of these AI tools as adjunctive decision support instruments.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/85360","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T17:53:11Z","timestamp":1783446791000},"page":"e85360-e85360","source":"Crossref","is-referenced-by-count":0,"title":["Magnetic Resonance Imaging\u2013Based Artificial Intelligence in Predicting Prostate Cancer Biochemical Recurrence: Systematic Review and Meta-Analysis"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2805-2178","authenticated-orcid":false,"given":"Yanjun","family":"Jin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5122-2799","authenticated-orcid":false,"given":"Tianzuo","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4241-7763","authenticated-orcid":false,"given":"Zhiyuan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,7,7]]},"reference":[{"issue":"21","key":"R1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers16213596","article-title":"Predicting biochemical recurrence of prostate cancer post-prostatectomy using artificial intelligence: a systematic review","volume":"16","author":"Liu","journal-title":"Cancers (Basel)"},{"issue":"10","key":"R2","volume":"53","author":"Clarke","journal-title":"Urology Times Journal"},{"issue":"3","key":"R3","doi-asserted-by":"publisher","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","journal-title":"Eur Urol"},{"issue":"1","key":"R4","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-11445-5","article-title":"Diagnostic systematic review and meta-analysis of machine learning in predicting biochemical recurrence of prostate cancer","volume":"15","author":"Ling","journal-title":"Sci Rep"},{"issue":"10","key":"R5","doi-asserted-by":"publisher","first-page":"4748","DOI":"10.1007\/s00261-025-04892-1","article-title":"MRI-based radiomics for prediction of biochemical recurrence in prostate cancer: a systematic review and meta-analysis","volume":"50","author":"Salimi","journal-title":"Abdom Radiol (NY)"},{"key":"R6","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2025.1602134","article-title":"Diagnostic accuracy of PSA derivatives for prostate cancer in patients with low prostate-specific antigen levels","volume":"15","author":"Kadeer","journal-title":"Front Oncol"},{"issue":"20","key":"R7","doi-asserted-by":"publisher","first-page":"4278","DOI":"10.1158\/1078-0432.CCR-25-0320","article-title":"Transrectal ultrasound-guided prostate biopsy with and without PSMA-PET-targeted prostate biopsy for prostate cancer diagnosis","volume":"31","author":"Jiao","journal-title":"Clin Cancer Res"},{"issue":"2","key":"R8","doi-asserted-by":"publisher","DOI":"10.3390\/jcm13020487","article-title":"Current approach to complications and difficulties during transrectal ultrasound-guided prostate biopsies","volume":"13","author":"Osama","journal-title":"J Clin Med"},{"issue":"16","key":"R9","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13162697","article-title":"Prostate MRI and PSMA-PET in the primary diagnosis of prostate cancer","volume":"13","author":"Cereser","journal-title":"Diagnostics (Basel)"},{"issue":"11","key":"R10","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15111342","article-title":"A narrative review of artificial intelligence in MRI-guided prostate cancer diagnosis: addressing key challenges","volume":"15","author":"Alis","journal-title":"Diagnostics (Basel)"},{"issue":"10","key":"R11","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.1038\/s41416-023-02441-5","article-title":"Biopsy-free AI-aided precision MRI assessment in prediction of prostate cancer biochemical recurrence","volume":"129","author":"Hou","journal-title":"Br J Cancer"},{"issue":"1","key":"R12","doi-asserted-by":"publisher","first-page":"100120","DOI":"10.1016\/j.engmed.2026.100120","article-title":"Clinical research on artificial intelligence medical diagnostic devices: a scoping review","volume":"3","author":"Zhang","journal-title":"EngMedicine"},{"issue":"1","key":"R13","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1038\/s41698-023-00481-x","article-title":"A deep learning model, NAFNet, predicts adverse pathology and recurrence in prostate cancer using MRIs","volume":"7","author":"Gu","journal-title":"npj Precis Onc"},{"issue":"1","key":"R14","doi-asserted-by":"publisher","DOI":"10.1186\/s13244-024-01865-8","article-title":"Evaluating the feasibility of AI-predicted bpMRI image features for predicting prostate cancer aggressiveness: a multi-center study","volume":"16","author":"Wang","journal-title":"Insights Imaging"},{"key":"R15","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":"1","key":"R16","doi-asserted-by":"publisher","DOI":"10.1186\/s13643-020-01542-z","article-title":"PRISMA-S: an extension to the PRISMA statement for reporting literature searches in systematic reviews","volume":"10","author":"Rethlefsen","journal-title":"Syst Rev"},{"issue":"10","key":"R17","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1001744","article-title":"Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist","volume":"11","author":"Moons","journal-title":"PLOS Med"},{"issue":"5","key":"R18","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1016\/j.eururo.2004.07.010","article-title":"Preoperative neural network using combined magnetic resonance imaging variables, prostate specific antigen, and Gleason score to predict prostate cancer recurrence after radical prostatectomy","volume":"46","author":"Poulakis","journal-title":"Eur Urol"},{"issue":"4","key":"R19","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1038\/s41391-021-00387-4","article-title":"PSA bounce, prognosis, and clues to the radiation response","volume":"24","author":"Kishan","journal-title":"Prostate Cancer Prostatic Dis"},{"key":"R20","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","journal-title":"BMJ"},{"issue":"3","key":"R21","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2018-026012","article-title":"Diagnostic accuracy of contemporary and high-sensitivity cardiac troponin assays used in serial testing, versus single-sample testing as a comparator, to triage patients suspected of acute non-ST-segment elevation myocardial infarction: a systematic review protocol","volume":"9","author":"Zhelev","journal-title":"BMJ Open"},{"issue":"12","key":"R22","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13123098","article-title":"Deep learning with quantitative features of magnetic resonance images to predict biochemical recurrence of radical prostatectomy: a multi-center study","volume":"13","author":"Yan","journal-title":"Cancers (Basel)"},{"key":"R23","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2288-14-25","article-title":"The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method","volume":"14","author":"IntHout","journal-title":"BMC Med Res Methodol"},{"issue":"4","key":"R24","doi-asserted-by":"publisher","DOI":"10.1016\/j.imr.2023.101014","article-title":"How to understand and report heterogeneity in a meta-analysis: the difference between I-squared and prediction intervals","volume":"12","author":"Borenstein","journal-title":"Integr Med Res"},{"issue":"11","key":"R25","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":"9","key":"R26","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1016\/j.jclinepi.2005.01.016","article-title":"The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed","volume":"58","author":"Deeks","journal-title":"J Clin Epidemiol"},{"issue":"18","key":"R27","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14184475","article-title":"Longitudinal changes and predictive value of multiparametric MRI features for prostate cancer patients treated with MRI-guided lattice extreme ablative dose (LEAD) boost radiotherapy","volume":"14","author":"Algohary","journal-title":"Cancers (Basel)"},{"key":"R28","doi-asserted-by":"publisher","DOI":"10.1177\/15330338231166766","article-title":"Predicting model of biochemical recurrence of prostate carcinoma (PCa-BCR) using MR perfusion-weighted imaging-based radiomics","volume":"22","author":"An","journal-title":"Technol Cancer Res Treat"},{"key":"R29","doi-asserted-by":"crossref","unstructured":"Mar\u00edn Castrill\u00f3n DM Fontaine P Gnep K Rd C Acosta O . Multiple kernel learning applied to the prediction of prostate cancer recurrence from MRI radiomic features. Presented at: 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE); Oct 28-30, 2019; Athens, Greece. [doi: 10.1109\/BIBE.2019.00183]","DOI":"10.1109\/BIBE.2019.00183"},{"issue":"18","key":"R30","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15184437","article-title":"T2-weighted MRI radiomic features predict prostate cancer presence and eventual biochemical recurrence","volume":"15","author":"Duenweg","journal-title":"Cancers (Basel)"},{"issue":"3","key":"R31","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.14293","article-title":"MRI-based radiomic features for identifying recurrent prostate cancer after proton radiation therapy","volume":"25","author":"Gumus","journal-title":"J Appl Clin Med Phys"},{"issue":"3","key":"R32","doi-asserted-by":"publisher","DOI":"10.1148\/rycan.230143","article-title":"Development and validation of a multimodality model based on whole-slide imaging and biparametric MRI for predicting postoperative biochemical recurrence in prostate cancer","volume":"6","author":"Hu","journal-title":"Radiol Imaging Cancer"},{"issue":"23","key":"R33","doi-asserted-by":"publisher","DOI":"10.3390\/jcm12237322","article-title":"Multi-institutional development and validation of a radiomic model to predict prostate cancer recurrence following radical prostatectomy","volume":"12","author":"Huynh","journal-title":"J Clin Med"},{"issue":"4","key":"R34","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1002\/jmri.26928","article-title":"Prediction of biochemical recurrence in prostate cancer patients who underwent prostatectomy using routine clinical prostate multiparametric MRI and decipher genomic score","volume":"51","author":"Jambor","journal-title":"J Magn Reson Imaging"},{"issue":"11","key":"R35","doi-asserted-by":"publisher","first-page":"3854","DOI":"10.3390\/app10113854","article-title":"A machine learning approach to predict an early biochemical recurrence after a radical prostatectomy","volume":"10","author":"Park","journal-title":"Appl Sci (Basel)"},{"key":"R36","doi-asserted-by":"publisher","DOI":"10.1088\/2057-1976\/ad8201","article-title":"Prediction of prostate cancer recurrence after radiotherapy using a fused machine learning approach: utilizing radiomics from pretreatment T2W MRI images with clinical and pathological information","volume":"PMID","author":"Piran Nanekaran","journal-title":"Biomed Phys Eng Express"},{"issue":"16","key":"R37","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15164163","article-title":"Prostate region-wise imaging biomarker profiles for risk stratification and biochemical recurrence prediction","volume":"15","author":"S\u00e1nchez Iglesias","journal-title":"Cancers (Basel)"},{"key":"R38","doi-asserted-by":"crossref","unstructured":"Septiers V Sosa-Marrero C Crevoisier R et al. Improved Prediction of Recurrence After Prostate Cancer Radiotherapy Using Multimodal Data and in Silico Simulations. Springer Nature Switzerland; 2025. [doi: 10.1007\/978-3-031-73376-5_20]","DOI":"10.1007\/978-3-031-73376-5_20"},{"issue":"6","key":"R39","doi-asserted-by":"publisher","first-page":"1626","DOI":"10.1002\/jmri.26178","article-title":"Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: preliminary findings","volume":"48","author":"Shiradkar","journal-title":"J Magn Reson Imaging"},{"key":"R40","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.841801","article-title":"Prostate surface distension and tumor texture descriptors from pre-treatment MRI are associated with biochemical recurrence following radical prostatectomy: preliminary findings","volume":"12","author":"Shiradkar","journal-title":"Front Oncol"},{"key":"R41","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1342104","article-title":"Deep learning-based radiomics model from pretreatment ADC to predict biochemical recurrence in advanced prostate cancer","volume":"14","author":"Wang","journal-title":"Front Oncol"},{"key":"R42","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.111095","article-title":"Evaluation of a deep learning-based algorithm for post-radiotherapy prostate cancer local recurrence detection using biparametric MRI","volume":"168","author":"Yilmaz","journal-title":"Eur J Radiol"},{"issue":"47","key":"R43","doi-asserted-by":"publisher","first-page":"78140","DOI":"10.18632\/oncotarget.11293","article-title":"An imaging-based approach predicts clinical outcomes in prostate cancer through a novel support vector machine classification","volume":"7","author":"Zhang","journal-title":"Oncotarget"},{"issue":"731","key":"R44","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.00731","article-title":"Radiomics of multiparametric MRI to predict biochemical recurrence of localized prostate cancer after radiation therapy","volume":"10","author":"Zhong","journal-title":"Front Oncol"},{"issue":"4","key":"R45","doi-asserted-by":"publisher","first-page":"264","DOI":"10.56434\/j.arch.esp.urol.20237604.30","article-title":"MRI-derived radiomics model to predict the biochemical recurrence of prostate cancer following seed brachytherapy","volume":"76","author":"Zhu","journal-title":"Arch Esp Urol"},{"issue":"3","key":"R46","doi-asserted-by":"publisher","first-page":"7771","DOI":"10.1097\/JS9.0000000000004584","article-title":"Optimizing recurrence prediction and risk stratification in prostate cancer using a 2.5D deep learning model: a multicenter MRI-based study","volume":"112","author":"Li","journal-title":"Int J Surg"},{"issue":"5","key":"R47","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering12050538","article-title":"MRMS-CNNFormer: a novel framework for predicting the biochemical recurrence of prostate cancer on multi-sequence MRI","volume":"12","author":"Lian","journal-title":"Bioengineering (Basel)"},{"key":"R48","doi-asserted-by":"publisher","DOI":"10.2174\/0115734056403104250527045320","article-title":"DWI-based biologically interpretable radiomic nomogram for predicting 1-year biochemical recurrence after radical prostatectomy: a deep learning, multicenter study","volume":"21","author":"Niu","journal-title":"Curr Med Imaging"},{"key":"R49","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinimag.2025.110579","article-title":"A multimodal automated deep learning-based model for predicting biochemical recurrence of prostate cancer following prostatectomy from baseline MRI, presurgical clinical covariates","volume":"126","author":"Simon","journal-title":"Clin Imaging"},{"issue":"9","key":"R50","doi-asserted-by":"publisher","first-page":"4320","DOI":"10.1007\/s00261-025-04873-4","article-title":"Bi-parametric MRI-based quantification radiomics model for the noninvasive prediction of histopathology and biochemical recurrence after prostate cancer surgery: a multicenter study","volume":"50","author":"Wu","journal-title":"Abdom Radiol (NY)"},{"issue":"6","key":"R51","doi-asserted-by":"publisher","DOI":"10.1111\/cbdd.70206","article-title":"Multimodal fusion of mpMRI radiomics, clinical features, and hematological biomarkers enhances machine learning-based prediction of biochemical recurrence in prostate cancer patients","volume":"106","author":"Zhu","journal-title":"Chem Biol Drug Des"},{"key":"R52","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2025.1674397","article-title":"Addressing the current challenges in the clinical application of AI-based radiomics for cancer imaging","volume":"12","author":"Xu","journal-title":"Front Med (Lausanne)"},{"issue":"4","key":"R53","doi-asserted-by":"publisher","DOI":"10.3892\/ol.2024.14321","article-title":"Diagnostic performance of F\u2011DCFPyL PET vs. Ga\u2011PSMA PET\/CT in patients with suspected prostate cancer: a systemic review and meta\u2011analysis","volume":"27","author":"Jiang","journal-title":"Oncol Lett"},{"issue":"1","key":"R54","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1186\/s12880-024-01278-5","article-title":"Radiomics diagnostic performance for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis","volume":"24","author":"Ma","journal-title":"BMC Med Imaging"},{"issue":"5","key":"R55","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14051115","article-title":"Lynch-like syndrome: potential mechanisms and management","volume":"14","author":"Mart\u00ednez-Roca","journal-title":"Cancers (Basel)"},{"issue":"1","key":"R56","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s13246-019-00730-z","article-title":"Multiparametric MRI and radiomics in prostate cancer: a review","volume":"42","author":"Sun","journal-title":"Australas Phys Eng Sci Med"},{"issue":"7","key":"R57","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1007\/s11547-024-01826-7","article-title":"Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study","volume":"129","author":"Bartolotta","journal-title":"Radiol Med"},{"issue":"3","key":"R58","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.240184","article-title":"Artificial intelligence for breast cancer screening: trade-offs between sensitivity and specificity","volume":"6","author":"Bahl","journal-title":"Radiol Artif Intell"},{"issue":"3","key":"R59","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1038\/s43018-026-01127-0","article-title":"Diagnostic accuracy, fairness and clinical implementation of AI for breast cancer screening: results of multicenter retrospective and prospective technical feasibility studies","volume":"7","author":"Kelly","journal-title":"Nat Cancer"},{"issue":"1","key":"R60","doi-asserted-by":"publisher","DOI":"10.1186\/s40779-023-00464-w","article-title":"What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?","volume":"10","author":"Zhao","journal-title":"Mil Med Res"},{"issue":"2","key":"R61","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.rcl.2017.10.008","article-title":"Prostate MR imaging for posttreatment evaluation and recurrence","volume":"56","author":"Gaur","journal-title":"Radiol Clin North Am"},{"issue":"6","key":"R62","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s13244-015-0426-9","article-title":"Pitfalls in interpreting mp-MRI of the prostate: a pictorial review with pathologic correlation","volume":"6","author":"Panebianco","journal-title":"Insights Imaging"},{"issue":"6","key":"R63","doi-asserted-by":"publisher","first-page":"852","DOI":"10.2214\/AJR.22.28665","article-title":"PI-RR: the prostate imaging for recurrence reporting system for mri assessment of local prostate cancer recurrence after radiation therapy or radical prostatectomy-a review","volume":"220","author":"Abreu-Gomez","journal-title":"AJR Am J Roentgenol"},{"issue":"2","key":"R64","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1007\/s00330-024-10853-x","article-title":"Accelerating breast MRI acquisition with generative AI models","volume":"35","author":"Okolie","journal-title":"Eur Radiol"},{"issue":"9","key":"R65","doi-asserted-by":"publisher","first-page":"1832","DOI":"10.1158\/0008-5472.CAN-21-3074","article-title":"Comprehensive evaluation of machine learning models and gene expression signatures for prostate cancer prognosis using large population cohorts","volume":"82","author":"Li","journal-title":"Cancer Res"},{"issue":"1","key":"R66","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01414-z","article-title":"Extensive benchmarking of a method that estimates external model performance from limited statistical characteristics","volume":"8","author":"El-Hay","journal-title":"NPJ Digit Med"},{"issue":"1","key":"R67","doi-asserted-by":"publisher","DOI":"10.1007\/s10238-025-01965-9","article-title":"AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions","volume":"26","author":"Hsu","journal-title":"Clin Exp Med"},{"key":"R68","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-026-12465-z","article-title":"Artificial-intelligence models vs. radiologists in the detection of clinically significant prostate cancer on mpMRI: a meta-analysis","author":"Andrade","journal-title":"Eur Radiol"},{"issue":"3","key":"R69","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/S1474-4422(15)00393-2","article-title":"MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines","volume":"15","author":"Filippi","journal-title":"Lancet Neurol"},{"issue":"1","key":"R70","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1002\/mp.13264","article-title":"Deep learning in medical imaging and radiation therapy","volume":"46","author":"Sahiner","journal-title":"Med Phys"},{"issue":"1","key":"R71","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-023-00703-w","article-title":"Data-centric artificial intelligence in oncology: a systematic review assessing data quality in machine learning models for head and neck cancer","volume":"10","author":"Adeoye","journal-title":"J Big Data"},{"issue":"2","key":"R72","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1007\/s00330-025-11898-2","article-title":"Diagnostic performance of deep learning for predicting glioma isocitrate dehydrogenase and 1p\/19q co-deletion in MRI: a systematic review and meta-analysis","volume":"36","author":"Farahani","journal-title":"Eur Radiol"},{"key":"R73","doi-asserted-by":"publisher","DOI":"10.22159\/ajpcr.2023.v16i11.48193","article-title":"Integrating artificial intelligence in disease diagnosis, treatment, and formulation development: a review","author":"Kumar","journal-title":"Asian J Pharm Clin Res"},{"key":"R74","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2021.12.004","article-title":"In-depth qualitative interviews identified barriers and facilitators that influenced chief investigators\u2019 use of core outcome sets in randomised controlled trials","volume":"144","author":"Hughes","journal-title":"J Clin Epidemiol"},{"issue":"1","key":"R75","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-025-02648-5","article-title":"Evaluating the properties of the fragility index of meta-analyses","volume":"25","author":"Xing","journal-title":"BMC Med Res Methodol"},{"issue":"4","key":"R76","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2025.101185","article-title":"Unraveling overoptimism and publication bias in ML-driven science","volume":"6","author":"Saidi","journal-title":"Patterns (N Y)"},{"issue":"1","key":"R77","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1186\/s13000-025-01686-3","article-title":"Fostering trust and interpretability: integrating explainable AI (XAI) with machine learning for enhanced disease prediction and decision transparency","volume":"20","author":"Agrawal","journal-title":"Diagn Pathol"},{"key":"R78","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m689","article-title":"Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies","volume":"368","author":"Nagendran","journal-title":"BMJ"},{"issue":"1","key":"R79","doi-asserted-by":"publisher","DOI":"10.3390\/biomedicines14010020","article-title":"Comparison of artificial intelligence and radiologists in MRI-based prostate cancer diagnosis: a meta-analysis of accuracy and effectiveness","volume":"14","author":"Chen","journal-title":"Biomedicines"},{"issue":"13","key":"R80","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":"3","key":"R81","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1038\/s41591-024-02850-w","article-title":"Heterogeneity and predictors of the effects of AI assistance on radiologists","volume":"30","author":"Yu","journal-title":"Nat Med"},{"issue":"11","key":"R82","doi-asserted-by":"publisher","first-page":"7463","DOI":"10.1007\/s00330-023-09882-9","article-title":"Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability","volume":"33","author":"Netzer","journal-title":"Eur Radiol"},{"issue":"4","key":"R83","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1016\/j.ajur.2024.12.001","article-title":"Advancements in artificial intelligence for prostate cancer: optimizing diagnosis, treatment, and prognostic assessment","volume":"12","author":"Arita","journal-title":"Asian J Urol"},{"issue":"1","key":"R84","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s00330-020-07148-2","article-title":"Challenges and solutions for introducing artificial intelligence (AI) in daily clinical workflow","volume":"31","author":"Kotter","journal-title":"Eur Radiol"},{"issue":"9","key":"R85","doi-asserted-by":"publisher","DOI":"10.3390\/jpm11090842","article-title":"Making radiomics more reproducible across scanner and imaging protocol variations: a review of harmonization methods","volume":"11","author":"Mali","journal-title":"J Pers Med"},{"issue":"9","key":"R86","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1097\/RLI.0000000000000666","article-title":"Variability and standardization of quantitative imaging: monoparametric to multiparametric quantification, radiomics, and artificial intelligence","volume":"55","author":"Hagiwara","journal-title":"Invest Radiol"},{"issue":"16","key":"R87","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14161835","article-title":"Enhancing the clinical utility of radiomics: addressing the challenges of repeatability and reproducibility in CT and MRI","volume":"14","author":"Teng","journal-title":"Diagnostics (Basel)"},{"issue":"8056","key":"R88","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":"1","key":"R89","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-69250-1","article-title":"Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data","volume":"10","author":"Sheller","journal-title":"Sci Rep"},{"issue":"1","key":"R90","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-73334-7","article-title":"A privacy-preserving expert system for collaborative medical diagnosis across multiple institutions using federated learning","volume":"14","author":"Markkandan","journal-title":"Sci Rep"},{"issue":"1","key":"R91","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1038\/s41585-025-01070-2","article-title":"The state of the art in artificial intelligence and digital pathology in prostate cancer","volume":"23","author":"Ni","journal-title":"Nat Rev Urol"}],"container-title":["Journal of Medical Internet Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jmir.org\/2026\/1\/e85360","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T22:25:50Z","timestamp":1785536750000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jmir.org\/2026\/1\/e85360"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,7]]},"references-count":91,"URL":"https:\/\/doi.org\/10.2196\/85360","relation":{},"ISSN":["1438-8871"],"issn-type":[{"value":"1438-8871","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,7]]},"article-number":"v28i21e85360"}}