{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T13:03:07Z","timestamp":1792242187251,"version":"4.4.0"},"reference-count":67,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T00:00:00Z","timestamp":1771804800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","ejropen.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Radiology Open"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.ejro.2026.100741","type":"journal-article","created":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T08:25:07Z","timestamp":1772526307000},"page":"100741","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"special_numbering":"C","title":["Convolutional neural networks for prostate cancer detection, classification, and segmentation: A systematic review and bibliometric analysis"],"prefix":"10.1016","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6870-6558","authenticated-orcid":false,"given":"Burak","family":"G\u00fclmez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.ejro.2026.100741_bib1","first-page":"17","article-title":"Cancer statistics, 2023","volume":"73","author":"Siegel","year":"2023","journal-title":"CA A Cancer Journal Clinicians"},{"key":"10.1016\/j.ejro.2026.100741_bib2","article-title":"Prostate Cancer Incidence and Mortality: Global Status and Temporal Trends in 89 Countries From 2000 to 2019","volume":"10","author":"Wang","year":"2022","journal-title":"Front. Public Health"},{"issue":"1","key":"10.1016\/j.ejro.2026.100741_bib3","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1038\/s41572-020-00243-0","article-title":"Prostate cancer","volume":"7","author":"Rebello","year":"2021","journal-title":"Nat. Rev. Dis. Prim."},{"key":"10.1016\/j.ejro.2026.100741_bib4","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1016\/S0022-5347(17)35233-3","article-title":"Comparison of digital rectal examination and serum prostate specific antigen in the early detection of prostate cancer: results of a multicenter clinical trial of 6,630 men","volume":"151","author":"Catalona","year":"1994","journal-title":"J. Urol."},{"key":"10.1016\/j.ejro.2026.100741_bib5","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/S0094-0143(03)00057-0","article-title":"PSA markers in prostate cancer detection","volume":"30","author":"Gretzer","year":"2003","journal-title":"Urol. Clin. North Am."},{"key":"10.1016\/j.ejro.2026.100741_bib6","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1016\/S0022-5347(05)65851-X","article-title":"Safety and morbidity of first and repeat transrectal ultrasound guided prostate needle biopsies: results of a prospective European prostate cancer detection study","volume":"166","author":"Djavan","year":"2001","journal-title":"J. Urol."},{"key":"10.1016\/j.ejro.2026.100741_bib7","article-title":"Comparison of Multiparametric Magnetic Resonance Imaging and Targeted Biopsy With Systematic Biopsy Alone for the Diagnosis of Prostate Cancer: A Systematic Review and Meta-analysis","volume":"2","author":"Sathianathen","year":"2019","journal-title":"JAMA Netw. Open."},{"key":"10.1016\/j.ejro.2026.100741_bib8","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.2214\/AJR.18.20536","article-title":"Interreader Variability of Prostate Imaging Reporting and Data System Version 2 in Detecting and Assessing Prostate Cancer Lesions at Prostate MRI","volume":"212","author":"Greer","year":"2019","journal-title":"AJR Am. J. Roentgenol."},{"key":"10.1016\/j.ejro.2026.100741_bib9","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1111\/bju.16452","article-title":"Perspectives on technology: Prostate Imaging\u2010Reporting and Data System (PI\u2010RADS) interobserver variability","volume":"134","author":"Taya","year":"2024","journal-title":"BJU Int"},{"key":"10.1016\/j.ejro.2026.100741_bib10","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1053\/hupa.2001.21135","article-title":"Interobserver reproducibility of Gleason grading of prostatic carcinoma: general pathologist","volume":"32","author":"Allsbrook Jr.","year":"2001","journal-title":"Hum. Pathol."},{"key":"10.1016\/j.ejro.2026.100741_bib11","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1080\/21681805.2016.1206619","article-title":"Interobserver variability in Gleason histological grading of prostate cancer","volume":"50","author":"Ozkan","year":"2016","journal-title":"Scand. J. Urol."},{"key":"10.1016\/j.ejro.2026.100741_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.imu.2022.100923","article-title":"Robust multi-modal prostate cancer classification via feature autoencoder and dual attention","volume":"30","author":"Li","year":"2022","journal-title":"Inform. Med. Unlocked"},{"issue":"1","key":"10.1016\/j.ejro.2026.100741_bib13","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1186\/s13014-024-02491-w","article-title":"The impact of multicentric datasets for the automated tumor delineation in primary prostate cancer using convolutional neural networks on 18F-PSMA-1007 PET","volume":"19","author":"Holzschuh","year":"2024","journal-title":"Radiat. Oncol."},{"issue":"6","key":"10.1016\/j.ejro.2026.100741_bib14","doi-asserted-by":"crossref","first-page":"5424","DOI":"10.21037\/qims-2024-2476","article-title":"U-Net benign prostatic hyperplasia-trained deep learning model for prostate ultrasound image segmentation in prostate cancer","volume":"15","author":"Jiao","year":"2025","journal-title":"Quant. Imaging Med. Surg."},{"key":"10.1016\/j.ejro.2026.100741_bib15","series-title":"Progress in Biomedical Optics and Imaging - Proceedings of","article-title":"Benchmarking image transformers for prostate cancer detection from ultrasound data","author":"Harmanani","year":"2024"},{"key":"10.1016\/j.ejro.2026.100741_bib16","doi-asserted-by":"crossref","DOI":"10.1016\/j.displa.2024.102699","article-title":"Identifying pathological groups from MRI in prostate cancer using graph representation learning","volume":"83","author":"Liu","year":"2024","journal-title":"Displays"},{"issue":"1","key":"10.1016\/j.ejro.2026.100741_bib17","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1186\/s12880-024-01336-y","article-title":"3D residual attention hierarchical fusion for real-time detection of the prostate capsule","volume":"24","author":"Wu","year":"2024","journal-title":"BMC Med. Imaging"},{"key":"10.1016\/j.ejro.2026.100741_bib18","first-page":"699","article-title":"Deep Learning for Image Analysis and Diagnosis Aid of Prostate Cancer","author":"da Silva","year":"2025","journal-title":"Proc. Int. Jt. Conf. Comput. Vis. Imaging Comput. Graph. Theory Appl. 3"},{"issue":"2","key":"10.1016\/j.ejro.2026.100741_bib19","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1002\/mp.17546","article-title":"Cross-shaped windows transformer with self-supervised pretraining for clinically significant prostate cancer detection in bi-parametric MRI","volume":"52","author":"Li","year":"2025","journal-title":"Med. Phys."},{"key":"10.1016\/j.ejro.2026.100741_bib20","doi-asserted-by":"crossref","first-page":"4812","DOI":"10.1109\/ACCESS.2024.3349409","article-title":"FCTformer: fusing convolutional operations and transformer for 3d rectal tumor segmentation in MR images","volume":"12","author":"Sang","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.ejro.2026.100741_bib21","doi-asserted-by":"crossref","DOI":"10.1007\/s00521-025-11386-0","article-title":"Integrative multi-omics approach for identifying and classifying high-risk patients in prostate cancer management","author":"Azzeh","year":"2025","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.ejro.2026.100741_bib22","article-title":"A multi-object deep neural network architecture to detect prostate anatomy in T2-weighted MRI: performance evaluation","volume":"2","author":"Baldeon-Calisto","year":"2022","journal-title":"Front. Nucl. Med."},{"issue":"1","key":"10.1016\/j.ejro.2026.100741_bib23","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1186\/s13014-024-02471-0","article-title":"Comparison of data fusion strategies for automated prostate lesion detection using mpMRI correlated with whole mount histology","volume":"19","author":"Gunashekar","year":"2024","journal-title":"Radiat. Oncol."},{"issue":"3","key":"10.1016\/j.ejro.2026.100741_bib24","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1007\/s00330-024-11299-x","article-title":"Prostate cancer MRI methodological radiomics score: a EuSoMII radiomics auditing group initiative","volume":"35","author":"Cavallo","year":"2024","journal-title":"Eur. Radiol."},{"issue":"1","key":"10.1016\/j.ejro.2026.100741_bib25","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s13244-023-01572-w","article-title":"METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII","volume":"15","author":"Kocak","year":"2024","journal-title":"Insights Imaging"},{"issue":"2","key":"10.1016\/j.ejro.2026.100741_bib26","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.media.2013.12.002","article-title":"Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge","volume":"18","author":"Litjens","year":"2014","journal-title":"Med. Image Anal."},{"issue":"5","key":"10.1016\/j.ejro.2026.100741_bib27","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1109\/TMI.2014.2303821","article-title":"Computer-aided detection of prostate cancer in MRI","volume":"33","author":"Litjens","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.ejro.2026.100741_bib28","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1515\/cdbme-2024-0116","article-title":"Classification of prostate cancer in 3d magnetic resonance imaging data based on convolutional neural networks","volume":"10","author":"Rippa","year":"2024","journal-title":"Curr. Dir. Biomed. Eng."},{"issue":"1","key":"10.1016\/j.ejro.2026.100741_bib29","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s11548-024-03153-8","article-title":"Robust prostate disease classification using transformers with discrete representations","volume":"20","author":"Santhirasekaram","year":"2025","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"4","key":"10.1016\/j.ejro.2026.100741_bib30","doi-asserted-by":"crossref","first-page":"369","DOI":"10.3390\/bioengineering12040369","article-title":"Advancing prostate cancer diagnostics: a ConvNeXt approach to multi-class classification in underrepresented populations","volume":"12","author":"Emegano","year":"2025","journal-title":"Bioengineering"},{"issue":"2","key":"10.1016\/j.ejro.2026.100741_bib31","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1002\/path.6373","article-title":"Stress testing deep learning models for prostate cancer detection on biopsies and surgical specimens","volume":"265","author":"Flannery","year":"2025","journal-title":"J. Pathol."},{"issue":"3","key":"10.1016\/j.ejro.2026.100741_bib32","doi-asserted-by":"crossref","DOI":"10.1002\/ima.70092","article-title":"An efficient parallel branch network for multi-class classification of prostate cancer from histopathological images","volume":"35","author":"Srivastava","year":"2025","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"10.1016\/j.ejro.2026.100741_bib33","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110776","article-title":"LightCM-PNet: a lightweight pyramid network for real-time prostate segmentation in transrectal ultrasound","volume":"156","author":"Wang","year":"2024","journal-title":"Pattern Recognit."},{"issue":"4","key":"10.1016\/j.ejro.2026.100741_bib34","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1007\/s00259-023-06551-3","article-title":"Deep learning-based whole-body characterization of prostate cancer lesions on [68Ga]Ga-PSMA-11 PET\/CT in patients with post-prostatectomy recurrence","volume":"51","author":"Huang","year":"2024","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"5","key":"10.1016\/j.ejro.2026.100741_bib35","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1007\/s00259-023-06555-z","article-title":"A convolutional neural network-based system for fully automatic segmentation of whole-body [68Ga]Ga-PSMA PET images in prostate cancer","volume":"51","author":"Jafari","year":"2024","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"10.1016\/j.ejro.2026.100741_bib36","article-title":"Cone-beam CT to CT image translation using a transformer-based deep learning model for prostate cancer adaptive radiotherapy","author":"Koike","year":"2025","journal-title":"J. Imaging Inform. Med."},{"key":"10.1016\/j.ejro.2026.100741_bib37","doi-asserted-by":"crossref","first-page":"167","DOI":"10.53759\/7669\/jmc202505013","article-title":"A contemporary CNN based classifier approach for intelligent diagnostic systems","author":"Chandrasekhara","year":"2025","journal-title":"J. Mach. Comput. 5(1)"},{"issue":"1","key":"10.1016\/j.ejro.2026.100741_bib38","doi-asserted-by":"crossref","first-page":"225","DOI":"10.3390\/app15010225","article-title":"A classifier model using fine-tuned convolutional neural network and transfer learning approaches for prostate cancer detection","volume":"15","author":"Sarates","year":"2025","journal-title":"Appl. Sci. (Switz. )"},{"issue":"14","key":"10.1016\/j.ejro.2026.100741_bib39","doi-asserted-by":"crossref","first-page":"7877","DOI":"10.1007\/s00521-024-09499-z","article-title":"Prostate cancer grading framework based on deep transfer learning and Aquila optimizer","volume":"36","author":"Balaha","year":"2024","journal-title":"Neural Comput. Appl."},{"issue":"1","key":"10.1016\/j.ejro.2026.100741_bib40","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1186\/s12938-024-01325-w","article-title":"Multi-branch CNNFormer: a novel framework for predicting prostate cancer response to hormonal therapy","volume":"23","author":"Abdelhalim","year":"2024","journal-title":"Biomed. Eng. Online"},{"key":"10.1016\/j.ejro.2026.100741_bib41","article-title":"A breakthrough approach for prostate cancer identification utilizing VGG-16 CNN model with migration learning","volume":"2024","author":"Ebin","year":"2024"},{"issue":"1","key":"10.1016\/j.ejro.2026.100741_bib42","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/s12672-024-01177-9","article-title":"Automated contouring of CTV and OARs in planning CT scans using novel hybrid convolution-transformer networks for prostate cancer radiotherapy","volume":"15","author":"Arjmandi","year":"2024","journal-title":"Discov. Oncol."},{"key":"10.1016\/j.ejro.2026.100741_bib43","series-title":"2024 International Conference on Intelligent Cybernetics Technology and Applications","first-page":"819","article-title":"Classification of prostate precancerous cells using DenseNet-201 and Inception-v3 Models","author":"Jusman","year":"2024"},{"key":"10.1016\/j.ejro.2026.100741_bib44","article-title":"Enhancing prostate cancer segmentation in bpMRI: integrating zonal awareness into attention-guided U-Net","volume":"11","author":"Wei","year":"2025","journal-title":"Digit. Health"},{"issue":"4","key":"10.1016\/j.ejro.2026.100741_bib45","doi-asserted-by":"crossref","first-page":"2665","DOI":"10.1002\/mp.16811","article-title":"Deep learning-based ultrasound auto-segmentation of the prostate with brachytherapy implanted needles","volume":"51","author":"Hampole","year":"2024","journal-title":"Med. Phys."},{"key":"10.1016\/j.ejro.2026.100741_bib46","article-title":"Prostate segmentation in MRI images using transfer learning based Mask R-CNN","volume":"20","author":"Shabbir","year":"2024","journal-title":"Curr. Med. Imaging"},{"issue":"3","key":"10.1016\/j.ejro.2026.100741_bib47","doi-asserted-by":"crossref","DOI":"10.1002\/nbm.5069","article-title":"Uncovering prostate cancer aggressiveness signal in T2-weighted MRI through a three-reference tissues normalization technique","volume":"37","author":"Algohary","year":"2024","journal-title":"NMR Biomed."},{"key":"10.1016\/j.ejro.2026.100741_bib48","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.euros.2024.02.005","article-title":"Deep learning model for realtime semantic segmentation during intraoperative robotic prostatectomy","volume":"62","author":"Gon Park","year":"2024","journal-title":"Eur. Urol. Open Sci."},{"key":"10.1016\/j.ejro.2026.100741_bib49","series-title":"ITC-CSCC 2022 - 37th International Technical Conference on Circuits\/Systems, Computers and Communications","first-page":"227","article-title":"Training Deep CNN\u2019s to Detect Prostate Cancer Lesion with Small Training Data","author":"Wiratchawa","year":"2022"},{"key":"10.1016\/j.ejro.2026.100741_bib50","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1007\/s12652-024-04751-9","article-title":"Multi image super resolution of MRI images using generative adversarial network","volume":"15","author":"Nimitha","year":"2024","journal-title":"J Ambient Intell Human Comput"},{"key":"10.1016\/j.ejro.2026.100741_bib51","doi-asserted-by":"crossref","unstructured":"Sharmin Sultana, Adam Robinson, Daniel Y. Song, and Junghoon Lee \"CNN-based hierarchical coarse-to-fine segmentation of pelvic CT images for prostate cancer radiotherapy\", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113151I (16 March 2020); doi:10.1117\/12.2549979.","DOI":"10.1117\/12.2549979"},{"key":"10.1016\/j.ejro.2026.100741_bib52","first-page":"167767","volume":"8","author":"Chaddad","year":"2020"},{"key":"10.1016\/j.ejro.2026.100741_bib53","doi-asserted-by":"crossref","unstructured":"Haritha. V, K. V. Archana, B. D, U. M. S, P. Sasireka and S. G P, \"Prostate Cancer Classification using Cat Swarm Optimization with Deep Learning on MRI Images,\" 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2023, pp. 1008-1013, doi: 10.1109\/ICECA58529.2023.10395331.","DOI":"10.1109\/ICECA58529.2023.10395331"},{"key":"10.1016\/j.ejro.2026.100741_bib54","doi-asserted-by":"crossref","DOI":"10.1016\/j.jocs.2024.102510","article-title":"Identifying pan-cancer and cancer subtype miRNAs using interpretable convolutional neural network","volume":"85","author":"Singh","year":"2025","journal-title":"J. Comput. Sci."},{"key":"10.1016\/j.ejro.2026.100741_bib55","doi-asserted-by":"crossref","first-page":"62234","DOI":"10.1109\/ACCESS.2024.3392633","article-title":"Gene Selection Based Cancer Classification With Adaptive Optimization Using Deep Learning Architecture","volume":"12","author":"Das","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.ejro.2026.100741_bib56","article-title":"Efficient prostate tissue classification with MobileNetV2: a comparative study","volume":"2024","author":"Sharma","year":"2024","journal-title":"Glob. Conf. Commun. Inf. Technol. GCCIT 2024"},{"issue":"3","key":"10.1016\/j.ejro.2026.100741_bib57","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1007\/s12652-022-04482-9","article-title":"An integrated framework based deep learning for cancer classification using microarray datasets","volume":"14","author":"Alrefai","year":"2023","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"10.1016\/j.ejro.2026.100741_bib58","series-title":"Progress in Biomedical Optics and Imaging - Proceedings of","first-page":"129260H","article-title":"CATS v2: Hybrid encoders for robust medical segmentation","author":"Li","year":"2024"},{"issue":"4","key":"10.1016\/j.ejro.2026.100741_bib59","doi-asserted-by":"crossref","DOI":"10.1016\/j.modpat.2024.100447","article-title":"HistoEM: a pathologist-guided and explainable workflow using histogram embedding for gland classification","volume":"37","author":"Ferrero","year":"2024","journal-title":"Mod. Pathol."},{"key":"10.1016\/j.ejro.2026.100741_bib60","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/5497120","article-title":"Light weighted healthcare CNN model to detect prostate cancer on multiparametric MRI","volume":"2022","author":"Soni","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"issue":"9","key":"10.1016\/j.ejro.2026.100741_bib61","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/acc77c","article-title":"Deep learning enables MV-based real-time image guided radiation therapy for prostate cancer patients","volume":"68","author":"Chrystall","year":"2023","journal-title":"Phys. Med. Biol."},{"key":"10.1016\/j.ejro.2026.100741_bib62","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1038\/s41598-023-27671-8","article-title":"Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones","volume":"13","author":"Zaridis","year":"2023","journal-title":"Sci Rep"},{"issue":"6","key":"10.1016\/j.ejro.2026.100741_bib63","doi-asserted-by":"crossref","first-page":"551","DOI":"10.4111\/icu.20240159","article-title":"Application of deep learning for semantic segmentation in robotic prostatectomy: comparison of convolutional neural networks and visual transformers","volume":"65","author":"Pak","year":"2024","journal-title":"Invest. Clin. Urol."},{"issue":"3n04","key":"10.1016\/j.ejro.2026.100741_bib64","doi-asserted-by":"crossref","DOI":"10.1142\/S021800142555002X","article-title":"A novel topic modeling framework for automated surveying of computer vision research in prostate cancer detection","volume":"39","author":"Fadaeidehcheshmeh","year":"2025","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"issue":"2","key":"10.1016\/j.ejro.2026.100741_bib65","first-page":"2557","article-title":"Prediction of uncertainty estimation and confidence calibration using fully convolutional neural network","volume":"75","author":"Gasmi","year":"2023","journal-title":"Comput. Mater. Contin."},{"issue":"4","key":"10.1016\/j.ejro.2026.100741_bib66","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.ijrobp.2024.10.021","article-title":"Patient-specific deep learning tracking framework for real-time 2d target localization in magnetic resonance imaging-guided radiation therapy","volume":"122","author":"Lombardo","year":"2025","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"10.1016\/j.ejro.2026.100741_bib67","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2023.110964","article-title":"Metadata-independent classification of MRI sequences using convolutional neural networks: successful application to prostate MRI","volume":"166","author":"Baumgartner","year":"2023","journal-title":"Eur. J. Radiol."}],"container-title":["European Journal of Radiology Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352047726000183?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352047726000183?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T05:35:36Z","timestamp":1792215336000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2352047726000183"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":67,"alternative-id":["S2352047726000183"],"URL":"https:\/\/doi.org\/10.1016\/j.ejro.2026.100741","relation":{},"ISSN":["2352-0477"],"issn-type":[{"value":"2352-0477","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Convolutional neural networks for prostate cancer detection, classification, and segmentation: A systematic review and bibliometric analysis","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology Open","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejro.2026.100741","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"100741"}}