{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T20:17:29Z","timestamp":1790626649844,"version":"4.1.0"},"reference-count":35,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012476","name":"Fundamental Research Funds for Central Universities of the Central South University","doi-asserted-by":"publisher","award":["1053320221234"],"award-info":[{"award-number":["1053320221234"]}],"id":[{"id":"10.13039\/501100012476","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017691","name":"Guangxi Key Research and Development Program","doi-asserted-by":"publisher","award":["2023SK2017"],"award-info":[{"award-number":["2023SK2017"]}],"id":[{"id":"10.13039\/501100017691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017691","name":"Guangxi Key Research and Development Program","doi-asserted-by":"publisher","award":["2021SK2014"],"award-info":[{"award-number":["2021SK2014"]}],"id":[{"id":"10.13039\/501100017691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018607","name":"Henan Province Science and Technology Innovation Talent Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100018607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016104","name":"Key Project of Research and Development Plan of Hunan Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81974397"],"award-info":[{"award-number":["81974397"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91859207"],"award-info":[{"award-number":["91859207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82272907"],"award-info":[{"award-number":["82272907"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81771873"],"award-info":[{"award-number":["81771873"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","ejradiology.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Radiology"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1016\/j.ejrad.2025.112063","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T14:11:56Z","timestamp":1742393516000},"page":"112063","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":11,"special_numbering":"C","title":["Machine learning models for enhanced diagnosis and risk assessment of prostate cancer with 68Ga-PSMA-617 PET\/CT"],"prefix":"10.1016","volume":"186","author":[{"given":"Wenhao","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongxiang","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaomei","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min-Feng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.ejrad.2025.112063_b0005","first-page":"7","article-title":"Cancer statistics, 2022","volume":"72","author":"Siegel","year":"2022","journal-title":"CA Can. J Clin."},{"issue":"15","key":"10.1016\/j.ejrad.2025.112063_b0010","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1056\/NEJMoa1606220","article-title":"10-year outcomes after monitoring, surgery, or radiotherapy for localized prostate cancer","volume":"375","author":"Hamdy","year":"2016","journal-title":"N. Engl. J. Med."},{"issue":"6","key":"10.1016\/j.ejrad.2025.112063_b0015","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1111\/apm.12533","article-title":"International Society of Urological Pathology (ISUP) grading of prostate cancer - an ISUP consensus on contemporary grading","volume":"124","author":"Egevad","year":"2016","journal-title":"APMIS"},{"issue":"22","key":"10.1016\/j.ejrad.2025.112063_b0020","doi-asserted-by":"crossref","first-page":"5039","DOI":"10.1002\/cncr.26169","article-title":"The CAPRA-S score: A straightforward tool for improved prediction of outcomes after radical prostatectomy","volume":"117","author":"Cooperberg","year":"2011","journal-title":"Cancer"},{"key":"10.1016\/j.ejrad.2025.112063_b0025","doi-asserted-by":"crossref","DOI":"10.1111\/his.14934","article-title":"Biopsy prostate cancer perineural invasion and tumour load are associated with positive posterolateral margins at radical prostatectomy: implications for planning of nerve-sparing surgery","author":"van der Slot","year":"2023","journal-title":"Histopathology"},{"issue":"1","key":"10.1016\/j.ejrad.2025.112063_b0030","doi-asserted-by":"crossref","first-page":"109","DOI":"10.2214\/AJR.13.10699","article-title":"Radiologist, be aware: ten pitfalls that confound the interpretation of multiparametric prostate MRI","volume":"202","author":"Rosenkrantz","year":"2014","journal-title":"AJR Am. J. Roentgenol."},{"issue":"19","key":"10.1016\/j.ejrad.2025.112063_b0035","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1056\/NEJMoa1801993","article-title":"MRI-targeted or standard biopsy for prostate-cancer diagnosis","volume":"378","author":"Kasivisvanathan","year":"2018","journal-title":"N. Engl. J. Med."},{"issue":"6","key":"10.1016\/j.ejrad.2025.112063_b0040","doi-asserted-by":"crossref","first-page":"786","DOI":"10.2967\/jnumed.118.219501","article-title":"Metaanalysis of 68Ga-PSMA-11 PET accuracy for the detection of prostate cancer validated by histopathology","volume":"60","author":"Hope","year":"2019","journal-title":"J. Nucl. Med."},{"issue":"7","key":"10.1016\/j.ejrad.2025.112063_b0045","doi-asserted-by":"crossref","first-page":"944","DOI":"10.2967\/jnumed.118.221010","article-title":"Combination of 68Ga-PSMA PET\/CT and Multiparametric MRI improves the detection of clinically significant prostate cancer: a lesion-by-lesion analysis","volume":"60","author":"Chen","year":"2019","journal-title":"J. Nucl. Med."},{"issue":"1","key":"10.1016\/j.ejrad.2025.112063_b0050","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.eururo.2023.03.001","volume":"84","author":"Chow","year":"2023","journal-title":"Eur. Urol."},{"issue":"8","key":"10.1016\/j.ejrad.2025.112063_b0055","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1001\/jamaoncol.2024.3196","article-title":"Fluorine-18 prostate-specific membrane antigen-1007 PET\/CT vs multiparametric MRI for locoregional staging of prostate cancer","volume":"10","author":"Mookerji","year":"2024","journal-title":"JAMA Oncol."},{"issue":"1","key":"10.1016\/j.ejrad.2025.112063_b0060","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1111\/bju.14603","article-title":"Prostate cancer heterogeneity: texture analysis score based on multiple magnetic resonance imaging sequences for detection, stratification and selection of lesions at time of biopsy","volume":"124","author":"Orczyk","year":"2019","journal-title":"BJU Int."},{"issue":"4","key":"10.1016\/j.ejrad.2025.112063_b0065","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: extracting more information from medical images using advanced feature analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur. J. Can."},{"key":"10.1016\/j.ejrad.2025.112063_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105316","article-title":"Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods","volume":"189","author":"Wildeboer","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"issue":"Suppl 2","key":"10.1016\/j.ejrad.2025.112063_b0075","doi-asserted-by":"crossref","first-page":"38S","DOI":"10.2967\/jnumed.118.220582","article-title":"Radiomics: data are also images","volume":"60","author":"Hatt","year":"2019","journal-title":"J. Nucl. Med."},{"key":"10.1016\/j.ejrad.2025.112063_b0080","doi-asserted-by":"crossref","unstructured":"Patterson SD. Data analysis--the Achilles heel of proteomics. Nat Biotechnol. 2003 Mar;21(3):221\u20132.","DOI":"10.1038\/nbt0303-221"},{"issue":"1","key":"10.1016\/j.ejrad.2025.112063_b0085","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1186\/s13045-020-01013-x","article-title":"Proteomic signatures of 16 major types of human cancer reveal universal and cancer-type-specific proteins for the identification of potential therapeutic targets","volume":"13","author":"Zhou","year":"2020","journal-title":"J. Hematol. Oncol."},{"issue":"1","key":"10.1016\/j.ejrad.2025.112063_b0090","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1080\/14789450.2018.1417846","article-title":"Prostate cancer proteomics: clinically useful protein biomarkers and future perspectives","volume":"15","author":"Intasqui","year":"2018","journal-title":"Expert Rev. Proteomics"},{"issue":"18","key":"10.1016\/j.ejrad.2025.112063_b0095","doi-asserted-by":"crossref","first-page":"9971","DOI":"10.3390\/ijms22189971","article-title":"Prostate cancer radiogenomics-from imaging to molecular characterization","volume":"22","author":"Ferro","year":"2021","journal-title":"Int. J. Mol. Sci."},{"issue":"1125","key":"10.1016\/j.ejrad.2025.112063_b0100","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20201331","article-title":"Radioproteomics in patients with ovarian cancer","volume":"94","author":"McCague","year":"2021","journal-title":"Br. J. Radiol."},{"issue":"6","key":"10.1016\/j.ejrad.2025.112063_b0105","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1007\/s00259-017-3670-z","article-title":"68Ga-PSMA PET\/CT: Joint EANM and SNMMI procedure guideline for prostate cancer imaging: version 1.0","volume":"44","author":"Fendler","year":"2017","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"8","key":"10.1016\/j.ejrad.2025.112063_b0110","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.neo.2017.05.002","volume":"19","author":"Chandrashekar","year":"2017","journal-title":"Neoplasia"},{"key":"10.1016\/j.ejrad.2025.112063_b0115","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.neo.2022.01.001","article-title":"UALCAN: An update to the integrated cancer data analysis platform","volume":"25","author":"Chandrashekar","year":"2022","journal-title":"Neoplasia"},{"issue":"2","key":"10.1016\/j.ejrad.2025.112063_b0120","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1038\/pcan.2015.1","article-title":"Evaluation of models predicting insignificant prostate cancer to select men for active surveillance of prostate cancer","volume":"18","author":"Wong","year":"2015","journal-title":"Prostate Can, Prostatic Dis."},{"issue":"6","key":"10.1016\/j.ejrad.2025.112063_b0125","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1016\/j.eururo.2013.03.058","article-title":"Multi-institutional validation of the CAPRA-S score to predict disease recurrence and mortality after radical prostatectomy","volume":"65","author":"Punnen","year":"2014","journal-title":"Eur. Urol."},{"issue":"3","key":"10.1016\/j.ejrad.2025.112063_b0130","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1016\/S0022-5347(05)66544-5","article-title":"A validated strategy for side specific prediction of organ confined prostate cancer: a tool to select for nerve sparing radical prostatectomy","volume":"165","author":"Graefen","year":"2001","journal-title":"J. Urol."},{"key":"10.1016\/j.ejrad.2025.112063_b0135","doi-asserted-by":"crossref","unstructured":"Steuber T, Graefen M, Haese A, Erbersdobler A, Chun FKH, Schlom T, et al. Validation of a nomogram for prediction of side specific extracapsular extension at radical prostatectomy. J Urol. 2006 Mar;175(3 Pt 1):939\u201344; discussion 944.","DOI":"10.1016\/S0022-5347(05)00342-3"},{"issue":"8","key":"10.1016\/j.ejrad.2025.112063_b0140","doi-asserted-by":"crossref","first-page":"22811","DOI":"10.1007\/s11042-023-16411-9","article-title":"A novel multi-objective wrapper-based feature selection method using quantum-inspired and swarm intelligence techniques","volume":"83","author":"Zouache","year":"2023","journal-title":"Multimed. Tools Appl."},{"issue":"1","key":"10.1016\/j.ejrad.2025.112063_b0145","first-page":"77","article-title":"Hybrid genetic and penguin search optimization algorithm (ga-Pseoa) for efficient flow shop scheduling solutions","volume":"22","author":"Mzili","year":"2024","journal-title":"Facta Univ-Ser Mech Eng."},{"issue":"1","key":"10.1016\/j.ejrad.2025.112063_b0150","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1007\/s42235-023-00436-9","article-title":"Improved manta ray foraging optimizer-based SVM for feature selection problems: a medical case study","volume":"21","author":"Got","year":"2024","journal-title":"J. Bionic Eng."},{"issue":"5","key":"10.1016\/j.ejrad.2025.112063_b0155","doi-asserted-by":"crossref","first-page":"1523","DOI":"10.1007\/s00259-021-05631-6","article-title":"Machine learning-based prediction of invisible intraprostatic prostate cancer lesions on 68Ga-PSMA-11 PET\/CT in patients with primary prostate cancer","volume":"49","author":"Yi","year":"2022","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"5","key":"10.1016\/j.ejrad.2025.112063_b0160","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1080\/14737159.2019.1604223","article-title":"Metabolomic profiling for the identification of novel diagnostic markers and therapeutic targets in prostate cancer: an update","volume":"19","author":"Lucarelli","year":"2019","journal-title":"Expert Rev. Mol. Diagn."},{"issue":"2","key":"10.1016\/j.ejrad.2025.112063_b0165","doi-asserted-by":"crossref","first-page":"910","DOI":"10.3390\/ijms24020910","article-title":"Emerging hallmarks of metabolic reprogramming in prostate cancer","volume":"24","author":"Lasorsa","year":"2023","journal-title":"Int. J. Mol. Sci."},{"issue":"1","key":"10.1016\/j.ejrad.2025.112063_b0170","doi-asserted-by":"crossref","first-page":"1176","DOI":"10.1038\/s41467-018-03573-6","article-title":"Integrative proteomics in prostate cancer uncovers robustness against genomic and transcriptomic aberrations during disease progression","volume":"9","author":"Latonen","year":"2018","journal-title":"Nat. Commun."},{"issue":"4","key":"10.1016\/j.ejrad.2025.112063_b0175","doi-asserted-by":"crossref","first-page":"488","DOI":"10.2967\/jnumed.118.222893","article-title":"Introduction to radiomics","volume":"61","author":"Mayerhoefer","year":"2020","journal-title":"J. Nucl. Med."}],"container-title":["European Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X25001494?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X25001494?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T10:01:44Z","timestamp":1778752904000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0720048X25001494"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":35,"alternative-id":["S0720048X25001494"],"URL":"https:\/\/doi.org\/10.1016\/j.ejrad.2025.112063","relation":{},"ISSN":["0720-048X"],"issn-type":[{"value":"0720-048X","type":"print"}],"subject":[],"published":{"date-parts":[[2025,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Machine learning models for enhanced diagnosis and risk assessment of prostate cancer with 68Ga-PSMA-617 PET\/CT","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejrad.2025.112063","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"112063"}}