{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T16:41:32Z","timestamp":1790959292166,"version":"4.1.0"},"reference-count":40,"publisher":"Elsevier BV","issue":"8","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81971580"],"award-info":[{"award-number":["81971580"]}],"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":["Z200027"],"award-info":[{"award-number":["Z200027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009331","name":"Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009331","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["academicradiology.org","clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Academic Radiology"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1016\/j.acra.2024.01.003","type":"journal-article","created":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T06:17:41Z","timestamp":1705645061000},"page":"3223-3234","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multiparametric MRI-Based Machine Learning Models for the Characterization of Cystic Renal Masses Compared to the Bosniak Classification, Version 2019: A Multicenter Study"],"prefix":"10.1016","volume":"31","author":[{"given":"Huanhuan","family":"Kang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanfang","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiping","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahui","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengqiu","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiyi","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baojun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dawei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0717-1748","authenticated-orcid":false,"given":"Haiyi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.acra.2024.01.003_bib1","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1148\/radiol.2019182646","article-title":"Bosniak classification of cystic renal masses, version 2019: an update proposal and needs assessment","volume":"292","author":"Silverman","year":"2019","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2024.01.003_bib2","first-page":"31","article-title":"The morphology of renal cystic disease","volume":"4","author":"Kissane","year":"1976","journal-title":"Perspect Nephrol Hypertens"},{"key":"10.1016\/j.acra.2024.01.003_bib3","doi-asserted-by":"crossref","first-page":"505.e9","DOI":"10.1016\/j.urolonc.2015.07.017","article-title":"Cystic renal cell carcinoma carries an excellent prognosis regardless of tumor size","volume":"33","author":"Winters","year":"2015","journal-title":"Urol Oncol"},{"key":"10.1016\/j.acra.2024.01.003_bib4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1148\/radiology.158.1.3510019","article-title":"The current radiological approach to renal cysts","volume":"158","author":"Bosniak","year":"1986","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2024.01.003_bib5","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1148\/radiol.14122908","article-title":"Follow-up for Bosniak category 2F cystic renal lesions","volume":"272","author":"Hindman","year":"2014","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2024.01.003_bib6","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.juro.2016.09.160","article-title":"Bosniak classification for complex renal cysts reevaluated: a systematic review","volume":"198","author":"Schoots","year":"2017","journal-title":"J Urol"},{"key":"10.1016\/j.acra.2024.01.003_bib7","doi-asserted-by":"crossref","first-page":"2239","DOI":"10.1007\/s00330-016-4631-9","article-title":"Malignancy rates and diagnostic performance of the Bosniak classification for the diagnosis of cystic renal lesions in computed tomography - a systematic review and meta-analysis","volume":"27","author":"Sevcenco","year":"2017","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2024.01.003_bib8","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1148\/radiol.2020200478","article-title":"MRI-based Bosniak classification of cystic renal masses, version 2019: interobserver agreement, impact of readers' experience, and diagnostic performance","volume":"297","author":"Bai","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2024.01.003_bib9","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.juro.2017.09.078","article-title":"Natural history of complex renal cysts: clinical evidence supporting active surveillance","volume":"199","author":"Chandrasekar","year":"2018","journal-title":"J Urol"},{"key":"10.1016\/j.acra.2024.01.003_bib10","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.2214\/AJR.17.18142","article-title":"Malignancy rate, histologic grade, and progression of Bosniak category III and IV complex renal cystic lesions","volume":"209","author":"Mousessian","year":"2017","journal-title":"AJR Am J Roentgenol"},{"key":"10.1016\/j.acra.2024.01.003_bib11","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1016\/j.eururo.2011.11.047","article-title":"A non-cancer-related survival benefit is associated with partial nephrectomy","volume":"61","author":"Sun","year":"2012","journal-title":"Eur Urol"},{"key":"10.1016\/j.acra.2024.01.003_bib12","doi-asserted-by":"crossref","first-page":"9579","DOI":"10.1007\/s00330-021-08006-5","article-title":"Bosniak classification version 2019: validation and comparison to original classification in pathologically confirmed cystic masses","volume":"31","author":"Yan","year":"2021","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2024.01.003_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2020.109270","article-title":"Bosniak classification of cystic renal masses version 2019 does not increase the interobserver agreement or the proportion of masses categorized into lower Bosniak classes for non-subspecialized readers on CT or MR","volume":"131","author":"Pacheco","year":"2020","journal-title":"Eur J Radiol"},{"key":"10.1016\/j.acra.2024.01.003_bib14","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.canlet.2019.02.054","article-title":"Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers","volume":"451","author":"Qian","year":"2019","journal-title":"Cancer Lett"},{"key":"10.1016\/j.acra.2024.01.003_bib15","doi-asserted-by":"crossref","first-page":"3207","DOI":"10.1002\/mp.13592","article-title":"Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT","volume":"46","author":"Uthoff","year":"2019","journal-title":"Med Phys"},{"key":"10.1016\/j.acra.2024.01.003_bib16","doi-asserted-by":"crossref","DOI":"10.1097\/RCT.0000000000001433","article-title":"Differentiating benign from malignant cystic renal masses: a feasibility study of computed tomography texture-based machine learning algorithms","author":"Miskin","year":"2023","journal-title":"J Comput Assist Tomogr"},{"key":"10.1016\/j.acra.2024.01.003_bib17","doi-asserted-by":"crossref","first-page":"4116","DOI":"10.1007\/s00330-021-08449-w","article-title":"Malignancy risk stratification of cystic renal lesions based on a contrast-enhanced CT-based machine learning model and a clinical decision algorithm","volume":"32","author":"Dana","year":"2022","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2024.01.003_bib18","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s00261-020-02629-w","article-title":"Stratification of cystic renal masses into benign and potentially malignant: applying machine learning to the bosniak classification","volume":"46","author":"Miskin","year":"2021","journal-title":"Abdom Radiol (NY)"},{"key":"10.1016\/j.acra.2024.01.003_bib19","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1007\/s00261-016-0797-5","article-title":"MRI evaluation of complex renal cysts using the Bosniak classification: a comparison to CT","volume":"41","author":"Ferreira","year":"2016","journal-title":"Abdom Radiol (NY)"},{"key":"10.1016\/j.acra.2024.01.003_bib20","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1148\/radiol.11111595","article-title":"The Bosniak renal cyst classification: 25 years later","volume":"262","author":"Bosniak","year":"2012","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2024.01.003_bib21","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1002\/jmri.27182","article-title":"Multiparametric MRI radiomic model for preoperative predicting WHO\/ISUP nuclear grade of clear cell renal cell carcinoma","volume":"52","author":"Li","year":"2020","journal-title":"J Magn Reson Imaging"},{"key":"10.1016\/j.acra.2024.01.003_bib22","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.210205","article-title":"Deployed deep learning kidney segmentation for polycystic kidney disease MRI","volume":"4","author":"Goel","year":"2022","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.acra.2024.01.003_bib23","doi-asserted-by":"crossref","first-page":"4765","DOI":"10.1007\/s00330-019-6003-8","article-title":"Influence of segmentation margin on machine learning-based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas","volume":"29","author":"Kocak","year":"2019","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2024.01.003_bib24","first-page":"660","article-title":"Addendum: B-spline interpolation in medical image processing","volume":"20","author":"Lehmann","year":"2001"},{"key":"10.1016\/j.acra.2024.01.003_bib25","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","article-title":"A guideline of selecting and reporting intraclass correlation coefficients for reliability research","volume":"15","author":"Koo","year":"2016","journal-title":"J Chiropr Med"},{"key":"10.1016\/j.acra.2024.01.003_bib26","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1007\/s00330-022-09102-w","article-title":"Proportion of malignancy in Bosniak classification of cystic renal masses version 2019 (v2019) classes: systematic review and meta-analysis","volume":"33","author":"McGrath","year":"2023","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2024.01.003_bib27","doi-asserted-by":"crossref","first-page":"W106","DOI":"10.2214\/AJR.08.1858","article-title":"Bayesian classifier for predicting malignant renal cysts on MDCT: early clinical experience","volume":"193","author":"Lee","year":"2009","journal-title":"AJR Am J Roentgenol"},{"key":"10.1016\/j.acra.2024.01.003_bib28","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.eururo.2016.02.029","article-title":"The 2016 WHO classification of tumours of the urinary system and male genital organs-part A: renal, penile, and testicular tumours","volume":"70","author":"Moch","year":"2016","journal-title":"Eur Urol"},{"key":"10.1016\/j.acra.2024.01.003_bib29","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The measurement of observer agreement for categorical data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"key":"10.1016\/j.acra.2024.01.003_bib30","article-title":"Enhanced and unenhanced: radiomics models for discriminating between benign and malignant cystic renal masses on CT images: a multi-center study","volume":"18","author":"Huang","year":"2023","journal-title":"PLoS One"},{"key":"10.1016\/j.acra.2024.01.003_bib31","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1148\/radiol.2019192515","article-title":"Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers-from the radiology editorial board","volume":"294","author":"Bluemke","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2024.01.003_bib32","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1148\/radiol.2018180513","article-title":"Deep learning-based method for fully automatic quantification of left ventricle function from cine MR images: a multivendor, multicenter study","volume":"290","author":"Tao","year":"2019","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2024.01.003_bib33","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.1016\/j.acra.2019.12.015","article-title":"Prediction of benign and malignant solid renal masses: machine learning-based CT texture analysis","volume":"27","author":"Erdim","year":"2020","journal-title":"Acad Radiol"},{"key":"10.1016\/j.acra.2024.01.003_bib34","doi-asserted-by":"crossref","DOI":"10.1002\/widm.1249","article-title":"Ensemble learning: a survey","volume":"8","author":"Sagi","year":"2018","journal-title":"WIREs Data Mining Knowl Discov"},{"key":"10.1016\/j.acra.2024.01.003_bib35","doi-asserted-by":"crossref","first-page":"W44","DOI":"10.2214\/AJR.19.21617","article-title":"Radiologic-radiomic machine learning models for differentiation of benign and malignant solid renal masses: comparison with expert-level radiologists","volume":"214","author":"Sun","year":"2020","journal-title":"AJR Am J Roentgenol"},{"key":"10.1016\/j.acra.2024.01.003_bib36","doi-asserted-by":"crossref","first-page":"1944","DOI":"10.1158\/1078-0432.CCR-19-0374","article-title":"Deep learning to distinguish benign from malignant renal lesions based on routine MR imaging","volume":"26","author":"Xi","year":"2020","journal-title":"Clin Cancer Res"},{"key":"10.1016\/j.acra.2024.01.003_bib37","doi-asserted-by":"crossref","first-page":"5260","DOI":"10.1007\/s00261-021-03241-2","article-title":"Robustness and performance of radiomic features in diagnosing cystic renal masses","volume":"46","author":"K\u00f6nik","year":"2021","journal-title":"Abdom Radiol ((NY))"},{"key":"10.1016\/j.acra.2024.01.003_bib38","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.211597","article-title":"Radiomic analysis: study design, statistical analysis, and other bias mitigation strategies","author":"Moskowitz","year":"2022","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2024.01.003_bib39","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1097\/JU.0000000000001438","article-title":"Prevalence of malignancy and histopathological association of Bosniak classification, version 2019 class III and IV cystic renal masses","volume":"205","author":"Tse","year":"2021","journal-title":"J Urol"},{"key":"10.1016\/j.acra.2024.01.003_bib40","doi-asserted-by":"crossref","first-page":"24.e21","DOI":"10.1016\/j.urolonc.2012.08.018","article-title":"Progression, interobserver agreement, and malignancy rate in complex renal cysts (\u2265 Bosniak category IIF)","volume":"32","author":"El-Mokadem","year":"2014","journal-title":"Urol Oncol"}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633224000035?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633224000035?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T22:28:51Z","timestamp":1778797731000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1076633224000035"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":40,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["S1076633224000035"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2024.01.003","relation":{},"ISSN":["1076-6332"],"issn-type":[{"value":"1076-6332","type":"print"}],"subject":[],"published":{"date-parts":[[2024,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multiparametric MRI-Based Machine Learning Models for the Characterization of Cystic Renal Masses Compared to the Bosniak Classification, Version 2019: A Multicenter Study","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2024.01.003","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}