{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T17:24:33Z","timestamp":1793381073426,"version":"4.5.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"vor","delay-in-days":38,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-026-02268-5","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T16:45:33Z","timestamp":1773074733000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multiphase CT-based deep learning radiomics nomogram models for preoperative WHO\/ISUP grading of clear cell renal cell carcinoma: a two-center validation study"],"prefix":"10.1186","volume":"26","author":[{"given":"Chunsen","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiling","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Buwei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongcheng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianhao","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunhan","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenfeng","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"issue":"5","key":"2268_CR1","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1016\/j.eururo.2022.08.019","volume":"82","author":"L Bukavina","year":"2022","unstructured":"Bukavina L, Bensalah K, Bray F, et al. Epidemiology of renal cell carcinoma: 2022 update. Eur Urology. 2022;82(5):529\u201342. https:\/\/doi.org\/10.1016\/j.eururo.2022.08.019.","journal-title":"Eur Urology"},{"issue":"3","key":"2268_CR2","doi-asserted-by":"publisher","first-page":"79","DOI":"10.14740\/wjon1279","volume":"11","author":"SA Padala","year":"2020","unstructured":"Padala SA, Barsouk A, Thandra KC, et al. Epidemiology of renal cell carcinoma. World J Oncol. 2020;11(3):79\u201387. https:\/\/doi.org\/10.14740\/wjon1279.","journal-title":"World J Oncol"},{"issue":"3","key":"2268_CR3","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/j.euo.2020.09.005","volume":"4","author":"Y Abu-Ghanem","year":"2021","unstructured":"Abu-Ghanem Y, Powles T, Capitanio U, et al. The impact of histological subtype on the incidence, timing, and patterns of recurrence in patients with renal cell carcinoma after surgery-results from RECUR consortium. Eur Urology Oncol. 2021;4(3):473\u201382. https:\/\/doi.org\/10.1016\/j.euo.2020.09.005.","journal-title":"Eur Urology Oncol"},{"key":"2268_CR4","doi-asserted-by":"publisher","unstructured":"Mattila KE, Vainio P, Jaakkola PM. Prognostic factors for localized clear cell renal cell carcinoma and their application in adjuvant therapy. Cancers. 2022;14(1). https:\/\/doi.org\/10.3390\/cancers14010239.","DOI":"10.3390\/cancers14010239"},{"issue":"1","key":"2268_CR5","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1111\/his.13735","volume":"74","author":"B Delahunt","year":"2019","unstructured":"Delahunt B, Eble JN, Egevad L, et al. Grading of renal cell carcinoma. Histopathology. 2019;74(1):4\u201317. https:\/\/doi.org\/10.1111\/his.13735.","journal-title":"Histopathology"},{"issue":"4","key":"2268_CR6","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1016\/j.eururo.2017.12.018","volume":"73","author":"GP Paner","year":"2018","unstructured":"Paner GP, Stadler WM, Hansel DE, et al. Updates in the eighth edition of the tumor-node-metastasis staging classification for Urologic Cancers. Eur Urology. 2018;73(4):560\u201369. https:\/\/doi.org\/10.1016\/j.eururo.2017.12.018.","journal-title":"Eur Urology"},{"key":"2268_CR7","doi-asserted-by":"publisher","unstructured":"Escudier B, Porta C, Schmidinger M, et al. Renal cell carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up [J]. Ann Oncol: Off J The Eur Soc Med Oncol. 2014;25Suppl(3):iii49\u201356. https:\/\/doi.org\/10.1093\/annonc\/mdu259.","DOI":"10.1093\/annonc\/mdu259"},{"issue":"1","key":"2268_CR8","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s12957-024-03302-y","volume":"22","author":"H Xu","year":"2024","unstructured":"Xu H, Xing Z, Ai K, et al. Patients with high nuclear grade pT1-ccRCC are more suitable for radical nephrectomy than partial nephrectomy: a multicenter retrospective study using propensity score. World J Surg Oncol. 2024;22(1):24. https:\/\/doi.org\/10.1186\/s12957-024-03302-y.","journal-title":"World J Surg Oncol"},{"issue":"5","key":"2268_CR9","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.eururo.2018.12.011","volume":"75","author":"PS Macklin","year":"2019","unstructured":"Macklin PS, Sullivan ME, Tapping CR, et al. Tumour seeding in the tract of percutaneous renal Tumour biopsy: a report on seven cases from a UK tertiary referral centre. Eur Urology. 2019;75(5):861\u201367. https:\/\/doi.org\/10.1016\/j.eururo.2018.12.011.","journal-title":"Eur Urology"},{"issue":"3","key":"2268_CR10","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1093\/ndt\/gfac177","volume":"38","author":"S Andrulli","year":"2023","unstructured":"Andrulli S, Rossini M, Gigliotti G, et al. The risks associated with percutaneous native kidney biopsies: a prospective study. Nephrol Dial Transplant. 2023;38(3):655\u201363. https:\/\/doi.org\/10.1093\/ndt\/gfac177.","journal-title":"Nephrol Dial Transplant"},{"issue":"1","key":"2268_CR11","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1148\/radiol.2401050061","volume":"240","author":"SG Silverman","year":"2006","unstructured":"Silverman SG, Gan YU, Mortele KJ, et al. Renal masses in the adult patient: the role of percutaneous biopsy. Radiology. 2006;240(1):6\u201322. https:\/\/doi.org\/10.1148\/radiol.2401050061.","journal-title":"Radiology"},{"issue":"5","key":"2268_CR12","doi-asserted-by":"publisher","first-page":"1340","DOI":"10.1016\/j.juro.2015.11.029","volume":"195","author":"HD Patel","year":"2016","unstructured":"Patel HD, Johnson MH, Pierorazio PM, et al. Diagnostic accuracy and risks of biopsy in the diagnosis of a renal mass suspicious for localized renal cell carcinoma: systematic review of the literature. J Urol. 2016;195(5):1340\u201347. https:\/\/doi.org\/10.1016\/j.juro.2015.11.029.","journal-title":"J Urol"},{"issue":"1","key":"2268_CR13","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1186\/s12894-024-01574-w","volume":"24","author":"J Shen","year":"2024","unstructured":"Shen J, Zou Y. Diagnostic value of contrast-enhanced CT in clear cell renal cell carcinoma: a systematic review and meta-analysis. BMC Urol. 2024;24(1):189. https:\/\/doi.org\/10.1186\/s12894-024-01574-w.","journal-title":"BMC Urol"},{"issue":"10","key":"2268_CR14","doi-asserted-by":"publisher","first-page":"2329","DOI":"10.1007\/s00345-019-03000-5","volume":"38","author":"R Suarez-Ibarrola","year":"2020","unstructured":"Suarez-Ibarrola R, Hein S, Reis G, et al. Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer. World J Urology. 2020;38(10):2329\u201347. https:\/\/doi.org\/10.1007\/s00345-019-03000-5.","journal-title":"World J Urology"},{"key":"2268_CR15","doi-asserted-by":"publisher","unstructured":"Dai C, Xiong Y, Zhu P, et al. Deep learning assessment of Small renal masses at contrast-enhanced multiphase CT. Radiology. 2024;311(2):e232178. https:\/\/doi.org\/10.1148\/radiol.232178.","DOI":"10.1148\/radiol.232178"},{"key":"2268_CR16","doi-asserted-by":"publisher","first-page":"111018","DOI":"10.1016\/j.ejrad.2023.111018","volume":"166","author":"P Nie","year":"2023","unstructured":"Nie P, Liu S, Zhou R, et al. A preoperative CT-based deep learning radiomics model in predicting the stage, size, grade and necrosis score and outcome in localized clear cell renal cell carcinoma: a multicenter study. Eur J Radiol. 2023;166:111018. https:\/\/doi.org\/10.1016\/j.ejrad.2023.111018.","journal-title":"Eur J Radiol"},{"key":"2268_CR17","doi-asserted-by":"publisher","unstructured":"Gui CP, Chen YH, Zhao HW, et al. Multimodal recurrence scoring system for prediction of clear cell renal cell carcinoma outcome: a discovery and validation study. The Lancet Digit Health. 2023;5(8):e515\u201324. doi: https:\/\/doi.org\/10.1016\/s2589-7500(23)00095-x.","DOI":"10.1016\/s2589-7500(23)00095-x"},{"key":"2268_CR18","doi-asserted-by":"publisher","first-page":"108738","DOI":"10.1016\/j.ejrad.2019.108738","volume":"121","author":"J Shu","year":"2019","unstructured":"Shu J, Wen D, Xi Y, et al. Clear cell renal cell carcinoma: machine learning-based computed tomography radiomics analysis for the prediction of WHO\/ISUP grade. Eur J Radiol. 2019;121:108738. https:\/\/doi.org\/10.1016\/j.ejrad.2019.108738.","journal-title":"Eur J Radiol"},{"key":"2268_CR19","doi-asserted-by":"publisher","first-page":"570396","DOI":"10.3389\/fonc.2020.570396","volume":"10","author":"X Yi","year":"2020","unstructured":"Yi X, Xiao Q, Zeng F, et al. Computed tomography radiomics for predicting pathological grade of renal cell carcinoma. Front Oncol. 2020;10:570396. https:\/\/doi.org\/10.3389\/fonc.2020.570396.","journal-title":"Front Oncol"},{"issue":"8","key":"2268_CR20","doi-asserted-by":"publisher","first-page":"6078","DOI":"10.1007\/s00330-020-07667-y","volume":"31","author":"Z Zheng","year":"2021","unstructured":"Zheng Z, Chen Z, Xie Y, et al. Development and validation of a CT-based nomogram for preoperative prediction of clear cell renal cell carcinoma grades. Eur Radiol. 2021;31(8):6078\u201386. https:\/\/doi.org\/10.1007\/s00330-020-07667-y.","journal-title":"Eur Radiol"},{"issue":"2","key":"2268_CR21","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee F, Jaeger PF, Kohl SA, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203\u201311. https:\/\/doi.org\/10.1038\/s41592-020-01008-z.","journal-title":"Nat Methods"},{"issue":"2","key":"2268_CR22","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563\u201377. https:\/\/doi.org\/10.1148\/radiol.2015151169.","journal-title":"Radiology"},{"key":"2268_CR23","doi-asserted-by":"publisher","first-page":"831112","DOI":"10.3389\/fonc.2022.831112","volume":"12","author":"Y Ma","year":"2022","unstructured":"Ma Y, Guan Z, Liang H, et al. Predicting the WHO\/ISUP grade of clear cell renal cell carcinoma through CT-Based tumoral and peritumoral radiomics. Front Oncol. 2022;12:831112. https:\/\/doi.org\/10.3389\/fonc.2022.831112.","journal-title":"Front Oncol"},{"key":"2268_CR24","doi-asserted-by":"publisher","first-page":"1467775","DOI":"10.3389\/fonc.2024.1467775","volume":"14","author":"Y Yang","year":"2024","unstructured":"Yang Y, Zhang Z, Zhang H, et al. Machine learning-based multiparametric MRI radiomics nomogram for predicting WHO\/ISUP nuclear grading of clear cell renal cell carcinoma. Front Oncol. 2024;14:1467775. https:\/\/doi.org\/10.3389\/fonc.2024.1467775.","journal-title":"Front Oncol"},{"issue":"1","key":"2268_CR25","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1186\/s13244-021-01107-1","volume":"12","author":"Y Xv","year":"2021","unstructured":"Xv Y, Lv F, Guo H, et al. Machine learning-based CT radiomics approach for predicting WHO\/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study. Insights Imag. 2021;12(1):170. https:\/\/doi.org\/10.1186\/s13244-021-01107-1.","journal-title":"Insights Imag"},{"issue":"1","key":"2268_CR26","doi-asserted-by":"publisher","first-page":"1425","DOI":"10.1038\/s41467-025-56784-z","volume":"16","author":"Y Xiong","year":"2025","unstructured":"Xiong Y, Yao L, Lin J, et al. Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses. Nat Commun. 2025;16(1):1425. https:\/\/doi.org\/10.1038\/s41467-025-56784-z.","journal-title":"Nat Commun"},{"issue":"5","key":"2268_CR27","doi-asserted-by":"publisher","first-page":"2970","DOI":"10.1097\/js9.0000000000001222","volume":"110","author":"S Chen","year":"2024","unstructured":"Chen S, Gao F, Guo T, et al. Deep learning-based multi-model prediction for disease-free survival status of patients with clear cell renal cell carcinoma after surgery: a multicenter cohort study. Int J Surg (Lond, Engl). 2024;110(5):2970\u201377. https:\/\/doi.org\/10.1097\/js9.0000000000001222.","journal-title":"Int J Surg (Lond, Engl)"},{"issue":"5","key":"2268_CR28","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1097\/rli.0000000000000842","volume":"57","author":"N Toda","year":"2022","unstructured":"Toda N, Hashimoto M, Arita Y, et al. Deep learning algorithm for fully automated detection of Small (\u22644 cm) renal cell carcinoma in contrast-enhanced computed tomography using a multicenter database. Invest Radiol. 2022;57(5):327\u201333. https:\/\/doi.org\/10.1097\/rli.0000000000000842.","journal-title":"Invest Radiol"},{"key":"2268_CR29","doi-asserted-by":"publisher","unstructured":"Xu L, Yang C, Zhang F, et al. Deep learning using CT images to grade clear cell renal cell carcinoma: development and validation of a prediction model. Cancers. 2022;14(11). https:\/\/doi.org\/10.3390\/cancers14112574.","DOI":"10.3390\/cancers14112574"},{"issue":"5","key":"2268_CR30","doi-asserted-by":"publisher","first-page":"1542","DOI":"10.1002\/jmri.27153","volume":"52","author":"Y Zhao","year":"2020","unstructured":"Zhao Y, Chang M, Wang R, et al. Deep learning based on MRI for differentiation of low- and high-grade in low-stage renal cell carcinoma. J Magn Reson Imag. 2020;52(5):1542\u201349. https:\/\/doi.org\/10.1002\/jmri.27153.","journal-title":"J Magn Reson Imag"},{"issue":"1","key":"2268_CR31","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1186\/s13244-023-01557-9","volume":"14","author":"S Wang","year":"2023","unstructured":"Wang S, Zhu C, Jin Y, et al. A multi-model based on radiogenomics and deep learning techniques associated with histological grade and survival in clear cell renal cell carcinoma. Insights Imag. 2023;14(1):207. https:\/\/doi.org\/10.1186\/s13244-023-01557-9.","journal-title":"Insights Imag"},{"key":"2268_CR32","unstructured":"Lundberg SM, S-I L. A unified approach to interpreting model predictions [Z]. Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, California, USA: Curran Associates Inc; 2017: 4768\u201377."},{"issue":"2","key":"2268_CR33","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2020","unstructured":"Selvaraju RR, Cogswell M, Das A, et al. Grad-CAM: visual Explanations from deep networks via Gradient-based localization. Int J Comput Vision. 2020;128(2):336\u201359. https:\/\/doi.org\/10.1007\/s11263-019-01228-7.","journal-title":"Int J Comput Vision"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-026-02268-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-026-02268-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-026-02268-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T08:35:35Z","timestamp":1776328535000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12880-026-02268-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,9]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2268"],"URL":"https:\/\/doi.org\/10.1186\/s12880-026-02268-5","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-7316367\/v1","asserted-by":"object"}]},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,9]]},"assertion":[{"value":"7 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was conducted in strict accordance with the Declaration of Helsinki and received approval from the Ethical Review Committee of the Tianjin Medical University Cancer Institute & Hospital (No: bc20251107). Due to the retrospective and deidentified data utilized in the study, the informed consent was waived.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable. This study used retrospective, de-identified data and no individual patient details, images, or videos are presented that would compromise anonymity.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"194"}}