{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T09:26:24Z","timestamp":1768296384803,"version":"3.49.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T00:00:00Z","timestamp":1748044800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T00:00:00Z","timestamp":1748044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"the Sichuan Science and Technology Program","award":["2025ZNSFSC1759"],"award-info":[{"award-number":["2025ZNSFSC1759"]}]},{"name":"the 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University","award":["ZYAI24048"],"award-info":[{"award-number":["ZYAI24048"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-025-01723-x","type":"journal-article","created":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T13:38:35Z","timestamp":1748093915000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhanced staging of renal cell carcinoma using tumor morphology features: model development and multi-source validation"],"prefix":"10.1038","volume":"8","author":[{"given":"Enyu","family":"Yuan","sequence":"first","affiliation":[]},{"given":"Yuntian","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Ben","family":"He","sequence":"additional","affiliation":[]},{"given":"ChunLei","family":"He","sequence":"additional","affiliation":[]},{"given":"Junchao","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Ting","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Ling","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Jin","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Song","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,24]]},"reference":[{"key":"1723_CR1","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.eururo.2018.08.036","volume":"75","author":"U Capitanio","year":"2019","unstructured":"Capitanio, U. et al. Epidemiology of renal cell carcinoma. Eur. Urol. 75, 74\u201384 (2019).","journal-title":"Eur. Urol."},{"key":"1723_CR2","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1016\/j.eururo.2022.03.006","volume":"82","author":"B Ljungberg","year":"2022","unstructured":"Ljungberg, B. et al. European Association of Urology Guidelines on renal cell carcinoma: the 2022 update. Eur. Urol. 82, 399\u2013410 (2022).","journal-title":"Eur. Urol."},{"key":"1723_CR3","doi-asserted-by":"publisher","first-page":"59","DOI":"10.2214\/ajr.148.1.59","volume":"148","author":"CD Johnson","year":"1987","unstructured":"Johnson, C. D., Dunnick, N. R., Cohan, R. H. & Illescas, F. F. Renal adenocarcinoma: CT staging of 100 tumors. Am. J. Roentgenol. 148, 59\u201363 (1987).","journal-title":"Am. J. Roentgenol."},{"key":"1723_CR4","first-page":"22","volume":"15","author":"A T\u00fcrkvatan","year":"2009","unstructured":"T\u00fcrkvatan, A. et al. Preoperative staging of renal cell carcinoma with multidetector CT. Diagn. Interv. Radiol. 15, 22\u201330 (2009).","journal-title":"Diagn. Interv. Radiol."},{"key":"1723_CR5","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1590\/S1677-55382012000500007","volume":"38","author":"Y Liu","year":"2012","unstructured":"Liu, Y., Song, T., Huang, Z., Zhang, S. & Li, Y. The accuracy of multidetector computed tomography for preoperative staging of renal cell carcinoma. Int. Braz. J. Urol. 38, 627\u2013636 (2012).","journal-title":"Int. Braz. J. Urol."},{"key":"1723_CR6","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1080\/21681805.2019.1675756","volume":"53","author":"AS Renard","year":"2019","unstructured":"Renard, A. S. et al. Is multidetector CT-scan able to detect T3a renal tumor before surgery?. Scand. J. Urol. 53, 350\u2013355 (2019).","journal-title":"Scand. J. Urol."},{"key":"1723_CR7","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1097\/01.rct.0000230009.31715.5b","volume":"30","author":"P Hallscheidt","year":"2006","unstructured":"Hallscheidt, P. et al. Multislice computed tomography in planning nephron-sparing surgery in a prospective study with 76 patients: comparison of radiological and histopathological findings in the infiltration of renal structures. J. Comput. Assist. Tomogr. 30, 869\u2013874 (2006).","journal-title":"J. Comput. Assist. Tomogr."},{"key":"1723_CR8","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1097\/RCT.0b013e318283bc8e","volume":"37","author":"AC Tsili","year":"2013","unstructured":"Tsili, A. C. et al. Perirenal fat invasion on renal cell carcinoma: evaluation with multidetector computed tomography-multivariate analysis. J. Comput. Assist. Tomogr. 37, 450\u2013457 (2013).","journal-title":"J. Comput. Assist. Tomogr."},{"key":"1723_CR9","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1097\/RCT.0000000000000588","volume":"41","author":"J Landman","year":"2017","unstructured":"Landman, J. et al. Preoperative computed tomography assessment for perinephric fat invasion: comparison with pathological staging. J. Comput. Assist. Tomogr. 41, 702\u2013707 (2017).","journal-title":"J. Comput. Assist. Tomogr."},{"key":"1723_CR10","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3892\/mco.2023.2607","volume":"18","author":"SM Fateh","year":"2023","unstructured":"Fateh, S. M. et al. Renal cell carcinoma T staging: diagnostic accuracy of preoperative contrast-enhanced computed tomography. Mol. Clin. Oncol. 18, 11 (2023).","journal-title":"Mol. Clin. Oncol."},{"key":"1723_CR11","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1186\/s13244-024-01739-z","volume":"15","author":"X Li","year":"2024","unstructured":"Li, X. et al. Radiomics predict the WHO\/ISUP nuclear grade and survival in clear cell renal cell carcinoma. Insights Imaging 15, 175 (2024).","journal-title":"Insights Imaging"},{"key":"1723_CR12","doi-asserted-by":"publisher","first-page":"111018","DOI":"10.1016\/j.ejrad.2023.111018","volume":"166","author":"P Nie","year":"2023","unstructured":"Nie, P. 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. 166, 111018 (2023).","journal-title":"Eur. J. Radiol."},{"key":"1723_CR13","doi-asserted-by":"publisher","first-page":"E37288","DOI":"10.1097\/MD.0000000000037288","volume":"103","author":"N Wang","year":"2024","unstructured":"Wang, N. et al. Study of radiomics based on dual-energy CT for nuclear grading and T-staging in renal clear cell carcinoma. Medicine103, E37288 (2024).","journal-title":"Medicine"},{"key":"1723_CR14","doi-asserted-by":"publisher","DOI":"10.1186\/s40644-023-00594-3","volume":"23","author":"MR Orton","year":"2023","unstructured":"Orton, M. R. et al. Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: insights from the TRACERx Renal study. Cancer Imaging 23, 76 (2023).","journal-title":"Cancer Imaging"},{"key":"1723_CR15","doi-asserted-by":"publisher","first-page":"2552","DOI":"10.1007\/s00330-021-08344-4","volume":"32","author":"NL Demirjian","year":"2022","unstructured":"Demirjian, N. L. et al. CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma. Eur. Radiol. 32, 2552\u20132563 (2022).","journal-title":"Eur. Radiol."},{"key":"1723_CR16","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.1111\/iju.14982","volume":"29","author":"W Shimada","year":"2022","unstructured":"Shimada, W. et al. Significance of tumor shape irregularity: radiomics analysis based on dynamic computed tomography for predicting pT3a upstaging in cT1b-2N0M0 renal cell carcinoma. Int. J. Urol. 29, 1387\u20131389 (2022).","journal-title":"Int. J. Urol."},{"key":"1723_CR17","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1097\/RCT.0b013e3182aa672a","volume":"38","author":"C Kim","year":"2014","unstructured":"Kim, C., Choi, H. J. & Cho, K. S. Diagnostic performance of multidetector computed tomography in the evaluation of perinephric fat invasion in renal cell carcinoma patients. J. Comput. Assist. Tomogr. 38, 268\u2013273 (2014).","journal-title":"J. Comput. Assist. Tomogr."},{"key":"1723_CR18","doi-asserted-by":"publisher","first-page":"20140504","DOI":"10.1259\/bjr.20140504","volume":"88","author":"HK Sokhi","year":"2015","unstructured":"Sokhi, H. K., Mok, W. Y. & Patel, U. Stage T3a renal cell carcinoma: Staging accuracy of CT for sinus fat, perinephric fat or renal vein invasion. Br. J. Radiol. 88, 20140504 (2015).","journal-title":"Br. J. Radiol."},{"key":"1723_CR19","doi-asserted-by":"publisher","first-page":"541","DOI":"10.2214\/AJR.21.25493","volume":"217","author":"AA Elkassem","year":"2021","unstructured":"Elkassem, A. A., Allen, B. C., Sharbidre, K. G., Rais-Bahrami, S. & Smith, A. D. Update on the role of imaging in clinical staging and restaging of renal cell carcinoma based on the AJCC 8th edition, from the AJR special series on cancer staging. Am. J. Roentgenol. 217, 541\u2013555 (2021).","journal-title":"Am. J. Roentgenol."},{"key":"1723_CR20","doi-asserted-by":"publisher","first-page":"489","DOI":"10.4103\/njcp.njcp_242_20","volume":"24","author":"L Damgaci","year":"2021","unstructured":"Damgaci, L., \u00d6zer, H. & Rona, G. Diagnostic value of MDCT in determining the perinephric fat tissue and renal sinus invasion in patients with clear cell renal cell carcinoma. Niger. J. Clin. Pract. 24, 489\u2013495 (2021).","journal-title":"Niger. J. Clin. Pract."},{"key":"1723_CR21","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.3348\/kjr.2020.0984","volume":"22","author":"JKJH Kim","year":"2021","unstructured":"Kim, J. K. J. H., Park, K. J., Kim, M. H. & Kim, J. K. J. H. Preoperative assessment of renal sinus invasion by renal cell carcinoma according to tumor complexity and imaging features in patients undergoing radical nephrectomy. Korean J. Radiol. 22, 1323\u20131331 (2021).","journal-title":"Korean J. Radiol."},{"key":"1723_CR22","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1093\/jjco\/hyz154","volume":"50","author":"J Teishima","year":"2020","unstructured":"Teishima, J. et al. Impact of radiological morphology of clinical T1 renal cell carcinoma on the prediction of upstaging to pathological T3. Jpn. J. Clin. Oncol. 50, 473\u2013478 (2020).","journal-title":"Jpn. J. Clin. Oncol."},{"key":"1723_CR23","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.euros.2022.12.003","volume":"48","author":"H Tanaka","year":"2023","unstructured":"Tanaka, H. et al. Defining tumour shape irregularity for preoperative risk stratification of clinically localised renal cell carcinoma. Eur. Urol. Open Sci. 48, 36\u201343 (2023).","journal-title":"Eur. Urol. Open Sci."},{"key":"1723_CR24","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.ejrad.2014.02.025","volume":"83","author":"C Kim","year":"2014","unstructured":"Kim, C., Choi, H. J. & Cho, K. S. Diagnostic value of multidetector computed tomography for renal sinus fat invasion in renal cell carcinoma patients. Eur. J. Radiol. 83, 914\u2013918 (2014).","journal-title":"Eur. J. Radiol."},{"key":"1723_CR25","doi-asserted-by":"crossref","unstructured":"Kocak, B. et al. CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging 14, (2023).","DOI":"10.1186\/s13244-023-01415-8"},{"key":"1723_CR26","doi-asserted-by":"publisher","unstructured":"National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC). The Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Cell Carcinoma Collection (CPTAC-CCRCC). https:\/\/doi.org\/10.7937\/k9\/tcia.2018.oblamn27 (2018).","DOI":"10.7937\/k9\/tcia.2018.oblamn27"},{"key":"1723_CR27","doi-asserted-by":"publisher","unstructured":"Akin, O. et al. The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma Collection (TCGA-KIRC). https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.V6PBVTDR (2016).","DOI":"10.7937\/K9\/TCIA.2016.V6PBVTDR"},{"key":"1723_CR28","doi-asserted-by":"publisher","unstructured":"Linehan, M. et al. The Cancer Genome Atlas Cervical Kidney Renal Papillary Cell Carcinoma Collection (TCGA-KIRP). https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.ACWOGBEF (2016).","DOI":"10.7937\/K9\/TCIA.2016.ACWOGBEF"},{"key":"1723_CR29","doi-asserted-by":"publisher","unstructured":"Linehan, M. W., Gautam, R., Sadow, C. A. & Levine, S. The Cancer Genome Atlas Kidney Chromophobe Collection (TCGA-KICH). https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.YU3RBCZN (2016).","DOI":"10.7937\/K9\/TCIA.2016.YU3RBCZN"},{"key":"1723_CR30","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1097\/PAS.0b013e31829a85d0","volume":"37","author":"K Trpkov","year":"2013","unstructured":"Trpkov, K. et al. Handling and staging of renal cell carcinoma: the International Society of Urological Pathology Consensus (ISUP) conference recommendations. Am. J. Surg. Pathol. 37, 1505\u20131517 (2013).","journal-title":"Am. J. Surg. Pathol."},{"key":"1723_CR31","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","volume":"31","author":"PA Yushkevich","year":"2006","unstructured":"Yushkevich, P. A. et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31, 1116\u20131128 (2006).","journal-title":"Neuroimage"},{"key":"1723_CR32","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","volume":"30","author":"A Fedorov","year":"2012","unstructured":"Fedorov, A. et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn. Reson. Imaging 30, 1323\u20131341 (2012).","journal-title":"Magn. Reson. Imaging"},{"key":"1723_CR33","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1148\/radiol.2020191145","volume":"295","author":"A Zwanenburg","year":"2020","unstructured":"Zwanenburg, A. et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295, 328\u2013338 (2020).","journal-title":"Radiology"},{"key":"1723_CR34","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011).","journal-title":"J. Mach. Learn. Res."},{"key":"1723_CR35","doi-asserted-by":"publisher","first-page":"e220232","DOI":"10.1148\/ryai.220232","volume":"5","author":"TJ Bradshaw","year":"2023","unstructured":"Bradshaw, T. J., Huemann, Z., Hu, J. & Rahmim, A. A guide to cross-validation for artificial intelligence in medical imaging. Radiol. Artif. Intell. 5, e220232 (2023).","journal-title":"Radiol. Artif. Intell."},{"key":"1723_CR36","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1148\/radiology.148.3.6878708","volume":"148","author":"JA Hanley","year":"1983","unstructured":"Hanley, J. A. & McNeil, B. J. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148, 839\u2013843 (1983).","journal-title":"Radiology"},{"key":"1723_CR37","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","volume":"44","author":"ER DeLong","year":"1988","unstructured":"DeLong, E. R., DeLong, D. M. & Clarke-Pearson, D. L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44, 837\u2013845 (1988).","journal-title":"Biometrics"},{"key":"1723_CR38","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/BF02295996","volume":"12","author":"Q McNEMAR","year":"1947","unstructured":"McNEMAR, Q. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12, 153\u2013157 (1947).","journal-title":"Psychometrika"},{"key":"1723_CR39","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.acra.2005.11.030","volume":"13","author":"BD Gallas","year":"2006","unstructured":"Gallas, B. D. One-shot estimate of MRMC variance: AUC. Acad. Radiol. 13, 353\u2013362 (2006).","journal-title":"Acad. Radiol."},{"key":"1723_CR40","first-page":"113160K","volume":"11316","author":"BJ Smith","year":"2020","unstructured":"Smith, B. J. & Hillis, S. L. Multi-reader multi-case analysis of variance software for diagnostic performance comparison of imaging modalities. Proc. SPIE Int. Soc. Opt. Eng. 11316, 113160K (2020).","journal-title":"Proc. SPIE Int. Soc. Opt. Eng."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01723-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01723-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01723-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T13:38:38Z","timestamp":1748093918000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01723-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,24]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1723"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-01723-x","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,24]]},"assertion":[{"value":"6 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"305"}}