{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T17:44:22Z","timestamp":1792518262604,"version":"4.5.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:00:00Z","timestamp":1792454400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:00:00Z","timestamp":1792454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Nautural Science Foundation of Jiangxi Province"},{"name":"Major Discipline Academic and Technological Leader Training Project"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int Urol Nephrol"],"DOI":"10.1007\/s11255-026-05451-3","type":"journal-article","created":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T17:22:44Z","timestamp":1792516964000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Non-contrast CT radiomics for identifying nonfunctioning kidneys: a multicenter development and external validation study"],"prefix":"10.1007","author":[{"given":"Yufei","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihan","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zechen","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5996-9752","authenticated-orcid":false,"given":"Liuru","family":"Bao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5925-6922","authenticated-orcid":false,"given":"Qiang","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,20]]},"reference":[{"issue":"10","key":"5451_CR1","doi-asserted-by":"publisher","first-page":"2937","DOI":"10.1681\/ASN.2006040368","volume":"17","author":"YC Ma","year":"2006","unstructured":"Ma YC, Zuo L, Chen JH et al (2006) Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease. J Am Soc Nephrol 17(10):2937\u20132944. https:\/\/doi.org\/10.1681\/ASN.2006040368","journal-title":"J Am Soc Nephrol"},{"issue":"4","key":"5451_CR2","doi-asserted-by":"publisher","first-page":"247","DOI":"10.7326\/0003-4819-145-4-200608150-00004","volume":"145","author":"AS Levey","year":"2006","unstructured":"Levey AS, Coresh J, Greene T et al (2006) Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med 145(4):247\u2013254. https:\/\/doi.org\/10.7326\/0003-4819-145-4-200608150-00004","journal-title":"Ann Intern Med"},{"issue":"1","key":"5451_CR3","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1186\/s12882-025-03972-0","volume":"26","author":"A Lanot","year":"2025","unstructured":"Lanot A, Akesson A, Nakano FK et al (2025) Enhancing individual glomerular filtration rate assessment: can we trust the equation? Development and validation of machine learning models to assess the trustworthiness of estimated GFR compared to measured GFR. BMC Nephrol 26(1):47. https:\/\/doi.org\/10.1186\/s12882-025-03972-0","journal-title":"BMC Nephrol"},{"issue":"8","key":"5451_CR4","doi-asserted-by":"publisher","first-page":"1649","DOI":"10.1007\/s11255-020-02771-w","volume":"53","author":"N Li","year":"2021","unstructured":"Li N, Huang H, Lv L et al (2021) Improving glomerular filtration rate estimation by semi-supervised learning: a development and external validation study. Int Urol Nephrol 53(8):1649\u20131658. https:\/\/doi.org\/10.1007\/s11255-020-02771-w","journal-title":"Int Urol Nephrol"},{"issue":"1","key":"5451_CR5","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1186\/s12967-017-1337-y","volume":"15","author":"X Liu","year":"2017","unstructured":"Liu X, Li N, Lv L et al (2017) Improving precision of glomerular filtration rate estimating model by ensemble learning. J Transl Med 15(1):231. https:\/\/doi.org\/10.1186\/s12967-017-1337-y","journal-title":"J Transl Med"},{"issue":"1","key":"5451_CR6","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-77618-w","volume":"14","author":"FK Nakano","year":"2024","unstructured":"Nakano FK, Akesson A, de Boer J et al (2024) Comparison between the EKFC-equation and machine learning models to predict glomerular filtration rate. Sci Rep 14(1):26383. https:\/\/doi.org\/10.1038\/s41598-024-77618-w","journal-title":"Sci Rep"},{"issue":"6","key":"5451_CR7","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1016\/j.kint.2022.01.034","volume":"101","author":"J van der Weijden","year":"2022","unstructured":"van der Weijden J, Mahesh SVK, van Londen M et al (2022) Early increase in single-kidney glomerular filtration rate after living kidney donation predicts long-term kidney function. Kidney Int 101(6):1251\u20131259. https:\/\/doi.org\/10.1016\/j.kint.2022.01.034","journal-title":"Kidney Int"},{"key":"5451_CR8","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2022.861895","volume":"13","author":"H Yin","year":"2022","unstructured":"Yin H, Liang W, Zhao D (2022) The application value of the renal region of interest corrected by computed tomography in single-kidney glomerular filtration rate for the evaluation of patients with moderate or severe hydronephrosis. Front Physiol 13:861895. https:\/\/doi.org\/10.3389\/fphys.2022.861895","journal-title":"Front Physiol"},{"issue":"1","key":"5451_CR9","doi-asserted-by":"publisher","first-page":"30","DOI":"10.5527\/wjn.v11.i1.30","volume":"11","author":"RA Gadelkareem","year":"2022","unstructured":"Gadelkareem RA, Mohammed N (2022) Unilateral hypoplastic kidney in adults: an experience of a tertiary-level urology center. World J Nephrol 11(1):30\u201338. https:\/\/doi.org\/10.5527\/wjn.v11.i1.30","journal-title":"World J Nephrol"},{"issue":"4","key":"5451_CR10","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, Albiges L, Abu-Ghanem Y et al (2022) European Association of Urology guidelines on renal cell carcinoma: the 2022 update. Eur Urol 82(4):399\u2013410. https:\/\/doi.org\/10.1016\/j.eururo.2022.03.006","journal-title":"Eur Urol"},{"issue":"4S","key":"5451_CR11","doi-asserted-by":"publisher","first-page":"S117","DOI":"10.1016\/j.kint.2023.10.018","volume":"105","author":"Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group","year":"2024","unstructured":"Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group (2024) KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int 105(4S):S117\u2013S314. https:\/\/doi.org\/10.1016\/j.kint.2023.10.018","journal-title":"Kidney Int"},{"issue":"3","key":"5451_CR12","doi-asserted-by":"publisher","first-page":"565","DOI":"10.2214\/ajr.138.3.565","volume":"138","author":"GF Gates","year":"1982","unstructured":"Gates GF (1982) Glomerular filtration rate: estimation from fractional renal accumulation of 99mTc-DTPA (stannous). AJR Am J Roentgenol 138(3):565\u2013570. https:\/\/doi.org\/10.2214\/ajr.138.3.565","journal-title":"AJR Am J Roentgenol"},{"issue":"12","key":"5451_CR13","doi-asserted-by":"publisher","first-page":"2596","DOI":"10.1097\/TP.0000000000003676","volume":"105","author":"JR Montgomery","year":"2021","unstructured":"Montgomery JR, Brown CS, Zondlak AN et al (2021) CT-measured cortical volume ratio is an accurate alternative to nuclear medicine split scan ratio among living kidney donors. Transplantation 105(12):2596\u20132605. https:\/\/doi.org\/10.1097\/TP.0000000000003676","journal-title":"Transplantation"},{"issue":"2","key":"5451_CR14","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1007\/s00330-016-4410-7","volume":"27","author":"F Gaillard","year":"2017","unstructured":"Gaillard F, Pavlov P, Tissier AM et al (2017) Use of computed tomography assessed kidney length to predict split renal GFR in living kidney donors. Eur Radiol 27(2):651\u2013659. https:\/\/doi.org\/10.1007\/s00330-016-4410-7","journal-title":"Eur Radiol"},{"issue":"11","key":"5451_CR15","doi-asserted-by":"publisher","first-page":"3143","DOI":"10.1007\/s00330-015-3741-0","volume":"25","author":"DK Choi","year":"2015","unstructured":"Choi DK, Choi SM, Park BH et al (2015) Measurement of renal function in a kidney donor: a comparison of creatinine-based and volume-based GFRs. Eur Radiol 25(11):3143\u20133150. https:\/\/doi.org\/10.1007\/s00330-015-3741-0","journal-title":"Eur Radiol"},{"issue":"1","key":"5451_CR16","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1148\/radiol.2521081873","volume":"252","author":"BR Herts","year":"2009","unstructured":"Herts BR, Sharma N, Lieber M, Freire M, Goldfarb DA, Poggio ED (2009) Estimating glomerular filtration rate in kidney donors: a model constructed with renal volume measurements from donor CT scans. Radiology 252(1):109\u2013116. https:\/\/doi.org\/10.1148\/radiol.2521081873","journal-title":"Radiology"},{"issue":"4","key":"5451_CR17","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","volume":"48","author":"P Lambin","year":"2012","unstructured":"Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4):441\u2013446. https:\/\/doi.org\/10.1016\/j.ejca.2011.11.036","journal-title":"Eur J Cancer"},{"issue":"2","key":"5451_CR18","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.232178","volume":"311","author":"C Dai","year":"2024","unstructured":"Dai C, Xiong Y, Zhu P et al (2024) Deep learning assessment of small renal masses at contrast-enhanced multiphase CT. Radiology 311(2):e232178. https:\/\/doi.org\/10.1148\/radiol.232178","journal-title":"Radiology"},{"issue":"4","key":"5451_CR19","doi-asserted-by":"publisher","first-page":"801","DOI":"10.2214\/AJR.16.17119","volume":"208","author":"N Agochukwu","year":"2017","unstructured":"Agochukwu N, Huber S, Spektor M, Goehler A, Israel GM (2017) Differentiating renal neoplasms from simple cysts on contrast-enhanced CT on the basis of attenuation and homogeneity. AJR Am J Roentgenol 208(4):801\u2013804. https:\/\/doi.org\/10.2214\/AJR.16.17119","journal-title":"AJR Am J Roentgenol"},{"issue":"1","key":"5451_CR20","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-56784-z","volume":"16","author":"Y Xiong","year":"2025","unstructured":"Xiong Y, Yao L, Lin J et al (2025) Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses. Nat Commun 16(1):1425. https:\/\/doi.org\/10.1038\/s41467-025-56784-z","journal-title":"Nat Commun"},{"issue":"2","key":"5451_CR21","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.210205","volume":"4","author":"A Goel","year":"2022","unstructured":"Goel A, Shih G, Riyahi S et al (2022) Deployed deep learning kidney segmentation for polycystic kidney disease MRI. Radiol Artif Intell 4(2):e210205. https:\/\/doi.org\/10.1148\/ryai.210205","journal-title":"Radiol Artif Intell"},{"issue":"6","key":"5451_CR22","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230043","volume":"5","author":"J Guo","year":"2023","unstructured":"Guo J, Goyal M, Xi Y et al (2023) Style transfer-assisted deep learning method for kidney segmentation at multiphase MRI. Radiol Artif Intell 5(6):e230043. https:\/\/doi.org\/10.1148\/ryai.230043","journal-title":"Radiol Artif Intell"},{"issue":"1","key":"5451_CR23","doi-asserted-by":"publisher","first-page":"142","DOI":"10.2214\/AJR.19.22125","volume":"215","author":"W Shi","year":"2020","unstructured":"Shi W, Liang X, Wu N, Zhang H, Yuan X, Tan Y (2020) Assessment of split renal function using a combination of contrast-enhanced CT and serum creatinine values for glomerular filtration rate estimation. AJR Am J Roentgenol 215(1):142\u2013147. https:\/\/doi.org\/10.2214\/AJR.19.22125","journal-title":"AJR Am J Roentgenol"},{"issue":"1","key":"5451_CR24","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1186\/s13244-025-01959-x","volume":"16","author":"Y Wang","year":"2025","unstructured":"Wang Y, Xu F, Han Q et al (2025) AI-based automatic estimation of single-kidney glomerular filtration rate and split renal function using non-contrast CT. Insights Imaging 16(1):84. https:\/\/doi.org\/10.1186\/s13244-025-01959-x","journal-title":"Insights Imaging"},{"issue":"1","key":"5451_CR25","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1007\/s00345-024-04921-6","volume":"42","author":"Q Zheng","year":"2024","unstructured":"Zheng Q, Ni X, Yang R et al (2024) UroAngel: a single-kidney function prediction system based on computed tomography urography using deep learning. World J Urol 42(1):238. https:\/\/doi.org\/10.1007\/s00345-024-04921-6","journal-title":"World J Urol"},{"issue":"11","key":"5451_CR26","doi-asserted-by":"publisher","first-page":"7169","DOI":"10.1097\/JS9.0000000000002030","volume":"110","author":"EA Jo","year":"2024","unstructured":"Jo EA, Lee J, Moon S et al (2024) The role of artificial intelligence measured preoperative kidney volume in predicting kidney function loss in elderly kidney donors: a multicenter cohort study. Int J Surg 110(11):7169\u20137176. https:\/\/doi.org\/10.1097\/JS9.0000000000002030","journal-title":"Int J Surg"},{"issue":"8","key":"5451_CR27","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1089\/end.2023.0488","volume":"38","author":"RL Correa-Medero","year":"2024","unstructured":"Correa-Medero RL, Jeong J, Patel B, Banerjee I, Abdul-Muhsin H (2024) Automated analysis of split kidney function from CT scans using deep learning and delta radiomics. J Endourol 38(8):817\u2013823. https:\/\/doi.org\/10.1089\/end.2023.0488","journal-title":"J Endourol"},{"issue":"1","key":"5451_CR28","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg SM, Erion G, Chen H et al (2020) From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2(1):56\u201367. https:\/\/doi.org\/10.1038\/s42256-019-0138-9","journal-title":"Nat Mach Intell"},{"key":"5451_CR29","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.q902","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins GS, Moons KGM, Dhiman P et al (2024) TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385:e078378. https:\/\/doi.org\/10.1136\/bmj.q902","journal-title":"BMJ"},{"issue":"21","key":"5451_CR30","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJM van Griethuysen","year":"2017","unstructured":"van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77(21):e104\u2013e107. https:\/\/doi.org\/10.1158\/0008-5472.CAN-17-0339","journal-title":"Cancer Res"},{"issue":"2","key":"5451_CR31","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1148\/radiol.2020191145","volume":"295","author":"A Zwanenburg","year":"2020","unstructured":"Zwanenburg A, Valli\u00e8res M, Abdalah MA et al (2020) The Image Biomarker Standardization Initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295(2):328\u2013338. https:\/\/doi.org\/10.1148\/radiol.2020191145","journal-title":"Radiology"},{"key":"5451_CR32","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1023\/A:1012487302797","volume":"46","author":"I Guyon","year":"2002","unstructured":"Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389\u2013422. https:\/\/doi.org\/10.1023\/A:1012487302797","journal-title":"Mach Learn"},{"issue":"3","key":"5451_CR33","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1002\/sim.5941","volume":"33","author":"PC Austin","year":"2014","unstructured":"Austin PC, Steyerberg EW (2014) Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers. Stat Med 33(3):517\u2013535. https:\/\/doi.org\/10.1002\/sim.5941","journal-title":"Stat Med"},{"issue":"1","key":"5451_CR34","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1466-7","volume":"17","author":"B Van Calster","year":"2019","unstructured":"Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW (2019) Calibration: the Achilles heel of predictive analytics. BMC Med 17(1):230. https:\/\/doi.org\/10.1186\/s12916-019-1466-7","journal-title":"BMC Med"},{"issue":"6","key":"5451_CR35","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1177\/0272989X06295361","volume":"26","author":"AJ Vickers","year":"2006","unstructured":"Vickers AJ, Elkin EB (2006) Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 26(6):565\u2013574. https:\/\/doi.org\/10.1177\/0272989X06295361","journal-title":"Med Decis Making"},{"issue":"1","key":"5451_CR36","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Series B Stat Methodol 58(1):267\u2013288. https:\/\/doi.org\/10.1111\/j.2517-6161.1996.tb02080.x","journal-title":"J R Stat Soc Series B Stat Methodol"},{"issue":"1","key":"5451_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2","volume":"78","author":"GW Brier","year":"1950","unstructured":"Brier GW (1950) Verification of forecasts expressed in terms of probability. Mon Weather Rev 78(1):1\u20133. https:\/\/doi.org\/10.1175\/1520-0493(1950)078%3c0001:VOFEIT%3e2.0.CO;2","journal-title":"Mon Weather Rev"},{"issue":"3","key":"5451_CR38","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","volume":"44","author":"ER DeLong","year":"1988","unstructured":"DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3):837\u2013845. https:\/\/doi.org\/10.2307\/2531595","journal-title":"Biometrics"},{"issue":"2","key":"5451_CR39","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1002\/sim.2929","volume":"27","author":"MJ Pencina","year":"2008","unstructured":"Pencina MJ, D\u2019Agostino RB Sr, D\u2019Agostino RB Jr, Vasan RS (2008) Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 27(2):157\u2013172. https:\/\/doi.org\/10.1002\/sim.2929","journal-title":"Stat Med"}],"container-title":["International Urology and Nephrology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11255-026-05451-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11255-026-05451-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11255-026-05451-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T17:22:45Z","timestamp":1792516965000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11255-026-05451-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,20]]},"references-count":39,"alternative-id":["5451"],"URL":"https:\/\/doi.org\/10.1007\/s11255-026-05451-3","relation":{},"ISSN":["1573-2584"],"issn-type":[{"value":"1573-2584","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,20]]},"assertion":[{"value":"13 September 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This retrospective study was approved by the Institutional Review Board of each participating center, which waived the requirement for written informed consent because only de-identified archived data were used (approval numbers NFEC-202502-K14, B2025-042-01, and KY2025-817-01).","order":2,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The requirement for written informed consent was waived by the institutional review boards of all participating centers because of the retrospective use of de-identified archived data.","order":3,"name":"Ethics","label":"Consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All images and data presented in this article are de-identified, and no individually identifiable information is included.","order":4,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The analysis code used in this study is available from the corresponding author on reasonable request.","order":5,"name":"Ethics","label":"Code availability","group":{"name":"EthicsHeading","label":"Declarations"}}]}}