{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T19:44:37Z","timestamp":1793907877507,"version":"4.8.0"},"reference-count":31,"publisher":"Elsevier BV","issue":"6","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"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":[[2025,6]]},"DOI":"10.1016\/j.acra.2024.12.050","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T02:01:42Z","timestamp":1737079302000},"page":"3464-3474","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"title":["End-to-End CT Radiomics-Based Pipeline for Predicting Renal Interstitial Fibrosis Grade in CKD Patients"],"prefix":"10.1016","volume":"32","author":[{"given":"Yue","family":"Ren","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongsheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuchao","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.acra.2024.12.050_bib1","doi-asserted-by":"crossref","first-page":"687","DOI":"10.3390\/antiox13060687","article-title":"Role of uremic toxins, oxidative stress, and renal fibrosis in chronic kidney disease","volume":"13","author":"Frak","year":"2024","journal-title":"Antioxidants"},{"key":"10.1016\/j.acra.2024.12.050_bib2","unstructured":"Collaboration GBDCKD. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study. 2017; Lancet, 395:2020; 709\u2013733. doi:\u00a010.1016\/S0140-6736(20)30045-3."},{"key":"10.1016\/j.acra.2024.12.050_bib3","doi-asserted-by":"crossref","first-page":"408","DOI":"10.3390\/ijms22010408","article-title":"Fibrosis in chronic kidney disease: pathogenesis and consequences","volume":"22","author":"Panizo","year":"2021","journal-title":"Int J Mol Sci"},{"key":"10.1016\/j.acra.2024.12.050_bib4","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1097\/MNH.0000000000000650","article-title":"Imaging of renal fibrosis","volume":"29","author":"Hysi","year":"2020","journal-title":"Curr Opin Nephrol Hypertens"},{"key":"10.1016\/j.acra.2024.12.050_bib5","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.trsl.2019.02.009","article-title":"Noninvasive assessment of renal fibrosis by magnetic resonance imaging and ultrasound techniques","volume":"209","author":"Jiang","year":"2019","journal-title":"Transl Res"},{"key":"10.1016\/j.acra.2024.12.050_bib6","doi-asserted-by":"crossref","first-page":"825","DOI":"10.7326\/0003-4819-158-11-201306040-00007","article-title":"Kidney disease: improving global outcomes chronic kidney disease guideline development work group m. evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline","volume":"158","author":"Stevens","year":"2013","journal-title":"Ann Intern Med"},{"key":"10.1016\/j.acra.2024.12.050_bib7","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1053\/j.ajkd.2018.10.011","article-title":"Update on the native kidney biopsy: core curriculum 2019","volume":"73","author":"Luciano","year":"2019","journal-title":"Am J Kidney Dis"},{"key":"10.1016\/j.acra.2024.12.050_bib8","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.2215\/CJN.14721219","article-title":"Major bleeding and risk of death after percutaneous native kidney biopsies: a French nationwide cohort study","volume":"15","author":"Halimi","year":"2020","journal-title":"Clin J Am Soc Nephrol"},{"key":"10.1016\/j.acra.2024.12.050_bib9","doi-asserted-by":"crossref","first-page":"2649","DOI":"10.1007\/s00261-023-03965-3","article-title":"Ultrasound-based radiomics analysis in the assessment of renal fibrosis in patients with chronic kidney disease","volume":"48","author":"Chen","year":"2023","journal-title":"Abdom Radiol"},{"key":"10.1016\/j.acra.2024.12.050_bib10","doi-asserted-by":"crossref","first-page":"362","DOI":"10.11152\/mu.2013.2066.183.per","article-title":"Value of ultrasound elastography in the diagnosis of native kidney fibrosis","volume":"18","author":"Peride","year":"2016","journal-title":"Med Ultrason"},{"key":"10.1016\/j.acra.2024.12.050_bib11","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1016\/j.jfma.2021.08.011","article-title":"Elastography ultrasound with machine learning improves the diagnostic performance of traditional ultrasound in predicting kidney fibrosis","volume":"121","author":"Zhu","year":"2022","journal-title":"J Formos Med Assoc"},{"key":"10.1016\/j.acra.2024.12.050_bib12","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1007\/s00261-021-03296-1","article-title":"Evaluation of renal fibrosis in various causes of glomerulonephritis by MR elastography: a clinicopathologic comparative analysis","volume":"47","author":"Guven","year":"2022","journal-title":"Abdom Radiol"},{"key":"10.1016\/j.acra.2024.12.050_bib13","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1186\/s13244-024-01736-2","article-title":"Assessing renal interstitial fibrosis using compartmental, non-compartmental, and model-free diffusion MRI approaches","volume":"15","author":"Hu","year":"2024","journal-title":"Insights Imaging"},{"key":"10.1016\/j.acra.2024.12.050_bib14","first-page":"w14442","article-title":"Diagnosis and assessment of renal fibrosis: the state of the art","volume":"147","author":"Berchtold","year":"2017","journal-title":"Swiss Med Wkly"},{"key":"10.1016\/j.acra.2024.12.050_bib15","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1111\/ctr.12784","article-title":"CT volumetry is superior to nuclear renography for prediction of residual kidney function in living donors","volume":"30","author":"Barbas","year":"2016","journal-title":"Clin Transplant"},{"key":"10.1016\/j.acra.2024.12.050_bib16","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0228626","article-title":"Detection and quantification of renal fibrosis by computerized tomography","volume":"15","author":"Cohen","year":"2020","journal-title":"PLoS One"},{"key":"10.1016\/j.acra.2024.12.050_bib17","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N Engl J Med"},{"key":"10.1016\/j.acra.2024.12.050_bib18","doi-asserted-by":"crossref","unstructured":"Improving the diagnosis of myocardial infarction with machine learning. Nat Med. 2023;29:1070\u20131. \u3008http:\/\/doi.org\/10.1038\/s41591\u2013023-02331\u20136\u3009.","DOI":"10.1038\/s41591-023-02331-6"},{"key":"10.1016\/j.acra.2024.12.050_bib19","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2024.12.050_bib20","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","article-title":"Radiomics: the bridge between medical imaging and personalized medicine","volume":"14","author":"Lambin","year":"2017","journal-title":"Nat Rev Clin Oncol"},{"key":"10.1016\/j.acra.2024.12.050_bib21","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1002\/cpt.3385","article-title":"Evaluating the effect of estimating renal function with the CKD-EPI 2021 equation on the ABCD-GENE score for clopidogrel response prediction","volume":"116","author":"AlSaeed","year":"2024","journal-title":"Clin Pharmacol Ther"},{"key":"10.1016\/j.acra.2024.12.050_bib22","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.230024","article-title":"TotalSegmentator: robust segmentation of 104 anatomic structures in CT images","volume":"5","author":"Wasserthal","year":"2023","journal-title":"Radiol Artif Intell"},{"key":"10.1016\/j.acra.2024.12.050_bib23","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat Methods"},{"key":"10.1016\/j.acra.2024.12.050_bib24","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational radiomics system to decode the radiographic phenotype","volume":"77","author":"van Griethuysen","year":"2017","journal-title":"Cancer Res"},{"key":"10.1016\/j.acra.2024.12.050_bib25","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.1038\/ncomms5006","article-title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"Aerts","year":"2014","journal-title":"Nat Commun"},{"key":"10.1016\/j.acra.2024.12.050_bib26","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1111\/ajt.14107","article-title":"The Banff 2015 kidney meeting report: current challenges in rejection classification and prospects for adopting molecular pathology","volume":"17","author":"Loupy","year":"2017","journal-title":"Am J Transplant"},{"key":"10.1016\/j.acra.2024.12.050_bib27","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.2215\/CJN.00020119","article-title":"Combination of functional magnetic resonance imaging and histopathologic analysis to evaluate interstitial fibrosis in kidney allografts","volume":"14","author":"Wang","year":"2019","journal-title":"Clin J Am Soc Nephrol"},{"key":"10.1016\/j.acra.2024.12.050_bib28","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1093\/ndt\/gfz129","article-title":"Quantitative assessment of renal structural and functional changes in chronic kidney disease using multi-parametric magnetic resonance imaging","volume":"35","author":"Buchanan","year":"2020","journal-title":"Nephrol Dial Transplant"},{"key":"10.1016\/j.acra.2024.12.050_bib29","doi-asserted-by":"crossref","first-page":"5211","DOI":"10.1007\/s00330-023-09674-1","article-title":"Value of multiparametric magnetic resonance imaging for evaluating chronic kidney disease and renal fibrosis","volume":"33","author":"Hua","year":"2023","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2024.12.050_bib30","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1007\/s10456-016-9524-7","article-title":"Contrast-enhanced CT imaging in patients with chronic kidney disease","volume":"19","author":"von Stillfried","year":"2016","journal-title":"Angiogenesis"},{"key":"10.1016\/j.acra.2024.12.050_bib31","doi-asserted-by":"crossref","DOI":"10.1016\/j.critrevonc.2021.103544","article-title":"State of the art of radiomic analysis in the clinical management of prostate cancer: a\u00a0systematic review","volume":"169","author":"Ghezzo","year":"2022","journal-title":"Crit Rev Oncol Hematol"}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633224010389?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633224010389?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T22:19:58Z","timestamp":1748643598000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1076633224010389"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":31,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["S1076633224010389"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2024.12.050","relation":{},"ISSN":["1076-6332"],"issn-type":[{"value":"1076-6332","type":"print"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"End-to-End CT Radiomics-Based Pipeline for Predicting Renal Interstitial Fibrosis Grade in CKD Patients","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2024.12.050","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Association of Academic Radiology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}