{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T15:38:54Z","timestamp":1792597134490,"version":"4.5.0"},"reference-count":34,"publisher":"Elsevier BV","issue":"10","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["R01 DK129541"],"award-info":[{"award-number":["R01 DK129541"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["R21 DK128668"],"award-info":[{"award-number":["R21 DK128668"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["amjtransplant.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["American Journal of Transplantation"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.ajt.2026.05.727","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T15:55:40Z","timestamp":1779292540000},"page":"2345-2355","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pathomic analysis of 5-year surveillance biopsies as predictors of kidney allograft loss"],"prefix":"10.1016","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2450-5233","authenticated-orcid":false,"given":"Pinaki","family":"Sarder","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aleksandar","family":"Denic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byron H.","family":"Smith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anindya S.","family":"Paul","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akshita","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harishwar Reddy","family":"Kasireddy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sumanth","family":"Devarasetty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel P.","family":"Border","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Walter D.","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark D.","family":"Stegall","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"9447","key":"10.1016\/j.ajt.2026.05.727_bib1","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.1016\/S0140-6736(04)17406-0","article-title":"Delayed graft function in kidney transplantation","volume":"364","author":"Perico","year":"2004","journal-title":"Lancet"},{"issue":"10","key":"10.1016\/j.ajt.2026.05.727_bib2","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.3390\/jpm12101557","article-title":"Delayed graft function in kidney transplant: risk factors, consequences and prevention strategies","volume":"12","author":"Ponticelli","year":"2022","journal-title":"J Pers Med"},{"issue":"4","key":"10.1016\/j.ajt.2026.05.727_bib3","doi-asserted-by":"crossref","DOI":"10.1016\/j.trre.2021.100629","article-title":"Emerging biomarkers of delayed graft function in kidney transplantation","volume":"35","author":"Mezzolla","year":"2021","journal-title":"Transplant Rev (Orlando)"},{"issue":"8","key":"10.1016\/j.ajt.2026.05.727_bib4","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","article-title":"Clinical-grade computational pathology using weakly supervised deep learning on whole slide images","volume":"25","author":"Campanella","year":"2019","journal-title":"Nat Med"},{"issue":"9","key":"10.1016\/j.ajt.2026.05.727_bib5","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.3390\/cancers16091686","article-title":"Digital pathology for better clinical practice","volume":"16","author":"Hijazi","year":"2024","journal-title":"Cancers (Basel)"},{"issue":"1","key":"10.1016\/j.ajt.2026.05.727_bib6","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1186\/s12967-024-05262-z","article-title":"Deep learning based digital pathology for predicting treatment response to first-line PD-1 blockade in advanced gastric cancer","volume":"22","author":"Liu","year":"2024","journal-title":"J Transl Med"},{"issue":"5","key":"10.1016\/j.ajt.2026.05.727_bib7","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1080\/14737159.2024.2346545","article-title":"Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer","volume":"24","author":"McCaffrey","year":"2024","journal-title":"Expert Rev Mol Diagn"},{"issue":"5","key":"10.1016\/j.ajt.2026.05.727_bib8","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1002\/path.6163","article-title":"Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects","volume":"260","author":"Verghese","year":"2023","journal-title":"J Pathol"},{"issue":"1","key":"10.1016\/j.ajt.2026.05.727_bib9","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1038\/s41746-024-01003-0","article-title":"Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer","volume":"7","author":"Zhang","year":"2024","journal-title":"NPJ Digit Med"},{"issue":"4","key":"10.1016\/j.ajt.2026.05.727_bib10","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1681\/ASN.2020050652","article-title":"Automated computational detection of interstitial fibrosis, tubular atrophy, and glomerulosclerosis","volume":"32","author":"Ginley","year":"2021","journal-title":"J Am Soc Nephrol"},{"issue":"5","key":"10.1016\/j.ajt.2026.05.727_bib11","doi-asserted-by":"crossref","first-page":"648","DOI":"10.34067\/KID.0000000000000116","article-title":"Clinical relevance of computationally derived attributes of peritubular capillaries from kidney biopsies","volume":"4","author":"Chen","year":"2023","journal-title":"Kidney360"},{"issue":"1","key":"10.1016\/j.ajt.2026.05.727_bib12","doi-asserted-by":"crossref","first-page":"e18","DOI":"10.1016\/S2589-7500(21)00211-9","article-title":"Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study","volume":"4","author":"Kers","year":"2022","journal-title":"Lancet Digit Health"},{"issue":"1","key":"10.1016\/j.ajt.2026.05.727_bib13","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1038\/s41467-023-36173-0","article-title":"Next-generation morphometry for pathomics-data mining in histopathology","volume":"14","author":"H\u00f6lscher","year":"2023","journal-title":"Nat Commun"},{"issue":"2","key":"10.1016\/j.ajt.2026.05.727_bib14","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1681\/ASN.2022040418","article-title":"Changes in glomerular volume, sclerosis, and ischemia at 5 years after kidney transplantation: incidence and correlation with late graft failure","volume":"34","author":"Denic","year":"2023","journal-title":"J Am Soc Nephrol"},{"issue":"9","key":"10.1016\/j.ajt.2026.05.727_bib15","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.34067\/KID.0000000000000514","article-title":"IFTA foci density: an unrecognized highly prognostic measurement of fibrosis in kidney transplant biopsies","volume":"5","author":"Denic","year":"2024","journal-title":"Kidney360"},{"issue":"7","key":"10.1016\/j.ajt.2026.05.727_bib16","doi-asserted-by":"crossref","DOI":"10.1097\/TXD.0000000000001652","article-title":"Mesangial expansion by morphometry at 5 y after kidney transplantation: incidence, risk factors, and association with graft loss","volume":"10","author":"Denic","year":"2024","journal-title":"Transplant Direct"},{"issue":"4","key":"10.1016\/j.ajt.2026.05.727_bib17","doi-asserted-by":"crossref","first-page":"1726","DOI":"10.34067\/KID.0000000000000299","article-title":"Correlating deep learning-based automated reference kidney histomorphometry with patient demographics and creatinine","volume":"1","author":"Lucarelli","year":"2023","journal-title":"Kidney360"},{"issue":"1","key":"10.1016\/j.ajt.2026.05.727_bib18","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1111\/ajt.14431","article-title":"Renal allograft histology at 10 years after transplantation in the tacrolimus era: evidence of pervasive chronic injury","volume":"18","author":"Stegall","year":"2018","journal-title":"Am J Transplant"},{"issue":"3","key":"10.1016\/j.ajt.2026.05.727_bib19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1200\/CCI.18.00157","article-title":"HistoQC: an open-source quality control tool for digital pathology slides","author":"Janowczyk","year":"2019","journal-title":"JCO Clin Cancer Inform"},{"issue":"11","key":"10.1016\/j.ajt.2026.05.727_bib20","doi-asserted-by":"crossref","first-page":"1795","DOI":"10.1097\/TP.0000000000002366","article-title":"A 2018 reference guide to the Banff classification of renal allograft pathology","volume":"102","author":"Roufosse","year":"2018","journal-title":"Transplantation"},{"key":"10.1016\/j.ajt.2026.05.727_bib21","article-title":"ComPRePS: unlocking scalable AI analysis for computational renal pathology","author":"Paul","year":"2026","journal-title":"bioRxiv"},{"issue":"1-2","key":"10.1016\/j.ajt.2026.05.727_bib22","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1162\/dint_r_00024","article-title":"FAIR principles: interpretations and implementation considerations","volume":"2","author":"Jacobsen","year":"2020","journal-title":"Data Intell"},{"issue":"1","key":"10.1016\/j.ajt.2026.05.727_bib23","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1038\/s43856-022-00138-z","article-title":"A user-friendly tool for cloud-based whole slide image segmentation with examples from renal histopathology","volume":"2","author":"Lutnick","year":"2022","journal-title":"Commun Med (Lond)"},{"key":"10.1016\/j.ajt.2026.05.727_bib24","author":"Yuxin Wu","year":"2019","journal-title":"Detectron2"},{"issue":"2","key":"10.1016\/j.ajt.2026.05.727_bib25","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1038\/s42256-019-0018-3","article-title":"An integrated iterative annotation technique for easing neural network training in medical image analysis","volume":"1","author":"Lutnick","year":"2019","journal-title":"Nat Mach Intell"},{"key":"10.1016\/j.ajt.2026.05.727_bib26","unstructured":"Gonzalez RC. Digital Image Processing Using MATLAB2004."},{"key":"10.1016\/j.ajt.2026.05.727_bib27","series-title":"Digital Image Processing","author":"Gonzalez","year":"2007"},{"key":"10.1016\/j.ajt.2026.05.727_bib28","article-title":"FUSION: a web-based application for in-depth exploration of multi-omics data with brightfield histology","author":"Border","year":"2024","journal-title":"bioRxiv."},{"key":"10.1016\/j.ajt.2026.05.727_bib29","unstructured":"Hastie T, Tibshirani R, Tibshirani RJ. Extended comparisons of best subset selection, forward stepwise selection, and the lasso. arXiv. 2017:arXiv:1707.08692. doi:10.48550\/arXiv.1707.08692."},{"issue":"1","key":"10.1016\/j.ajt.2026.05.727_bib30","first-page":"91","article-title":"An introduction to survival statistics: Kaplan-Meier analysis","volume":"7","author":"Dudley","year":"2016","journal-title":"J Adv Pract Oncol"},{"key":"10.1016\/j.ajt.2026.05.727_bib31","first-page":"120390S","article-title":"HistoLens: a generalizable tool for increasing accessibility and interpretability of quantitative analyses in digital pathology","volume":"12039","author":"Border","year":"2022","journal-title":"Proc SPIE Int Soc Opt Eng."},{"key":"10.1016\/j.ajt.2026.05.727_bib32","series-title":"Wiley StatsRef: Statistics Reference Online","article-title":"Cox proportional hazards model","author":"Cai","year":"2014"},{"issue":"2","key":"10.1016\/j.ajt.2026.05.727_bib33","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1111\/j.1399-0012.2011.01506.x","article-title":"Superiority of virtual microscopy versus light microscopy in transplantation pathology","volume":"26","author":"Ozluk","year":"2012","journal-title":"Clin Transplant."},{"issue":"2","key":"10.1016\/j.ajt.2026.05.727_bib34","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1111\/tri.13340","article-title":"A method to reduce variability in scoring antibody-mediated rejection in renal allografts: implications for clinical trials - a retrospective study","volume":"32","author":"Smith","year":"2019","journal-title":"Transpl Int"}],"container-title":["American Journal of Transplantation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1600613526009949?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1600613526009949?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T14:41:39Z","timestamp":1792593699000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1600613526009949"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":34,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2026,10]]}},"alternative-id":["S1600613526009949"],"URL":"https:\/\/doi.org\/10.1016\/j.ajt.2026.05.727","relation":{},"ISSN":["1600-6135"],"issn-type":[{"value":"1600-6135","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Pathomic analysis of 5-year surveillance biopsies as predictors of kidney allograft loss","name":"articletitle","label":"Article Title"},{"value":"American Journal of Transplantation","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ajt.2026.05.727","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Inc. on behalf of American Society of Transplantation & American Society of Transplant Surgeons.","name":"copyright","label":"Copyright"}]}}