{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T06:57:03Z","timestamp":1781333823698,"version":"3.54.1"},"reference-count":29,"publisher":"Elsevier BV","issue":"7","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T00:00:00Z","timestamp":1779753600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","ejso.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Surgical Oncology"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.ejso.2026.111911","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T16:12:00Z","timestamp":1779725520000},"page":"111911","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable and non-linear machine learning models for quantitative ICG perfusion assessment in colorectal cancer anastomosis: A feasibility study"],"prefix":"10.1016","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8434-7428","authenticated-orcid":false,"given":"Paulina","family":"Daniluk-Marsy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5152-7660","authenticated-orcid":false,"given":"Johannes","family":"Castelein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karol","family":"Polom","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronald J.H.","family":"Borra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejso.2026.111911_bib1","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1177\/15533506211072678","article-title":"Fluorescence imaging in colorectal surgery: an updated review and future trends","volume":"29","author":"Daniluk","year":"2022","journal-title":"Surg Innov"},{"key":"10.1016\/j.ejso.2026.111911_bib2","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1097\/SLA.0000000000004412","article-title":"Consensus conference statement on the general use of near-infrared fluorescence imaging and indocyanine green guided surgery: results of a modified Delphi study","volume":"275","author":"Dip","year":"2022","journal-title":"Ann Surg"},{"key":"10.1016\/j.ejso.2026.111911_bib3","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.jamcollsurg.2014.09.015","article-title":"Perfusion assessment in laparoscopic left-sided\/anterior resection (PILLAR II): a multi-institutional study","volume":"220","author":"Jafari","year":"2015","journal-title":"J Am Coll Surg"},{"key":"10.1016\/j.ejso.2026.111911_bib4","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s00464-019-06730-0","article-title":"Intraoperative angiography with indocyanine green to assess anastomosis perfusion in patients undergoing laparoscopic colorectal resection: results of a multicenter randomized controlled trial","volume":"34","author":"De Nardi","year":"2020","journal-title":"Surg Endosc"},{"key":"10.1016\/j.ejso.2026.111911_bib5","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1111\/codi.15037","article-title":"A study investigating the perfusion of colorectal anastomoses using fluorescence angiography: results of the FLAG randomized trial","volume":"22","author":"Alekseev","year":"2020","journal-title":"Colorectal Dis"},{"key":"10.1016\/j.ejso.2026.111911_bib6","doi-asserted-by":"crossref","first-page":"4281","DOI":"10.1007\/s00464-020-07735-w","article-title":"Intraoperative use of fluorescence with indocyanine green reduces anastomotic leak rates in rectal cancer surgery: an individual participant data analysis","volume":"34","author":"Arezzo","year":"2020","journal-title":"Surg Endosc"},{"key":"10.1016\/j.ejso.2026.111911_bib7","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1016\/S2468-1253(24)00198-5","article-title":"Indocyanine green near-infrared fluorescence bowel perfusion assessment to prevent anastomotic leakage in minimally invasive colorectal surgery (AVOID): a multicentre, randomised, controlled, phase 3 trial","volume":"9","author":"Faber","year":"2024","journal-title":"Lancet Gastroenterol Hepatol"},{"key":"10.1016\/j.ejso.2026.111911_bib8","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1089\/lap.2019.0788","article-title":"Quantitative indocyanine green fluorescence imaging used to predict anastomotic leakage focused on rectal stump during laparoscopic anterior resection","volume":"30","author":"Iwamoto","year":"2020","journal-title":"J Laparoendosc Adv Surg Tech"},{"key":"10.1016\/j.ejso.2026.111911_bib9","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1080\/13645706.2020.1762655","article-title":"Intra-arterial versus negative-staining of embryonal resection borders with indocyanine green (ICG) fluorescence for total mesorectal excision in colorectal cancer - an experimental feasibility study in a porcine model","volume":"31","author":"Frank","year":"2022","journal-title":"Minim Invasive Ther Allied Technol"},{"key":"10.1016\/j.ejso.2026.111911_bib10","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1055\/s-0039-1700548","article-title":"Preliminary use of indocyanine green fluorescence angiography and value in predicting the vascular supply of tissues needed to perform cloacal, anorectal malformation, and Hirschsprung reconstructions","volume":"30","author":"Rentea","year":"2020","journal-title":"Eur J Pediatr Surg"},{"key":"10.1016\/j.ejso.2026.111911_bib11","doi-asserted-by":"crossref","first-page":"6824","DOI":"10.1007\/s00464-023-10140-8","article-title":"Quantification of indocyanine green near-infrared fluorescence bowel perfusion assessment in colorectal surgery","volume":"37","author":"Faber","year":"2023","journal-title":"Surg Endosc"},{"key":"10.1016\/j.ejso.2026.111911_bib12","doi-asserted-by":"crossref","first-page":"6945","DOI":"10.3748\/wjg.v26.i44.6945","article-title":"Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery","volume":"26","author":"Park","year":"2020","journal-title":"World J Gastroenterol"},{"key":"10.1016\/j.ejso.2026.111911_bib13","doi-asserted-by":"crossref","DOI":"10.3390\/jcm12103550","article-title":"Quantification of indocyanine green fluorescence imaging in general, visceral and transplant surgery","volume":"12","author":"Pollmann","year":"2023","journal-title":"J Clin Med"},{"key":"10.1016\/j.ejso.2026.111911_bib14","doi-asserted-by":"crossref","first-page":"6361","DOI":"10.1007\/s00464-023-09963-2","article-title":"Clinical application of machine learning and computer vision to indocyanine green quantification for dynamic intraoperative tissue characterisation: how to do it","volume":"37","author":"Hardy","year":"2023","journal-title":"Surg Endosc"},{"key":"10.1016\/j.ejso.2026.111911_bib15","doi-asserted-by":"crossref","first-page":"3135","DOI":"10.1007\/s00259-024-06731-9","article-title":"Intraoperative near infrared functional imaging of rectal cancer using artificial intelligence methods - now and near future state of the art","volume":"51","author":"Boland","year":"2024","journal-title":"Eur J Nucl Med Mol Imag"},{"key":"10.1016\/j.ejso.2026.111911_bib16","doi-asserted-by":"crossref","DOI":"10.1007\/s00464-025-12312-0","article-title":"AUGUR-AI: a real-time artificial intelligence interpreter for indocyanine green fluorescence angiography in colorectal surgery","author":"Mc Entee","year":"2025","journal-title":"Surg Endosc"},{"key":"10.1016\/j.ejso.2026.111911_bib17","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0317771","article-title":"A novel flexible near-infrared endoscopic device that enables real-time artificial intelligence fluorescence tissue characterization","volume":"20","author":"Gallagher","year":"2025","journal-title":"PLoS One"},{"key":"10.1016\/j.ejso.2026.111911_bib18","doi-asserted-by":"crossref","first-page":"3511","DOI":"10.1007\/s00464-021-08673-x","article-title":"Quantitative evaluation of colon perfusion after high versus low ligation in rectal surgery by indocyanine green: a pilot study","volume":"36","author":"Han","year":"2022","journal-title":"Surg Endosc"},{"key":"10.1016\/j.ejso.2026.111911_bib19","doi-asserted-by":"crossref","first-page":"3602","DOI":"10.1007\/s00464-022-09819-1","article-title":"Anastomotic perfusion assessment with indocyanine green in robot-assisted low-anterior resection, a multicenter study of interobserver variation","volume":"37","author":"Larsen","year":"2023","journal-title":"Surg Endosc"},{"key":"10.1016\/j.ejso.2026.111911_bib20","doi-asserted-by":"crossref","DOI":"10.3390\/cancers14143508","article-title":"Machine learning models and multiparametric magnetic resonance imaging for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer","volume":"14","author":"Herrero Vicent","year":"2022","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.ejso.2026.111911_bib21","doi-asserted-by":"crossref","DOI":"10.3390\/cancers15051460","article-title":"Dynamic NIR fluorescence imaging and machine learning framework for stratifying high vs. low Notch-Dll4 expressing host microenvironment in triple-negative breast cancer","volume":"15","author":"Shafiee","year":"2023","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.ejso.2026.111911_bib22","doi-asserted-by":"crossref","first-page":"1715","DOI":"10.1007\/s13304-024-01883-7","article-title":"Intraoperative left-sided colorectal anastomotic testing in clinical practice: a multi-treatment machine-learning analysis of the iCral3 prospective cohort","volume":"76","author":"Catarci","year":"2024","journal-title":"Updates Surg"},{"key":"10.1016\/j.ejso.2026.111911_bib23","doi-asserted-by":"crossref","DOI":"10.3390\/cancers17050761","article-title":"Advances in surgery and sustainability: the use of AI systems and reusable devices in laparoscopic colorectal surgery","volume":"17","author":"Iwai","year":"2025","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.ejso.2026.111911_bib24","doi-asserted-by":"crossref","DOI":"10.3390\/cancers11060800","article-title":"Machine-learning-based prediction of treatment outcomes using MR imaging-derived quantitative tumor information in patients with sinonasal squamous cell carcinomas: a preliminary study","volume":"11","author":"Fujima","year":"2019","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.ejso.2026.111911_bib25","first-page":"428","article-title":"Practical perfusion quantification in multispectral endoscopic video: using the minutes after ICG administration to assess tissue pathology","volume":"2021","author":"Epperlein","year":"2022","journal-title":"AMIA Ann Symposium Proc"},{"key":"10.1016\/j.ejso.2026.111911_bib26","doi-asserted-by":"crossref","DOI":"10.1007\/s10916-025-02140-z","article-title":"Prediction of the risk of adverse clinical outcomes with machine learning techniques in patients with noncommunicable diseases","volume":"49","author":"Hern\u00e1ndez-Arango","year":"2025","journal-title":"J Med Syst"},{"key":"10.1016\/j.ejso.2026.111911_bib27","doi-asserted-by":"crossref","DOI":"10.3390\/cancers13236013","article-title":"Machine learning models that integrate tumor texture and perfusion characteristics using low-dose breast computed tomography are promising for predicting histological biomarkers and treatment failure in breast cancer patients","volume":"13","author":"Park","year":"2021","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.ejso.2026.111911_bib28","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1007\/s13304-022-01329-y","article-title":"Indocyanine green fluorescence in robot-assisted minimally invasive esophagectomy with intrathoracic anastomosis: a prospective study","volume":"75","author":"de Groot","year":"2023","journal-title":"Updates Surg"},{"key":"10.1016\/j.ejso.2026.111911_bib29","doi-asserted-by":"crossref","DOI":"10.3390\/cancers16101900","article-title":"Prediction of lymph node metastasis in T1 colorectal cancer using artificial intelligence with Hematoxylin and eosin-stained whole-slide-images of endoscopic and surgical resection specimens","volume":"16","author":"Song","year":"2024","journal-title":"Cancers (Basel)"}],"container-title":["European Journal of Surgical Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0748798326005305?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0748798326005305?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T06:46:00Z","timestamp":1781333160000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0748798326005305"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":29,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["S0748798326005305"],"URL":"https:\/\/doi.org\/10.1016\/j.ejso.2026.111911","relation":{},"ISSN":["0748-7983"],"issn-type":[{"value":"0748-7983","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Interpretable and non-linear machine learning models for quantitative ICG perfusion assessment in colorectal cancer anastomosis: A feasibility study","name":"articletitle","label":"Article Title"},{"value":"European Journal of Surgical Oncology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejso.2026.111911","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"111911"}}