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We aimed to develop and validate prognostic radiomics-based machine learning models in a multicenter, prospectively collected drug-eluting microsphere TACE cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed 76 patients (176 lesions) from the prospective CIREL registry trial. Radiomic features were extracted from each lesion. We tested three types of imaging markers: general radiomics, intensity-based features, and lesion volume. For each, we derived baseline and delta features, reflecting the difference in feature vector values between baseline and first follow-up. Using a center-based split, we trained genetic\/evolutionary machine learning models to predict survival and lesion-level response.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The median age of the final study population with baseline imaging was 66 years (IQR, 59\u201371), with 67.1% (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200951) of patients identifying as male. On external validation, the baseline intensity algorithm was the only significant survival-prediction model (AUC\u2009=\u20090.79, 95% CI\u2009=\u20090.57\u20130.95;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.011), outperforming baseline radiomics (AUC\u2009=\u20090.69, 95% CI\u2009=\u20090.47\u20130.86;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.100) and baseline volume (AUC\u2009=\u20090.56, 95% CI\u2009=\u20090.37\u20130.74;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.574). Radiomic prediction models stratified patients into distinct overall survival risk groups, with low-risk patients showing a median survival of 696 days versus 453 days (log-rank\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.0267). Integrating imaging features with laboratory variables improved lesion-level response assessment (AUC\u2009=\u20090.86, 95% CI\u2009=\u20090.66\u20130.99;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.006), but did not enhance OS prediction. Lesion-level response was best identified by delta radiomics (AUC\u2009=\u20090.83, 95% CI\u2009=\u20090.63\u20130.97;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.008).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Radiomics-based machine learning models could predict overall survival in patients treated with irinotecan-TACE, offering a potential tool for patient selection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Can radiomics and machine learning predict outcomes in patients with colorectal liver metastases treated with irinotecan-TACE, aiding in patient stratification and selection?<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Baseline intensity features predicted overall survival (AUC\u2009=\u20090.79), while delta radiomics identified lesion response (AUC\u2009=\u20090.83) in a multicenter cohort.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>These models can help identify patients likely to benefit from irinotecan-TACE and lesions most responsive to treatment. Further development would enable personalized therapy that may improve survival and reduce unnecessary interventions in non-responders.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-026-12573-w","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T11:56:40Z","timestamp":1777463800000},"page":"6981-6996","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Radiomics-based outcome prediction for irinotecan-TACE in colorectal liver metastases: advanced analysis from the prospective CIREL trial"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2617-8128","authenticated-orcid":false,"given":"Zuhir","family":"Bodalal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco Javier","family":"Mendoza Ferrad\u00e1s","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga","family":"Maxouri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberto","family":"Iezzi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aleksandar","family":"Gjoreski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stavros","family":"Spiliopoulos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zoltan","family":"Bansaghi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Belarmino","family":"Gon\u00e7alves","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bleranda","family":"Zeka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathalie","family":"Kaufmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julien","family":"Taieb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Regina","family":"Beets-Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philippe L.","family":"Pereira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fernando G\u00f3mez","family":"Mu\u00f1oz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,29]]},"reference":[{"key":"12573_CR1","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1016\/j.jcmgh.2023.08.012","volume":"16","author":"RK Patel","year":"2023","unstructured":"Patel RK, Rahman S, Schwantes IR et al (2023) Updated management of colorectal cancer liver metastases: scientific advances driving modern therapeutic innovations. 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B.Z. and N.K. are affiliated with Next Research GmbH. Z.B. is a member of the Scientific Editorial Board of\n                      European Radiology\n                      and, as such, did not participate in the selection or review processes for this article. All other co-authors have no relevant conflicts of interest to declare.","order":2,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"One of the authors (Z.B.) has significant statistical experience.","order":3,"name":"Ethics","label":"Statistics and biometry","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Written informed consent was obtained from all patients at their respective centers upon enrollment into the CIREL registry.","order":4,"name":"Ethics","label":"Informed consent","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Institutional Review Board approval was obtained (IRBd22-317).","order":5,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Some study subjects have been previously reported in PMIDs: 32974773, 39954388, and 36294499. All prior research has focused on the clinical characterization of these patients; no prior medical image analysis study has been conducted.","order":6,"name":"Ethics","label":"Study subjects or cohorts overlap","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"order":7,"name":"Ethics","label":"Methodology","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}}]}}