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Largest tumors were segmented semiautomatically using pre-procedural\n                      <jats:sup>18<\/jats:sup>\n                      F-FDG PET images. Radiomics features were extracted, clinical information were collected. Two datasets were created comprising radiomics-only and clinico-radiomic features. Datasets were divided 60:40 for training and testing. Top 5 features were selected based on feature importances. Random Forest, Extreme Gradient Boosting, Logistic Regression models were trained. Test-set area under the curves (AUCs) for predicting post-treatment target lesion local progression were calculated and compared using DeLong\u2019s test. Sensitivity, specificity, accuracy and F1 scores were calculated at the optimal cut-offs.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Seventy-four patients out of 96 patients were included. Top five selected features in the radiomics-only dataset were Coarseness, IMC1, Zone Entropy, Size-Zone Non-Uniformity, and Strength. In the clinico-radiomic dataset, AST and ALT levels were substituted among the top five features. Radiomics-only features demonstrated AUCs ranging from 0.90 (95% CI 0.71\u20131) to 0.81 (95% CI 0.51\u20131) in the test-set while clinico-radiomics dataset AUCs varied between 0.88 (95% CI 0.51\u20131) and 0.84 (0.62\u20131).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      <jats:sup>18<\/jats:sup>\n                      F-FDG PET radiomics based models can predict the local response to TARE in patients with CRCLM, in this series.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11604-026-01949-z","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T04:11:44Z","timestamp":1769573504000},"page":"1062-1070","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["18F-FDG PET radiomics model for predicting TARE response in patients with colorectal cancer liver metastases"],"prefix":"10.1007","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4008-3395","authenticated-orcid":false,"given":"Osman Melih","family":"Topcuoglu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3345-9208","authenticated-orcid":false,"given":"Onur","family":"Tuncer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muge","family":"Oral","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5620-2881","authenticated-orcid":false,"given":"Ay\u015fegul","family":"Gormez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Turkay","family":"Toklu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0912-8214","authenticated-orcid":false,"given":"Nalan Alan","family":"Selcuk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"1949_CR1","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, Hosny A, Aucoin N, Narayan V, et al. 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