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The aim of this study was to evaluate radiomics for distinguishing GISTs from other intra-abdominal tumors, and in GISTs, predict the <jats:italic>c-KIT<\/jats:italic>, <jats:italic>PDGFRA<\/jats:italic>, <jats:italic>BRAF<\/jats:italic> mutational status, and mitotic index (MI). Patients diagnosed at the Erasmus MC between 2004 and 2017, with GIST or non-GIST intra-abdominal tumors and a contrast-enhanced venous-phase CT, were retrospectively included. Tumors were segmented, from which 564 image features were extracted. Prediction models were constructed using a combination of machine learning approaches. The evaluation was performed in a 100\u2009\u00d7\u2009random-split cross-validation. Model performance was compared to that of three radiologists. One hundred twenty-five GISTs and 122 non-GISTs were included. The GIST vs. non-GIST radiomics model had a mean area under the curve (AUC) of 0.77. Three radiologists had an AUC of 0.69, 0.76, and 0.84, respectively. The radiomics model had an AUC of 0.52 for <jats:italic>c-KIT<\/jats:italic>, 0.56 for <jats:italic>c-KIT <\/jats:italic>exon 11, and 0.52 for the MI. The numbers of <jats:italic>PDGFRA<\/jats:italic>, <jats:italic>BRAF<\/jats:italic>, and other <jats:italic>c-KIT<\/jats:italic> mutations were too low for analysis. Our radiomics model was able to distinguish GISTs from non-GISTs with a performance similar to three radiologists, but less observer dependent. Therefore, it may aid in the early diagnosis of GIST, facilitating rapid referral to specialized treatment centers. 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