ArticleAbdominal radiology (New York)2026
Can radiomics outperform CT morphological features in diagnosing ovarian clear cell carcinoma? A multicenter study.
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPlatinum-resistant OCCC misdiagnosis risks ineffective chemotherapy. CT morphology is widely used for diagnosis, and our prior single-center study showed radiomics is feasible. Whether radiomics adds value beyond morphology remains unclear. This multicenter study compares CT, radiomics, and integrated models for OCCC diagnosis.
methods457 patients with epithelial ovarian cancer (training = 280, internal testing = 69, external testing = 108). Two radiologists assessed 10 CT morphological features. From CT, 1,218 radiomic features were ICC-filtered (≥ 0.8) + JMIM selection. Three logistic regression models were built: traditional (clinical + CT morphology), radiomics (selected features, output as rad-score), and integrated (traditional + rad-score). Performance was evaluated using ROC analysis, and rad-score correlation with morphological features was examined.
resultsOf 457 patients, 96 (21%) had OCCC. In internal testing set, the integrated model achieved the highest AUC (0.890) but was not significantly superior to the traditional model (0.840, p = 0.210) or the radiomics model (0.811, p = 0.051); both integrated and traditional models had 100% sensitivity versus 75.0% for radiomics. In external testing, the integrated model maintained an AUC of 0.850, compared with 0.837 for the traditional model (p = 0.645) and 0.809 for the radiomics model (p = 0.102); sensitivity was 81.8%, 77.3%, and 81.8%, respectively. The rad-score significantly correlated with multiple CT morphological features.
conclusionsIn this multicenter head-to-head comparison, radiomics did not significantly improve AUC over traditional CT features for OCCC diagnosis. Although sensitivity differences between models were not statistically significant, the integrated model achieved numerically higher and more stable sensitivity across both cohorts, suggesting potential clinical value in reducing missed diagnoses and avoiding ineffective neoadjuvant chemotherapy. These findings warrant further validation in larger prospective cohorts.
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