ArticleJournal of Cancer2026
MRI-Based Habitat Radiomics for Differentiating Early-Stage Endometrial Carcinoma from Submucous Leiomyoma: A Multicenter Validation Study.
Article in Journal of Cancer, 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
Objective: This study aimed to investigate the utility of magnetic resonance imaging (MRI)-based habitat radiomics for preoperatively distinguishing early-stage endometrial carcinoma (EC) from submucous leiomyoma (SML). Materials and Methods: A retrospective study was conducted on uterine lesions patients who underwent MRI from three hospitals. The k-means clustering algorithm was applied to segment the MRI into distinct habitats based on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) maps. Radiomic features were extracted from whole-tumor and these habitats and selected by the Pearson correlation coefficient and least absolute shrinkage and selection operator (LASSO) regression. A logistic regression (LR) model was constructed by these radiomics in the training set. Results: A total of 231 eligible patients were incorporated, 97 EC and 134 SML confirmed by histopathology. In the training cohort, the AUCs of the models based on features from the whole-tumor, habitat_1, habitat_2, and habitat_3 were 0.826, 0.787, 0.770, and 0.907, respectively, while in the test and external validation cohorts, the corresponding AUCs were 0.774/0.751, 0.486/0.608, 0.663/0.514, and 0.858/0.881. Compared with whole-tumor model, habitat_3 model demonstrated incrementally improved predictive performance in the external validation cohort (0.881 [95% CI: 0.799-0.934]). Conclusion: MRI-based habitat radiomics offers incremental improvement for preoperative differentiation between early-stage EC and SML.
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