ArticleOral radiology2026
Deep learning-based automated masseter muscle area on routine CT stratifies survival in oral cancer.
Article in Oral radiology, 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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6 authors.
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Abstract
objectivesTo develop and validate a deep learning model for automated masseter muscle segmentation on routine head and neck CT and to evaluate whether the derived masseter muscle area is associated with overall survival in oral cancer. MATERIALS AND
methodsA U-Net-based model was trained using a model-development cohort (n = 348) with preoperative CT and bioelectrical impedance analysis. Masseter muscle area was measured on an axial slice at the maxillary sinus floor, and sex-specific cutoffs for low masseter muscle area were derived against sarcopenia defined by appendicular skeletal muscle index. Prognostic value of AI-derived masseter muscle area (AI-MMA) was tested in an independent cohort of primary oral cancer patients (n = 247) using Kaplan-Meier analysis and Cox proportional hazards models.
resultsSegmentation performance was high (Dice similarity coefficient, 0.92). AI-MMA correlated strongly with manual MMA in males and females (r = 0.892 and r = 0.896, respectively; both p < 0.001). Low AI-MMA was associated with poorer overall survival. In multivariable analysis, low AI-MMA remained an independent predictor of mortality (hazard ratio [HR], 2.584; 95% CI, 1.132-5.898; p = 0.024), together with stage III-IV disease (HR, 5.811; 95% CI, 2.130-15.860; p < 0.001) and low body mass index (HR, 2.572; 95% CI, 1.162-5.693; p = 0.020).
conclusionsAutomated AI-MMA from routine staging CT provides an objective prognostic biomarker in oral cancer.
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