ArticleNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2026
MRI-based clinical radiomics nomogram for the prognostic prediction of adult diffuse low-grade gliomas.
Article in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 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
backgroundAdult diffuse low-grade glioma (DLGG) is a heterogeneous tumor, making accurate prognostic prediction challenging. This study aimed to develop and validate a clinical-radiomics model for predicting progression-free survival (PFS) in DLGG patients.
methodsPatients from The Cancer Genome Atlas Low-Grade Glioma (TCGA-LGG) formed the training cohort. Radiomic features were extracted from tumor regions on preoperative MRI. A radiomics model was constructed using Least Absolute Shrinkage and Selection Operator (LASSO) regression. This was integrated with clinical factors to build a combined clinical-radiomics model. The model was externally validated using an independent cohort from the First Affiliated Hospital of Chongqing Medical University (CQMU). Performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).
resultsThe radiomics-score (hazard ratio [HR]: 3.98, 95% confidence interval [CI]: 1.20–13.27, P = 0.024) and radiotherapy (HR: 0.07, 95% CI: 0.01–0.66, P = 0.021) were independent prognostic factors. The clinical-radiomics model demonstrated superior performance to the radiomics-only model. In the training set, the clinical-radiomics model achieved an area under the curve (AUC) of 0.97 compared to 0.86 for the radiomics model. This superior performance was maintained in external validation, with AUCs of 0.92 and 0.89 for the clinical-radiomics model versus 0.81 and 0.84 for the radiomics model.
conclusionsThe clinical-radiomics model demonstrated superior performance over the standalone radiomics model in predicting PFS in DLGGs, thus providing valuable insights for patient management strategies.
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