Radiomics Nomogram Building From Multiparametric MRI to Predict Grade in Patients With Glioma: A Cohort Study

Background Accurate classification of gliomas is crucial for prescribing therapy and assessing the prognosis of patients. Purpose To develop a radiomics nomogram using multiparametric MRI for predicting glioma grading. Study Type Retrospective. Population This study involved 85 patients (training cohort: n = 56; validation cohort: n = 29) with pathologically confirmed gliomas. Field Strength/Sequence 1.5T MR, containing contrast‐enhanced T1‐weighted (CET1WI), axial T2‐weighted (T2WI), and apparent diffusion coefficient (ADC) sequences. Assessment A region of interest of the tumor was delineated. A total of 652 radiomics features were extracted and were reduced using least absolute shrinkage and selection operator regression. Statistical Testing Radiomic signature, participant's age, and gender were analyzed as potential predictors to perform logistic regression analysis and develop a prediction model of glioma grading, and a radiomics nomogram was used to represent this model. The performance of the nomogram was assessed in terms of discrimination, calibration, and clinical value in glioma grading. Results The radiomic signature was significantly associated with glioma grade (P