Preoperative magnetic resonance imaging-based nomogram for predicting overall survival in patients with glioma: a multicenter study.

BACKGROUND: Glioma prognostication relies on histopathological and molecular assessment requiring tissue sampling. Accessible tools for noninvasive preoperative prognostication remain limited. METHODS: This multicenter retrospective study included 1992 patients (1207 in the training dataset [TD] and 785 in the external validation dataset [EVD]). A preoperative clinicoradiological nomogram (PCR nomogram) was developed using Cox regression with age and visual magnetic resonance imaging features. A histopathology-based model incorporated tumor type and grade according to the 2021 World Health Organization (WHO) classification, and a clinicoradiological-histopathological (CRH) model combined these histopathological variables with the PCR nomogram variables. Performance was evaluated using the C-index and time-dependent area under the receiver operating characteristic curve (tdAUROC). Maximally selected rank statistics were used to define a risk-score cutoff for high- and low-risk groups. Overall survival (OS) was compared using Kaplan-Meier curves and the log-rank test. RESULTS: In the EVD, the PCR nomogram achieved a C-index of 0.736, compared with 0.701 for the histopathology-based model (P < 0.001) and 0.759 for the CRH model (P < 0.001). The PCR nomogram's 1-, 3-, and 5-year tdAUROCs were 0.764, 0.850, and 0.900, compared with 0.725, 0.822, and 0.918 for the histopathology-based model and 0.796, 0.866, and 0.930 for the CRH model, respectively. High-risk patients showed shorter OS than low-risk patients (hazard ratio, 5.485; 95% confidence interval, 4.261-7.062; P < 0.0001). CONCLUSIONS: The PCR nomogram may provide a readily accessible and interpretable tool for noninvasive preoperative OS prediction and prognostic stratification in adults with diffuse glioma.