PURPOSE: To construct a comprehensive model for predicting the prognosis of patients with glioblastoma (GB) using a radiomics method and integrating clinical risk factors, tumor microenvironment (TME), and imaging characteristics. MATERIALS AND METHODS: In this retrospective study, we included 148 patients (85 males and 63 females
median age 53 years) with isocitrate dehydrogenase-wildtype GB between January 2016 and April 2022. Patients were randomly divided into the training (n = 104) and test (n = 44) sets. The best feature combination related to GB overall survival (OS) was selected using LASSO Cox regression analyses. Clinical, radiomics, clinical-radiomics, clinical-TME, and clinical-radiomics-TME models were established. The models' concordance index (C-index) was evaluated. The survival curve was drawn using the Kaplan-Meier method, and the prognostic stratification ability of the model was tested. RESULTS: LASSO Cox analyses were used to screen the factors related to OS in patients with GB, including MGMT (hazard ratio [HR] = 0.642
95% CI 0.414-0.997
P = 0.046), TERT (HR = 1.755
95% CI 1.095-2.813
P = 0.019), peritumoral edema (HR = 1.013
95% CI 0.999-1.027
P = 0.049), tumor purity (TP
HR = 0.982
95% CI 0.964-1.000
P = 0.054), CD163 + tumor-associated macrophages (TAMs
HR = 1.049
95% CI 1.021-1.078
P <
0.001), CD68 + TAMs (HR = 1.055
95% CI 1.018-1.093
P = 0.004), and the six radiomics features. The clinical-radiomics-TME model had the best survival prediction ability, the C‑index was 0.768 (0.717-0.819). The AUC of 1‑, 2‑, and 3‑year OS prediction in the test set was 0.842, 0.844, and 0.795, respectively. CONCLUSION: The clinical-radiomics-TME model is the most effective for predicting the survival of patients with GB. Radiomics features, TP, and TAMs play important roles in the prognostic model.