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. 2025 Apr;201(4):398-410.
doi: 10.1007/s00066-024-02283-x. Epub 2024 Sep 9.

Integrated MRI radiomics, tumor microenvironment, and clinical risk factors for improving survival prediction in patients with glioblastomas

Qing Zhou  1   2   3 , Xiaoai Ke  1   2   3 , Jiangwei Man  4   5 , Jian Jiang  1   4   2   3 , Jialiang Ren  6 , Caiqiang Xue  1   4   2   3 , Bin Zhang  1   4   2   3 , Peng Zhang  4   7 , Jun Zhao  1   2   3 , Junlin Zhou  8   9   10   11   12
Affiliations

Integrated MRI radiomics, tumor microenvironment, and clinical risk factors for improving survival prediction in patients with glioblastomas

Qing Zhou et al. Strahlenther Onkol. 2025 Apr.

Abstract

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.

Keywords: Glioblastoma; Prediction model; Prognosis; Radiomics; Tumor microenvironment.

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Conflict of interest statement

Declarations. Conflict of interest: Q. Zhou, X. Ke, J. Man, J. Jiang, J. Ren, C. Xue, B. Zhang, P. Zhang, J. Zhao, and J. Zhou declare that they have no competing interests. Ethical standards: Ethical approval (approval number: 2020A-070) was obtained from our institutional ethics review board; the requirement for informed consent was waived owing to the retrospective nature of the study. The authors declare that this article does not contain any personal information that could lead to identification of the patients.

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