What question did this study set out to answer?

This study aims to enhance glioma grading and prognostic assessment by integrating habitat radiomics with conventional MRI features.

May 15, 2026Open Access

Application of a multimodal MRI model integrating radiomics and habitat features for predicting glioma pathology and prognosis

Puntos clave

This study aims to enhance glioma grading and prognostic assessment by integrating habitat radiomics with conventional MRI features.
Retrospective analysis of 185 patients with glioma using preoperative multimodal MRI, including T1WI, T2W-FLAIR, and T1W CE.
Automated whole-tumor segmentation and extraction of 2,264 radiomics features and 61 habitat features using machine learning algorithms.
Model performance evaluated through AUC metrics, accuracy, sensitivity, and specificity, along with five-fold cross-validation.
Habitat model showed superior sensitivity in detecting tumor heterogeneity across prediction tasks.
Integrated model achieved AUC of 0.916 for glioma grading, 0.877 for IDH mutation status, 0.859 for Ki-67 LI, and 0.906 for 2-year survival prediction.
Age correlated strongly with tumor grade, IDH mutation status, and Ki-67 LI, indicating their predictive value for postoperative survival.

Resumen

BACKGROUND: Accurate grading and prognostic assessment of glioma requires integrating key molecular biomarkers, including IDH mutation status and the Ki-67 proliferation index. However, current radiomics studies often focus on single-task predictions and rely on manual tumor segmentation, which fails to capture intratumoral spatial heterogeneity. This study proposes an automated whole-tumor segmentation-based multimodal MRI approach integrating habitat radiomics to achieve noninvasive, multitask prediction of WHO grade, IDH mutation, Ki-67 labeling index (LI), and 2-year postoperative survival in glioma. METHODS: This retrospective study enrolled 185 patients with pathologically confirmed glioma. Preoperative multimodal MRI - including T1-weighted imaging (T1WI), T2-weighted fluid-attenuated inversion recovery (T2W-FLAIR), and T1-weighted contrast-enhanced imaging (T1W CE) - was acquired for analysis. Using the uAI Research Portal platform, we performed automated whole-tumor segmentation and subsequent feature extraction, deriving 2,264 radiomics features and 61 habitat-based features. Predictive models were developed using multiple machine learning algorithms, and feature selection was rigorously performed within the training folds of a five-fold cross-validation to prevent overfitting. Model performance was evaluated using AUC, accuracy, sensitivity, and specificity, with statistical comparisons conducted performed DeLong's test. RESULTS: The habitat model exhibited superior sensitivity in capturing tumor heterogeneity across all four prediction tasks. Building on this, the integrated model combining habitat and conventional radiomics features, achieved the highest overall predictive performance, with AUCs of 0.916 (95% CIs: 0.858-0.975) for glioma grading, 0.877 (95% CIs: 0.828-0.926) for IDH mutation status, 0.859 (95% CIs: 0.788-0.930) for Ki-67 LI, and 0.906 (95% CIs: 0.837-0.974) for 2-year survival prediction, consistently outperforming single-modality models. SHAP interpretability analysis revealed that patient age exhibited strong correlation with tumor grade, IDH mutation status, and Ki-67 LI. Furthermore, tumor grade, IDH status, and Ki-67 LI demonstrated potential predictive value for 2-year postoperative survival. CONCLUSIONS: The automated habitat radiomics framework effectively quantified intratumoral spatial heterogeneity in glioma. When combined with conventional radiomics, it significantly enhanced accuracy in predicting key molecular and clinical endpoints.

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