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Background <p>Accurate grading and prognostic assessment of glioma requires integrating key molecular biomarkers, including <i>IDH</i> 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, <i>IDH</i> mutation, Ki-67 labeling index (LI), and 2-year postoperative survival in glioma.</p> Methods <p>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.</p> Results <p>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 <i>IDH</i> 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, <i>IDH</i> mutation status, and Ki-67 LI. Furthermore, tumor grade, <i>IDH</i> status, and Ki-67 LI demonstrated potential predictive value for 2-year postoperative survival.</p> Conclusions <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月12日

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  • Lianxi Sun,
  • Yifeng Yang,
  • Zehong Cao,
  • Danping Yang,
  • Ningfang Du,
  • Yawen Lu,
  • Meijing Yan,
  • Jiajin Li,
  • Feng Shi,
  • Xinhua Zhou,
  • Xuhao Fang,
  • Guangwu Lin,
  • Shihong Li

摘要

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.