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<p>Brain invasion is an independent diagnostic criterion for WHO grade 2 meningiomas, and preoperative prediction of brain invasion in meningiomas is crucial for guiding treatment decisions. Therefore, we constructed a radiomics model that integrated structural and diffusion-weighted images to predict brain invasion of meningiomas. Seven hundred and twenty-three consecutive patients with pathologically confirmed meningiomas between 2013 and 2022 were retrospectively studied. Radiomics features of the brain-to-tumor interface region were extracted from structural MRI and DWI-derived apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator (LASSO) method was utilized to select radiomics features. A linear predictor of brain invasion was constructed using a logistic regression classifier. The model’s performance was evaluated using receiver operating characteristic (ROC) curve analysis. Additionally, decision curve analysis (DCA) was performed to evaluate the clinical utility of the established models. A nomogram was developed for a combined model that incorporates clinical features, along with radiomics scores derived from structural images and ADC maps. DeLong test and integrated discrimination improvement (IDI) were used to compare the diagnostic efficiency of different models. Six radiomics features from structural MRI, six radiomics features from ADC, the volume of peritumoral edema, and gender were selected to construct the combined model. This model achieved the highest AUC and sensitivity for predicting brain invasion in both the training (AUC = 0.897, 95%CI: 0.857 to 0.936, sensitivity = 0.911) and test sets (AUC = 0.871, 95%CI: 0.806 to 0.936, sensitivity = 0.895). It outperformed the structural model (AUC = 0.691) and the structural and clinical model (AUC = 0.812). The IDI demonstrated a significant improvement in predictive value when ADC radiomic features were added to the combined model. The incorporation of ADC radiomics into the MRI radiomic model improved the diagnostic performance for identifying brain invasion in meningiomas.</p>

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Integrating diffusion-weighted MRI radiomics features to predict brain invasion of meningiomas

  • 期刊论文

  • 发表日期：

    2025年12月8日

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  • Zongmeng Wang,
  • Lihong Chen,
  • Ye Li,
  • Dingfu Wei,
  • Yizhu Chen,
  • Xiaodan Chen,
  • Sihui Liu,
  • Yichao Zhang,
  • Tianjin Zhong,
  • Peirong Jiang,
  • Haixia Li,
  • Yunjing Xue,
  • Lin Lin

摘要

Brain invasion is an independent diagnostic criterion for WHO grade 2 meningiomas, and preoperative prediction of brain invasion in meningiomas is crucial for guiding treatment decisions. Therefore, we constructed a radiomics model that integrated structural and diffusion-weighted images to predict brain invasion of meningiomas. Seven hundred and twenty-three consecutive patients with pathologically confirmed meningiomas between 2013 and 2022 were retrospectively studied. Radiomics features of the brain-to-tumor interface region were extracted from structural MRI and DWI-derived apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator (LASSO) method was utilized to select radiomics features. A linear predictor of brain invasion was constructed using a logistic regression classifier. The model’s performance was evaluated using receiver operating characteristic (ROC) curve analysis. Additionally, decision curve analysis (DCA) was performed to evaluate the clinical utility of the established models. A nomogram was developed for a combined model that incorporates clinical features, along with radiomics scores derived from structural images and ADC maps. DeLong test and integrated discrimination improvement (IDI) were used to compare the diagnostic efficiency of different models. Six radiomics features from structural MRI, six radiomics features from ADC, the volume of peritumoral edema, and gender were selected to construct the combined model. This model achieved the highest AUC and sensitivity for predicting brain invasion in both the training (AUC = 0.897, 95%CI: 0.857 to 0.936, sensitivity = 0.911) and test sets (AUC = 0.871, 95%CI: 0.806 to 0.936, sensitivity = 0.895). It outperformed the structural model (AUC = 0.691) and the structural and clinical model (AUC = 0.812). The IDI demonstrated a significant improvement in predictive value when ADC radiomic features were added to the combined model. The incorporation of ADC radiomics into the MRI radiomic model improved the diagnostic performance for identifying brain invasion in meningiomas.