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Background <p>Brain invasion serves as an independent diagnostic criterion for the diagnosis of WHO grade 2 meningiomas. This study aimed to evaluate the value of radiomic models constructed with features extracted from different regions of the brain-to-tumor interface to predict potential brain invasion of meningiomas.</p> Methods <p>The cohort of this retrospective multicenter study included 915 consecutive patients from three centers with histopathologically confirmed meningiomas. Brain invasion status was determined separately using predefined criteria based primarily on histopathological findings, with operative records used as supplementary information. Nine radiomic models were developed using radiomic features extracted from nine different regions of interest: 3–5&#xa0;mm regions of the brain-to-tumor interface (BTI), brain-to-tumor inner interface (BTII), and brain-to-tumor outer interface (BTOI). The features were derived from T1W, T2-FLAIR, and CE-T1W images. A logistic regression classifier was trained for model development. Model discrimination was evaluated via receiver operating characteristic (ROC) analysis, while clinical utility and incremental predictive value were quantified using decision curve analysis (DCA) and the integrated discrimination improvement (IDI) index.</p> Results <p>As compared to models constructed with features from the 3–5&#xa0;mm BTII and BTI regions, the BTOI radiomic models demonstrated enhanced predictive capability for meningioma brain invasion. The area under the curve (AUC) values for the 3-, 4-, and 5-mm BTOI models were 0.845, 0.845, and 0.905 with the test cohort, and 0.818, 0.825, and 0.874 with the external validation cohort, respectively. DCA confirmed the clinical superiority of the BTOI models, which provided the highest net benefit across various threshold probabilities. Moreover, the IDI index indicated that the BTOI features offered statistically significant incremental value for invasion risk stratification as compared to the BTII and BTI models (<i>p</i> &lt; 0.05).</p> Conclusions <p>The BTOI radiomic models demonstrated superior performance in predicting brain invasion in meningiomas compared with models constructed from the BTI and BTII regions. These findings suggest that the peritumoral brain tissue adjacent to the tumor may contain particularly informative imaging characteristics related to invasion and provide imaging-based support for optimizing ROI delineation.</p>

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Can brain-to-tumour outer interface radiomics improve the efficiency of MRI in predicting the brain invasion of meningiomas?

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月29日

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  • Zongmeng Wang,
  • Dingfu Wei,
  • Weiwei Xia,
  • Ye Li,
  • Xiaofang Zhou,
  • Mingwei Sun,
  • Sihui Liu,
  • Haixia Li,
  • Yang Song,
  • Jie Wu,
  • Yuting Chen,
  • Yunjing Xue,
  • Lin Lin

摘要

Background

Brain invasion serves as an independent diagnostic criterion for the diagnosis of WHO grade 2 meningiomas. This study aimed to evaluate the value of radiomic models constructed with features extracted from different regions of the brain-to-tumor interface to predict potential brain invasion of meningiomas.

Methods

The cohort of this retrospective multicenter study included 915 consecutive patients from three centers with histopathologically confirmed meningiomas. Brain invasion status was determined separately using predefined criteria based primarily on histopathological findings, with operative records used as supplementary information. Nine radiomic models were developed using radiomic features extracted from nine different regions of interest: 3–5 mm regions of the brain-to-tumor interface (BTI), brain-to-tumor inner interface (BTII), and brain-to-tumor outer interface (BTOI). The features were derived from T1W, T2-FLAIR, and CE-T1W images. A logistic regression classifier was trained for model development. Model discrimination was evaluated via receiver operating characteristic (ROC) analysis, while clinical utility and incremental predictive value were quantified using decision curve analysis (DCA) and the integrated discrimination improvement (IDI) index.

Results

As compared to models constructed with features from the 3–5 mm BTII and BTI regions, the BTOI radiomic models demonstrated enhanced predictive capability for meningioma brain invasion. The area under the curve (AUC) values for the 3-, 4-, and 5-mm BTOI models were 0.845, 0.845, and 0.905 with the test cohort, and 0.818, 0.825, and 0.874 with the external validation cohort, respectively. DCA confirmed the clinical superiority of the BTOI models, which provided the highest net benefit across various threshold probabilities. Moreover, the IDI index indicated that the BTOI features offered statistically significant incremental value for invasion risk stratification as compared to the BTII and BTI models (p < 0.05).

Conclusions

The BTOI radiomic models demonstrated superior performance in predicting brain invasion in meningiomas compared with models constructed from the BTI and BTII regions. These findings suggest that the peritumoral brain tissue adjacent to the tumor may contain particularly informative imaging characteristics related to invasion and provide imaging-based support for optimizing ROI delineation.