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Background <p>Meningioma consistency critically impacts surgical planning, as soft tumors are easier to resect than hard tumors. Current assessments of tumor consistency using MRI are subjective and lack quantitative accuracy. Integrating deep learning and radiomics could enhance the predictive accuracy of meningioma consistency.</p> Methods <p>A retrospective study analyzed 204 meningioma patients from two centers: the Second Affiliated Hospital of Guangzhou Medical University and the Southern Theater Command Hospital PLA. Three models—a radiomics model (Rad_Model), a deep learning model (DL_Model), and a combined model (DLR_Model)—were developed. Model performance was evaluated using AUC, accuracy, sensitivity, specificity, and precision.</p> Results <p>The DLR_Model outperformed other models across all cohorts. In the training set, it achieved AUC 0.957, accuracy of 0.908, and precision of 0.965. In the external test cohort, it maintained superior performance with an AUC of 0.854, accuracy of 0.778, and precision of 0.893, surpassing both the Rad_Model (AUC = 0.768) and DL_Model (AUC = 0.720). Combining radiomics and deep learning features improved predictive performance and robustness.</p> Conclusion <p>Our study introduced and evaluated a deep learning radiomics model (DLR-Model) to accurately predict the consistency of meningiomas, which has the potential to improve preoperative assessments and surgical planning.</p>

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Multimodal deep learning-based radiomics for meningioma consistency prediction: integrating T1 and T2 MRI in a multi-center study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Huanjie Lin,
  • Yubiao Yue,
  • Li Xie,
  • Bingbing Chen,
  • Weifeng Li,
  • Fan Yang,
  • Qinrong Zhang,
  • Huai Chen

摘要

Background

Meningioma consistency critically impacts surgical planning, as soft tumors are easier to resect than hard tumors. Current assessments of tumor consistency using MRI are subjective and lack quantitative accuracy. Integrating deep learning and radiomics could enhance the predictive accuracy of meningioma consistency.

Methods

A retrospective study analyzed 204 meningioma patients from two centers: the Second Affiliated Hospital of Guangzhou Medical University and the Southern Theater Command Hospital PLA. Three models—a radiomics model (Rad_Model), a deep learning model (DL_Model), and a combined model (DLR_Model)—were developed. Model performance was evaluated using AUC, accuracy, sensitivity, specificity, and precision.

Results

The DLR_Model outperformed other models across all cohorts. In the training set, it achieved AUC 0.957, accuracy of 0.908, and precision of 0.965. In the external test cohort, it maintained superior performance with an AUC of 0.854, accuracy of 0.778, and precision of 0.893, surpassing both the Rad_Model (AUC = 0.768) and DL_Model (AUC = 0.720). Combining radiomics and deep learning features improved predictive performance and robustness.

Conclusion

Our study introduced and evaluated a deep learning radiomics model (DLR-Model) to accurately predict the consistency of meningiomas, which has the potential to improve preoperative assessments and surgical planning.