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Background <p>Vestibular schwannomas (VS) and meningiomas (MM) are the most common benign tumors in the cerebellopontine angle (CPA), often presenting with similar clinical symptoms. Accurate preoperative differentiation is essential for appropriate treatment planning.</p> Objective <p>To develop an MRI-based habitat radiomics model for the differential diagnosis of VS and MM in the CPA, integrating multi-parameter MRI data and clinical features.</p> Methods <p>This retrospective study included 176 patients diagnosed with CPA VS or MM from three medical centers: Guangyuan Central Hospital, Nanchong Central Hospital, and Affiliated Hospital of North Sichuan Medical College. 101 patients (68 VS, 33 MM) met the inclusion criteria. Preoperative MRI scans (T2WI and CE-T1WI) were analyzed using a sliding window method for feature extraction, followed by image preprocessing and Gaussian Mixture Model (GMM) clustering for habitat subregion segmentation.Habitat radiomics models based on T2WI, CE-T1WI, and fused T2WI &amp; CE-T1WI were constructed respectively, and a combined habitat-clinical model was developed by integrating clinical features, and the models were compared across multiple different machine learning classifiers.</p> Results <p>Multi-parameter models, combining T2WI and CE-T1WI features, outperformed single-parameter models, achieving AUC values of 0.987 in the training set and 0.974 in the test set. A combined model integrating habitat radiomics and clinical features showed superior diagnostic accuracy. Decision Curve Analysis and calibration curves confirmed its clinical utility and reliability.</p> Conclusion <p>MRI-based habitat radiomics, when integrated with clinical features, provides a robust, noninvasive method for differentiating CPA VS from MM, offering potential for personalized treatment strategies.</p>

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Development of a multi-parameter MRI-based habitat radiomics model for differential diagnosis of vestibular schwannoma and meningioma in the cerebellopontine angle: a multi-center study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Xiyuan Tang,
  • Xueyun Deng,
  • Yanfen Zhao,
  • Shuang Li,
  • Yuanchuan Wang,
  • Bo Tan,
  • Xudong Song

摘要

Background

Vestibular schwannomas (VS) and meningiomas (MM) are the most common benign tumors in the cerebellopontine angle (CPA), often presenting with similar clinical symptoms. Accurate preoperative differentiation is essential for appropriate treatment planning.

Objective

To develop an MRI-based habitat radiomics model for the differential diagnosis of VS and MM in the CPA, integrating multi-parameter MRI data and clinical features.

Methods

This retrospective study included 176 patients diagnosed with CPA VS or MM from three medical centers: Guangyuan Central Hospital, Nanchong Central Hospital, and Affiliated Hospital of North Sichuan Medical College. 101 patients (68 VS, 33 MM) met the inclusion criteria. Preoperative MRI scans (T2WI and CE-T1WI) were analyzed using a sliding window method for feature extraction, followed by image preprocessing and Gaussian Mixture Model (GMM) clustering for habitat subregion segmentation.Habitat radiomics models based on T2WI, CE-T1WI, and fused T2WI & CE-T1WI were constructed respectively, and a combined habitat-clinical model was developed by integrating clinical features, and the models were compared across multiple different machine learning classifiers.

Results

Multi-parameter models, combining T2WI and CE-T1WI features, outperformed single-parameter models, achieving AUC values of 0.987 in the training set and 0.974 in the test set. A combined model integrating habitat radiomics and clinical features showed superior diagnostic accuracy. Decision Curve Analysis and calibration curves confirmed its clinical utility and reliability.

Conclusion

MRI-based habitat radiomics, when integrated with clinical features, provides a robust, noninvasive method for differentiating CPA VS from MM, offering potential for personalized treatment strategies.