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.
7 affiliations
- 1Department of Neurosurgery, Guangyuan Central Hospital, Guangyuan, Sichuan, 628000, China.
- 2Department of Neurosurgery, Dazhou First People's Hospital (Sichuan Provincial People's Hospital Chuandong Hospital), Dazhou, Sichuan, 635000, China.
- 3Department of Radiology, Xi'an Qinhuang Hospital, Xi'an, Shanxi, 710600, China.
- 4Department of Pain Medicine, Guang'an People's Hospital, Guang'an, Sichuan, 638000, China.
- 5Department of Neurosurgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, 637000, China.
- 6Department of Neurosurgery, Guangyuan Central Hospital, Guangyuan, Sichuan, 628000, China. [email protected].
- 7Department of Neurosurgery, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, 637000, China. [email protected].
Abstract
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.
Related
- Spontaneous resolution of vestibular schwannoma: A singular phenomenon.
- Divergent Schwann cell-immune cell trajectories across vestibular schwannoma subtypes mirror nerve injury-repair programs.
- Bilateral Vision Loss due to Fulminant Hypertensive Optic Neuropathy Mimicking Cortical Blindness in a Patient with Co-existing Vestibular Schwannoma: A Case Report
- Intraoperative detection of a false-negative facial motor evoked potential by direct facial nerve stimulation during vestibular schwannoma surgery: A case report.
- Longitudinal tumor response dynamics after staged gamma knife radiosurgery combined with bevacizumab in sporadic vestibular schwannoma
BibTeX
@article{tang2026development,
title = {{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.}},
author = {Xiyuan Tang and Xueyun Deng and Yanfen Zhao and Shuang Li and Yuanchuan Wang and Bo Tan and Xudong Song},
journal = {BMC cancer},
year = {2026},
month = jul,
doi = {10.1186/s12885-026-16612-z},
url = {https://europepmc.org/article/MED/42928133},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - 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.
AU - Xiyuan Tang
AU - Xueyun Deng
AU - Yanfen Zhao
AU - Shuang Li
AU - Yuanchuan Wang
AU - Bo Tan
AU - Xudong Song
PY - 2026
DA - 2026/07/29
JO - BMC cancer
DO - 10.1186/s12885-026-16612-z
UR - https://europepmc.org/article/MED/42928133
AB - 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.
SN - 1471-2407
ER -