Trial reportBioMed research international2022
A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas.
Trial report in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- [The role of histopathology in the diagnosis and management of intracranial meningiomas. Hospital experience in Kinshasa (Democratic Republic of Congo)].Medecine tropicale et sante internationale · 2025Article
- Diagnostic performance of amide proton transfer-weighted imaging and four advanced diffusion-weighted MRI models for meningioma grading and subtyping.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Multisequence magnetic resonance imaging habitat analysis for pre-operative meningioma grade prediction.Quantitative imaging in medicine and surgery · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: We aim to develop and validate a machine learning model by enhanced MRI to determine the pathological grading of meningiomas with unsupervised clustering image analysis method, which are multihabitat to reflect the inherent heterogeneity of tumors. Materials and Methods: A total of 120 patients with meningiomas confirmed by postoperative pathology were included in the study, including 60 patients with low-grade meningiomas (WHO grade I) and 60 patients with high-grade meningiomas (WHO grade II and WHO grade III). All patients underwent complete head enhanced magnetic resonance scans before surgery or any anti-tumor treatment. Enrolled patients in the group received surgical resection and obtained postoperative pathological data. The patients in the training group (84 people) and the test group (36 people) were randomly divided into two groups according to the ratio of 7 to 3. Multi-habitat features were extracted from MRI images based on enhanced T1. Machine learning method was used to model, which was used to distinguish high-grade meningioma from low-grade meningioma. At the same time, the obtained machine learning model was calibrated and evaluated. Results: In patients with low-grade meningioma and high-grade meningioma, we found significant differences in Silhouette coefficient ( Conclusions: Multi-habitat analysis based on enhanced MRI (T1) could accurately predict the pathological grading of meningiomas. This unsupervised image-based method could reflect the direct heterogeneity between high-grade meningiomas and low-grade meningiomas, which is of great significance for patients' treatment and prevention of recurrence.
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