A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas.
Xinghao WangJia LiJing SunWenjuan LiuLinkun CaiPengfei ZhaoZhenghan YangHan LvZhen-Chang WangPublished in: BioMed research international (2022)
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.
Keyphrases
- low grade
- high grade
- machine learning
- end stage renal disease
- single cell
- chronic kidney disease
- ejection fraction
- newly diagnosed
- deep learning
- climate change
- magnetic resonance imaging
- artificial intelligence
- prognostic factors
- big data
- patient reported outcomes
- contrast enhanced
- combination therapy
- smoking cessation