Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging.
Yae Won ParkJongmin OhSeng Chan YouKyunghwa HanSung Soo AhnYoon Seong ChoiJong Hee ChangSe Hoon KimSeung-Koo LeePublished in: European radiology (2018)
• Preoperative, noninvasive differentiation of the meningioma grade is important because it influences the treatment strategy. • Radiomics feature-based machine learning classifiers of T1C images, ADC, and FA maps are useful for differentiating meningioma grades. • In benign meningiomas, there were significant differences in the various texture parameters between fibroblastic and non-fibroblastic meningioma subtypes.
Keyphrases
- machine learning
- contrast enhanced
- deep learning
- diffusion weighted
- optic nerve
- artificial intelligence
- lymph node metastasis
- big data
- diffusion weighted imaging
- magnetic resonance imaging
- magnetic resonance
- optical coherence tomography
- convolutional neural network
- patients undergoing
- computed tomography
- combination therapy
- replacement therapy