id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-330284-r3l6hdrk	Gao, Min	Machine Learning-Based Radiomics Predicting Tumor Grades and Expression of Multiple Pathologic Biomarkers in Gliomas	2020-09-11		.txt	text/plain	4941	273	51	The present study aimed to use conventional machine learning algorithms to predict the tumor grades and pathologic biomarkers on magnetic resonance imaging (MRI) data. The radiomic features were extracted from enhanced MRI images, and three frequently-used machine-learning models of LC, Support Vector Machine (SVM), and Random Forests (RF) were built for four predictive tasks: (1) glioma grades, (2) Ki67 expression level, (3) GFAP expression level, and (4) S100 expression level in gliomas. CONCLUSION: The machine-learning based radiomics approach can provide a non-invasive method for the prediction of glioma grades and expression levels of multiple pathologic biomarkers, preoperatively, with favorable predictive accuracy and stability. Three machine-learning-based models (LR, SVM, and RF) were built to perform the tasks: (1) classify the glioma grades, and (2) predict the expression levels of Ki67, S100, and GFAP. The machine-learning based radiomics approach was applied to predict glioma grades and the expression levels of pathologic biomarkers Ki67, GFAP, and S100 in low or high.	./cache/cord-330284-r3l6hdrk.txt	./txt/cord-330284-r3l6hdrk.txt