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Published on: January 9, 2019
A quantitative model based on clinically relevant MRI features differentiates lower grade gliomas and glioblastoma
Hang Cao1, E Zeynep Erson-Omay2, Xuejun Li1
1Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, 410008, China.
Objectives:
To establish a quantitative MR model that uses clinically relevant features of tumor location and tumor volume to differentiate lower grade glioma (LRGG, grades II and III) and glioblastoma (GBM, grade IV).
Methods:
We extracted tumor location and tumor volume (enhancing tumor, non-enhancing tumor, peritumor edema) features from 229 The Cancer Genome Atlas (TCGA)-LGG and TCGA-GBM cases. Through two sampling strategies, i.e., institution-based sampling and repeat random sampling (10 times, 70% training set vs 30% validation set), LASSO (least absolute shrinkage and selection operator) regression and nine-machine learning method-based models were established and evaluated.
Results:
Principal component analysis of 229 TCGA-LGG and TCGA-GBM cases suggested that the LRGG and GBM cases could be differentiated by extracted features. For nine machine learning methods, stack modeling and support vector machine achieved the highest performance (institution-based sampling validation set, AUC > 0.900, classifier accuracy > 0.790; repeat random sampling, average validation set AUC > 0.930, classifier accuracy > 0.850). For the LASSO method, regression model based on tumor frontal lobe percentage and enhancing and non-enhancing tumor volume achieved the highest performance (institution-based sampling validation set, AUC 0.909, classifier accuracy 0.830). The formula for the best performance of the LASSO model was established.
Conclusions:
Computer-generated, clinically meaningful MRI features of tumor location and component volumes resulted in models with high performance (validation set AUC > 0.900, classifier accuracy > 0.790) to differentiate lower grade glioma and glioblastoma.
Key Points:
• Lower grade glioma and glioblastoma have significant different location and component volume distributions. • We built machine learning prediction models that could help accurately differentiate lower grade gliomas and GBM cases. We introduced a fast evaluation model for possible clinical differentiation and further analysis.
