Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma

Purpose: To evaluate the performance of a machine learning method based on texture features in multi-parametric magnetic resonance imaging (MRI) to differentiate a glioblastoma multiforme (GBM) from a primary cerebral nervous system lymphoma (PCNSL). Materials and methods: We included 70 patients who underwent contrast enhanced brain MRI at 3 T with brain tumors diagnosed as GBM (n= 45) and PCNSL (n= 25) in this retrospective study. Twelve histograms and texture parameters were assessed on T2-weighted images (T2WIs), apparent diffusion coefficient maps, relative cerebral blood volume (rCBV) map, and contrast-enhanced T1-weighted images (CE-T1WIs). A prediction model was developed using a machine learning method (univariate logistic regression and multivariate eXtreme gradient boosting-XGBoost) and the area under the receiver operating characteristic curve of this model was calculated via 10-fold cross validation. In addition, the performance of the machine learning method was compared with the judgments of two board certified radiologists. Results: With the univariate logistic regression model, the standard deviation of rCBV offered the highest AUC (0.86), followed by mean value of rCBV (0.83), skewness of CE-T1WI (0.78), mean value of CET1 (0.78), and max value of rCBV (0.77). The AUC of the XGBoost was significantly higher than the two radiologists (0.98 vs. 0.84; p < 0.01 and 0.98 vs. 0.79; p < 0.01, respectively). Conclusion: The performance of machine learning based on histogram and texture features in multi-parametric MRI was superior to that of conventional cut-off method and the board certified radiologists to differentiate a GBM from a PCNSL.