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Machine Learning-Based Preoperative Predicting TERT Promoter Mutation and EGFR Gene Amplification Phenotype in IDH
Yan Su1,2, Wei Guo1,2, Yuwei Pan1
1From the Department of Radiology (Y.S., W.G., Y.P., P.H., D.S.), The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, P.R. China.
Background And Purpose:
The telomerase reverse transcriptase (TERT) gene promoter mutation is a crucial factor for identifying an isocitrate dehydrogenase (IDH) wild-type glioblastoma with poor prognosis, and the epidermal growth factor receptor (EGFR) amplification may be a potential prognostic factor. The purpose of this study was to investigate the value of the tumor habitats imaging model on advanced MRI in predicting TERT promoter mutation and EGFR gene amplification phenotype of IDH wild-type glioblastoma.
Materials And Methods:
One hundred seventy-nine patients with pretreatment conventional MRI, DWI, and DSC-PWI were included. The data were divided into the training set (n=112), test set (n=29), and time-independent validation set (n=38). Based on the ADC and CBV map, the solid tumor area was split into several habitat subregions using the k-means clustering algorithm (hypovascular hypercellular area, hypervascular area, and hypovascular hypocellular area). In the training set, TERT promoter mutation and EGFR gene amplification phenotype prediction models were constructed using the random forest method. The reliability of prediction models was validated in the test and the time-independent validation sets. Receiver operating characteristic (ROC) curve analysis, calibration curve, and decision curve analysis (DCA) were used.
Results:
The area under the curve (AUC) of the training, test, and validation sets of the TERT promoter prediction model was 0.877, 0.783, and 0.796, respectively. The accuracy of the TERT promoter prediction model was 82.1%, 75.9%, and 76.3%, respectively. The AUCs of the 3 sets for the EGFR gene amplification status prediction model were 0.877, 0.784, and 0.878, respectively. The accuracy of the EGFR gene amplification status prediction model was 79.5%, 75.9%, and 89.5%, respectively. Moreover, the prediction probability of these models was in good agreement with the actual result.
Conclusions:
The tumor habitat imaging model based on advanced MRI was useful for accurately predicting TERT promoter mutation and EGFR amplification status in IDH wild-type glioblastoma.
