Predicting telomerase reverse transcriptase promoter mutation status in glioblastoma by whole-tumor multi-sequence...

This study aimed to determine the feasibility of preoperative multi-sequence magnetic resonance texture analysis (MRTA) for predicting TERT promoter mutation status in IDH-wildtype glioblastoma (IDHwt GB).

The clinical and imaging data of 111 patients with IDHwt GB at our hospital between November 2018 and June 2023 were retrospectively analyzed as the training set, and those of 23 patients with IDHwt GB between July 2023 and November 2023 were interpreted as the validation set. We used molecular sequencing results to classify the training set into TERT promoter mutation and wildtype groups. Textural features of the whole-tumor volume were extracted, including T2-weighted imaging (T2WI), T2-fluid-attenuated inversion recovery, apparent diffusion coefficient (ADC) map, and contrast-enhanced T1-weighted imaging (CE-T1). All textural features were obtained using open-source pyradiomics. After feature selection, logistic regression was used to build prediction models, and a nomogram was generated. Finally, the model was validated using validation cohort.

The CE-T1_Model (AUC 0.704) had a better predictive ability than the T2_Model (AUC 0.684) and ADC_Model (AUC 0.624). The MRI_Combined_Model (CE-T1, T2, and ADC texture features) (AUC 0.780) had a better predictive ability than the Clinical_Model (AUC 0.758). The Combined_Model (CE-T1, T2, ADC texture features, and clinical features) had the best predictive performance (AUC 0.871), with a sensitivity, specificity, and accuracy of 82.60 %, 83.30 %, and 80.18 %, respectively. The AUC, sensitivity, specificity, and accuracy in the validation cohort were 0.775, 86.70 %, 75.00 %, and 69.57 %, respectively.

Whole-tumor multi-sequence MRTA can be used as non-invasive quantitative parameters to assist in the preoperative clinical prediction of TERT promoter mutation status in IDHwt GB.