Nomogram of magnetic resonance imaging (MRI) histogram analysis to predict telomerase reverse transcriptase promoter mutation status in glioblastoma.

Abstract

Telomerase reverse transcriptase promoter (pTERT) status is a strong biomarker to diagnose and predict the prognosis of glioblastoma (GBM). In this study, we explored the predictive value of preoperative magnetic resonance imaging (MRI) histogram analysis in the form of nomogram for evaluating pTERT mutation status in GBM. The clinical and imaging data of 181 patients with GBM at our hospital between November 2018 and April 2023 were retrospectively assessed. We used the molecular sequencing results to classify the datasets into pTERT mutations (C228T and C250T) and pTERT-wildtype groups. FireVoxel software was used to extract preoperative T1-weighted contrast-enhanced (T1C) histogram parameters of GBM patients. The T1C histogram parameters were compared between groups. Univariate and multivariate logistic regression analyses were used to construct the nomogram, and the predictive efficacy of model was evaluated using calibration and decision curves. Receiver operating characteristic curve was used to assess model performance. Patient age and percentage of unenhanced tumor area showed statistically significant differences between the pTERT mutation and pTERT-wildtype groups (P<0.001). Among the T1C histogram features, the maximum, standard deviation (SD), variance, coefficient of variation (CV), skewness, 5th, 10th, 25th, 95th and 99th percentiles were statistically significantly different between groups (P=0.000-0.040). Multivariate logistic regression analysis showed that age, percentage of unenhanced tumor area, SD and CV were independent risk factors for predicting pTERT mutation status in GBM patients. The logistic regression model based on these four features showed a better sample predictive performance, and the area under the curve (AUC) [95% confidence interval (CI)], accuracy, sensitivity, specificity were 0.842 (0.767-0.917), 0.796, 0.820, and 0.729, respectively. There were no significant differences in the T1C histogram parameters between the C228T and C250T groups (P=0.055-0.854). T1C histogram parameters can be used to evaluate pTERT mutations status in GBM. A nomogram based on conventional MRI features and T1C histogram parameters is a reliable tool for the pTERT mutation status, allowing for non-invasive radiological prediction before surgery.