ATRX status in patients with gliomas: Radiomics analysis

Abstract

The aim of this study was to develop a noninvasive radiomics analysis model based on preoperative multiparameter MRI to predict the status of the biomarker alpha thalassemia/mental retardation X-linked syndrome (ATRX) in glioma noninvasively. Material and methods: A cohort of 123 patients diagnosed with gliomas (World Health Organization grades II–IV) who underwent surgery and was treated at our center between January 2016 and July 2020, was enrolled in this retrospective study. Radiomics features were extracted from MR T1WI, T2WI, T2FLAIR, CE-T1WI, and ADC images. Patients were randomly split into training and validation sets at a ratio of 4:1. A radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) to train the SVM model using the training set. The prediction accuracy and area under curve and other evaluation indexes were used to explore the performance of the model established in this study for predicting the ATRX mutation state. Results: Fifteen radiomic features were selected to generate an ATRX-associated radiomic signature using the LASSO logistic regression model. The area under curve for ATRX mutation (ATRX(−)) on training set was 0.93 (95% confidence interval [CI]: 0.87–1.0), with the sensitivity, specificity and accuracy being 0.91, 0.82 and 0.88, while on the validation set were 0.84 (95% CI: 0.63–0.91), with the sensitivity, specificity and accuracy of 0.73, 0.86, and 0.79, respectively. Conclusions: These results indicate that radiomic features derived from preoperative MRI facilitat efficient prediction of ATRX status in gliomas, thus providing a novel evaluation method for noninvasive imaging biomarkers.

Comparison of MRI Sequences to Predict ATRX Status Using Radiomics-Based Machine Learning

“…The prediction of ATRX mutation status in glioma was also proved by Leng et al [ 22 ] 2022 using a Lasso regression algorithm model and fifteen radiomics features on different MRI sequences, reaching an AUC on the training set of 0.93 and 0.84 on the validation set.…”

Changes in radiomic and radiologic features in meningiomas after radiation therapy

“…Kurtosis, a radiomic feature commonly seen in T1WI and CE-T1WI, is a measure of the peakedness of the distribution of values in meningioma ROI, meaning that higher values do not converge to the mean but spread toward the tail of a normal distribution [25]. Radiomic features reflect the microscopic heterogeneity within tumors associated with radiation therapy.…”

Changes in Radiomic and Radiologic Features in Meningiomas after Radiation Therapy

“…Kurtosis, a radiomic feature commonly seen in T1WI and CE-T1WI, is a measure of the peakedness of the distribution of values in meningioma ROI, meaning that higher values do not converge to the mean but spread toward the tail of a normal distribution [22]. Radiomic features re ect the microscopic heterogeneity within tumors associated with radiation therapy.…”