• Linlin Meng, Ran Zhang, Liangguo Fa, Lulu Zhang, Linlin Wang, and Guangrui Shao.
• Department of Radiology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
• Medicine (Baltimore). 2022 Sep 16; 101 (37): e30189.

Material And MethodsA 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.ResultsFifteen 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.ConclusionsThese 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.Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.

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