Automated machine learning to predict the co‐occurrence of isocitrate dehydrogenase mutations and O 6 ‐methylguanine‐DNA methyltransferase promoter methylation in patients with gliomas

Combining isocitrate dehydrogenase mutation (IDHmut) with O 6 ‐ methylguanine ‐DNA methyltransferase promoter methylation (MGMTmet) has been identified as a critical prognostic molecular marker for gliomas. The aim of this study was to determine the ability of glioma radiomics features from magnetic resonance imaging (MRI) to predict the co‐occurrence of IDHmut and MGMTmet by applying the tree‐based pipeline optimization tool (TPOT), an automated machine learning (autoML) approach. This was a retrospective study, in which 162 patients with gliomas were evaluated, including 58 patients with co‐occurrence of IDHmut and MGMTmet and 104 patients with other status comprising: IDH wildtype and MGMT unmethylated ( n  = 67), IDH wildtype and MGMTmet ( n  = 36), and IDHmut and MGMT unmethylated ( n  = 1). Three‐dimensional (3D) T1‐weighted images, gadolinium‐enhanced 3D T1‐weighted images (Gd‐3DT1WI), T2‐weighted images, and fluid‐attenuated inversion recovery (FLAIR) images acquired at 3.0 T were used. Radiomics features were extracted from FLAIR and Gd‐3DT1WI images. The TPOT was employed to generate the best machine learning pipeline, which contains both feature selector and classifier, based on input feature sets. A 4‐fold cross‐validation was used to evaluate the performance of automatically generated models. For each iteration, the training set included 121 subjects, while the test set included 41 subjects. Student's t ‐test or a chi‐square test was applied on different clinical characteristics between two groups. Sensitivity, specificity, accuracy, kappa score, and AUC were used to evaluate the performance of TPOT‐generated models. Finally, we compared the above metrics of TPOT‐generated models to identify the best‐performing model. Patients' ages and grades between two groups were significantly different ( p  = 0.002 and p  = 0.000, respectively). The 4‐fold cross‐validation showed that gradient boosting classifier trained on shape and textual features from the Laplacian‐of‐Gaussian‐filtered Gd‐3DT1 achieved the best performance (average sensitivity = 81.1%, average specificity = 94%, average accuracy = 89.4%, average kappa score = 0.76, average AUC = 0.951). Using autoML based on radiomics features from MRI, a high discriminatory accuracy was achieved for predicting co‐occurrence of IDHmut and MGMTmet in gliomas. Level of Evidence 3 Technical Efficacy Stage 3