Prediction of H3K27M alteration in midline gliomas of the brain using radiomics: A multi-institute study.

Abstract

Background: Noninvasive prediction of H3K27M-altered Diffuse midline gliomas is important because of the involvement of deep locations and proximity to eloquent structures. We aim to predict H3K27M alteration in midline gliomas using radiomics features of T2W images.

Methods: Radiomics features extracted from 124 subjects (69 H3K27M-altered/55 H3K27M-wild type). T2W images were resampled to 1 × 1 × 1mm³ voxel size, preprocessed, and normalized for artifact correction, intensity variations. The feature set was normalized and subjected to reduction by variance thresholding, correlation coefficient thresholding, and sequential feature selector. Adaptive synthesis oversampling technique was used to oversample the training data. Random forest classifier (RFC), Decision tree classifier (DTC), and K-nearest neighbors classifier (KNN) were trained over the training dataset and the performance was assessed over the internal test dataset and external test data set (52 subjects: 33 H3K27M-altered/19-H3K27M-wild type).

Results: DTC achieved a validation score of 77.33% (5-fold cross-validation) and an accuracy of 80.64%, 75% on internal and external test datasets. RFC achieved a validation score of 80.7% (5-fold cross-validation) an accuracy of 80.6%, and 73% on internal and external test datasets. DTC achieved a validation score of 78.67% (5-fold cross-validation) an accuracy of 80.64%, and 61.53% on internal and external test datasets. The accuracy score of DTC, RFC, and KNN on the internal test dataset was approximately 80% while on the external test dataset, DTC achieved 75% accuracy, RFC achieved 73% accuracy and KNN achieved 65.1% accuracy.

Conclusions: H3K27M alteration is a potential immunotherapeutic marker and is associated with poor prognosis and radiomics features extracted from conventional T2W-images can help in identifying H3K27M-altered cases non-invasively with high precision.