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

Abstract

Abstract Background Non-invasive 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-wildtype). T2W-images were resampled to 1x1x1mm voxel-size, pre-processed, and normalized for artefact correction, intensity variations. Feature-set was normalized, subjected to reduction by variance thresholding, correlation coefficient thresholding, and sequential feature selector. Adaptive synthesis oversampling technique was used to over-sample 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-wildtype). Results DTC achieved validation score of 77.33% (5-fold cross-validation) and accuracy of 80.64%, 75% on internal and external test dataset. RFC achieved validation score of 80.7% (5-fold cross-validation) and accuracy of 80.6%, 73% on internal and external test dataset. DTC achieved validation score of 78.67% (5-fold cross-validation) and accuracy of 80.64%, 61.53% on internal and external test dataset. Accuracy score of DTC, RFC, and KNN on 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. Conclusion 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.