Survival classification of Gliomas through a novel enhancement-based strategy for class overlap of radiomics features

Abstract

Conventional radiomics-based models precisely engineered image-based features from Magnetic Resonance Imaging (MRI) to extract the predictive patterns for High-Grade Gliomas (HGGs) survival prediction. But an in-depth exploration and assessment of these extracted features are still not conducted. To the best of our knowledge, this is the first study that perceives the range distributions of radiomics features and gains insight into the problem of class overlap among these features. A novel class-wise feature enhancement strategy addresses the ambiguous data regions in the extracted features without any data loss. This strategy explicitly tunes the data values of only two classes and retains the data of the third class to achieve excellent data separability. The enhancement depends on the difference in the feature values of the two classes and incorporates the scalability of this difference using different scaling factors. Furthermore, Box-Cox and logarithmic transformations are employed to overcome the non-normality of the enhanced features. Consequently, ablation experimentation is conducted to substantiate the classification metrics with pre- and post-enhancement cases. BraTS 2020 benchmark is employed, demonstrating that the proposed approach performs competitively in classifying HGG patients into three survival groups, namely, short, mid, and long survivors. It achieves an overall testing classification accuracy, precision, recall, and F1-score of 0.994, 0.993, 0.996, and 0.994, respectively. Therefore, instead of directly utilizing these raw extracted features, this strategy eliminates the overlapped class regions without any information loss and has proven to be a significant step for HGG survival classification applications.