Brain Tumor Survival Prediction using Radiomics Features

TL;DR

This study presents a radiomics-based machine learning method for multi-class brain tumor survival prediction, achieving high accuracy on BraTS 2019 data, emphasizing engineered features over deep learning.

Contribution

It introduces a three-step radiomics approach with feature extraction and machine learning classification, outperforming existing methods in brain tumor prognosis prediction.

Findings

Achieved 76.5% accuracy on BraTS 2019 dataset.

Identified key radiomic features influencing prognosis.

Outperformed previous state-of-the-art results.

Abstract

Surgery planning in patients diagnosed with brain tumor is dependent on their survival prognosis. A poor prognosis might demand for a more aggressive treatment and therapy plan, while a favorable prognosis might enable a less risky surgery plan. Thus, accurate survival prognosis is an important step in treatment planning. Recently, deep learning approaches have been used extensively for brain tumor segmentation followed by the use of deep features for prognosis. However, radiomics-based studies have shown more promise using engineered/hand-crafted features. In this paper, we propose a three-step approach for multi-class survival prognosis. In the first stage, we extract image slices corresponding to tumor regions from multiple magnetic resonance image modalities. We then extract radiomic features from these 2D slices. Finally, we train machine learning classifiers to perform the classification. We evaluate our proposed approach on the publicly available BraTS 2019 data and achieve an accuracy of 76.5% and precision of 74.3% using the random forest classifier, which to the best of our knowledge are the highest reported results yet. Further, we identify the most important features that contribute in improving the prediction.