Brain Tumor Survival Prediction using Radiomics Features
This work addresses the problem of accurate survival prognosis for brain tumor patients to inform surgery and treatment planning, representing an incremental improvement over existing methods.
The paper tackles brain tumor survival prediction by proposing a three-step approach using radiomics features from MRI slices and machine learning classifiers, achieving an accuracy of 76.5% and precision of 74.3% on the BraTS 2019 dataset, which are reported as the highest results to date.
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.