Abstract
GB (Glioblastoma WHO Grade 4) is the most aggressive type of brain tumor in adults that has a short survival rate even after aggressive treatment with surgery and radiation therapy. The changes in magnetic resonance imaging (MRI) for patients with GB after radiotherapy are indicative of either radiation-induced necrosis (RN) or recurrent brain tumor (rBT). Screening for rBT and RN at an early stage is crucial for facilitating faster treatment and better outcomes for the patients. Differentiating rBT from RN is challenging as both may present with similar radiological and clinical characteristics on MRI. Moreover, learning-based rBT versus RN classification using MRI may suffer from class imbalance due to a lack of patient data. While synthetic data generation using generative models has shown promise to address class imbalances, the underlying data representation may be different in synthetic or augmented data. This study proposes computational modeling with statistically rigorous repeated random sub-sampling to balance the subset sample size for rBT and RN classification. The proposed pipeline includes multiresolution radiomic feature (MRF) extraction followed by feature selection with statistical significance testing (p<0.05). The five-fold cross validation results show the proposed model with MRF features classifies rBT from RN with an area under the curve (AUC) of 0.892±0.055. Moreover, considering the dependence between survival time and censoring time (where patients are not followed up until death), the feasibility of using MRF radiomic features as a non-invasive biomarker to identify patients who are at higher risk of recurrence or radiation necrosis is demonstrated. The cross-validated results show that the MRF model provides the best overall survival prediction with an AUC of 0.77±0.032. Comparison with state-of-the-art methods suggest the proposed method is effective in RN versus rBT classification and patient survivability prediction.