  GBM (Glioblastoma multiforme) 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 on magnetic resonance imaging (MRI)
for patients with GBM 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 lack of patient data. While synthetic data generation
using generative models has shown promise to address class imbalance, 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 0.8920+-.055. Moreover, considering the dependence between survival
time and censor time (where patients are not followed up until death), we
demonstrate 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. The cross-validated results show that the MRF model
provides the best overall performance with an AUC of 0.770+-.032.
