Prediction of Rapid Early Progression and Survival Risk with
  Pre-Radiation MRI in WHO Grade 4 Glioma Patients
Walia Farzana Mustafa M Basree Norou Diawara Zeina A. Shboul Sagel Dubey Marie M Lockhart Mohamed Hamza Joshua D. Palmer Khan M. Iftekharuddin
Recent clinical research describes a subset of glioblastoma patients that
exhibit REP prior to start of radiation therapy. Current literature has thus
far described this population using clinicopathologic features. To our
knowledge, this study is the first to investigate the potential of conventional
ra-diomics, sophisticated multi-resolution fractal texture features, and
different molecular features (MGMT, IDH mutations) as a diagnostic and
prognostic tool for prediction of REP from non-REP cases using computational
and statistical modeling methods. Radiation-planning T1 post-contrast (T1C) MRI
sequences of 70 patients are analyzed. Ensemble method with 5-fold cross
validation over 1000 iterations offers AUC of 0.793 with standard deviation of
0.082 for REP and non-REP classification. In addition, copula-based modeling
under dependent censoring (where a subset of the patients may not be followed
up until death) identifies significant features (p-value <0.05) for survival
probability and prognostic grouping of patient cases. The prediction of
survival for the patients cohort produces precision of 0.881 with standard
deviation of 0.056. The prognostic index (PI) calculated using the fused
features suggests that 84.62% of REP cases fall under the bad prognostic group,
suggesting potentiality of fused features to predict a higher percentage of REP
cases. The experimental result further shows that mul-ti-resolution fractal
texture features perform better than conventional radiomics features for REP
and survival outcomes.