  This study presents a multi-faceted approach combining stereotactic biopsy
with standard clinical open-craniotomy for sample collection, voxel-wise
analysis of MR images, regression-based Generalized Additive Models (GAM), &
whole-exome sequencing. This work aims to demonstrate the potential of machine
learning algorithms to predict variations in cellular & molecular tumor
characteristics. This retrospective study enrolled ten treatment-naive patients
with radiologically confirmed glioma (5 WHO grade II, 5 WHO grade IV). Each
patient underwent a multiparametric MR scan (T1W, T1W-CE, T2W, T2W-FLAIR, DWI)
prior to surgery (27.9+/-34.0 days). During standard craniotomy procedure, at
least 1 stereotactic biopsy was collected from each patient, with screenshots
of the sample locations saved for spatial registration to pre-surgical MR data.
Whole-exome sequencing was performed on flash-frozen tumor samples,
prioritizing the signatures of five glioma-related genes: IDH1, TP53, EGFR,
PIK3CA, & NF1. Regression was implemented with a GAM using a univariate shape
function for each predictor. Standard receiver operating characteristic
analyses were used to evaluate detection, with AUC (area under curve)
calculated for each gene target & MR contrast combination. The mean AUC for the
five gene targets & 31 MR contrast combinations was 0.75+/-0.11; individual
AUCs were as high as 0.96 for both IDH1 & TP53 with T2W-FLAIR & ADC & 0.99 for
EGFR with T2W & ADC. An average AUC of 0.85 across the five mutations was
achieved using the combination of T1W, T2W-FLAIR, & ADC. These results suggest
the possibility of predicting exome-wide mutation events from non-invasive, in
vivo imaging by combining stereotactic localization of glioma samples & a
semi-parametric deep learning method. This approach holds potential for
refining targeted therapy by better addressing the genomic heterogeneity of
glioma tumors.
