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This study presents a multi-faceted approach combining stereotactic biopsywith standard clinical open-craniotomy for sample collection, voxel-wiseanalysis of MR images, regression-based Generalized Additive Models (GAM), &whole-exome sequencing. This work aims to demonstrate the potential of machinelearning algorithms to predict variations in cellular & molecular tumorcharacteristics. This retrospective study enrolled ten treatment-naive patientswith radiologically confirmed glioma (5 WHO grade II, 5 WHO grade IV). Eachpatient underwent a multiparametric MR scan (T1W, T1W-CE, T2W, T2W-FLAIR, DWI)prior to surgery (27.9+/-34.0 days). During standard craniotomy procedure, atleast 1 stereotactic biopsy was collected from each patient, with screenshotsof 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 shapefunction for each predictor. Standard receiver operating characteristicanalyses were used to evaluate detection, with AUC (area under curve)calculated for each gene target & MR contrast combination. The mean AUC for thefive gene targets & 31 MR contrast combinations was 0.75+/-0.11; individualAUCs were as high as 0.96 for both IDH1 & TP53 with T2W-FLAIR & ADC & 0.99 forEGFR with T2W & ADC. An average AUC of 0.85 across the five mutations wasachieved using the combination of T1W, T2W-FLAIR, & ADC. These results suggestthe possibility of predicting exome-wide mutation events from non-invasive, invivo imaging by combining stereotactic localization of glioma samples & asemi-parametric deep learning method. This approach holds potential forrefining targeted therapy by better addressing the genomic heterogeneity ofglioma tumors.

Mapping Whole Exome Sequencing to In Vivo Imaging with Stereotactic Localization and Deep Learning