Objective Selection of GBM Imaging Features for Clinical Trials Bradley Erickson, Daniel Blezek, Panos Korfiatis, Jian Su, Ross Mitchell Background: We focus on identifying MRI features that predict patient survival and response in both low grade and high grade brain tumors. Some molecular markers of tumors have a substantial impact on optimal therapy and survival that impacts clinical decision making. There are reports of imaging identifying some markers, but not in robust trials. Physiologic and functional imaging methods have been used to identify markers of true progression and pseudoprogression, but again have not been robustly validated. ADC has been reported to predict MGMT methylation in one group of nearly 100 subjects but others trying to repeat the results in another group of 100 patients found no difference. We intend to develop tools to make robust validation and application feasible for multi-site trials. Texture measures have been reported to predict 1p19q status in oligodendrogliomas and also MGMT methylation status for GBMs. Robust validation and comparison of various texture measures has not been performed. We have built a library of many standard and some novel texture measures total of 22 classes of texture measures). Below is the ROC curve for one recent test. One putative biomarker for differentiating tumor progression from pseudoprogression is the relative cerebral blood volume (rCBV) derived from DSC perfusion. Given this, we first studied the reliability of rCBV measurements by comparing 3 FDA- approved softwares. There was no difference in prediction accuracy, but they got different cases correct. 3T was better than 1.5T. This work has been presented at RSNA 2013 and submitted for publication. We have also created a digital phantom for DSC that allows variable leakage, noise, and uniformity and evaluated commercial packages. This was also presented at RSNA. We also are evaluating diffusion imaging (ADC) as a marker of tumor progression. Calculation is more straightforward, and we are seeing similar results, though ROI creation is critical variation is present. We are working on robust & automated ROI creation to make this viable for widespread use. An early step in building our program was to create an efficient environment for building out the image database and associated metadata. We evaluated tools like XNAT but felt there were substantial shortcomings. We elected to use an open-source asset management system for store data and metadata, and built a connector library that lets us easily select image sets for analysis, and to store back results (including many versions of results). We use iPython notebooks for the vast majority of computation. The architecture of this environment is shown in figure 1. We hope to share and further develop this in the future for data collection, algorithm development, and deployment of decision support systems. This system would likely be able to achieve the decision support infrastructure use case of the BIDS WG. We are developing a U24 grant for the purpose of refining and gaining broader deployment of this system. N=82 (52 1p19q del) Train on 57; Test on features 4-fold cross val SVM with rbf AUC = 0.82