Abstract 869: Leveraging a novel analysis pipeline for outcomes predictions from routine MRIs

Abstract

Abstract There is a desperate need for approaches that allow us to use routinely gathered clinical and imaging data to make outcomes predictions for patients. The acquisition of MRI images forms a standard part of clinical care for cancer patients, especially for malignant brain tumor patients. As these digital MRI scans are performed regularly, the associated digital MRI data also grows and can form the basis for longitudinal assessment of disease and prediction of clinical outcomes. However, standard modalities are coupled with non-standard modalities in most cases (some of which is institution specific), making it difficult to organize and standardize these images for analysis. In addition, for a rare cancer such as malignant brain tumors, multi-institutional collaborations are necessary in order to have appropriate power for analyses, thereby complicating the issues around organization and standardization of images for analysis. In addition, there are likely to be discrepancies among data formats intra-institutionally. To address these shortcomings and to expedite the research process, we developed a general digital MRI analysis pipeline that is able to organize messy data, automatically preprocess, segment, extract features, and produce results that are ready for statistical analysis and discovery. Such a pipeline with modular segments for major stages in the process is beneficial to investigators who are analyzing MRI data and hope to save time from the tedium of organization and data management. With a focus on efficiency, extensibility, and reproducibility, the tools that form this pipeline also aim to increase the accessibility of complex imaging analysis to a wider audience by leveraging artificial intelligence. While pipelines exist in the literature, most are not focused on clinical MRI images of brain tumors and quickly become deprecated as software dependencies change, resulting in outdated and potentially buggy software. Our pipeline implements the latest Python version using object-oriented principles, employing PEP 8 style, and is optimized to run on modern hardware thus ensuring extensibility. The pipeline itself runs intuitively via the command line interface (CLI) that requires minimal input parameters to perform its subtasks but will have the option of accepting other parameters. We showcase features of this new MRI analysis pipeline, using malignant brain tumors as an exemplar, given their disproportionate contribution to cancer morbidity and mortality. Citation Format: Vachan Vadmal, Kristin Waite, Jill S. Barnholtz-Sloan. Leveraging a novel analysis pipeline for outcomes predictions from routine MRIs [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 869.