IMG-03. HARNESSING COMPUTATIONAL IMAGING, SCALABLE CLOUD-BASED WORKFLOWS, AND MULTI-MODAL DATA ANALYTICS WITH INTEROPERABLE SOFTWARE PLATFORMS IN PEDIATRIC NEURO-ONCOLOGY; THE ROAD AHEAD FOR CHILDREN’S BRAIN TUMOR NETWORK (CBTN)

Abstract

Abstract Neuroimaging is acquired as part of the clinical standard of care for treatment of brain tumor patients. Capitalizing on this data and integration of imaging with other data types to perform predictive analytics is an increasing need. Here, we review the efforts at CBTN in the past couple of years to establish user-friendly workflows to support the entire imaging data lifecycle and to bridge access to multi-modal datasets with scalable analytics scale, to empower researchers to make breakthrough discoveries that will advance patient care. An automated deep learning-based brain extraction and tumor subregion segmentation model based on a multi-institutional dataset was developed that can reliably segment the MRI scans of children across a variety of brain tumor histologies. This model was subsequently integrated into an end-to-end imaging pipeline for collecting, managing, and analyzing clinically acquired radiology exams of the CBTN consortium with processing of 12,500 exams to date. A public website was also created to allow users to search and explore the dataset based on sequence labels, and clinical and imaging attributes. In addition, we established a scalable workflow and processes for de-identification, ingestion, quality control, and management of digital pathology slides on CBTN (over 8,000 slides to date). Finally, we established interoperability between imaging (Flywheel) and molecular (CAVATICA) platforms deployed in cloud ecosystems which allows streamlined, user-friendly workflows to ingest and harmonize files, perform cohort selection, prepare data using standard processing packages and cloud resources, and conduct analysis on extracted multi-modal feature sets. The described workflow lays the foundation for broad use of imaging studies and access to multi-modal datasets and analytics, with the goal of empowering researchers to make breakthroughs in patient care. The next step is to apply these tools in the context of clinical trials.