Morphomics
Abstract
Cross-sectional images are used by surgeons for operative planning. These images contain vast amounts of additional data specific to that patient which are never assessed by clinicians. Analytic morphomics is a novel approach to clinical image analysis and may inform perioperative risk evaluations, adding objectivity to the subjective “eyeball test”. Our recent work has shown strong relationships between patient morphometric characteristics on preoperative imaging and surgical outcomes following major surgery. We propose to utilize these morphomics to predict various medical outcomes
Intellectual Merit
This proposal leverages novel technologies and available data to communicate surgical risk. The surgical literature is replete with studies of the risk factors for poor surgical outcomes, but little effort has been made to bring this to the bedside and affect surgical decision-making. This proposal will extract data from preoperative images to inform clinical decisions. Over 90% of patients have cross-sectional imaging prior to major surgery. Within these images exist a robust amount of patient specific data well suited for preoperative risk assessment. Developing methods to optimally utilize these scans and to inform clinical decision-making builds upon our innovative work in these areas.
Broader Impact
The decision to operate surgical procedures appears to be at the root-cause of many preventable peri-operative poor outcomes. Identifying and improving factors that enhance surgical decision-making would have an obvious value for patients. We will use novel morphomics to build such risk prediction models.
Use of FutureGrid
will be running statistical analysis on morphomics, mostly supervised learning, such as glm, random forrest, elastic net.
Scale Of Use
running analysis a few times per weeks. need more cpus to conduct it parallelly. (100 nodes and 30G memory)
Futuregrid is a resource provider for