Automated Software for Point-of-cCare Testing to Identify Cancer-Associated Malnutrition

Fast-Track proposals will be accepted. Direct-to-Phase II proposals will NOT be accepted. Number of anticipated awards: 2-3 Budget (total costs, per award): Phase I: up to $400,000 for up to 12 months Phase II: up to $2,000,000 for up to 2 years PROPOSALS THAT EXCEED THE BUDGET OR PROJECT DURATION LISTED ABOVE MAY NOT BE FUNDED. Summary The NIH Office of Disease Prevention recently held a Pathways to Prevention workshop, which explored the evidence for nutritional interventions and cancer health outcomes. A report of the workshop by an independent panel recommended baseline screening for nutrition risk following cancer diagnosis and repeated through treatment. This recommendation is supported by evidence linking cancer-associated malnutrition to poorer outcomes, including decreased treatment completion, greater healthcare utilization, and overall worse survival. The poor outcomes are driven substantially by the depletion of skeletal muscle, such as in sarcopenia, and by the emerging abnormal body composition phenotype of low muscle mass and high adipose tissue or sarcopenic obesity. Nutritional screening is the first step in the identification and treatment of patients with or at risk for malnutrition, especially those patients with cancer types that have the highest prevalence of malnutrition including upper gastrointestinal, head and neck, hematological, gynecological, colorectal, and lung cancers. Several quick and simple-to-administer questionnaire-based screening tools validated in the oncology setting capture changes in appetite and unintentional weight loss; they are often short, easy to administer, and can be incorporated into the electronic health record (EHR). However, they fail to capture abnormal body composition, which is fundamental for the identification of hidden abnormalities, such as sarcopenia and myosteatosis. State-of-the-art approaches based on diagnostic imaging are available to quantify the depletion of skeletal muscle and abnormal body composition changes that occur in patients with cancer. For example, CT scans, which are accessible in most cancer populations for routine diagnosis and follow-up of treatment response, can be re-purposed' for assessing muscle and adipose tissue and is considered gold standard methodology. Biomedical image segmentation and automated segmentation of skeletal muscle and adipose tissue from CT scans provides a time-efficient, clinic-friendly, and accurate assessment of muscle and adipose tissues. Developing an automated nutrition screener that combines the questionnaire-based tools with diagnostic imaging would greatly improve the identification of cancer patients with or at risk for malnutrition and will aid in the optimal timing for nutritional intervention.