CMA: Marker-assisted prevention and risk stratification (MAPRS): Artificial Intelligence Endoscopy for Colorectal Cancer Prevention (CMA1)

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This collaborative merit review application (CMA) aims to advance the precision management of cancers, specifically marker-assisted prevention and risk stratification (MAPRS) of colorectal cancers (CRCs). The third most common cancer in the USA, CRC accounts for nearly 10% of all cancers among Veterans. MAPRS stems from a group of investigators from the VA Colorectal Cancer Cellgenomics Collaborative (VA4C), created with the support of a VA Field-based Meeting Award. The VA4C aims to advance basic/translational research on the prevention, early detection, diagnosis, prognosis and treatment of CRCs. The proposed CMAs aim to disrupt these limitations and significantly advance CRC prevention, detection, risk stratification and precision treatment by advancing MAPRS. MAPRS-CMA aims to: CMA1) develop artificial intelligence-enhanced endoscopy for colorectal cancer prevention; CMA2) examine mucin-based markers to improve endoscopic detection, resection, histological classification and surveillance of neoplastic polyps; CMA3) validate tissue and blood-based combinatorial biomarker panels derived from functional pathway-specific studies to improve risk stratification; and CMA4) examine the potential of cellgenomic drug-response profiling for precision CRC treatment. The main objective of our project, CMA1, is to create and establish within the VA an infrastructure to enable us to develop, validate, and deploy machine learning (ML) /artificial intelligence (AI) models to enhance endoscopy. The past decade has seen an explosion in biophotonic technologies to more precisely diagnose and treat colonic neoplasia. The result is, however, increasingly information-dense imaging to interpret and interact with during procedures. Not surprisingly, technological enhancement of practice has remained restricted to experts at academic centers. Our hypothesis is that reliable real-time polyp histology can be enabled for any operator by computer- assisted diagnosis using ML/AI. This capability would finally open the door to widespread adoption of cost-saving, ASGE-sanctioned resect-and-discard and leave-behind paradigms for diminutive polyps. Thus, the specific aims of this project are: Aim 1: To create a large, scalable labeled endoscopic databank for ML/AI research comprised of clinical image data uploaded from multiple VA centers. Aim 2: To utilize this image repository to develop and validate ML/AI models that enable real-time histology of polyps as well as Aim 3: To develop ML models for computer assisted polyp detection in conjunction with mucin-based fluorescent biomarkers for widefield detection. Aim 4: Use ML/AI to help predict CRC drug response based on combined clinical factors and cellgenomic data.