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CCE 4: Bridging Clinical Expertise Using Predictive Computational Cancer Models

This predictive computational cancer model utilizes over 3,000 complete patient histories to offer patient-specific predictions for colorectal cancer risk. The model, based on a semi-mechanistic approach, considers factors such as age, gender, symptoms, previous test results, and procedure conditions to enhance CRC surveillance post-screening colonoscopy. Research questions include exploring the impact of colonoscopy preparation quality on clinic burden, patient costs, CRC risks, and the cost-effectiveness of follow-up guidelines.

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CCE 4: Bridging Clinical Expertise Using Predictive Computational Cancer Models

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  1. CCE 4: Bridging Clinical Expertise Using Predictive Computational Cancer Models • CRC screening and follow-up • Semi-mechanistic model of CRC development • Patient-specific predictions • Age • Gender • Symptoms • Previous test results • Procedure conditions • >3,000 complete longitudinal patient histories • Progress: Built the model & tuned to data Lieberman et al. Five-year colon surveillance after screening colonoscopy. Gastroenterology, 133: 1077-1085, 2007.

  2. CRC risks for an individual patient(tool on cceHUB)

  3. Research Questions / Model Uses • Effect of colonoscopy preparation quality on… • Clinic burden • Patient costs • CRC risks • Cost-effectiveness of colonoscopy follow-up guidelines • Risks, costs, and mortality by • Age, gender, and test history • Design colonoscopy follow-up “rules” • (Incorporate other screening tests)

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