Abstract A044: Developing a digital twin framework for colorectal cancer outcome prediction and robust clinical trial simulation
Abstract
Digital twin (DT) technology enables in silico patient representations for counterfactual outcome simulations and precise target population identification, thereby facilitating in silico hypothesis testing while reducing clinical trial costs and resource utilization. We present a novel DT framework for colorectal cancer (CRC) patient outcome prediction and clinical trial simulation.
We used multiomics data from 2,100 CRC patients from our Precision 360™ database. The data were partitioned into training (80%), validation (10%), and testing (10%) cohorts. The DT architecture incorporates an encoder-decoder neural network framework that compresses patient data into latent vectors (serving as digital twins) and subsequently decodes clinically relevant observables including overall survival (OS). Training employed a self-supervised approach with dual optimization criteria: minimization of reconstruction loss and maximization of latent space entropy. Training proceeded for 10,000 epochs until convergence of validation and training curves. We conducted two validation experiments related to AST/ALT ratio, a parameter shown to be associated with overall survival: (1) to assess how well the DT could generate prognosis-related hypotheses, we compared the predictions made by the DT and those from univariate analysis between AST/ALT ratio at baseline (closet lab value after initial diagnosis) and OS using concordance indices (CI); and (2) evaluation of counterfactual stability through…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 2,100 participants or records.
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