From Algorithms to Clinical Utility: A Systematic Review of Individualized Risk Prediction Models for Colorectal Cancer

Abstract

Individualized risk prediction models for colorectal cancer (CRC) play a pivotal role in shaping risk-based screening approaches, garnering attention for use in informed decision making by patients and clinicians. While the incorporation of new predictors and the development of advanced yet complex prediction models can enhance model performance, their practical implementation in clinical settings remains challenging. This systematic review assessed individualized CRC risk prediction models for their validity and potential clinical utility. Utilizing the Cochrane Collaboration methods and PROBAST tool, we conducted comprehensive searches across key databases and risk of bias assessment, respectively. Out of 41 studies included evaluating 44 risk prediction models, 12 conventional and 3 composite models underwent external validation. All risk models exhibited varying discriminatory accuracy, with the area under the curve (AUCs) ranging from 0.57 to 0.90. However, most studies showed an unclear or high risk of bias, with concerns about applicability. Of the five models with promising clinical utility, only two underwent external validation and one employed a decision curve analysis. These models demonstrated a discriminating and well-calibrated performance. While high-performing CRC risk prediction models exist, a need for transparent reporting of performance metrics and their clinical utility persists. Further research on this area is needed to facilitate the integration of these models into clinical practice, particularly in CRC screening.