Core Self-Reported Variables for Next-Generation Colorectal Cancer Risk Prediction: An International Expert Consensus Study.

BACKGROUND

Colorectal cancer remains a major global health burden, and although population-based screening is beneficial, current programs have notable limitations. Risk-stratified screening has been proposed, yet existing prediction models vary in performance and in the inclusion of predictors. Data-adaptive modeling (machine learning) may strengthen risk prediction but depends on high-quality input data. Expert consensus methods offer a structured way to identify clinically meaningful variables for future tools.

OBJECTIVE

To establish expert consensus on the selection of relevant variables and refinement of self-reported questionnaire items for next-generation colorectal cancer risk-prediction tools.

METHODS

A modified Delphi study was conducted with seventeen internationally recognized experts. The process consisted of anonymized, email-based rounds aimed at refining a questionnaire for colorectal cancer risk prediction. Across three iterative rounds, experts rated item relevance, proposed revisions, and identified missing domains while reviewing group-level summaries of ratings and comments. Items not meeting predefined thresholds were removed, and validated lifestyle-related items were incorporated. Consensus was defined as 15 of 17 experts rating an item within a similar three-point range.

RESULTS

All seventeen experts completed all rounds. The final I-CRC-RISK Questionnaire comprises 23 items across seven categories: Demography, Health Status, Family Health History, Dietary Patterns, Physical Activity, Smoking, and Alcohol Consumption. Expert consensus was reached for 19 items.

CONCLUSION

The I-CRC-RISK Questionnaire provides an expert-informed set of self-reported variables to support future risk prediction efforts. It offers a structured basis for further validation and psychometric testing before prospective data collection may be undertaken. This foundational work could enhance the predictive performance of both traditional and machine-learning approaches by improving the consistency and relevance of input data.