Cancer medicine · 2026
Core Self-Reported Variables for Next-Generation Colorectal Cancer Risk Prediction: An International Expert Consensus Study.
СтатьяConsensus StatementНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Dubois Hanna, Torbjörnsson Eva, Norrbom Jessica, Hubbard Alan, Enskär Mirna, Phillips Rachael V, van der Laan Mark, Blom Johannes
Аннотация
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.
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.