Artificial intelligence-based models for colorectal cancer diagnosis using laboratory tests: an exploratory retrospective case-control study.

Background: Colorectal cancer (CRC) is frequently diagnosed at advanced stages owing to the insidious nature of its early clinical manifestations and the lack of validated screening modalities. This diagnostic delay presents a substantial therapeutic challenge, underscoring the need to develop population-based screening strategies. Based on laboratory data, this study constructed a diagnostic model for CRC to explore a screening strategy for population-based tumor census. Methods: In this retrospective case-control study, we analyzed anonymized laboratory parameters and baseline demographic and clinical characteristics of patients with histologically confirmed CRC and age-matched healthy controls undergoing routine health checkups at our tertiary care center between January 2021 and December 2022. Non-parametric comparisons between groups were conducted using the Mann-Whitney Results: After rigorous screening, this study included 1,068 patients with histologically confirmed CRC and 1,068 age- and sex-matched healthy controls. Comparative analysis using the Mann-Whitney Conclusions: An effective risk stratification model for CRC screening was developed using routine clinical laboratory parameters and the readily available demographic variable of age. However, being derived from a single-center retrospective cohort, the model requires further validation in multi-center, prospective studies to confirm its generalizability and mitigate potential overfitting.