Objective:

To design and validate a noninvasive screening model for advanced colorectal neoplasia utilizing routine blood tumor markers and inflammatory indices, and to develop an online calculator for assessing individual risk.

Approach:

Key Findings:

• 210 out of 1,290 patients were diagnosed with advanced colorectal neoplasms.
• Independent predictors identified include CEA, SII, NLR, ALB, and PLR.
• XGBoost model achieved the best performance with an AUC of 0.956 (95% CI 0.936–0.976).
• SII was identified as the most influential predictor in the SHAP analysis.

Interpretation:

A machine learning model based on key blood markers can effectively support noninvasive screening for advanced colorectal neoplasms.

Conclusion:

The online calculator is a convenient and practical resource for evaluating risk on an individual basis in clinical practice.

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