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Quantitative Fecal Immunochemical Test for Advanced Colorectal Neoplasia Risk Stratification.

Hewei Wu1, Zhongxue Han1, Iqtida Ahmed Mirza1

  • 1Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China.

Cancer Epidemiology, Biomarkers & Prevention : a Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology
|March 3, 2026
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Summary

A new model effectively identifies individuals at high risk for advanced colorectal neoplasia (ACRN) using quantitative fecal immunochemical tests (qFIT) and clinical data. This tool aids in prioritizing colonoscopies and improving colorectal cancer screening efficiency.

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Area of Science:

  • Colorectal Cancer Research
  • Diagnostic Model Development
  • Clinical Risk Stratification

Background:

  • Developing accurate risk stratification models for advanced colorectal neoplasia (ACRN) is crucial for effective screening.
  • Quantitative fecal immunochemical testing (qFIT) combined with clinical data offers a promising approach.

Purpose of the Study:

  • To develop and externally validate a risk stratification model for ACRN.
  • To integrate qFIT results with easily accessible clinical variables.

Main Methods:

  • A multicenter study with development (3209 individuals) and validation (1678 individuals) sets was utilized.
  • Multivariable logistic regression identified significant risk factors for ACRN.
  • Model performance was assessed using AUROC and the Hosmer-Lemeshow test, followed by risk tier categorization.

Main Results:

  • Key risk factors identified include age, sex, BMI, residence, alcohol intake, smoking, and fecal hemoglobin concentration.
  • The model showed strong predictive performance (AUROC = 0.870) and good calibration (P = 0.929).
  • ACRN prevalence increased significantly across risk tiers: 1.66% (low), 4.50% (intermediate), 28.45% (high), and 60.64% (very-high).

Conclusions:

  • The developed model effectively stratifies ACRN risk, guiding prompt colonoscopy for high-risk individuals.
  • This approach can enhance colorectal cancer (CRC) screening strategies and optimize resource allocation.
  • The model may improve the detection rate of advanced adenoma compared to standard FIT.