The Use of Blood-Based Biomarkers in the Prediction of Colorectal Neoplasia at the Time of Primary Screening Colonoscopy Among Average-Risk Patients: A Systematic Literature Review

  • 0Department of Community Health Sciences, University of Calgary, Calgary, AB T2N 1N4, Canada.

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Summary

This summary is machine-generated.

Risk prediction models (RPMs) for colorectal cancer (CRC) screening are improving with blood-based biomarkers. A systematic review found models using complete blood counts and plasma metabolites show promise for early detection.

Area Of Science

  • Oncology
  • Biomarkers
  • Epidemiology

Background

  • Risk prediction models (RPMs) for colorectal cancer (CRC) can enhance screening strategies.
  • Incorporating blood-based biomarkers into RPMs may improve their clinical utility for CRC detection.
  • This review focuses on RPMs for CRC screening in average-risk populations undergoing colonoscopy.

Purpose Of The Study

  • To systematically review studies on RPMs for CRC that evaluate blood-based biomarkers.
  • To assess the impact of these biomarkers on predicting clinical outcomes during screening colonoscopy.

Main Methods

  • Systematic literature search of MEDLINE, Web of Science, and PubMed databases (up to April 2024).
  • Inclusion of studies developing or validating RPMs for CRC or its precursors in average-risk screening populations.
  • Analysis of reported biomarker performance, including area under the curve (AUC) and specificity.

Main Results

  • Sixteen studies (2015-2024) were included, focusing on CRC (16 studies) and high-risk adenomas (1 study).
  • Complete blood count (CBC) achieved an AUC of 0.82 and specificity of 0.88.
  • A plasma metabolite panel demonstrated the highest performance with an AUC of 0.99.

Conclusions

  • The use of biomarkers in RPMs for CRC screening is a growing area of research.
  • Many RPMs lack robust internal/external validation and clear implementation strategies.
  • Further research is needed to evaluate and integrate improved biomarker-based RPMs into CRC screening frameworks.