A prediction model for metachronous colorectal cancer: development and validation

Ye Zhang ^1,2, Amalia Karahalios ¹, Aung Ko Win ^1,2,3

¹Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Victoria, 3010, Australia.
²University of Melbourne Center for Cancer Research, Victorian Comprehensive Cancer Center, University of Melbourne, Victoria, 3010, Australia.
³Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville Victoria, 3050, Australia.
⁴Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton, 3800, Victoria, Australia.
⁵Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Parkville, 3050, Victoria, Australia.
⁶Department of Gastroenterology, The Alfred, Monash University, Victoria, 3800, Australia.
⁷Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Victoria, 3010, Australia.
⁸Genomic Medicine Institute, Cleveland Clinic, Cleveland, Ohio, USA.
⁹Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, Ohio, USA.
¹⁰Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Phoenix, Arizona, USA.
¹¹Department of Colorectal Medicine and Genetics, University of Melbourne, Royal Melbourne Hospital, Parkville, Australia.

⁰Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Victoria, 3010, Australia.

Journal of the National Cancer Institute +

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July 16, 2025

View abstract on PubMed

Summary

A new model predicts the 10-year risk of developing metachronous colorectal cancer (CRC) after an initial diagnosis. Factors like BMI, smoking, and tumor characteristics improve risk assessment for tailored surveillance strategies.

Area Of Science

Oncology
Epidemiology
Biostatistics

Background

Estimating metachronous colorectal cancer (CRC) risk is crucial for personalized surveillance strategies.
Current methods lack precision in identifying individuals at high risk for secondary CRC.
Developing a robust risk prediction model can guide post-diagnosis management.

Purpose Of The Study

To develop and validate a predictive model for estimating the 10-year risk of metachronous CRC.
To identify key clinical and pathological factors associated with metachronous CRC development.

Main Methods

A large cohort of 6,085 population-based CRC cases diagnosed between 1997-2012 were analyzed.
Cox regression with LASSO penalization identified predictors of metachronous CRC.
Internal validation using bootstrapping assessed model discrimination and calibration.

Main Results

138 cases (2.3%) developed metachronous CRC over a median of 12 years.
Predictors included BMI, smoking, physical activity, family history, synchronous CRC, tumor stage, grade, histology, DNA mismatch repair status, and age at diagnosis.
The model demonstrated good validity with a c-statistic of 0.65 and calibration slope of 0.873.

Conclusions

A validated risk prediction model can estimate individual 10-year metachronous CRC risk.
The model incorporates readily available clinical variables for practical application.
This tool can facilitate risk-stratified surveillance for colorectal cancer survivors.