Development and Validation of Risk Prediction Models for Colorectal Cancer in Patients with Symptoms

Wei Xu, Ines Mesa-Eguiagaray, Theresa Kirkpatrick, Jennifer Devlin, Stephanie Brogan, Patricia Turner, Chloe Macdonald, Michelle Thornton, Xiaomeng Zhang, Yazhou He, Xue Li, Maria Timofeeva, Susan Farrington, Farhat Din, Malcolm Dunlop, Evropi Theodoratou*


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We aimed to develop and validate prediction models incorporating demographics, clinical features, and a weighted genetic risk score (wGRS) for individual prediction of colorectal cancer (CRC) risk in patients with gastroenterological symptoms. Prediction models were developed with internal validation [CRC Cases: n = 1686/Controls: n = 963]. Candidate predictors included age, sex, BMI, wGRS, family history, and symptoms (changes in bowel habits, rectal bleeding, weight loss, anaemia, abdominal pain). The baseline model included all the non-genetic predictors. Models A (baseline model + wGRS) and B (baseline model) were developed based on LASSO regression to select predictors. Models C (baseline model + wGRS) and D (baseline model) were built using all variables. Models’ calibration and discrimination were evaluated through the Hosmer-Lemeshow test (calibration curves were plotted) and C-statistics (corrected based on 1000 bootstrapping). The models’ prediction performance was: model A (corrected C-statistic = 0.765); model B (corrected C-statistic = 0.753); model C (corrected C-statistic = 0.764); and model D (corrected C-statistic = 0.752). Models A and C, that integrated wGRS with demographic and clinical predictors, had a statistically significant improved prediction performance. Our findings suggest that future application of genetic predictors holds significant promise, which could enhance CRC risk prediction. Therefore, further investigation through model external validation and clinical impact is merited.

TidsskriftJournal of Personalized Medicine
Udgave nummer7
Antal sider16
StatusUdgivet - jul. 2023

Bibliografisk note

Funding Information:
This research was funded by Cancer Research UK, grant number: C348/A12076. E.T. is supported by a Cancer Research UK Career Development Fellowship (C31250/A22804). M.G.D. as Project Leader with the MRC Human Genetics Unit Centre is supported by Grant (U127527198).


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