Predicting the risk for colorectal cancer with personal characteristics and fecal immunochemical test

Medicine (Baltimore). 2018 May;97(18):e0529. doi: 10.1097/MD.0000000000010529.


We aimed to predict colorectal cancer (CRC) based on the demographic features and clinical correlates of personal symptoms and signs from Tianjin community-based CRC screening data.A total of 891,199 residents who were aged 60 to 74 and were screened in 2012 were enrolled. The Lasso logistic regression model was used to identify the predictors for CRC. Predictive validity was assessed by the receiver operating characteristic (ROC) curve. Bootstrapping method was also performed to validate this prediction model.CRC was best predicted by a model that included age, sex, education level, occupations, diarrhea, constipation, colon mucosa and bleeding, gallbladder disease, a stressful life event, family history of CRC, and a positive fecal immunochemical test (FIT). The area under curve (AUC) for the questionnaire with a FIT was 84% (95% CI: 82%-86%), followed by 76% (95% CI: 74%-79%) for a FIT alone, and 73% (95% CI: 71%-76%) for the questionnaire alone. With 500 bootstrap replications, the estimated optimism (<0.005) shows good discrimination in validation of prediction model.A risk prediction model for CRC based on a series of symptoms and signs related to enteric diseases in combination with a FIT was developed from first round of screening. The results of the current study are useful for increasing the awareness of high-risk subjects and for individual-risk-guided invitations or strategies to achieve mass screening for CRC.

Publication types

  • Observational Study

MeSH terms

  • Aged
  • Area Under Curve
  • China
  • Colorectal Neoplasms / diagnosis*
  • Early Detection of Cancer / methods*
  • Feces / chemistry
  • Female
  • Humans
  • Logistic Models
  • Male
  • Mass Screening / methods*
  • Middle Aged
  • ROC Curve
  • Reproducibility of Results
  • Risk Assessment / methods
  • Surveys and Questionnaires