Improved risk scoring systems for colorectal cancer screening in Shanghai, China

Abstract

Abstract Background An optimal risk‐scoring system enables more targeted offers for colonoscopy in colorectal cancer (CRC) screening. This analysis aims to develop and validate scoring systems using parametric and non‐parametric methods for average‐risk populations. Methods Screening data of 807,695 subjects and 2806 detected cases in the first‐round CRC screening program in Shanghai were used to develop risk‐predictive models and scoring systems using logistic‐regression (LR) and artificial‐neural‐network (ANN) methods. Performance of established scoring systems was evaluated using area under the receiver operating characteristic curve (AUC), calibration, sensitivity, specificity, number of high‐risk individuals and potential detection rates of CRC. Results Age, sex, CRC in first‐degree relatives, chronic diarrhoea, mucus or bloody stool, history of any cancer and faecal‐immunochemical‐test (FIT) results were identified as predictors for the presence of CRC. The AUC of LR‐based system was 0.642 when using risk factors only in derivation set, and increased to 0.774 by further incorporating one‐sample FIT results, and to 0.808 by including two‐sample FIT results, while those for ANN‐based systems were 0.639, 0.763 and 0.805, respectively. Better calibrations were observed for the LR‐based systems than the ANN‐based ones. Compared with the currently used initial tests, parallel use of FIT with LR‐based systems resulted in improved specificities, less demands for colonoscopy and higher detection rates of CRC, while parallel use of FIT with ANN‐based systems had higher sensitivities; incorporating FIT in the scoring systems further increased specificities, decreased colonoscopy demands and improved detection rates of CRC. Conclusions Our results indicate the potentials of LR‐based scoring systems incorporating one‐ or two‐sample FIT results for CRC mass screening. External validation is warranted for scaling‐up implementation in the Chinese population.

Keywords

• colorectal cancer
• data mining
• risk model
• risk score
• screening