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

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...

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Published in:	Cancer medicine (Malden, MA) MA), 2022-05, Vol.11 (9), p.1972-1983
Main Authors:	Wu, Wei‐Miao, Gu, Kai, Yang, Yi‐Hui, Bao, Ping‐Ping, Gong, Yang‐Ming, Shi, Yan, Xu, Wang‐Hong, Fu, Chen
Format:	Article
Language:	eng
Subjects:	Cancer Cancer Prevention Cancer screening China - epidemiology Colonoscopy Colorectal cancer Colorectal carcinoma Colorectal Neoplasms - diagnosis Colorectal Neoplasms - epidemiology data mining Diagnosis Diarrhea Early Detection of Cancer - methods Feces Humans Mass Screening - methods Medical screening Mucus Neural networks Occult Blood Polyps Population Prediction models Regression analysis Risk assessment Risk Factors risk model risk score screening Variables
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Summary:	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. The established scoring systems derived from the logistic regression (LR) models, incorporating one‐ or two‐sample faecal immunochemical test (FIT) results as a predictor, have the potential to triage high‐risk individuals for colonoscopy in mass screening of colorectal cancer (CRC). More importantly, the cut‐off points of the scoring systems can be adjusted flexibly, facilitating the choices of cut‐off values for populations with abundant or limited resources.
ISSN:	2045-7634 2045-7634