Clinical value evaluation of serum markers for early diagnosis of colorectal cancer

BACKGROUND: Early screening for colorectal cancer (CRC) is important in clinical practice. However, the currently methods are inadequate because of high cost and low diagnostic value. AIM: To develop a new examination method based on the serum biomarker panel for the early detection of CRC. METHODS: Three hundred and fifty cases of CRC, 300 cases of colorectal polyps and 360 cases of normal controls. Combined with the results of area under curve (AUC) and correlation analysis, the binary Logistic regression analysis of the remaining indexes which is in accordance with the requirements was carried out, and discriminant analysis, classification tree and artificial neural network analysis were used to analyze the remaining indexes at the same time. RESULTS: By comparison of these methods, we obtained the ability to distinguish CRC from healthy control group, malignant disease group and benign disease group. Artificial neural network had the best diagnostic value when compared with binary logistic regression, discriminant analysis, and classification tree. The AUC of CRC and the control group was 0.992 (0.987, 0.997), sensitivity and specificity were 98.9% and 95.6%. The AUC of the malignant disease group and benign group was 0.996 (0.992, 0.999), sensitivity and specificity were 97.4% and 96.7%. CONCLUSION: Artificial neural network diagnosis method can improve the sensitivity and specificity of the diagnosis of CRC, and a novel assistant diagnostic method was built for the early detection of CRC.