Published online Feb 15, 2020. doi: 10.4251/wjgo.v12.i2.219
Peer-review started: December 18, 2019
First decision: January 13, 2020
Revised: January 17, 2020
Accepted: February 8, 2020
Article in press: February 8, 2020
Published online: February 15, 2020
Processing time: 59 Days and 2.2 Hours
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.
Blood-based screening method attract the public because of their noninvasive, and multiparameter method was demonstrated to increase the diagnostic value.
We aimed to conduct a retrospective analysis. By multiparameter methods combined detection were used to determine the tumor marker diagnostic value for detection of CRC.
350 CRC, 300 colorectal polyps and 360 normal controls were enrolled. Combined with the results of area under curve, the binary Logistic regression analysis, and discriminant analysis, classification tree and artificial neural network were used to analyze the diagnostic value.
For distinguishing CRC from healthy control group, malignant disease group and benign disease group. Artificial neural network had the best diagnostic value when compared with the other methods. The area under the curve of CRC and the control group was 0.992 (0.987, 0.997), sensitivity and specificity were 98.9% and 95.6%. The area under the curve of the malignant disease group and benign group was 0.996 (0.992, 0.999), sensitivity and specificity were 97.4% and 96.7%.
Artificial neural network diagnosis method can provide a novel assistant diagnostic method was built for the early detection of CRC.
Although we have built a multiparameter diagnostic model, the sample size was relatively small, and the diagnostic model has not been validated. Multi-center and larger sample size to validate its diagnostic value.