Open AccessEstablishment of multiple diagnosis models for colorectal cancer with artificial neural networks
Author(s) -
Qiang Wang,
Jianchang Wei,
Zhuanpeng Chen,
Tong Zhang,
Junbin Zhong,
Bingzheng Zhong,
Ping Yang,
Wanglin Li,
Jie Cao
Publication year - 2019
Publication title -
oncology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.766
H-Index - 54
eISSN - 1792-1082
pISSN - 1792-1074
DOI - 10.3892/ol.2019.10010
Subject(s) - receiver operating characteristic , artificial neural network , colorectal cancer , cross validation , learning vector quantization , carcinoembryonic antigen , artificial intelligence , cancer , machine learning , oncology , medicine , algorithm , computer science
The current study aimed to develop multiple diagnosis models for colorectal cancer (CRC) based on data from The Cancer Genome Atlas database and analysis with artificial neural networks in order to enhance CRC diagnosis methods. A genetic algorithm and mean impact value were used to select genes to be used as numerical encoded parameters to reflect cancer metastasis or aggression. Back propagation and learning vector quantization neural networks were used to build four diagnosis models: Cancer/Normal, M0/M1, carcinoembryonic antigen (CEA) <5/≥5 and Clinical stage I-II/III-IV. The performance of each model was evaluated by predictive accuracy (ACC), the area under the receiver operating characteristic curve (AUC) and a 10-fold cross-validation test. The ACC and AUC of the Cancer/Normal, M0/M1, CEA and Clinical stage models were 100%, 1.000; 87.14%, 0.670; 100%, 1.000; and 100%, 1.000, respectively. The 10-fold cross-validation test of the ACC values and sensitivity for each test were 93.75-99.39%, 1.0000; 80.58-88.24%, 0.9286-1.0000; 67.21-92.31%, 0.7091-1.0000; and 59.13-68.85%, 0.6017-0.6585, respectively. The diagnosis models developed in the current study combined gene expression profiling data and artificial intelligence algorithms to create tools for improved diagnosis of CRC.