Artificial neural networks for diagnosis and survival prediction in colon cancer

被引:125
作者
Ahmed, Farid E. [1 ]
机构
[1] E Carolina Univ, Brody Sch Med, Leo W Jenkins Canc Ctr, Dept Radiat Oncol, Greenville, NC 27858 USA
关键词
D O I
10.1186/1476-4598-4-29
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
ANNs are nonlinear regression computational devices that have been used for over 45 years in classification and survival prediction in several biomedical systems, including colon cancer. Described in this article is the theory behind the three-layer free forward artificial neural networks with backpropagation error, which is widely used in biomedical fields, and a methodological approach to its application for cancer research, as exemplified by colon cancer. Review of the literature shows that applications of these networks have improved the accuracy of colon cancer classification and survival prediction when compared to other statistical or clinicopathological methods. Accuracy, however, must be exercised when designing, using and publishing biomedical results employing machine-learning devices such as ANNs in worldwide literature in order to enhance confidence in the quality and reliability of reported data.
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页数:12
相关论文
共 53 条
[1]   Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays [J].
Alon, U ;
Barkai, N ;
Notterman, DA ;
Gish, K ;
Ybarra, S ;
Mack, D ;
Levine, AJ .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 1999, 96 (12) :6745-6750
[2]  
ASTION ML, 1992, CLIN CHEM, V38, P34
[3]   An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers [J].
Ball, G ;
Mian, S ;
Holding, F ;
Allibone, RO ;
Lowe, J ;
Ali, S ;
Li, G ;
McCardle, S ;
Ellis, IO ;
Creaser, C ;
Rees, RC .
BIOINFORMATICS, 2002, 18 (03) :395-404
[4]   Artificial neural networks: fundamentals, computing, design, and application [J].
Basheer, IA ;
Hajmeer, M .
JOURNAL OF MICROBIOLOGICAL METHODS, 2000, 43 (01) :3-31
[5]   APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO CLINICAL MEDICINE [J].
BAXT, WG .
LANCET, 1995, 346 (8983) :1135-1138
[6]   Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions [J].
Bottaci, L ;
Drew, PJ ;
Hartley, JE ;
Hadfield, MB ;
Farouk, R ;
Lee, PWR ;
Macintyre, IMC ;
Duthie, GS ;
Monson, JRT .
LANCET, 1997, 350 (9076) :469-472
[7]  
Burke H. B., 2001, CANCER S, V91, P857
[8]  
Dayhoff JE, 2001, CANCER, V91, P1615, DOI 10.1002/1097-0142(20010415)91:8+<1615::AID-CNCR1175>3.0.CO
[9]  
2-L
[10]   A TECHNIQUE FOR USING NEURAL-NETWORK ANALYSIS TO PERFORM SURVIVAL ANALYSIS OF CENSORED-DATA [J].
DELAURENTIIS, M ;
RAVDIN, PM .
CANCER LETTERS, 1994, 77 (2-3) :127-138