title: Artificial neural networks distinguish among subtypes of neoplastic colorectal lesions.
abstract: OBJECTIVE
  There is a subtle distinction between sporadic colorectal adenomas and cancers (SAC) and inflammatory bowel disease (IBD)-associated dysplasias and cancers. However, this distinction is clinically important because sporadic adenomas are usually managed by polypectomy alone, whereas IBD-related high-grade dysplasias mandate subtotal colectomy. The current study evaluated the ability of artificial neural networks (ANNs) based on complementary DNA (cDNA) microarray data to discriminate between these 2 types of colorectal lesions.
  
  
  METHODS
  We hybridized cDNA microarrays, each containing 8064 cDNA clones, to RNAs derived from 39 colorectal neoplastic specimens. Hierarchical clustering was performed, and an ANN was constructed and trained on a set of 5 IBD-related dysplasia or cancer (IBDNs) and 22 SACs.
  
  
  RESULTS
  Hierarchical clustering based on all 8064 clones failed to correctly categorize the SACs and IBDNs. However, the ANN correctly diagnosed 12 of 12 blinded samples in a test set (3 IBDNs and 9 SACs). Furthermore, using an iterative process based on the computer programs GeneFinder, Cluster, and MATLAB, we reduced the number of clones used for diagnosis from 8064 to 97. Even with this reduced clone set, the ANN retained its capacity for correct diagnosis. Moreover, cluster analysis performed with these 97 clones now separated the 2 types of lesions.
  
  
  CONCLUSIONS
  Our results suggest that ANNs have the potential to discriminate among subtly different clinical entities, such as IBDNs and SACs, as well as to identify gene subsets having the power to make these diagnostic distinctions.
authors.affiliation: Department of Medicine, Division of Gastroenterology and Greenebaum Cancer Center, University of Maryland School of Medicine, Baltimore, Maryland, USA.
authors.forename: Florin M
authors.initials: FM
authors.lastname: Selaru
authors.forename: Yan
authors.initials: Y
authors.lastname: Xu
authors.forename: Jing
authors.initials: J
authors.lastname: Yin
authors.forename: Tong
authors.initials: T
authors.lastname: Zou
authors.forename: Thomas C
authors.initials: TC
authors.lastname: Liu
authors.forename: Yuriko
authors.initials: Y
authors.lastname: Mori
authors.forename: John M
authors.initials: JM
authors.lastname: Abraham
authors.forename: Fumiaki
authors.initials: F
authors.lastname: Sato
authors.forename: Suna
authors.initials: S
authors.lastname: Wang
authors.forename: Charlie
authors.initials: C
authors.lastname: Twigg
authors.forename: Andreea
authors.initials: A
authors.lastname: Olaru
authors.forename: Valentina
authors.initials: V
authors.lastname: Shustova
authors.forename: Anatoly
authors.initials: A
authors.lastname: Leytin
authors.forename: Prodromos
authors.initials: P
authors.lastname: Hytiroglou
authors.forename: David
authors.initials: D
authors.lastname: Shibata
authors.forename: Noam
authors.initials: N
authors.lastname: Harpaz
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Meltzer
mesh_terms: D000236:Adenoma / Q000175:diagnosis* / Q000235:genetics*; D018256:Adenomatous Polyps / Q000175:diagnosis; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000175:diagnosis; D018938:Caco-2 Cells; D015179:Colorectal Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D004273:DNA, Neoplasm / Q000032:analysis; D003937:Diagnosis, Differential; D005103:Expert Systems; D005260:Female; D019073:HT29 Cells; D006367:HeLa Cells; D006801:Humans; D007938:Leukemia / Q000175:diagnosis; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D013274:Stomach Neoplasms / Q000175:diagnosis
chemical_list: D004273:DNA, Neoplasm