Artificial neural networks distinguish among subtypes of neoplastic colorectal lesions.
Τίτλος | Artificial neural networks distinguish among subtypes of neoplastic colorectal lesions. |
Publication Type | Journal Article |
Year of Publication | 2002 |
Authors | Selaru, F. M., Xu Y., Yin J., Zou T., Liu T. C., Mori Y., Abraham J. M., Sato F., Wang S., Twigg C., Olaru A., Shustova V., Leytin A., Hytiroglou P., Shibata D., Harpaz N., & Meltzer S. J. |
Journal | Gastroenterology |
Volume | 122 |
Issue | 3 |
Pagination | 606-13 |
Date Published | 2002 Mar |
ISSN | 0016-5085 |
Λέξεις κλειδιά | Adenoma, Adenomatous Polyps, Adult, Aged, Aged, 80 and over, Breast Neoplasms, Caco-2 Cells, Colorectal Neoplasms, Diagnosis, Differential, DNA, Neoplasm, Expert Systems, Female, HeLa Cells, HT29 Cells, Humans, Leukemia, Male, Middle Aged, Neural Networks, Computer, Oligonucleotide Array Sequence Analysis, Stomach Neoplasms |
Abstract | BACKGROUND & AIMS: 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. |
DOI | 10.1053/gast.2002.31904 |
Alternate Journal | Gastroenterology |
PubMed ID | 11874992 |
Grant List | CA 77057 / CA / NCI NIH HHS / United States CA 85069 / CA / NCI NIH HHS / United States CA95323 / CA / NCI NIH HHS / United States DK 47717 / DK / NIDDK NIH HHS / United States |