Abstract | BACKGROUND: METHODS: Twenty-eight primary esophageal squamous cell carcinomas were used. Gene expression profiles of all primary tumors were obtained by cDNA microarray. Lymph node metastasis-related genes were extracted with use of Significance Analysis of Microarrays (SAM). Predictive accuracy for lymph node metastasis was calculated by evaluation of 28 cases by ANNs with leave-one-out cross-n. The results were compared with those of other analyses such as clustering or predictive scoring (LMS). RESULTS: Our ANN model could predict lymph node metastasis most accurately with 60 clones. The highest predictive accuracy for lymph node metastasis by ANN was 10 of 13 (77%) in newly added cases that were not used for gene selection by SAM and 24 of 28 (86%) in all cases (sensitivity: 15/17, 88%; specificity: 9/11, 82%). Predictive accuracy of LMS was 9 of 13 (69%) in newly added cases and 24 of 28 (86%) in all cases (sensitivity: 17/17, 100%; specificity: 7/11, 67%). It was difficult to extract useful information for the prediction of lymph node metastasis by clustering analysis. CONCLUSIONS:
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Authors | Takatsugu Kan, Yutaka Shimada, Fumiaki Sato, Tetsuo Ito, Kan Kondo, Go Watanabe, Masato Maeda, Seiji Yamasaki, Stephen J Meltzer, Masayuki Imamura |
Journal | Annals of surgical oncology
(Ann Surg Oncol)
Vol. 11
Issue 12
Pg. 1070-8
(Dec 2004)
ISSN: 1068-9265 [Print] United States |
PMID | 15545505
(Publication Type: Journal Article)
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Topics |
- Adult
- Aged
- Aged, 80 and over
- Carcinoma, Squamous Cell
(genetics, pathology)
- Esophageal Neoplasms
(genetics, pathology)
- Female
- Forecasting
- Gene Expression Profiling
- Humans
- Lymphatic Metastasis
- Male
- Middle Aged
- Neural Networks, Computer
- Oligonucleotide Array Sequence Analysis
- Prognosis
- Reverse Transcriptase Polymerase Chain Reaction
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