Prediction of lymph node metastasis with use of artificial neural networks based on gene expression profiles in esophageal squamous cell carcinoma.

BACKGROUND: The aim of the study was (1) to detect candidate genes involved in lymph node metastasis in esophageal cancers and (2) to investigate whether we can estimate and predict occurrence of lymph node metastasis by analyzing artificial neural networks (ANNs) using these gene subsets.
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: ANN had superior potential in comparison with other methods of analysis for the prediction of lymph node metastasis. This systematic analysis combining SAM with ANN was very useful for the prediction of lymph node metastasis in esophageal cancers and could be applied clinically in the near future.