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Artificial neural network in studying factors of hepatic cancer recurrence after hepatectomy

贺佳;贺宪民;张智坚

  Objective: To explore the affecting factors of liver cancer recurrence after hepatectomy. Methods: The BP artificial neural network - Cox regression was introduced to analyze the factors of recurrence in 1 457 patients. Results: The affecting factors statistically significant to liver cancer prognosis was selected. There were 18 factors to be selected by uni-factor analysis, and 9 factors to be selected by multi-factor analysis. Conclusion: The 9 factors selected can be used as important indexes to evaluate the recurrence of liver cancer after hepatectomy. The artificial neural network is a better method to analyze the clinical data, which provides scientific and objective data for evaluating prognosis of liver cancer.……