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

• 作 者: 贺佳 张智坚
• 出 处: Journal of Medical Colleges of PLA
• 发表日期: 2002年01期
• 关键词: recurrence hepatectomy prognosis thrombus statistically quantitatively affecting

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.

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. . 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. neural network is a better method to analyze the clinical data, which provides scientific and objective data for evaluating prognosis of liver cancer.