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

贺佳; 贺宪民; 张智坚

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

Updated：2026-03-12

- Artificial neural network in studying factors of hepatic cancer recurrence after hepatectomy
- Artificial neural network in studying factors of hepatic cancer recurrence after hepatectomy
- 解放军医学杂志（英文版） 2002年第1期页码：65-68
- Affiliations：
- Author bio：
- Funds：
  
  Supported by the National Natural Science Foundation of China （No. 39770835）
- DOI：
  中图分类号： R735.7
- 纸质出版：2002
- Accepted：
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Artificial neural network in studying factors of hepatic cancer recurrence after hepatectomy[J]. 解放军医学杂志（英文版）, 2002,(1):65-68.

[1]贺佳,,,,,,,,,,贺宪民,,,,,,,,,,张智坚.Artificial neural network in studying factors of hepatic cancer recurrence after hepatectomy[J].Journal of Medical Colleges of PLA,2002(01):65-68.
Artificial neural network in studying factors of hepatic cancer recurrence after hepatectomy[J]. 解放军医学杂志（英文版）, 2002,(1):65-68. DOI：

[1]贺佳,,,,,,,,,,贺宪民,,,,,,,,,,张智坚.Artificial neural network in studying factors of hepatic cancer recurrence after hepatectomy[J].Journal of Medical Colleges of PLA,2002(01):65-68. DOI：

摘要

Abstract

<正>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.

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