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Updated: Sep 17, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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机器学习算法预测乳腺癌发病率风险:基于生物化学生物标志物的数据驱动的回顾性研究

Qianqian Guo1,2,3,4, Peng Wu5, Junhao He6,7

  • 1State Key Laboratory of Traditional Chinese Medicine Syndrome/Breast Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou, Guangdong, China.

BMC cancer
|July 2, 2025
PubMed
概括

这项研究确定了胺转移酶 (GGT) 和胺转移酶 (ALT) 作为用于预测乳腺癌风险的新型血液生物标志物. 整合这些生物标志物的机器学习模型显示了改善早期检测的前景.

关键词:
生化生物标志物 生化生物标志物乳腺癌 乳腺癌 乳腺癌机器学习 机器学习风险因素 风险因素 风险因素验证 验证 验证 验证

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