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与ALD和MASLD相关的肝细胞癌的个性化死亡风险分层使用机器学习方法.

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机器学习,特别是随机森林,准确地预测肝细胞癌 (HCC) 患者的死亡率与酒精相关的肝病 (ALD) 和代谢功能障碍相关的脂肪性肝病 (MASLD). 肝功能和炎症标志物是关键预测因素,表现优于瘤标志物.

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人工智能的人工智能随机的森林 随机的森林与酒精相关的肝脏疾病肝细胞癌是肝细胞癌.机器学习是机器学习.与代谢功能障碍相关的脂肪性肝病.预后 预后 预后

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科学领域:

  • 肝病学 肝病学是一种肝病学.
  • 机器学习在医学中的应用
  • 在瘤学瘤学.

背景情况:

  • 肝细胞癌 (HCC) 流行病学正在转变,与酒精相关的肝病 (ALD) 和代谢功能障碍相关的脂肪性肝病 (MASLD) 成为发达国家的主要驱动因素.
  • 早期识别这些特定的HCC患者群体的预后死亡因素对于及时干预至关重要.

研究的目的:

  • 确定ALD和MASLD的HCC患者诊断时死亡率的关键预后因素.
  • 评估各种机器学习 (ML) 算法在预测这一群体内的死亡率方面的表现.
  • 使用随机森林 (RF) 算法建立参考预测模型.

主要方法:

  • 一项追溯的多中心队列研究,涉及91名患者,在2008年至2023年期间被诊断患有ALD或MASLD相关的HCC.
  • 收集人口统计,临床和生化数据.
  • 实现和比较多个ML算法 (RF,SVM,决策树,天真贝叶斯,KNN) 与贝叶斯优化用于超参数调整.

主要成果:

  • 随机森林 (RF) 算法表现出优异的预测性能 (AUC:0.91,精度:90.67%,F1得分:91.05%),显著优于其他ML模型.
  • 死亡率的关键预测因素包括血清白蛋白,CRP/白蛋白比率,巴塞罗那诊所肝癌 (BCLC) 阶段,以及白蛋白-白氨 (ALBI) 评分.
  • MELD 3.0显示出比其他MELD得分变异更高的预测准确度,而α-fetoprotein (AFP) 的预后价值有限.

结论:

  • 肝功能标志物和全身炎症指标比瘤标志物更有效地预测ALD和MASLD相关的HCC的早期死亡率.
  • 射频算法显示,在这种患者群体中,个性化预后具有显著的潜力.
  • 在临床广泛采用之前,需要在独立数据集中对射频模型进行外部验证.