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基于预后营养指数的机器学习模型,用于预测HCC患者经历了切除术的长期结果

Nan Zhang¹, Ke Lin¹, Bin Qiao¹

¹Division of Interventional Ultrasound, Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.

Cancer medicine

|October 23, 2024

概括

使用预后营养指数 (PNI) 的新机器学习模型准确地预测了切除后肝细胞癌 (HCC) 患者的长期存活率. 这种基于PNI的Aorsf模型为HCC监测,预防和治疗提供了改进的策略.

科学领域:

肝细胞癌研究肝细胞癌研究
机器学习在瘤学中
预测因素分析预测因素分析

背景情况:

在局部切除后,肝细胞癌 (HCC) 的生存预测仍然具有挑战性.
确定可靠的预后标志物对于优化患者管理至关重要.
预后营养指数 (PNI) 是营养状况和炎症的潜在指标,影响癌症的结果.

研究的目的:

开发和验证机器学习 (ML) 模型,用于预测HCC患者在局部切除后的长期存活率.
根据预后营养指数 (PNI) 确定最佳的ML模型.
将开发的ML模型的性能与现有的预后工具进行比较.

主要方法:

从848名接受局部切除 (2009-2019) 的原发性HCC患者的数据分析.
使用一致性指数 (C指数),C/D AUC和Brier分数构建和评估多个ML模型.
使用部分依赖图 (PDP) 和夏普利添加式扩展 (SHAP) 解释最佳模型.

主要成果:

性酸酶,手术前的α-fetoprotein,PNI,瘤数量和大小是关键的预后因素.
在培训和验证队列中,Aorsf模型表现出优异的表现 (例如,验证C指数:0.793).
Aorsf模型显示了1至9年生存期的显著预测准确性,在临床实用性方面表现优于其他模型.

结论:

基于PNI的Aorsf模型对预测HCC患者在切除后的长期存活有效.
该模型通过为监测,预防和治疗策略提供信息来增强HCC研究.
Aorsf模型为个性化HCC患者管理提供了一个有价值的工具.

关键词:

肝细胞癌是肝细胞癌.当地的剥离.机器学习模型机器学习模型预后预后预后预后营养指数的预后.

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