基于机器学习的分层集群分析:在切除肝细胞癌后的预后中基于性别的差异
Vivian Resende1,2, Diamantis I Tsilimigras1, Yutaka Endo1
1Department of Surgery, Wexner Medical Center and James Comprehensive Cancer Center, The Ohio State University, 395 W. 12th Ave., Suite 670, Columbus, OH, USA.
World journal of surgery
|September 30, 2023
概括
机器学习确定了经过切除的肝细胞癌 (HCC) 患者的三个不同的预后组. 女性占主导地位的群体显示出最有利的无病生存 (DFS) 结果.
科学领域:
- 肝胆道手术是肝胆道的手术.
- 在瘤学瘤学.
- 机器学习在医学中的应用
背景情况:
- 肝细胞癌 (HCC) 在切除后呈现异质,结果可变.
- 预测患者的预后对于有效的治疗计划至关重要.
研究的目的:
- 应用机器学习来将HCC患者分为不同的预后组.
- 确定与不同长期生存结果相关的术前特征.
主要方法:
- 从966名接受治疗切除的HCC患者的手术前数据进行分层集群分析.
- 对国际多机构数据库 (2000-2020) 的分析.
- 在已识别的集群中评估无病生存 (DFS).
主要成果:
- 根据手术前的因素确定了三个不同的患者群.
- 集群1 (16.5%):女性,高阿尔法-胎蛋白 (AFP),中等瘤负担得分 (TBS).女性,高阿尔法-胎蛋白 (AFP),中等瘤负担得分 (TBS).
- 集群2 (55.6%):男性,HBV病史,中级AFP,较低的TBS.
- 第三组 (27.8%):年长的男性,低HBV/HCV,中间AFP,高炎症评分,高TBS.
- 各集群的中位数DFS下降 (集群1:未达到;集群2: 34个月;集群3: 19个月).
结论:
- 集群分析有效地将HCC患者分为预后组.
- 手术前的特征预测了HCC切除后的无病生存期.
- 女性占主导地位的群体表现出最有利的预后.
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