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基于机器的学习层次聚类分析:肝癌切除术后预后的性别差异。

Machine-Based Learning Hierarchical Cluster Analysis: Sex-Based Differences in Prognosis Following Resection of Hepatocellular Carcinoma.

机构信息

Department of Surgery, Wexner Medical Center and James Comprehensive Cancer Center, The Ohio State University, 395 W. 12th Ave., Suite 670, Columbus, OH, USA.

Federal University of Minas Gerais School of Medicine, Belo Horizonte, Brazil.

出版信息

World J Surg. 2023 Dec;47(12):3319-3327. doi: 10.1007/s00268-023-07194-z. Epub 2023 Sep 30.

Abstract

BACKGROUND

Patients with hepatocellular carcinoma (HCC) may have a heterogeneous presentation, as well as different long-term outcomes following surgical resection. We sought to use machine learning to cluster patients into different prognostic groups based on preoperative characteristics.

METHODS

Patients who underwent curative-intent liver resection for HCC between 2000 and 2020 were identified from a large international multi-institutional database. A hierarchical cluster analysis was performed based on preoperative factors to characterize patterns of presentation and define disease-free survival (DFS).

RESULTS

Among 966 with HCC, 3 distinct clusters were identified: Cluster 1 (n = 160, 16.5%), Cluster 2 (n = 537, 55.6%) and Cluster 3 (n = 269, 27.8%). Cluster 1 (n = 160, 16.5%) consisted of female patients (n = 160, 100%), low inflammation-based scores, intermediate tumor burden score (TBS) (median: 4.71) and high alpha-fetoprotein (AFP) levels (median 41.3 ng/mL); Cluster 2 consisted of male individuals (n = 537, 100%), mainly with a history of HBV infection (n = 429, 79.9%), low inflammation-based scores, intermediate AFP levels (median 26.0 ng/mL) and lower TBS (median 4.49); Cluster 3 was comprised of older patients (median age 68 years) predominantly male (n = 248, 92.2%) who had low incidence of HBV/HCV infection (7.1% and 8.2%, respectively), intermediate AFP levels (median 16.8 ng/mL), high inflammation-based scores and high TBS (median 6.58). Median DFS worsened incrementally among the three different clusters with Cluster 3 having the lowest DFS (Cluster 1: median not reached; Cluster 2: 34 months, 95% CI 23.0-48.0, Cluster 3: 19 months, 95% CI 15.0-29.0, p < 0.05).

CONCLUSION

Cluster analysis classified HCC patients into three distinct prognostic groups. Cluster assignment predicted DFS following resection of HCC with the female cluster having the most favorable prognosis following HCC resection.

摘要

背景

肝细胞癌(HCC)患者的表现可能存在异质性,并且手术后的长期预后也不同。我们试图使用机器学习根据术前特征将患者分为不同的预后组。

方法

从一个大型国际多机构数据库中确定了 2000 年至 2020 年间接受根治性肝切除术治疗 HCC 的患者。基于术前因素进行层次聚类分析,以描述表现模式并定义无病生存(DFS)。

结果

在 966 例 HCC 患者中,确定了 3 个不同的簇:簇 1(n=160,16.5%)、簇 2(n=537,55.6%)和簇 3(n=269,27.8%)。簇 1(n=160,16.5%)由女性患者组成(n=160,100%),炎症评分低,肿瘤负荷评分(TBS)中等(中位数:4.71),甲胎蛋白(AFP)水平高(中位数 41.3ng/mL);簇 2 由男性个体组成(n=537,100%),主要有乙型肝炎病毒(HBV)感染史(n=429,79.9%),炎症评分低,AFP 水平中等(中位数 26.0ng/mL),TBS 较低(中位数 4.49);簇 3 由年龄较大的患者(中位年龄 68 岁)组成,主要为男性(n=248,92.2%),HBV/HCV 感染发生率低(分别为 7.1%和 8.2%),AFP 水平中等(中位数 16.8ng/mL),炎症评分高,TBS 高(中位数 6.58)。三个不同簇之间的中位 DFS 逐渐恶化,其中簇 3 的 DFS 最低(簇 1:中位未达到;簇 2:34 个月,95%CI 23.0-48.0;簇 3:19 个月,95%CI 15.0-29.0,p<0.05)。

结论

聚类分析将 HCC 患者分为三个不同的预后组。聚类分配预测了 HCC 切除术后的 DFS,女性簇在 HCC 切除术后具有最有利的预后。

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