基于反事实理论的机器学习模型用于肝细胞癌患者的治疗决策

IF 4.2 3区 医学 Q2 ONCOLOGY Journal of Hepatocellular Carcinoma Pub Date : 2024-08-30 eCollection Date: 2024-01-01 DOI:10.2147/JHC.S470550
Xiaoqin Wei, Fang Wang, Ying Liu, Zeyong Li, Zhong Xue, Mingyue Tang, Xiaowen Chen
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引用次数: 0

摘要

目的:基于使用临床和放射组学特征的机器学习模型,预测接受肝切除术和经动脉化疗栓塞术(TACE)治疗的患者的疗效:回顾性队列研究收集了2016年6月至2021年7月期间首次治疗为肝切除术或TACE的HCC患者。为确保治疗效果与治疗方式之间的因果关系,根据倾向评分匹配原则获得完全匹配的患者,并将其作为独立的试验队列。为控制未匹配患者的偏倚,采用了治疗的逆概率加权,并将加权结果作为训练队列。临床特征通过单变量和多变量cox比例危险回归分析进行筛选,放射组学特征通过相关性分析和随机生存森林进行筛选。结合临床和放射组学特征,构建了机器学习模型(Deathhepatectomy 和 DeathTACE)来预测患者在治疗(肝切除术和 TACE)后的死亡概率,并推荐最佳治疗方案。此外,还构建了一个预后模型来预测所有患者的生存时间:共招募了418名接受肝切除术(n=267,平均年龄为58岁±11岁[标准差];228名男性)或TACE(n=151,平均年龄为59岁±13岁[标准差];127名男性)的HCC患者。在构建机器学习模型Deathhepatectomy和DeathTACE后,患者被分为肝切除术首选组和TACE首选组。在肝切除术首选组中,肝切除术的生存时间明显长于TACE(训练组:P < 0.001;测试组:P < 0.001),反之亦然。此外,预后模型对总生存期也有很高的预测能力:机器学习模型可以预测肝切除术和 TACE 的结果差异,预后模型可以预测 HCC 患者的总生存期。
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A Machine Learning Model Based on Counterfactual Theory for Treatment Decision of Hepatocellular Carcinoma Patients.

Purpose: To predict the efficacy of patients treated with hepatectomy and transarterial chemoembolization (TACE) based on machine learning models using clinical and radiomics features.

Patients and methods: Patients with HCC whose first treatment was hepatectomy or TACE from June 2016 to July 2021 were collected in the retrospective cohort study. To ensure a causal effect of treatment effect and treatment modality, perfectly matched patients were obtained according to the principle of propensity score matching and used as an independent test cohort. Inverse probability of treatment weighting was used to control bias for unmatched patients, and the weighted results were used as the training cohort. Clinical characteristics were selected by univariate and multivariate analysis of cox proportional hazards regression, and radiomics features were selected using correlation analysis and random survival forest. The machine learning models (Deathhepatectomy and DeathTACE) were constructed to predict the probability of patient death after treatment (hepatectomy and TACE) by combining clinical and radiomics features, and an optimal treatment regimen was recommended. In addition, a prognostic model was constructed to predict the survival time of all patients.

Results: A total of 418 patients with HCC who received either hepatectomy (n=267, mean age, 58 years ± 11 [standard deviation]; 228 men) or TACE (n=151, mean age, 59 years ± 13 [standard deviation]; 127 men) were recruited. After constructing the machine learning models Deathhepatectomy and DeathTACE, patients were divided into the hepatectomy-preferred and TACE-preferred groups. In the hepatectomy-preferred group, hepatectomy had a significantly prolonged survival time than TACE (training cohort: P < 0.001; testing cohort: P < 0.001), and vise versa for the TACE-preferred group. In addition, the prognostic model yielded high predictive capability for overall survival.

Conclusion: The machine learning models could predict the outcomes difference between hepatectomy and TACE, and prognostic models could predict the overall survival for HCC patients.

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来源期刊
CiteScore
0.50
自引率
2.40%
发文量
108
审稿时长
16 weeks
期刊最新文献
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