基于阿替利珠单抗和贝伐珠单抗治疗的肝细胞癌患者的生存预测的分类和回归树。
Classification and Regression Trees to Predict for Survival for Patients With Hepatocellular Carcinoma Treated With Atezolizumab and Bevacizumab.
机构信息
Division of Hematology/Oncology, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TX.
Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA.
出版信息
JCO Clin Cancer Inform. 2024 Aug;8:e2300220. doi: 10.1200/CCI.23.00220.
PURPOSE
Systemic therapy with atezolizumab and bevacizumab can extend life for patients with advanced hepatocellular carcinoma (HCC). However, there is substantial variability in response to therapy and overall survival. Although current prognostic models have been validated in HCC, they primarily consider covariates that may be reflective of the severity of the underlying liver disease of patients with HCC. We developed and internally validated a classification and regression tree (CART) to identify patient characteristics associated with risks of early mortality, at or before 6 months from treatment initiation.
METHODS
This retrospective cohort study used the nationwide Flatiron Health electronic health record-derived deidentified database and included patients with a diagnosis of HCC after January 1, 2020, who received initial systemic therapy with atezolizumab and bevacizumab. CART was developed from available baseline clinical and demographic information to predict mortality within 6 months from treatment initiation. Model characteristics were compared to the albumin-bilirubin (ALBI) model and was further validated against a contemporary validation cohort of patients after a data update.
RESULTS
A total of 293 patients were analyzed. The CART identified seven cohorts of patients from baseline demographic and laboratory characteristics. The model had an area under the receiver operating curve (AUROC) of 0.739 (95% CI, 0.683 to 0.794) for predicting 6-month mortality. This model was internally valid and performed more favorably than the ALBI model, which had an AUROC of 0.608 (95% CI, 0.557 to 0.660). The model applied to the contemporary validation cohort (n = 111) had an AUROC of 0.666 (95% CI, 0.506 to 0.826).
CONCLUSION
Using CART, we identified unique cohorts of patients with HCC treated with atezolizumab and bevacizumab with distinct risks of early mortality. This approach outperformed the ALBI model and used clinical and laboratory characteristics that are readily available to oncologists caring for these patients.
目的
阿替利珠单抗联合贝伐珠单抗的系统治疗可延长晚期肝细胞癌（HCC）患者的生命。然而，对治疗的反应和总生存率存在很大差异。虽然目前的预后模型已经在 HCC 中得到验证，但它们主要考虑的是可能反映 HCC 患者基础肝病严重程度的协变量。我们开发并内部验证了一个分类和回归树（CART），以确定与治疗开始后 6 个月内（或之前）早期死亡风险相关的患者特征。
方法
本回顾性队列研究使用了全国范围的 Flatiron Health 电子健康记录衍生的去识别数据库，纳入了 2020 年 1 月 1 日以后诊断为 HCC 并接受初始阿替利珠单抗联合贝伐珠单抗系统治疗的患者。CART 从可用的基线临床和人口统计学信息中开发，以预测治疗开始后 6 个月内的死亡率。比较了模型特征与白蛋白-胆红素（ALBI）模型，并在数据更新后对患者的当代验证队列进行了进一步验证。
结果
共分析了 293 例患者。CART 根据基线人口统计学和实验室特征确定了 7 个患者队列。该模型预测 6 个月死亡率的受试者工作特征曲线下面积（AUROC）为 0.739（95%CI，0.683 至 0.794）。该模型具有内部有效性，性能优于 ALBI 模型，后者的 AUROC 为 0.608（95%CI，0.557 至 0.660）。该模型应用于当代验证队列（n=111）时的 AUROC 为 0.666（95%CI，0.506 至 0.826）。
结论
使用 CART，我们确定了接受阿替利珠单抗联合贝伐珠单抗治疗的 HCC 患者具有独特的早期死亡风险队列。这种方法优于 ALBI 模型，并使用了临床和实验室特征，这些特征对治疗这些患者的肿瘤学家来说是易于获得的。
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