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杜琪, 殷欣, 任正刚. 随机生存森林模型预测肝动脉化疗栓塞治疗肝细胞癌患者的预后[J]. 中国临床医学, 2024, 31(2): 177-185. DOI: 10.12025/j.issn.1008-6358.2024.20240002
引用本文: 杜琪, 殷欣, 任正刚. 随机生存森林模型预测肝动脉化疗栓塞治疗肝细胞癌患者的预后[J]. 中国临床医学, 2024, 31(2): 177-185. DOI: 10.12025/j.issn.1008-6358.2024.20240002
DU Qi, YIN Xin, REN Zhenggang. Random survival forest model predicts the prognosis of patients with hepatocellular carcinoma treated by transcatheter arterial chemoembolization[J]. Chinese Journal of Clinical Medicine, 2024, 31(2): 177-185. DOI: 10.12025/j.issn.1008-6358.2024.20240002
Citation: DU Qi, YIN Xin, REN Zhenggang. Random survival forest model predicts the prognosis of patients with hepatocellular carcinoma treated by transcatheter arterial chemoembolization[J]. Chinese Journal of Clinical Medicine, 2024, 31(2): 177-185. DOI: 10.12025/j.issn.1008-6358.2024.20240002

随机生存森林模型预测肝动脉化疗栓塞治疗肝细胞癌患者的预后

Random survival forest model predicts the prognosis of patients with hepatocellular carcinoma treated by transcatheter arterial chemoembolization

  • 摘要:
    目的 采用随机生存森林算法分析影响肝动脉化疗栓塞(transcatheter arterial chemoembolization,TACE)治疗不可切除肝细胞癌(hepatocellular carcinoma,HCC)患者的预后因素,并构建预后模型。
    方法 回顾性选择2014年1月至2017年12月复旦大学附属中山医院肝肿瘤内科收治的一线治疗为TACE的HCC患者636例,并按照7∶3比例划分为训练集(n=445)和验证集(n=191)。根据患者的临床数据、实验室指标及随访生存数据,建立Cox比例风险模型和基于机器学习算法的随机生存森林模型,并评估2种模型的预测能力。
    结果 肿瘤负荷、年龄、基线G-谷氨酰转肽酶水平、基线甲胎蛋白水平和白蛋白-胆红素分级是影响TACE治疗不能切除HCC患者的独立预后因素。Cox回归模型的训练集1年、3年、5年的ROC曲线下面积(area under the curve,AUC)为0.782、0.796和0.791,验证集为0.750、0.766和0.766。随机生存森林模型训练集1年、3年和5年AUC为0.896、0.894和0.875,验证集为0.743、0.763和0.770。随机生存森林模型能将患者区分为预后好组和预后差组,两组生存期差异有统计学意义(P<0.05)。决策曲线显示随机生存森林模型的净获益优于Cox比例风险模型。
    结论 随机生存森林模型是预测TACE治疗不可切除HCC患者预后的可靠工具。

     

    Abstract:
    Objective A random survival forest algorithm was applied to explore the prognostic factors and develop the prognosis model for patients with unresectable hepatocellular carcinoma (HCC) after transcatheter arterial chemoembolization (TACE).
    Methods Retrospective selection of 636 HCC patients treated with TACE as first-line treatment in the Department of Hepatology, Zhongshan Hospital, Fudan University from January 2014 to December 2017. The patients were divided into a training set (n=445) and a validation set (n=191) in a 7∶3 ratio. Based on the clinical data, laboratory indicators and follow-up survival of patients, the Cox proportional-hazards regression model and the random survival forest model based on machine learning algorithm was developed, and the predictive ability of the two models was evaluated.
    Results The tumor burden, age, baseline gamma-glutamyl transpeptidase (GGT) level, baseline alpha-fetoprotein (AFP) level and albumin-bilirubin grade (ALBI) were independent factors affecting the prognosis of unresectable HCC patients treated with TACE. In the Cox model, the 1-year, 3-year and 5-year AUC of the training set was 0.782, 0.796 and 0.791, respectively, and the validation set was 0.750, 0.766 and 0.766, respectively. The 1-year, 3-year and 5-year AUC of the training set in the random survival forest model was 0.896, 0.894 and 0.875, respectively, and validation set was 0.743, 0.763 and 0.770, respectively. Random survival forest model could distinguish patients into good prognosis group and poor prognosis group, and the overall survival of these two groups was significantly different (P < 0.05). The decision curve analysis showed that the net benefit of the random survival forest model was better than that of the Cox proportional-hazards model.
    Conclusions The random survival forest model is a reliable tool for predicting the prognosis of unresectable HCC patients treated with TACE.

     

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