Construction and validation of nomograms for predicting the prognosis of late-stage hepatocellular carcinoma
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目的 构建和验证用于预测晚期肝细胞癌总生存期和癌症特异性生存期的Nomogram模型。 方法 本研究采用回顾性队列研究的方法，收集SEER数据库中2010—2015年病理诊断为晚期肝细胞癌的患者，共2 382例，使用R语言函数按1∶1的比例随机分为建模组(n＝1 191)和内部验证组(n＝1 191)，并采用χ2检验验证两组间可比性。选取苏州大学附属张家港医院的62例晚期肝细胞癌患者作为外部验证组。基于建模组中的单因素和多因素COX回归分析，构建6个月和12个月的总生存期和癌症特异性生存期的Nomogram预测模型。一致性指数(C-index)、校准图、受试者工作特征(ROC)曲线和Kaplan-Meier生存分析检测Nomogram模型的评估能力，并在内部验证组、外部验证组中进行验证。通过决策曲线分析(DCA)衡量模型的临床效用。 结果 基于COX回归分析构建Nomogram模型，纳入模型的危险因素包括性别、组织学分级、T分期、N分期、M分期、肿瘤大小、骨转移、甲胎蛋白、原发部位手术、放疗和化疗。总生存期模型中，建模组、内部验证组和外部验证组的C-index分别为0.729(95%CI：0.711~0.747)、0.721(95%CI：0.705~0.737)和0.860(95%CI：0.831~0.889)，癌症特异性生存期模型中分别为0.732(95%CI：0.714~0.750)、0.725(95%CI：0.707~0.743)和0.862(95%CI：0.829~0.895)。校准图、ROC曲线和Kaplan-Meier生存曲线显示Nomogram模型有良好的预测能力，DCA显示Nomogram模型的临床应用价值优于其他传统临床模型。 结论 构建了用于预测晚期肝细胞癌总生存期和癌症特异性生存期的Nomogram模型，并进行了验证。 Objective To construct and validate prognostic nomograms predicting overall survival (OS) and cancer-specific survival (CSS) of patients with late-stage hepatocellular carcinoma (HCC). Methods A retrospective cohort study was used in this report. Screened 2382 late-stage HCC patients obtained from Surveillance, Epidemiology, and End Results (SEER) database (2010—2015), were randomly classified into the training cohort and the internal validation cohort by using the function in R software according to the ratio of 1∶1. Chi-square test was applied to verify the comparability of data between two groups. The external validation cohort (n=62) were collected from the Affiliated Zhangjiagang Hospital of Soochow University. Based on univariate and multivariate COX regression analyses in the training cohort, this study constructed nomograms for 6- and 12- month OS and CSS. Concordance index (C-index), calibration plots, the receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves were applied to measure the performance of nomograms in the training cohort and to validate nomograms in two validation cohorts. The clinical utility was measured by decision curve analysis (DCA). Results Two nomograms were constructed. The identified risk factors included sex, Edmondson-Steiner grade, T stage, N stage, M stage, tumor size, bone metastasis, Alpha-fetoprotein (AFP), surgery of primary site, radiation and chemotherapy. The C-index for OS in the training and two validation cohorts was 0.729(95%CI: 0.711-0.747), 0.721(95%CI: 0.705-0.737) and 0.860(95CI: 0.831-0.889), respectively. The C-index for CSS in the training and two validation cohorts was 0.732(95%CI: 0.714-0.750), 0.725(95%CI: 0.707-0.743) and 0.862(95%CI: 0.829-0.895), respectively. Afterwards, for nomograms in the training and two validation cohorts, C-index and calibration plots expressed great predictive accuracy and concordance. ROC curves and Kaplan-Meier survival curves demonstrated good prognostic ability. Furthermore, nomograms performed superior to other models. DCA showed substantial clinical utility. Conclusion This study has developed and validated nomograms predicting 6- and 12- month OS and CSS of patients with late-stage HCC, which may be useful to develop the individualized treatment.
Objective To construct and validate prognostic nomograms predicting overall survival (OS) and cancer-specific survival (CSS) of patients with late-stage hepatocellular carcinoma (HCC). Methods A retrospective cohort study was used in this report. Screened 2382 late-stage HCC patients obtained from Surveillance, Epidemiology, and End Results (SEER) database (2010—2015), were randomly classified into the training cohort and the internal validation cohort by using the function in R software according to the ratio of 1∶1. Chi-square test was applied to verify the comparability of data between two groups. The external validation cohort (n=62) were collected from the Affiliated Zhangjiagang Hospital of Soochow University. Based on univariate and multivariate COX regression analyses in the training cohort, this study constructed nomograms for 6- and 12- month OS and CSS. Concordance index (C-index), calibration plots, the receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves were applied to measure the performance of nomograms in the training cohort and to validate nomograms in two validation cohorts. The clinical utility was measured by decision curve analysis (DCA). Results Two nomograms were constructed. The identified risk factors included sex, Edmondson-Steiner grade, T stage, N stage, M stage, tumor size, bone metastasis, Alpha-fetoprotein (AFP), surgery of primary site, radiation and chemotherapy. The C-index for OS in the training and two validation cohorts was 0.729(95%CI: 0.711-0.747), 0.721(95%CI: 0.705-0.737) and 0.860(95CI: 0.831-0.889), respectively. The C-index for CSS in the training and two validation cohorts was 0.732(95%CI: 0.714-0.750), 0.725(95%CI: 0.707-0.743) and 0.862(95%CI: 0.829-0.895), respectively. Afterwards, for nomograms in the training and two validation cohorts, C-index and calibration plots expressed great predictive accuracy and concordance. ROC curves and Kaplan-Meier survival curves demonstrated good prognostic ability. Furthermore, nomograms performed superior to other models. DCA showed substantial clinical utility. Conclusion This study has developed and validated nomograms predicting 6- and 12- month OS and CSS of patients with late-stage HCC, which may be useful to develop the individualized treatment.
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