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GALAD 和 BALAD-2 血清学模型在肝细胞癌诊断和患者生存预测中的作用。

Role of the GALAD and BALAD-2 Serologic Models in Diagnosis of Hepatocellular Carcinoma and Prediction of Survival in Patients.

机构信息

Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, United Kingdom.

Department of Gastroenterology and Hepatology, Ogaki Municipal Hospital, Gifu, Japan.

出版信息

Clin Gastroenterol Hepatol. 2016 Jun;14(6):875-886.e6. doi: 10.1016/j.cgh.2015.12.042. Epub 2016 Jan 13.

Abstract

BACKGROUND & AIMS: GALAD and BALAD-2 are statistical models for estimating the likelihood of the presence of hepatocellular carcinoma (HCC) in individual patients with chronic liver disease and the survival of patients with HCC, respectively. Both models use objective measures, particularly the serum markers α-fetoprotein (AFP), AFP-L3, and des-γ-carboxyprothrombin. We aimed to validate these models in an international cohort of patients with HCC and assess their clinical performance.

METHODS

We collected data on cancer diagnosis and outcomes of 6834 patients (2430 with HCC and 4404 with chronic liver disease) recruited from Germany, Japan, and Hong Kong. We also collected data from 229 patients with other hepatobiliary tract cancers (cholangiocarcinoma or pancreatic adenocarcinoma) and 92 healthy individuals (controls). For reference, the original UK cohort (on which the GALAD model initially was built and BALAD-2 was validated) was included in the analysis. We assessed the effects of tumor size and etiology on GALAD model performance, and its ability to correctly discriminate HCC from other hepatobiliary cancers. We assessed the performance of BALAD-2 in patients with different stages of HCC.

RESULTS

In all cohorts, the area under the receiver operating characteristic curve (AUROC), quantifying the ability of GALAD to discriminate patients with HCC from patients with chronic liver disease, was greater than 0.90-similar to the series on which the model originally was built (AUROC, 0.97). GALAD discriminated patients with HCC from those with other hepatobiliary cancers with an AUROC value of 0.95; values were slightly lower for patients with small unifocal HCCs, ranging from 0.85 to 0.95. Etiology and treatment of chronic viral hepatitis had no effect on the performance of this model. BALAD-2 analysis assigned patients with HCC to 4 distinct prognostic groups-overall and when patients were stratified according to disease stage.

CONCLUSIONS

We validated the performance of the GALAD and BALAD-2 models for the diagnosis of HCC and predicting patient survival, respectively (based on levels of the serum markers AFP, AFP-L3, and des-γ-carboxyprothrombin), in an international cohort of almost 7000 patients. These systems might be used in HCC surveillance and determination of patient prognosis.

摘要

背景与目的

GALAD 和 BALAD-2 分别是用于估计慢性肝病患者中肝细胞癌(HCC)存在可能性和 HCC 患者生存率的统计模型。这两个模型都使用客观指标,特别是血清标志物甲胎蛋白(AFP)、AFP-L3 和脱γ-羧基凝血酶原。我们旨在验证这些模型在国际 HCC 患者队列中的表现,并评估它们的临床性能。

方法

我们收集了来自德国、日本和中国香港的 6834 名患者(2430 名 HCC 患者和 4404 名慢性肝病患者)的癌症诊断和结局数据。我们还收集了 229 名其他肝胆管癌(胆管癌或胰腺腺癌)患者和 92 名健康个体(对照)的数据。作为参考,最初建立 GALAD 模型并验证 BALAD-2 的英国原始队列也包含在分析中。我们评估了肿瘤大小和病因对 GALAD 模型性能的影响,以及其正确区分 HCC 与其他肝胆癌的能力。我们评估了 BALAD-2 在不同 HCC 阶段患者中的表现。

结果

在所有队列中,GALAD 区分 HCC 患者和慢性肝病患者的接收者操作特征曲线下面积(AUROC)均大于 0.90-与最初建立该模型的系列相似(AUROC,0.97)。GALAD 区分 HCC 患者和其他肝胆癌患者的 AUROC 值为 0.95;对于小的单发 HCC 患者,该值略低,范围为 0.85 至 0.95。慢性病毒性肝炎的病因和治疗对该模型的性能没有影响。BALAD-2 分析将 HCC 患者分配到 4 个不同的预后组-整体和根据疾病阶段对患者进行分层时。

结论

我们在近 7000 名患者的国际队列中验证了 GALAD 和 BALAD-2 模型分别用于诊断 HCC 和预测患者生存的性能(基于血清标志物 AFP、AFP-L3 和脱γ-羧基凝血酶原的水平)。这些系统可能用于 HCC 监测和患者预后的确定。

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