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一种用于肝细胞癌患者微血管侵犯的新型预测评分模型的开发与验证

Development and validation of a novel predictive scoring model for microvascular invasion in patients with hepatocellular carcinoma.

作者信息

Zhao Hui, Hua Ye, Dai Tu, He Jian, Tang Min, Fu Xu, Mao Liang, Jin Huihan, Qiu Yudong

机构信息

Department of Hepatopancreatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, Jiangsu, China; Department of Hepatopancreatobiliary Surgery, Nanjing Medical University Affiliated Wuxi Second People's Hospital, Wuxi, Jiangsu, China.

Department of Neurology, Nanjing Medical University Affiliated Wuxi Second People's Hospital, Wuxi, Jiangsu, China.

出版信息

Eur J Radiol. 2017 Mar;88:32-40. doi: 10.1016/j.ejrad.2016.12.030. Epub 2016 Dec 27.

Abstract

PURPOSE

Microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) cannot be accurately predicted preoperatively. This study aimed to establish a predictive scoring model of MVI in solitary HCC patients without macroscopic vascular invasion.

METHODS

A total of 309 consecutive HCC patients who underwent curative hepatectomy were divided into the derivation (n=206) and validation cohort (n=103). A predictive scoring model of MVI was established according to the valuable predictors in the derivation cohort based on multivariate logistic regression analysis. The performance of the predictive model was evaluated in the derivation and validation cohorts.

RESULTS

Preoperative imaging features on CECT, such as intratumoral arteries, non-nodular type of HCC and absence of radiological tumor capsule were independent predictors for MVI. The predictive scoring model was established according to the β coefficients of the 3 predictors. Area under receiver operating characteristic (AUROC) of the predictive scoring model was 0.872 (95% CI, 0.817-0.928) and 0.856 (95% CI, 0.771-0.940) in the derivation and validation cohorts. The positive and negative predictive values were 76.5% and 88.0% in the derivation cohort and 74.4% and 88.3% in the validation cohort. The performance of the model was similar between the patients with tumor size ≤5cm and >5cm in AUROC (P=0.910).

CONCLUSIONS

The predictive scoring model based on intratumoral arteries, non-nodular type of HCC, and absence of the radiological tumor capsule on preoperative CECT is of great value in the prediction of MVI regardless of tumor size.

摘要

目的

肝细胞癌(HCC)患者的微血管侵犯(MVI)术前无法准确预测。本研究旨在建立无肉眼可见血管侵犯的孤立性HCC患者MVI的预测评分模型。

方法

将309例接受根治性肝切除术的连续HCC患者分为推导队列(n = 206)和验证队列(n = 103)。基于多因素逻辑回归分析,根据推导队列中的有价值预测因素建立MVI预测评分模型。在推导队列和验证队列中评估预测模型的性能。

结果

术前CT增强扫描(CECT)的影像特征,如瘤内动脉、非结节型HCC及无肿瘤放射学包膜,是MVI的独立预测因素。根据这3个预测因素的β系数建立预测评分模型。该预测评分模型在推导队列和验证队列中的受试者操作特征曲线下面积(AUROC)分别为0.872(95%CI,0.817 - 0.928)和0.856(95%CI,0.771 - 0.940)。推导队列中的阳性和阴性预测值分别为76.5%和88.0%,验证队列中分别为74.4%和88.3%。在AUROC方面,肿瘤大小≤5cm和>5cm的患者之间模型性能相似(P = 0.910)。

结论

基于术前CECT上的瘤内动脉、非结节型HCC及无肿瘤放射学包膜的预测评分模型,无论肿瘤大小,在预测MVI方面都具有重要价值。

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