一种基于影像组学的列线图，用于术前预测肝细胞癌患者肝切除术后肝衰竭。

A radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure in patients with hepatocellular carcinoma.

作者信息

Cai Wei, He Baochun, Hu Min, Zhang Wenyu, Xiao Deqiang, Yu Hao, Song Qi, Xiang Nan, Yang Jian, He Songsheng, Huang Yaohuan, Huang Wenjie, Jia Fucang, Fang Chihua

机构信息

Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China; Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

出版信息

Surg Oncol. 2019 Mar;28:78-85. doi: 10.1016/j.suronc.2018.11.013. Epub 2018 Nov 14.

DOI:10.1016/j.suronc.2018.11.013

PMID:30851917

Abstract

OBJECTIVES

To develop and validate a radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC).

METHODS

One hundred twelve consecutive HCC patients who underwent hepatectomy were included in the study pool (training cohort: n = 80, validation cohort: n = 32), and another 13 patients were included in a pilot prospective analysis. A total of 713 radiomics features were extracted from portal-phase computed tomography (CT) images. A logistic regression was used to construct a radiomics score (Rad-score). Then a nomogram, including Rad-score and other risk factors, was built with a multivariate logistic regression model. The discrimination, calibration and clinical utility of nomogram were evaluated.

RESULTS

The Rad-score could predict PHLF with an AUC of 0.822 (95% CI, 0.726-0.917) in the training cohort and of 0.762 (95% CI, 0.576-0.948) in the validation cohort; however, the approach could not completely outmatch the existing methods (CP [Child-Pugh], MELD [Model of End Stage Liver Disease], ALBI [albumin-bilirubin]). The individual predictive nomogram that included the Rad-score, MELD and performance status (PS) showed better discrimination with an AUC of 0.864 (95% CI, 0.786-0.942), which was higher than the AUCs of the conventional methods (nomogram vs CP, MELD, and ALBI at P < 0.001, P < 0.005, and P < 0.005, respectively). In the validation cohort, the nomogram discrimination was also superior to those of the other three methods (AUC: 0.896; 95% CI, 0.774-1.000). The calibration curves showed good agreement in both cohorts, and the decision curve analysis of the entire cohort revealed that the nomogram was clinically useful. A pilot prospective analysis showed that the radiomics nomogram could predict PHLF with an AUC of 0.833 (95% CI, 0.591-1.000).

CONCLUSIONS

A nomogram based on the Rad-score, MELD, and PS can predict PHLF.

摘要

目的

开发并验证一种基于放射组学的列线图，用于术前预测肝细胞癌（HCC）患者肝切除术后肝衰竭（PHLF）。

方法

112例连续接受肝切除术的HCC患者纳入研究队列（训练队列：n = 80，验证队列：n = 32），另外13例患者纳入前瞻性初步分析。从门静脉期计算机断层扫描（CT）图像中提取713个放射组学特征。采用逻辑回归构建放射组学评分（Rad-score）。然后使用多变量逻辑回归模型建立包含Rad-score和其他危险因素的列线图。评估列线图的区分度、校准度和临床实用性。

结果

Rad-score在训练队列中预测PHLF的AUC为0.822（95%CI，0.726 - 0.917），在验证队列中为0.762（95%CI，0.576 - 0.948）；然而，该方法不能完全优于现有方法（Child-Pugh分级[CP]、终末期肝病模型[MELD]、白蛋白-胆红素分级[ALBI]）。包含Rad-score、MELD和体能状态（PS）的个体预测列线图显示出更好的区分度，AUC为0.864（95%CI，0.786 - 0.942），高于传统方法的AUC（列线图与CP、MELD和ALBI比较，P分别<0.001、<0.005和<0.005）。在验证队列中，列线图的区分度也优于其他三种方法（AUC：0.896；95%CI，0.774 - 1.000）。校准曲线在两个队列中显示出良好的一致性，整个队列的决策曲线分析表明列线图具有临床实用性。前瞻性初步分析显示，放射组学列线图预测PHLF的AUC为0.833（95%CI，0.591 - 1.000）。