A radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure in patients with hepatocellular carcinoma
A radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure in patients with hepatocellular carcinoma
复制标题
基于放射组学的列线图用于术前预测肝细胞癌患者肝切除术后肝功能衰竭
DOI：
10.1016/j.suronc.2018.11.013
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发表时间：
2019-03-01
影响因子：
2.3
通讯作者：
Fang, Chihua
中科院分区：
文献类型：
--
作者：
Cai, Wei;He, Baochun;Fang, Chihua
目的：开发和验证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：12 名连续接受肝切除术的 HCC 患者被纳入研究池（训练队列：n = 80，验证队列：n = 32），另外 13 名患者被纳入试点前瞻性分析。从门脉期计算机断层扫描 (CT) 图像中提取了总共 713 个放射组学特征。使用逻辑回归来构建放射组学评分（Rad-score）。然后使用多元逻辑回归模型构建列线图，包括 Rad 评分和其他风险因素。评估列线图的辨别力、校准和临床实用性。结果：Rad-score 可以预测 PHLF，训练组中的 AUC 为 0.822（95% CI，0.726-0.917），验证组中的 AUC 为 0.762（95% CI，0.576-0.948）；然而，该方法无法完全超越现有方法（CP [Child-Pugh]、MELD [终末期肝病模型]、ALBI [白蛋白-胆红素]）。包括 Rad 评分、MELD 和体能状态 (PS) 在内的个体预测列线图显示出更好的辨别力，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 分别为 P < 0.001、P < 0.005 和 P < 0.005）。在验证队列中，列线图辨别力也优于其他三种方法（AUC：0.896；95% CI，0.774-1.000）。两个队列的校准曲线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在临床上有用。初步前瞻性分析表明，放射组学列线图可以预测 PHLF，AUC 为 0.833（95% CI，0.591-1.000）。结论：基于 Rad 评分、MELD 和 PS 的列线图可以预测 PHLF。
Objectives: To develop and validate a radiomics-based nomogram for the preoperative prediction of post-hepatectomy 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.