构建包含体成分参数的列线图预测肝癌术后早期复发的研究

Development and External Validation of a Nomogram Including Body Composition Parameters for Predicting Early Recurrence of Hepatocellular Carcinoma After Hepatectomy.

机构信息

Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong 250012, China.

出版信息

Acad Radiol. 2023 Dec;30(12):2940-2953. doi: 10.1016/j.acra.2023.05.022. Epub 2023 Oct 4.

DOI:10.1016/j.acra.2023.05.022

PMID:37798207

Abstract

RATIONALE AND OBJECTIVES

Body composition, including adipose and muscle tissues, evaluated by computer tomography is correlated with the prognosis of hepatocellular carcinoma (HCC). However, its relationship with early recurrence (ER) remains unclear. This study aimed at establishing and validating a nomogram based on body composition and clinicopathological indices to predict ER of HCC.

MATERIALS AND METHODS

One hundred ninety-five patients from institution A formed the training cohort and internal validation cohort, and 50 patients from institution B formed the external validation cohort. Independent predictors of ER were identified using LASSO and Cox regression analyses. The performance of nomogram was evaluated using the calibration curve, concordance index (C-index), area under the curve (AUC), and decision curve analysis (DCA).

RESULTS

After data screening, the nomogram was constructed using eight independent predictors of ER, including the tumor size, alpha fetoprotein, body mass index, Edmondson Steiner grade, visceral adipose tissue radiodensity, intermuscular adipose tissue index, intramuscular adipose tissue content, and skeletal muscle area. The calibration curve exhibited excellent concordances, with C-indices of 0.808 (95%CI: 0.771-0.860), 0.802 (95%CI: 0.747-0.942), and 0.804 (95%CI: 0.701-0.861) in training, internal validation, and external validation cohorts, respectively. In addition, compared to conventional staging systems and pure clinical model, the nomogram exhibited a higher AUC and wider range of threshold probabilities in DCA, which indicated better discriminative ability and greater clinical benefit. Finally, patients with nomogram scores of <183.07, 183.07-243.09, and >243.09 were considered to have low, moderate, and high risks of ER, respectively.

CONCLUSION

The nomogram exhibits excellent ER predictive ability for patients with HCC who underwent hepatectomy.

摘要

背景与目的

计算机断层扫描评估的身体成分，包括脂肪组织和肌肉组织，与肝细胞癌（HCC）的预后相关。然而，其与早期复发（ER）的关系尚不清楚。本研究旨在建立和验证一个基于身体成分和临床病理指标的列线图，以预测 HCC 的 ER。

材料与方法

来自机构 A 的 195 名患者组成了训练队列和内部验证队列，来自机构 B 的 50 名患者组成了外部验证队列。使用 LASSO 和 Cox 回归分析确定 ER 的独立预测因素。使用校准曲线、一致性指数（C-index）、曲线下面积（AUC）和决策曲线分析（DCA）评估列线图的性能。

结果

经过数据筛选，该列线图使用 8 个 ER 的独立预测因素构建，包括肿瘤大小、甲胎蛋白、体重指数、Edmondson-Steiner 分级、内脏脂肪组织密度、肌间脂肪组织指数、肌肉内脂肪组织含量和骨骼肌面积。校准曲线显示出极好的一致性，训练、内部验证和外部验证队列的 C 指数分别为 0.808（95%CI：0.771-0.860）、0.802（95%CI：0.747-0.942）和 0.804（95%CI：0.701-0.861）。此外，与传统分期系统和纯临床模型相比，列线图在 DCA 中具有更高的 AUC 和更宽的阈值概率范围，这表明其具有更好的判别能力和更大的临床获益。最后，将列线图评分<183.07、183.07-243.09 和>243.09 的患者分别归类为低、中、高 ER 风险。