Mao Xin-Cheng, Shi Shuo, Yan Lun-Jie, Wang Han-Chao, Ding Zi-Niu, Liu Hui, Pan Guo-Qiang, Zhang Xiao, Han Cheng-Long, Tian Bao-Wen, Wang Dong-Xu, Tan Si-Yu, Dong Zhao-Ru, Yan Yu-Chuan, Li Tao
Department of General Surgery, Qilu Hospital, Shandong University, 107 West Wen Hua Road, Jinan, 250012, China.
Department of Radiology, Qilu Hospital, Shandong University, Jinan, China.
Biomark Res. 2023 Oct 4;11(1):87. doi: 10.1186/s40364-023-00527-z.
The presence of microvascular invasion (MVI) will impair the surgical outcome of hepatocellular carcinoma (HCC). Adipose and muscle tissues have been confirmed to be associated with the prognosis of HCC. We aimed to develop and validate a nomogram based on adipose and muscle related-variables for preoperative prediction of MVI in HCC.
One hundred fifty-eight HCC patients from institution A (training cohort) and 53 HCC patients from institution B (validation cohort) were included, all of whom underwent preoperative CT scan and curative resection with confirmed pathological diagnoses. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied to data dimensionality reduction and screening. Nomogram was constructed based on the independent variables, and evaluated by external validation, calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA).
Histopathologically identified MVI was found in 101 of 211 patients (47.9%). The preoperative imaging and clinical variables associated with MVI were visceral adipose tissue (VAT) density, intramuscular adipose tissue index (IMATI), skeletal muscle (SM) area, age, tumor size and cirrhosis. Incorporating these 6 factors, the nomogram achieved good concordance index of 0.79 (95%CI: 0.72-0.86) and 0.75 (95%CI: 0.62-0.89) in training and validation cohorts, respectively. In addition, calibration curve exhibited good consistency between predicted and actual MVI probabilities. ROC curve and DCA of the nomogram showed superior performance than that of models only depended on clinical or imaging variables. Based on the nomogram score, patients were divided into high (> 273.8) and low (< = 273.8) risk of MVI presence groups. For patients with high MVI risk, wide-margin resection or anatomical resection could significantly improve the 2-year recurrence free survival.
By combining 6 preoperative independently predictive factors of MVI, a nomogram was constructed. This model provides an optimal preoperative estimation of MVI risk in HCC patients, and may help to stratify high-risk individuals and optimize clinical decision making.
微血管侵犯（MVI）的存在会损害肝细胞癌（HCC）的手术效果。脂肪组织和肌肉组织已被证实与HCC的预后相关。我们旨在开发并验证一种基于脂肪和肌肉相关变量的列线图，用于术前预测HCC中的MVI。
纳入来自机构A的158例HCC患者（训练队列）和来自机构B的53例HCC患者（验证队列），所有患者均接受了术前CT扫描及根治性切除，并经病理确诊。应用最小绝对收缩和选择算子（LASSO）逻辑回归进行数据降维和筛选。基于自变量构建列线图，并通过外部验证、校准曲线、受试者工作特征（ROC）曲线和决策曲线分析（DCA）进行评估。
211例患者中有101例（47.9%）经组织病理学鉴定存在MVI。与MVI相关的术前影像和临床变量包括内脏脂肪组织（VAT）密度、肌内脂肪组织指数（IMATI）、骨骼肌（SM）面积、年龄、肿瘤大小和肝硬化。纳入这6个因素后，列线图在训练队列和验证队列中的一致性指数分别为0.79（95%CI：0.72 - 0.86）和0.75（95%CI：0.62 - 0.89）。此外，校准曲线显示预测的MVI概率与实际概率之间具有良好的一致性。列线图的ROC曲线和DCA显示其性能优于仅依赖临床或影像变量的模型。根据列线图评分，将患者分为MVI存在风险高（> 273.8）和低（<= 273.8）两组。对于MVI风险高的患者，宽切缘切除或解剖性切除可显著提高2年无复发生存率。
通过结合6个术前MVI的独立预测因素，构建了列线图。该模型为HCC患者的MVI风险提供了最佳的术前估计，可能有助于对高危个体进行分层并优化临床决策。
