Author:
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
Abstract
Abstract
Background and aim
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.
Methods
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).
Results
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.
Conclusion
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.
Funder
the Taishan Scholars Program of Shandong Province
National Natural Science Foundation of China
Major basic research of Shandong Provincial Natural Science Foundation
Independent Cultivation of Innovative Team from Universities in Jinan
Publisher
Springer Science and Business Media LLC
Subject
Biochemistry (medical),Clinical Biochemistry,Molecular Medicine