A model based on adipose and muscle-related indicators evaluated by CT images for predicting microvascular invasion in HCC patients

Biomarker Research

Research

Zhao-Ru Dong¹  Xin-Cheng Mao¹  Bao-Wen Tian¹  Hui Liu¹  Dong-Xu Wang¹  Zi-Niu Ding¹  Si-Yu Tan¹  Cheng-Long Han¹  Yu-Chuan Yan¹  Lun-Jie Yan¹  Guo-Qiang Pan¹  Xiao Zhang¹  Tao Li¹  Shuo Shi²  Han-Chao Wang³ 

[1] Department of General Surgery, Qilu Hospital, Shandong University, 107 West Wen Hua Road, 250012, Jinan, China;Department of Radiology, Qilu Hospital, Shandong University, Jinan, China;Institute for Financial Studies, Shandong University, 250100, Jinan, China;

关键词: Hepatocellular carcinoma;    Microvascular invasion;    Adipose and muscle tissues;    Computed tomography;    Nomogram;    Survival analysis;   

DOI  :  10.1186/s40364-023-00527-z

 received in 2023-03-31, accepted in 2023-09-20,  发布年份 2023

来源: Springer

【 摘 要 】

Background and aimThe 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.MethodsOne 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).ResultsHistopathologically 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.ConclusionBy 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.

【 授权许可】

CC BY   
© Yumed Inc. and BioMed Central Ltd., part of Springer Nature 2023

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