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. 2023 Oct 4;11(1):87.
doi: 10.1186/s40364-023-00527-z.

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

Xin-Cheng Mao  1 , Shuo Shi  2 , Lun-Jie Yan  1 , Han-Chao Wang  3 , Zi-Niu Ding  1 , Hui Liu  1 , Guo-Qiang Pan  1 , Xiao Zhang  1 , Cheng-Long Han  1 , Bao-Wen Tian  1 , Dong-Xu Wang  1 , Si-Yu Tan  1 , Zhao-Ru Dong  1 , Yu-Chuan Yan  1 , Tao Li  4
Affiliations

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

Xin-Cheng Mao et al. Biomark Res. .

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.

Keywords: Adipose and muscle tissues; Computed tomography; Hepatocellular carcinoma; Microvascular invasion; Nomogram; Survival analysis.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart of the included HCC patients
Fig. 2
Fig. 2
Selecting influence factors by using LASSO regression model. a LASSO coefficient curves for 28 predictors. b Identification of the best punishment coefficient lambda in LASSO regression model
Fig. 3
Fig. 3
Nomogram model for predicting MVI of HCC patients (a) and the calibration curve of the nomogram in training (b) and validation cohorts (c)
Fig. 4
Fig. 4
ROC curve (a-b) and DCA (c-d) of the nomogram, imaging and clinical models in training and validation cohorts
Fig. 5
Fig. 5
Kaplan–Meier curves of OS and 2-year RFS for patients with different risks scores in training cohort (a, b) and validation cohort (c, d)
Fig. 6
Fig. 6
Kaplan–Meier curves of OS for high-risk patients under different surgical approaches, resection methods and surgical margins in training cohort (a-c) and validation cohort (d-f)
Fig. 7
Fig. 7
Kaplan–Meier curves of 2-RFS for high-risk patients under different surgical approaches, resection methods and surgical margins in training cohort (a-c) and validation cohort (d-f)
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