Preoperative Assessment of Abdominal Adipose Tissue to Predict Microvascular Invasion in Small Hepatocellular Carcinoma

The potential of 18F-FDG PET/CT metabolic parameter-based nomogram in predicting the microvascular invasion of hepatocellular carcinoma before liver transplantation

PURPOSE

Microvascular invasion (MVI) is a critical factor in predicting the recurrence and prognosis of hepatocellular carcinoma (HCC) after liver transplantation (LT). However, there is a lack of reliable preoperative predictors for MVI. The purpose of this study is to evaluate the potential of an 18F-FDG PET/CT-based nomogram in predicting MVI before LT for HCC.

METHODS

83 HCC patients who obtained 18F-FDG PET/CT before LT were included in this retrospective research. To determine the parameters connected to MVI and to create a nomogram for MVI prediction, respectively, Logistic and Cox regression models were applied. Analyses of the calibration curve and receiver operating characteristic (ROC) curves were used to assess the model's capability to differentiate between clinical factors and metabolic data from PET/CT images.

RESULTS

Among the 83 patients analyzed, 41% were diagnosed with histologic MVI. Multivariate logistic regression analysis revealed that Child-Pugh stage, alpha-fetoprotein, number of tumors, CT Dmax, and Tumor-to-normal liver uptake ratio (TLR) were significant predictors of MVI. A nomogram was constructed using these predictors, which demonstrated strong calibration with a close agreement between predicted and actual MVI probabilities. The nomogram also showed excellent differentiation with an AUC of 0.965 (95% CI 0.925-1.000).

CONCLUSION

The nomogram based on 18F-FDG PET/CT metabolic characteristics is a reliable preoperative imaging biomarker for predicting MVI in HCC patients before undergoing LT. It has demonstrated excellent efficacy and high clinical applicability.

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

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.

Preoperative prediction of early recurrence of HBV-related hepatocellular carcinoma (≤5 cm) by visceral adipose tissue index

Background

This study aimed to investigate whether visceral adipose tissue index (VATI) is a significant risk factor for the early recurrence (ER) of HBV-related hepatocellular carcinoma (HCC) (≤5 cm) after hepatectomy.

Methods

The recruited cohort patients who were positive for hepatitis B virus, presented with surgically confirmed HCC (≤5 cm) from Army Medical University (internal training cohort: n = 192) and Chongqing Medical University (external validation group: n = 46). We measured VATI, subcutaneous adipose tissue index (SATI) via computed tomography (CT). ER was defined as recurrence within 2 years after hepatectomy. The impact of parameters on outcome after hepatectomy for HCC was analyzed.

Results

Univariate analysis showed that alpha-fetoprotein levels (p = 0.044), body mass index (BMI) (p < 0.001), SATI (p < 0.001), and VATI (p < 0.001) were significantly different between ER and non-ER groups in internal training cohort. Multivariate analysis identified VATI as an independent risk factor for ER (odds ratio = 1.07, 95% confidence interval: 1.047-1.094, p < 0.001), with a AUC of 0.802, based on the cut-off value of VATI, which was divided into high risk (≥37.45 cm²/m²) and low risk (<37.45 cm²/m²) groups. The prognosis of low risk group was significantly higher than that of high risk group (p < 0.001). The AUC value of VATI in external validation group was 0.854.

Conclusion

VATI was an independent risk factor for the ER, and higher VATI was closely related to poor outcomes after hepatectomy for HBV-related HCC (≤5 cm).