Analysis of Related Risk Factors of Microvascular Invasion in Hepatocellular Carcinoma

Prediction of microvascular invasion in hepatocellular carcinoma using a preoperative serum C-reactive protein-based nomogram

Microvascular invasion (MVI) diagnosis relies on postoperative pathological examinations, underscoring the urgent need for a novel diagnostic method. C-Reactive Protein (CRP), has shown significant relevance to hepatocellular carcinoma (HCC) prognosis. This study aims to explore the relationship between preoperative serum CRP levels and microvascular invasion in hepatocellular carcinoma and develop a nomogram model for predicting MVI. Patients were categorized into MVI-positive and MVI-negative groups for analysis. Serum CRP levels were compared between the two groups. And then use LASSO regression to screen variables and build a nomogram. CRP levels showed significant differences between the MVI-positive and MVI-negative groups. Multivariable logistic regression analysis identified CRP (OR = 4.85, P < 0.001), lnAFP (OR = 3.11, P < 0.001), WBC count (OR = 2.73, P = 0.003), and tumor diameter (OR = 2.38, P = 0.01) as independent predictors of MVI. A nomogram based on these variables showed good predictive performance in both the training and validation cohorts with dual validation. The clinical prediction nomogram model, which includes serum CRP levels, WBC count, tumor diameter, and serum AFP levels, showed good performance in predicting MVI in both the training and validation cohorts.

Preoperative evaluation of MRI features and inflammatory biomarkers in predicting microvascular invasion of combined hepatocellular cholangiocarcinoma

PURPOSE

Microvascular invasion (MVI) is a significant prognostic factor in combined hepatocellular cholangiocarcinoma (cHCC-CCA). However, its diagnosis relies on postoperative histopathologic analysis. This study aims to identify preoperative inflammatory biomarkers and MR-imaging features that can predict MVI in cHCC-CCA.

METHODS

This retrospective study enrolled 119 patients with histopathologically confirmed cHCC-CCA between January 2016 and December 2021. Two radiologists, unaware of the clinical data, independently reviewed all MR image features. Univariable and multivariable analyses were performed to determine the independent predictors for MVI among inflammatory biomarkers and MRI characteristics. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the diagnostic performance.

RESULTS

Multivariable logistic regression analysis identified four variables significantly associated with MVI (p < 0.05), including two inflammatory biomarkers [albumin-to-alkaline phosphatase ratio (AAPR) and aspartate aminotransferase-to-neutrophil ratio index (ANRI)] and two MRI features (non-smooth tumor margin and arterial phase peritumoral enhancement). A combined model for predicting MVI was constructed based on these four variables, with an AUC of 0.802 (95% CI 0.719-0.870). The diagnostic efficiency of the combined model was higher than that of the imaging model.

CONCLUSION

Inflammatory biomarkers and MRI features could be potential predictors for MVI in cHCC-CCA. The combined model, derived from inflammatory biomarkers and MRI features, showed good performance in preoperatively predicting MVI in cHCC-CCA patients.

Preoperative prediction and risk assessment of microvascular invasion in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common and highly lethal tumors worldwide. Microvascular invasion (MVI) is a significant risk factor for recurrence and poor prognosis after surgical resection for HCC patients. Accurately predicting the status of MVI preoperatively is critical for clinicians to select treatment modalities and improve overall survival. However, MVI can only be diagnosed by pathological analysis of postoperative specimens. Currently, numerous indicators in serology (including liquid biopsies) and imaging have been identified to effective in predicting the occurrence of MVI, and the multi-indicator model based on deep learning greatly improves accuracy of prediction. Moreover, several genes and proteins have been identified as risk factors that are strictly associated with the occurrence of MVI. Therefore, this review evaluates various predictors and risk factors, and provides guidance for subsequent efforts to explore more accurate predictive methods and to facilitate the conversion of risk factors into reliable predictors.

Retracted: Analysis of Related Risk Factors of Microvascular Invasion in Hepatocellular Carcinoma

[This retracts the article DOI: 10.1155/2022/8195512.].