Correlation Between Dynamic Contrast-Enhanced CT Imaging Signs and Differentiation Grade and Microvascular Invasion of Hepatocellular Carcinoma.

OBJECTIVE

This study aimed to investigate how dynamic contrast-enhanced CT imaging signs correlate with the differentiation grade and microvascular invasion (MVI) of hepatocellular carcinoma (HCC), and to assess their predictive value for MVI when combined with clinical characteristics.

METHODS

We conducted a retrospective analysis of clinical data from 232 patients diagnosed with HCC at our hospital between 2021 and 2022. All patients underwent preoperative enhanced CT scans, laboratory tests, and postoperative pathological examinations. Among the 232 patients, 89 were identified as MVI-positive and 143 as MVI-negative. Regarding tumor differentiation, 56 patients were well-differentiated, 145 moderately, and 31 poorly. Multivariate logistic regression analysis was employed to establish a prediction model for variables showing significant differences. Additionally, the diagnostic performance of various indicators were evaluated using ROC analysis.

RESULTS

Among the qualitative data, significant differences (P<0.05) were observed between the MVI-positive and MVI-negative groups in 5 items such as peritumoral enhancement. In terms of quantitative data, the MVI-positive group exhibited higher maximum tumor length, AST, ALT, AFP levels and the ALBI score (P<0.05). Conversely, CT values in the arterial phase (AP), portal venous phase (PVP), and PT levels were lower in the MVI-positive group (P<0.05). Multivariate Logistic regression analysis identified ALBI score, PT level, CT value in PVP, and tumor capsule as independent risk factors for MVI occurrence (AUC: 0.71, 0.58, 0.66, and 0.60). The combined diagnostic AUC value was 0.82 (95% CI: 0.76-0.87). Significant differences were found among different differentiation grade groups in 10 items such as non-smooth tumor margin (P<0.05).

CONCLUSION

Preoperative dynamic contrast-enhanced CT examination in patients with HCC can be utilized to predict the presence of MVI. When combined with clinical characteristics, these imaging signs demonstrate good predictive performance for MVI status. Furthermore, this approach has significant implications for determining the differentiation grade of tumors.