Clinical Nomogram Model for Pre-Operative Prediction of Microvascular Invasion of Hepatocellular Carcinoma before Hepatectomy

Jen-Lung Chen ¹, Yaw-Sen Chen ¹, Kun-Chou Hsieh ¹

⁰Department of General Surgery, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan.

Medicina (Kaunas, Lithuania) +

|

September 28, 2024

View abstract on PubMed

Summary

A new nomogram model accurately predicts microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients before surgery. This tool helps surgeons tailor treatment strategies to improve patient outcomes and survival rates.

Area Of Science

Hepatocellular Carcinoma (HCC) Research
Surgical Oncology
Diagnostic Biomarkers

Background

Microvascular invasion (MVI) is a critical prognostic factor in hepatocellular carcinoma (HCC), significantly affecting recurrence and survival post-liver resection.
Accurate pre-operative prediction of MVI is essential for optimizing surgical planning and patient management strategies in HCC.
Current methods for MVI assessment often rely on post-operative pathological examination, highlighting the need for reliable pre-operative predictive tools.

Purpose Of The Study

To develop and validate a nomogram model for predicting the probability of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients based on pre-operative clinical features.
To identify significant clinical and tumor-related factors that contribute to the pre-operative prediction of MVI in HCC.
To provide a practical tool for surgeons to estimate MVI risk before hepatectomy, thereby informing treatment decisions.

Main Methods

A retrospective study involving 489 HCC patients, with cohorts for derivation (2012-2015) and validation (2016-2019).
A regression-based nomogram model was constructed using pre-operative clinical and tumor characteristics.
Key predictors identified included AFP, platelet count, GOT/GPT ratio, albumin-alkaline phosphatase ratio (AAR), ALBI score, and Geriatric Nutritional Risk Index (GNRI).

Main Results

The nomogram identified AFP, platelet count, GOT/GPT ratio, AAR, ALBI score, and GNRI as significant predictors of MVI, with Youden index scores ranging from 0.112 to 0.287.
Higher nomogram scores correlated with an increased probability of MVI.
The model demonstrated predictive accuracy rates between 55.9% and 64.4% and precision rates between 54.3% and 68.2%. MVI positivity was associated with significantly poorer overall survival (p < 0.001).

Conclusions

The developed nomogram model, integrating readily available clinical factors, provides a reliable and well-calibrated tool for predicting MVI in HCC patients pre-operatively.
This predictive model can assist surgeons in assessing MVI risk, enabling personalized surgical strategies and optimized post-operative care plans.
Implementing this nomogram can potentially improve the overall prognosis and management of HCC patients by facilitating informed clinical decision-making.

Keywords:

hepatocellular carcinoma microvascular invasion nomogram outcome recurrence