Development and validation of a nomogram to predict prognosis of patients with combined hepatocellular-cholangiocarcinoma after hepatic resection

TL;DR

This study creates a prediction model to estimate survival chances for patients with a rare liver cancer after surgery.

Contribution

A novel nomogram model using non-invasive blood markers to predict prognosis in cHCC-CCA patients post-hepatic resection.

Findings

The nomogram model showed strong predictive ability for survival rates at 1, 3, and 5 years.

Factors like lymphatic metastasis and blood markers were found to significantly influence survival outcomes.

Abstract

Background: Combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CCA) is a rare primary liver cancer characterized by a low incidence but a poor prognosis. The purpose of the study was to develop a clinical prediction model utilizing non-invasive blood markers to effectively evaluate the prognosis of cHCC-CCA patients following hepatic resection.

Methods: The retrospective analysis was conducted on 125 patients with cHCC-CCA who underwent hepatic resection between April 2013 and October 2022. All cHCC-CCA patients were randomly assigned to the training group (n = 63) and the validation group (n =62). A nomogram based on patient clinical factors was established using cox regression analysis. Receiver operating characteristic curves (ROCs) were used to assess the predictive performance of the model. Calibration and decision curves were employed to evaluate the model's prediction accuracy and goodness of fit.

Results: Multivariate analysis revealed significant associations between lymphatic metastasis, microvascular invasion (MVI), gamma-glutamyl transpeptidase to albumin ratio (GAR), carcinoembryonic antigen (CEA), prothrombin time (PT), alpha-fetoprotein (AFP), hepatitis B virus (HBV), and overall survival. Based on these prognostic factors, a nomogram model was established and validated using the validation set. Calibration curves demonstrated good consistency in the 1-year, 3-year, and 5-year survival rates of patients. Additionally, the ROC analysis indicated the model's strong predictive ability, and the decision curves confirmed its clinical applicability.

Conclusion: This study successfully developed a nomogram model for predicting survival outcomes in patients with cHCC-CCA following hepatectomy.

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