Predicting Metastasis in Cholangiocarcinoma: A New Clinical Prediction Model

Cholangiocarcinoma (CCA) is a highly malignant form of cancer, with 5-year survival rates ranging from 20% to 30%. In an effort to more accurately predict the potential of distal metastasis in patients with CCA, Caixia Fang, MD, and colleagues sought to develop a new clinical prediction model to determine the risk of this occurrence in patients with intrahepatic CCA.

A cohort of 345 patients with CCA from the Fifth Medical Center of Chinese PLA General Hospital were divided into 2 groups based on distal metastasis (n=21) and nondistal metastasis (n=324). Least absolute shrinkage and selection operator regression models were used to determine relevant parameters and to compare clinical information among patients in each group.

Univariate and multivariate regression analyses were used to develop a predictive heat map that included risk factor parameters such as hypertension, cholesterol, degree of differentiation, and margin invasion, among others. Decision curve analysis (DCA) was applied to determine the model’s utility in clinical applications, and an overall survival analysis was conducted using Kaplan-Meier estimates.

The heat map identified 4 independent risk factors of distal metastasis in patients with CCA, including the presence of tumor marker CA199, cholesterol, hypertension, and margin invasion. The developed nomogram was found to provide significant diagnostic accuracy, with an area under the curve level of 0.882 (95% CI, 0.843-0.914). Calibration plots and DCA further demonstrated the model’s high clinical utility.

This nomogram model can serve as an accurate predictor of distal metastasis in patients with CCA and can potentially be utilized to guide clinical decision-making and provide accurate prognosis and treatment.