Development of a risk score to predict portal vein tumor thrombosis in patients with hepatocellular carcinoma

BackgroundPortal vein tumor thrombosis (PVTT) is a common complication of hepatocellular carcinoma and is one of the most negative prognostic factors. The management of patients with PVTT is challenging. The aim of the study was to develop a score predictive of tumor thrombosis. MethodsData from a large cohort of 2243 hepatocellular carcinoma patients (all stages) recorded in the Progetto Epatocarcinoma Campania (January 2013-April 2021) database were analyzed. To construct the score, univariate generalized estimated equation models, the bootstrap approach for internal validation, and a regression coefficient-based scoring system were used. ResultsPVTT (any location) was found in 14.4% of cases and was related to shorter survival. Males, younger patients, and symptomatic cases were more prevalent among the PVTT group. At multivariate analysis, size >= 5 cm, massive or infiltrative hepatocellular carcinoma growth, and alpha-fetoprotein >= 400 ng/mL were significantly associated with PVTT. A risk prediction score of PVTT based on eight variables was developed. Using a continuous score, the risk was associated with an odds ratio (OR) of 1.30 (1.27-1.34; P < 0.001). Considering a dichotomous score >8 versus a score <= 8 the OR for PVTT was 11.33 (8.55-15.00; P < 0.001). ConclusionThe risk score for PVTT might be useful for clinicians to optimize hepatocellular carcinoma management by picking out patients with more aggressive cancers and higher mortality rates. Prospective validation of the score is needed before its application in daily clinical practice.

Automated summary

The study aimed to develop a predictive score for tumor thrombosis in hepatocellular carcinoma patients. Analysis of data from 2243 patients revealed that PVTT was associated with poorer prognosis and correlated with tumor size, growth pattern, and alpha-fetoprotein level. A risk prediction score based on eight variables was developed, which can help clinicians identify patients with more aggressive cancers and higher mortality rates for optimized management of hepatocellular carcinoma.