Outcome scoring systems for locoregional therapy in hepatocellular carcinoma: a systematic review.

Authors

Cyrus Daruwalla, Evelyn Calderon, Christo Mathew, Lauren Mignogna, Ajit Vyas, David Sada, Rayhan Hai, Benjamin Hines, Muzammil Hanif, Ewout W Steyerberg, Hashem B El-Serag, Ruben Hernaez

Published in

Gut. Jul 09, 2026. Epub Jul 09, 2026.

Abstract

Locoregional therapy (LRT) is frequently used as bridging therapy to transplantation/resection or palliative treatment for hepatocellular carcinoma (HCC). Multiple prediction models have been developed for prognosis and treatment response among patients undergoing LRTs.
We aimed to systematically review the methodological quality and performance of clinical risk scores predicting outcomes in patients with HCC treated with LRT.
EMBASE and PubMed were searched from inception to 18 March 2026. Our main outcome was the concordance statistic, or area under the receiver operating characteristic curve (AUROC), to predict survival and other tumour-related and liver-related outcomes.
130 studies met the inclusion criteria, resulting in 179 individual scoring systems. The total population was 70 061 patients, 21% female (4-51%), most with Barcelona Clinic Liver Cancer Stage B (39%). Risk scores commonly incorporated tumour parameters, liver function tests, cirrhosis staging and comorbidities. AUROC values ranged from 0.56 to 0.94. 16 studies (12.3%) had low risk of bias, while most had high risk of bias. Of these, the Y-scoring system, Cheng et al nomogram and Li et al nomogram showed the highest discrimination for overall survival in palliative LRT (AUROC >0.87). Separate pooled analyses showed that the Hepatoma Arterial-embolisation Prognostic (HAP) and modified Hepatoma Arterial-embolisation Prognostic II (mHAP-II) scores had the highest performance (pooled AUROC >0.72), while the Six-and-Twelve (0.68) and Albumin-Bilirubin (ALBI) scores (0.60) demonstrated lower performance.
Several outcome scoring systems are promising for specific LRTs and populations. However, most models have high risk of bias, underscoring the need for further development and validation.

PMID:
42425732
Bibliographic data and abstract were imported from PubMed on 10 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.