Evidence map›Paper›PMID 41730232›Full record

ArticleHepatology communications2026

Validation of longitudinal biomarker screening algorithms for HCC detection in patients with cirrhosis.

Amit G Singal, Qingchun Jin, Jorge Marrero, Neehar D Parikh, Anna S Lok, Camden Lopez, Stephanie Page-Lester, Lewis R Roberts, Rajender Reddy, Michelle Luster and 6 more

Abstract readValidation Study
In one paragraph

Article in Hepatology communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Amit G SingalDepartment of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA.
Qingchun JinDepartment of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
Jorge MarreroDepartment of Internal Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Neehar D ParikhDepartment of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Anna S LokDepartment of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Camden LopezPublic Health Science Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.
Stephanie Page-LesterPublic Health Science Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.
Lewis R RobertsDivision of Gastroenterology and Hepatology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA.
Rajender ReddyDepartment of Internal Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Michelle LusterBaylor College of Medicine, Houston, Texas, USA.
Saira KhaderiBaylor College of Medicine, Houston, Texas, USA.
Sumeet K AsraniBaylor Scott & White Health, Dallas, Texas, USA.
Hashem B El-SeragBaylor College of Medicine, Houston, Texas, USA.
Fasiha KanwalBaylor College of Medicine, Houston, Texas, USA.
Ziding FengPublic Health Science Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.
Nabihah TayobDepartment of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.ORCID 0000-0001-6088-167

Funding

The Early Detection Research Network: Data Management and Coordinating CenterU24CA086368 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI RUTH D ETZIONI, Ziding Feng · 2010 to 2026
$75.7M
Prevention of Hepatocellular Carcinoma Related to Metabolic SyndromeP01CA263025 · NCI · BAYLOR COLLEGE OF MEDICINE · PI Christopher I. Amos · 2022 to 2026
$8.9M
Risk Stratification for and Early Detection of Liver CancerU01CA230997 · NCI · BAYLOR COLLEGE OF MEDICINE · PI Jagpreet Chhatwal, Hashem B El-Serag · 2018 to 2026
$6.4M
Consortium on Translational Research in Early Detection of Liver Cancer:Data Management and Coordinating Center (DMCC)U24CA230144 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Ziding Feng · 2018 to 2026
$5.8M
Clinical Validation Center for Hepatocellular CarcinomaU01CA271887 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI FASIHA KANWAL, JORGE A MARRERO · 2022 to 2026
$4.7M
Precision Risk Stratification and Screening for HCC among Patients with Indeterminate Liver NodulesU01CA283935 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI HOSHIDA, YUJIN, SINGAL, AMIT · 2023 to 2025
$3.9M
Precision Screening for Hepatocellular Carcinoma in Patients with CirrhosisR01CA222900 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI SINGAL, AMIT · 2018 to 2023
$2.4M
Biomarker screening algorithms for the improved early detection of hepatocellular carcinomaR01CA230503 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI TAYOB, NABIHAH · 2019 to 2025
$1.6M
NCI NIH HHS P01 CA263025NCI NIH HHS R01 CA222900NCI NIH HHS R01 CA230503NCI NIH HHS U01 CA230997NCI NIH HHS U01 CA271887NCI NIH HHS U01 CA283935NCI NIH HHS U24 CA086368NCI NIH HHS U24 CA230144
6 · The paper itself

Abstract

backgroundBlood-based biomarker panels, including GALAD, have been proposed as an alternative to abdominal ultrasound for hepatocellular carcinoma (HCC) surveillance. Studies suggest longitudinal evaluation of biomarkers can improve test performance; however, no large studies have validated these findings.

methodsWe leveraged the HCC Early Detection Strategy (HEDS) study (n=1019 patients with cirrhosis; 99 incident HCC) and Texas HCC Consortium (THCCC) (n=2345 patients with cirrhosis; 126 incident HCC) to compare the performance of fixed-threshold GALAD (cutoff of -1.36), and 4 longitudinal algorithms: longitudinal GALAD, PALAD, mFB-ALAD, and mPEB-ALAD. The HEDS cohort was used to derive longitudinal algorithms, and external validation was performed in THCCC. Patient-level sensitivity and test-level false-positive rate (FPR) were examined overall and across subgroups by age, sex, and cirrhosis etiology.

resultsIn THCCC, fixed-threshold GALAD had higher sensitivity in the 6 months before HCC diagnosis (71.4%, 95% CI: 62.9%-81.7%) than longitudinal GALAD (55.1%, 95% CI: 49.3-66.4), PALAD (55.1%, 95% CI: 50.0%-70.5%), mPEB-ALAD (53.1%, 95% CI: 43.6%-62.0%), and mFB-ALAD (42.9%, 95% CI: 32.3-48.5). However, fixed-threshold GALAD had a higher FPR (25.4%, 95% CI: 23.7%-26.9%) compared with longitudinal GALAD (15.0%, 95% CI: 12.8%-15.8%), PALAD (13.0%, 95% CI: 10.2%-14.6%), and mFB-ALAD (11.2%, 95% CI: 9.6%-12.1%) but comparable to mPEB-ALAD (22.9%, 95% CI: 21.2%-23.8%). In subgroup analyses, fixed-threshold GALAD had the highest FPR in males (35.6%) and those aged ≥65 (43.5%); longitudinal algorithms had significantly lower FPRs in subgroups but with lower sensitivity for HCC.

conclusionsImprovements in FPRs with longitudinal GALAD algorithms, as compared with fixed-threshold GALAD, are offset by significantly decreased sensitivity for HCC detection.

Indexed as

AlgorithmsBiomarkers, TumorCarcinoma, HepatocellularEarly Detection of CancerLiver CirrhosisLiver NeoplasmsAgedFemaleHumansLongitudinal StudiesMaleMiddle AgedSensitivity and SpecificityBiomarkers, TumorbiomarkerGALADliver cancerscreeningultrasound

Identifiers

PMID41730232
PMCPMC12922931

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.