Evidence map›Paper›PMID 32389887›Full record

ArticleClinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association2021

Serum Levels of α-Fetoprotein Increased More Than 10 Years Before Detection of Hepatocellular Carcinoma.

David M Hughes, Sarah Berhane, C A Emily de Groot, Hidenori Toyoda, Toshifumi Tada, Takashi Kumada, Shinji Satomura, Naoshi Nishida, Masatoshi Kudo, Toru Kimura and 6 more

Open access · hybridAbstract read
In one paragraph

Article in Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
3.6field-weighted citation impact, top 6% of its field
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

19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.

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  16. The effect of random-effects misspecification on classification accuracy.The international journal of biostatistics · 2021
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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 at 9 institutions in 2 countries.

David M HughesDepartment of Biostatistics.
Sarah BerhaneDepartment of Biostatistics.
C A Emily de GrootDepartment of Economics, University of St Andrews, St Andrews, United Kingdom.
Hidenori ToyodaDepartment of Gastroenterology and Hepatology, Ogaki Municipal Hospital, Ogaki, Japan.
Toshifumi TadaDepartment of Gastroenterology and Hepatology, Ogaki Municipal Hospital, Ogaki, Japan.
Takashi KumadaDepartment of Nursing, Gifu Kyoritsu University, Ogaki, Japan.
Shinji SatomuraDepartment of Molecular Biochemistry and Clinical Investigation, Osaka University Graduate School of Medicine, Osaka, Japan.
Naoshi NishidaDepartment of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan.
Masatoshi KudoDepartment of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan.
Toru KimuraDepartment of Gastroenterology and Hepatology, Osaka Red Cross Hospital, Osaka, Japan.
Yukio OsakiDepartment of Gastroenterology and Hepatology, Osaka Red Cross Hospital, Osaka, Japan; Department of Internal Medicine, Meiwa Hospital, Nishinomiya, Japan.
Ruwanthi Kolamunage-DonaDepartment of Biostatistics.
Ruben AmorosSchool of Mathematics.
Tom BirdMedical Research Council Centre for Inflammation Research, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom.
Marta Garcίa-FiñanaDepartment of Biostatistics.
Philip JohnsonDepartment of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom. Electronic address: Philip.Johnson@liverpool.ac.uk.
Kindai University · JPOgaki Municipal Hospital · JPCentre for Inflammation Research · GBGifu Kyoritsu University · JPMiwa Hospital · JPOsaka Red Cross Hospital · JPThe University of Osaka · JPUniversity of Liverpool · GBUniversity of St Andrews · GB

Funding

Cancer Research UK 26813Chief Scientist Office CGA/17/19Medical Research Council MR/L010909/1Medical Research Council MR/M013227/1Medical Research Council MR/R024847/1Wellcome Trust 107492Wellcome Trust WT107492/Z/15/Z
6 · The paper itself

Abstract

BACKGROUND &

aimsUltrasound (US)-based screening has been recommended for patients with an increased risk of hepatocellular carcinoma (HCC). US analysis, however, is limited in patients who are obese or have small tumors. The addition of serum level of α-fetoprotein (AFP) measurements to US analysis can increase detection of HCC. We analyzed data from patients with chronic liver disease, collected over 15 years in an HCC surveillance program, to develop a model to assess risk of HCC.

methodsWe collected data from 3450 patients with chronic liver disease undergoing US surveillance in Japan from March 1998 through April 2014, and followed them up for a median of 8.83 years. We performed longitudinal discriminant analysis of serial AFP measurements (median number of observations/patient, 56; approximately every 3 months) to develop a model to determine the risk of HCC. We validated the model using data from 2 cohorts of patients with chronic liver disease in Japan (404 and 2754 patients) and 1 cohort in Scotland (1596 patients).

resultsHCC was detected in 413 patients (median tumor diameter, 1.8 cm), during a median follow-up time of 6.60 years. In the development data set, the model identified patients who developed HCC with an area under the curve of 0.78; it correctly identified 74.3% of patients who did develop HCC, and 72.9% of patients who did not. Overall, 73.1% of patients were classified correctly. The model could be used to assign patients to a high-risk group (27.5 HCCs/1000 patient-years) vs a low-risk group (4.9 HCCs/1000 patient-years). A similar performance was observed when the model was used to assess patients with cirrhosis. Analysis of the validation cohorts produced similar results.

conclusionsWe developed and validated a model to identify patients with chronic liver disease who are at risk for HCC based on change in serum AFP level over time. The model could be used to assign patients to high-risk vs low-risk groups, and might be used to select patients for surveillance.

Indexed as

Carcinoma, HepatocellularLiver Neoplasmsalpha-FetoproteinsFetal ProteinsHumansLiver Cirrhosisalpha-FetoproteinsFetal ProteinsBiomarkerLiver CancerPrognostic FactorStratification

Identifiers

PMID32389887
PMCPMC7611145
OpenAlexW3023707554

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.