Evidence map›Paper›PMID 41137918›Full record

ArticleEuropean radiology2026

Hepatocellular carcinoma risk stratification to identify patients suitable for intensive surveillance in viral hepatitis: the SELECT score.

Yeun-Yoon Kim, Won Chang, Jeong Min Lee, Se Woo Kim, Jae Seok Bae, Jeongin Yoo, Sun Kyung Jeon, HeeSoo Kim, Young Hoon Kim, Jin-Young Choi and 6 more

Abstract readMulticenter Study
In one paragraph

Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors.

Yeun-Yoon Kim *Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Won Chang *Department of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Jeong Min LeeDepartment of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
Se Woo KimDepartment of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Jae Seok BaeDepartment of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
Jeongin YooDepartment of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
Sun Kyung JeonDepartment of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
HeeSoo KimDepartment of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
Young Hoon KimDepartment of Radiology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Jin-Young ChoiDepartment of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Eun Ju ChoDepartment of Internal Medicine and Liver Research Institute, Seoul National University Hospital and College of Medicine, Seoul, Republic of Korea.
Yun Bin LeeDepartment of Internal Medicine and Liver Research Institute, Seoul National University Hospital and College of Medicine, Seoul, Republic of Korea.
Sook-Hyang JeongDepartment of Internal Medicine and Liver Research Institute, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Do Young KimDepartment of Internal Medicine and Institute of Gastroenterology, Yonsei University College of Medicine, Seoul, Republic of Korea.
Yunhee ChoiDivision of Biostatistics, Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Republic of Korea. yunhchoi1@gmail.com.
Jeong Hee YoonDepartment of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea. cinamon1@snu.ac.kr.ORCID http://orcid.org/0000-0002-9925-9973

Funding

Korea National Research Foundation NRF-2021R1C1C1004569
6 · The paper itself

Abstract

objectiveA risk-stratification strategy can improve the effectiveness of intensive hepatocellular carcinoma (HCC) surveillance with an alternative modality. However, such strategies and prediction models incorporating ultrasound features remain undeveloped for a suitable population. Therefore, we developed and validated an HCC risk prediction model using ultrasound features in patients with viral hepatitis who are potentially eligible for intensive surveillance. MATERIALS AND

methodsThis retrospective multicenter study included 17,557 HCC-naïve patients with viral hepatitis who underwent US surveillance between 2005 and 2015. In the development dataset (n = 7918), clinical and US features were analyzed to establish the prediction model. Factors associated with HCC were identified by multivariable Cox regression analysis. Model performance was compared to existing prediction models in internal (n = 3393) and external (n = 6246) validation datasets.

resultsThe SELECT model included age, male sex, diabetes, serum albumin and alanine aminotransferase levels, platelet count, and ultrasound-detected cirrhosis and multiple cirrhotic nodules. In the external validation dataset, the low-, intermediate-, and high-risk groups had 0.8%, 6.9%, and 16.1% 5-year cumulative HCC incidence, respectively. In those with an estimated annual HCC incidence ≥ 2.5% (SELECT score > -2.04), the 5-year cumulative HCC incidence was 15.5%. The SELECT model had better discrimination capability than aMAP, THRI, ADRESS-HCC, the Velazquez score, and mPAGE-B (Uno C-index, 0.791 vs. 0.740, 0.668, 0.658, 0.650, and 0.740, respectively; all adjusted p < 0.001).

conclusionThe SELECT model better estimated HCC risk than other models in viral hepatitis patients. Intensive surveillance with alternative modalities may be considered based on this model. KEY POINTS: Question Ultrasound features have not been incorporated into hepatocellular carcinoma risk prediction models, despite ultrasound being the primary surveillance modality. Findings The SELECT model, incorporating demographics, laboratory findings and ultrasound features (cirrhosis and multiple cirrhotic nodules), demonstrated superior performance compared to existing models. Clinical relevance The SELECT model effectively identifies viral hepatitis patients with ≥ 2.5% annual HCC risk who would benefit from intensive surveillance using alternative imaging modalities, optimizing resource allocation and achieving higher diagnostic yield.

Indexed as

Carcinoma, HepatocellularHepatitis, Viral, HumanLiver NeoplasmsAdultAgedFemaleHumansIncidenceMaleMiddle AgedRetrospective StudiesRisk AssessmentRisk FactorsUltrasonographyCirrhosisHepatitis BHepatitis CHepatocellular carcinomaUltrasound

Identifiers

PMID41137918
PMCPMC13035526

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.