Evidence map›Paper›PMID 41680345›Full record

ReviewJournal of gastrointestinal cancer2026

Nonoperative Management of Hepatic Adenomas: A Review.

Ian C Garbarine, Jordan M Cloyd

Abstract readReview
In one paragraph

Review in Journal of gastrointestinal cancer, 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

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

2 authors.

Ian C GarbarineDepartment of Surgery, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA.
Jordan M CloydDivision of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA. jordan.cloyd@osumc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatic adenomas (HAs) are relatively common benign liver tumors that carry small risks of hemorrhage and malignant transformation, both of which increase with tumor size. While traditionally linked to estrogen-containing oral contraceptives (OCPs), other risk factors including obesity have been associated with HA formation and growth. Treatment strategies depend on the risk of malignant transformation or hemorrhage, tumor size, histological subtype, and patient-specific factors. While traditionally, surgical resection has been the standard recommendation for large, symptomatic, or high-risk tumors, several non-operative strategies are now available to minimize morbidity and optimize outcomes. Active surveillance is recommended for patients with small (< 5 cm), asymptomatic HAs without other risk factors. Loco-regional therapies, including transarterial embolization and radiofrequency ablation, have been used effectively, particularly for small tumors. Mounting evidence suggests that interventions aimed at weight loss, either through lifestyle modifications or bariatric surgery, are efficacious in reducing HA size. Recent data support expanding non-operative management for even large tumors (> 5 cm), as they can respond to lifestyle interventions. Several subgroups of patients warrant special consideration, such as pregnant patients, males, and patients with hepatic adenomatosis. A personalized, multidisciplinary approach remains essential as non-operative strategies continue to expand along with emerging targeted therapies.

Indexed as

Adenoma, Liver CellLiver NeoplasmsEmbolization, TherapeuticFemaleHumansMalePregnancyRisk FactorsHepatic adenomaHepatic adenomatosisHepatocellular adenomaLiver adenomaLiver cell adenomaNon-operative management

Identifiers

PMID41680345
PMCPMC12901198

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Registered trials

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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.