Evidence map›Paper›PMID 40149253›Full record

ArticleCancers2025

Accurate Diagnosis of High-Risk Pulmonary Nodules Using a Non-Invasive Epigenetic Biomarker Test.

Pei-Hsing Chen, Tung-Ming Tsai, Tzu-Pin Lu, Hsiao-Hung Lu, Dorian Pamart, Aristotelis Kotronoulas, Marielle Herzog, Jacob Vincent Micallef, Hsao-Hsun Hsu, Jin-Shing Chen

Abstract read
In one paragraph

Article in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Pei-Hsing ChenInstitute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei City 106, Taiwan.ORCID 0000-0002-9512-5409
Tung-Ming TsaiDepartment of Surgical Oncology, National Taiwan University Cancer Center, College of Medicine, National Taiwan University, Taipei City 106, Taiwan.
Tzu-Pin LuDepartment of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei City 100, Taiwan.ORCID 0000-0003-3697-0386
Hsiao-Hung LuDepartment of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei City 100, Taiwan.
Dorian PamartBelgian Volition SRL, 22 Rue Phocas Lejeune, Parc Scientifique Crealys, 5032 Isnes, Belgium.
Aristotelis KotronoulasBelgian Volition SRL, 22 Rue Phocas Lejeune, Parc Scientifique Crealys, 5032 Isnes, Belgium.ORCID 0000-0001-9695-0876
Marielle HerzogBelgian Volition SRL, 22 Rue Phocas Lejeune, Parc Scientifique Crealys, 5032 Isnes, Belgium.ORCID 0000-0003-1311-101X
Jacob Vincent MicallefVolition Diagnostics UK Limited, 93-95 Gloucester Place, London W1U 6JQ, UK.
Hsao-Hsun HsuDepartment of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei City 100, Taiwan.
Jin-Shing ChenDepartment of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei City 100, Taiwan.

Funding

Ministry of Science and Technology, Taiwan MOST 113-2314-B-002-038-MY3VolitionRX Limited no related grant number
6 · The paper itself

Abstract

BACKGROUND/

objectivesAccurate non-invasive tests to improve early detection and diagnosis of lung cancer are urgently needed. However, no regulatory-approved blood tests are available for this purpose. We aimed to improve pulmonary nodule classification to identify malignant nodules in a high-prevalence patient group.

methodsThis study involved 806 participants with undiagnosed nodules larger than 5 mm, focusing on assessing nucleosome levels and histone modifications (H3.1 and H3K27Me3) in circulating blood. Nodules were classified as malignant or benign. For model development, the data were randomly divided into training (n = 483) and validation (n = 121) datasets. The model's performance was then evaluated using a separate testing dataset (n = 202).

resultsAmong the patients, 755 (93.7%) had a tissue diagnosis. The overall malignancy rate was 80.4%. For all datasets, the areas under curves were as follows: training, 0.74; validation, 0.86; and test, 0.79 (accuracy range: 0.80-0.88). Sensitivity showed consistent results across all datasets (0.91, 0.95, and 0.93, respectively), whereas specificity ranged from 0.37 to 0.64. For smaller nodules (5-10 mm), the model recorded accuracy values of 0.76, 0.88, and 0.85. The sensitivity values of 0.91, 1.00, and 0.94 further highlight the robust diagnostic capability of the model. The performance of the model across the reporting and data system (RADS) categories demonstrated consistent accuracy.

conclusionsOur epigenetic biomarker panel detected non-small-cell lung cancer early in a high-risk patient group with high sensitivity and accuracy. The epigenetic biomarker model was particularly effective in identifying high-risk lung nodules, including small, part-solid, and non-solid nodules, and provided further evidence for validation.

Indexed as

circulating nucleosomesepigenetic biomarkershistone PTMsliquid biopsylung cancerpulmonary nodule

Identifiers

PMID40149253
PMCPMC11940740

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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.