Evidence map›Paper›PMID 40368830›Full record

ArticleInternal medicine (Tokyo, Japan)2025

Quantitative Assessment of High-resolution Computer Tomography Imaging in a Super-responder to Nintedanib Therapy in a Patient with Idiopathic Pulmonary Fibrosis.

Junko Itano, Mitsunori Ishiga, Makoto Fujii, Megumi Takemoto, Noriko Hayashibara, Goro Kimura, Yasushi Tanimoto

Abstract readCase Reports
In one paragraph

Article in Internal medicine (Tokyo, Japan), 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

7 authors.

Junko ItanoDepartment of Allergy and Respiratory Medicine, National Hospital Organization Minami-Okayama Medical Center, Japan.
Mitsunori IshigaDepartment of Allergy and Respiratory Medicine, National Hospital Organization Minami-Okayama Medical Center, Japan.
Makoto FujiiDepartment of Allergy and Respiratory Medicine, National Hospital Organization Minami-Okayama Medical Center, Japan.
Megumi TakemotoDepartment of Radiology, National Hospital Organization Minami-Okayama Medical Center, Japan.
Noriko HayashibaraDepartment of Radiology, National Hospital Organization Minami-Okayama Medical Center, Japan.
Goro KimuraDepartment of Allergy and Respiratory Medicine, National Hospital Organization Minami-Okayama Medical Center, Japan.
Yasushi TanimotoDepartment of Allergy and Respiratory Medicine, National Hospital Organization Minami-Okayama Medical Center, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nintedanib inhibits disease progression in patients with idiopathic pulmonary fibrosis (IPF) and dramatically improves the lung function in patients known to be super-responders (SRs). However, there are no reports on quantitative high-resolution computed tomography (HRCT), and the HRCT imaging characteristics of SRs remain unknown. We herein present the case of a 66-year-old man with IPF who was a SR to nintedanib treatment, which showed a marked improvement compared to other patients with IPF upon quantitative HRCT evaluation using the artificial intelligence (AI) software 3D Slicer. This study is worth reporting because a quantitative HRCT assessment using AI may be necessary to understand the SR characteristics.

Indexed as

Idiopathic Pulmonary FibrosisIndolesTomography, X-Ray ComputedAgedArtificial IntelligenceHumansMaleTreatment OutcomeIndolesnintedanib3D Slicerartificial intelligenceidiopathic pulmonary fibrosisnintedanibquantitative high-resolution computer tomographysuper-responder

Identifiers

PMID40368830
PMCPMC12183406

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.