Evidence map›Paper›PMID 38936886›Full record

ArticleIn vivo (Athens, Greece)

Application of Innovative 3D Pathological Tactic for Diagnosis of Organizing Pneumonia.

Y I Hsia, Yu Hsin Tsai, Pin-Kuei Fu, Chih Jung Chen

Abstract read
In one paragraph

Article in In vivo (Athens, Greece). 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
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0citing papers in PubMed
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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

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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

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4 · The record

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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

4 authors.

Y I HsiaDepartment of Pathology and Laboratory Medicine, Taichung Veterans General Hospital, Taichung, Taiwan, R.O.C.
Yu Hsin TsaiDepartment of Pathology and Laboratory Medicine, Taichung Veterans General Hospital, Taichung, Taiwan, R.O.C.
Pin-Kuei FuDivision of Clinical Research, Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan, R.O.C.
Chih Jung ChenDepartment of Pathology and Laboratory Medicine, Taichung Veterans General Hospital, Taichung, Taiwan, R.O.C.; cjchen1016@vghtc.gov.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

aimThe pathological diagnosis of organizing pneumonia (OP) relies on conventional traditional histopathological analysis, which involves examining stained thin slices of tissue. However, this method often results in suboptimal diagnostic objectivity due to low tissue sampling rates. This study aimed to assess the efficacy of tissue-clearing and infiltration-enhanced 3D spatial imaging techniques for elucidating the tissue architecture of OP. MATERIALS AND

methodsH&E staining, 3D imaging technology, and AI-assisted analysis were employed to facilitate the construction of a multidimensional tissue architecture using six OP patient specimens procured from Taichung Veterans General Hospital, enabling a comprehensive morphological assessment.

resultsSpecimens underwent H&E staining and exhibited Masson bodies and varying degrees of interstitial fibrosis. Furthermore, we conducted a comprehensive study of 3D images of the pulmonary histology reconstructed through an in-depth pathology analysis, and uncovered heterogenous distributions of fibrosis and Masson bodies across different depths of the OP specimens.

conclusionIntegrating 3D imaging for OP with AI-assisted analysis permits a substantially enhanced visualization and delineation of complex histological pulmonary disorders such as OP. The synergistic application of conventional histopathology with novel 3D imaging elucidated the sophisticated spatial configuration of OP, revealing the presence of Masson bodies and interstitial fibrosis. This methodology transcends conventional pathology constraints and paves the way for advanced algorithmic approaches to enhance precision in the detection, classification, and clinical management of lung pathologies.

Indexed as

Imaging, Three-DimensionalCryptogenic Organizing PneumoniaHumansLungMaleOrganizing Pneumonia3D pathologyartificial intelligenceorganizing pneumonia

Identifiers

PMID38936886
PMCPMC11215595

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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.