Evidence map›Paper›PMID 40093557›Full record

ArticleJournal of medical imaging (Bellingham, Wash.)2025

Using a fully automated, quantitative fissure integrity score extracted from chest CT scans of emphysema patients to predict endobronchial valve response.

Dallas K Tada, Grace H Kim, Jonathan G Goldin, Pangyu Teng, Kalyani Vyapari, Ashley Banola, Fereidoun Abtin, Michael McNitt-Gray, Matthew S Brown

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical imaging (Bellingham, Wash.), 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

9 authors.

Dallas K TadaThe University of California, Los Angeles (UCLA), David Geffen School of Medicine at UCLA, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, Los Angeles, California, United States.
Grace H KimThe University of California, Los Angeles (UCLA), David Geffen School of Medicine at UCLA, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, Los Angeles, California, United States.ORCID https://orcid.org/0000-0003-1225-3489
Jonathan G GoldinThe University of California, Los Angeles (UCLA), David Geffen School of Medicine at UCLA, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, Los Angeles, California, United States.
Pangyu TengThe University of California, Los Angeles (UCLA), David Geffen School of Medicine at UCLA, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, Los Angeles, California, United States.ORCID https://orcid.org/0000-0002-5582-5339
Kalyani VyapariThe University of California, Los Angeles (UCLA), David Geffen School of Medicine at UCLA, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, Los Angeles, California, United States.
Ashley BanolaThe University of California, Los Angeles (UCLA), David Geffen School of Medicine at UCLA, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, Los Angeles, California, United States.
Fereidoun AbtinThe University of California, Los Angeles (UCLA), David Geffen School of Medicine at UCLA, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, Los Angeles, California, United States.
Michael McNitt-GrayThe University of California, Los Angeles (UCLA), David Geffen School of Medicine at UCLA, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, Los Angeles, California, United States.ORCID https://orcid.org/0000-0003-3004-4613
Matthew S BrownThe University of California, Los Angeles (UCLA), David Geffen School of Medicine at UCLA, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, Los Angeles, California, United States.

Funding

Robust Clinical Translation of CT Imaging Biomarker in COPD for EBV Patient SelectionR01HL153964 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BROWN, MATTHEW S, GOLDIN, JONATHAN G · 2021 to 2024
$2.7M
NHLBI NIH HHS R01 HL153964
6 · The paper itself

Abstract

Purpose: We aim to develop and validate a prediction model using a previously developed fully automated quantitative fissure integrity score (FIS) extracted from pre-treatment CT images to identify suitable candidates for endobronchial valve (EBV) treatment. Approach: We retrospectively collected 96 anonymized pre- and post-treatment chest computed tomography (CT) exams from patients with moderate to severe emphysema and who underwent EBV treatment. We used a previously developed fully automated, deep learning-based approach to quantitatively assess the completeness of each fissure by obtaining the FIS for each fissure from each patient's pre-treatment CT exam. The response to EBV treatment was recorded as the amount of targeted lobe volume reduction (TLVR) compared with target lobe volume prior to treatment as assessed on the pre- and post-treatment CT scans. EBV placement was considered successful with a TLVR of Results: ROC analysis of the training set provided an AUC of 0.83, and the determined FIS threshold was 89.5%. Using this threshold on the test set achieved an accuracy of 81.6%, specificity (Sp) of 90.9%, sensitivity (Sn) of 77.8%, positive predictive value (PPV) of 62.5%, and negative predictive value of 95.5%. Conclusions: A model using the quantified FIS shows potential as a predictive biomarker for whether a targeted lobe will achieve successful volume reduction from EBV treatment.

Indexed as

emphysemaendobronchial valvefissure integrity scorelobe volume reductionprediction model

Identifiers

PMID40093557
PMCPMC11906092

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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.