Evidence map›Paper›PMID 35782232›Full record

ArticleQuantitative imaging in medicine and surgery2022

Quantitative analysis of high-resolution computed tomography features of idiopathic pulmonary fibrosis: a structure-function correlation study.

Haishuang Sun, Min Liu, Han Kang, Xiaoyan Yang, Peiyao Zhang, Rongguo Zhang, Huaping Dai, Chen Wang

Open access · diamondAbstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 13% of its field
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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. A systematic review of the role of quantitative CT in the prognostication and disease monitoring of interstitial lung disease.European respiratory review : an official journal of the European Respiratory Society · 2025
    Pooled it
  2. Article
  3. Article
  4. Radiomics and Artificial Intelligence in Pulmonary Fibrosis.Journal of imaging informatics in medicine · 2025
    Review
  5. Review
  6. Observational
  7. Article
  8. Article
  9. Article
  10. Article
  11. 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

8 authors at 5 institutions in 1 country.

Haishuang SunDepartment of Respiratory Medicine, The First Hospital of Jilin University, Changchun, China.
Min LiuDepartment of Radiology, China-Japan Friendship Hospital, Beijing, China.
Han KangInstitute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, China.
Xiaoyan YangDepartment of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing, China.
Peiyao ZhangDepartment of Radiology, China-Japan Friendship Hospital, Beijing, China.
Rongguo ZhangInstitute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, China.
Huaping DaiDepartment of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing, China.
Chen WangDepartment of Respiratory Medicine, The First Hospital of Jilin University, Changchun, China.
China-Japan Friendship Hospital · CNInferVision (China) · CNFirst Hospital of Jilin University · CNJilin University · CNPeking Union Medical College Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The quantitative analysis of high-resolution computed tomography (HRCT) is increasingly being used to quantify the severity and evaluate the prognosis of disease. Our aim was to quantify the HRCT features of idiopathic pulmonary fibrosis (IPF) and identify their association with pulmonary function tests. Methods: This was a retrospective, single-center, clinical research study. Patients with IPF were retrospectively included. Pulmonary segmentation was performed using the deep learning-based method. Radiologists manually segmented 4 findings of IPF, including honeycombing (HC), reticular pattern (RE), traction bronchiectasis (TRBR), and ground glass opacity (GGO). Pulmonary vessels were segmented with the automatic integration segmentation method. All segmentation results were quantified by the corresponding segmentation software. Correlations between the volume of the 4 findings on HRCT, volume of the lesions at different sites, pulmonary vascular-related parameters, and pulmonary function tests were analyzed. Results: A total of 101 IPF patients (93 males) with a median age of 63 years [interquartile range (IQR), 58 to 68 years] were included in this study. Total lesion extent demonstrated a stronger negative correlation with diffusion capacity for carbon monoxide (DLco) compared to HC, RE, and TRBR [total lesion ratio, correlation coefficient (r) =-0.67, P<0.001; HC, r=-0.45, P<0.001; RE, r=-0.41, P<0.001; TRBR, r=-0.25, P<0.05, respectively]. Correlations with lung function were similar among various lesion sites with r from -0.38 to -0.61 (P<0.001). Pulmonary artery volume (PAV) displayed a slightly increased positive association with the DLco compared to total pulmonary vascular volume (PVV); for PAV, r=0.41 and P<0.001 and for total PVV, r=0.36 and P<0.001. Additionally, total lesion extent, HC, and RE indicated a negative relationship with vascular-related parameters, and the strength of the correlations was independent of lesion site. Conclusions: Quantitative analysis of HRCT features of IPF indicated a decline in function and an aggravation of vascular destruction with increasing lesion extent. Furthermore, a positive correlation between vascular-related parameters and pulmonary function was confirmed. This co-linearity indicated the potential of vascular-related parameters as new objective markers for evaluating the severity of IPF.

Indexed as

high-resolution computed tomography (HRCT)Idiopathic pulmonary fibrosis (IPF)pulmonary vesselquantitative analysis

Identifiers

PMID35782232
PMCPMC9246749
OpenAlexW4283836171

What OpenQuestion holds

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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.