Evidence map›Paper›PMID 35205693›Full record

ArticleCancers2022

Quantitative Analysis of Radiation-Associated Parenchymal Lung Change.

Edward Chandy, Adam Szmul, Alkisti Stavropoulou, Joseph Jacob, Catarina Veiga, David Landau, James Wilson, Sarah Gulliford, John D Fenwick, Maria A Hawkins and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 33% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

12 authors at 4 institutions in 1 country.

Edward ChandyCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
Adam SzmulCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
Alkisti StavropoulouCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
Joseph JacobCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
Catarina VeigaCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
David LandauUCL Cancer Institute, University College London, London WC1E 6BT, UK.
James WilsonMedical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
Sarah GullifordMedical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
John D FenwickInstitute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 3GE, UK.
Maria A HawkinsMedical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.ORCID 0000-0002-6669-0628
Crispin HileyUCL Cancer Institute, University College London, London WC1E 6BT, UK.
Jamie R McClellandCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
University College London · GBRoyal Sussex County Hospital · GBUniversity College Hospital · GBUniversity of Liverpool · GB

Funding

Cancer Research UK 21993Cancer Research UK 28990Wellcome Trust 209553/Z/17/Z
6 · The paper itself

Abstract

We present a novel classification system of the parenchymal features of radiation-induced lung damage (RILD). We developed a deep learning network to automate the delineation of five classes of parenchymal textures. We quantify the volumetric change in classes after radiotherapy in order to allow detailed, quantitative descriptions of the evolution of lung parenchyma up to 24 months after RT, and correlate these with radiotherapy dose and respiratory outcomes. Diagnostic CTs were available pre-RT, and at 3, 6, 12 and 24 months post-RT, for 46 subjects enrolled in a clinical trial of chemoradiotherapy for non-small cell lung cancer. All 230 CT scans were segmented using our network. The five parenchymal classes showed distinct temporal patterns. Moderate correlation was seen between change in tissue class volume and clinical and dosimetric parameters, e.g., the Pearson correlation coefficient was ≤0.49 between V30 and change in Class 2, and was 0.39 between change in Class 1 and decline in FVC. The effect of the local dose on tissue class revealed a strong dose-dependent relationship. Respiratory function measured by spirometry and MRC dyspnoea scores after radiotherapy correlated with the measured radiological RILD. We demonstrate the potential of using our approach to analyse and understand the morphological and functional evolution of RILD in greater detail than previously possible.

Indexed as

deep learninglung cancerradiotherapy-induced lung damage

Identifiers

PMID35205693
PMCPMC8870325
OpenAlexW4213062199

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.