Evidence map›Paper›PMID 38355768›Full record

ArticleScientific reports2024

Distinguishing recurrence from radiation-induced lung injury at the time of RECIST progressive disease on post-SABR CT scans using radiomics.

Salma Dammak, Stephanie Gulstene, David A Palma, Sarah A Mattonen, Suresh Senan, Aaron D Ward

Open access · goldAbstract read
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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.6field-weighted citation impact, top 18% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Lung metastases.Nature reviews. Disease primers · 2025
    Review
  4. Review
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

6 authors at 4 institutions in 2 countries.

Salma DammakBaines Imaging Research Laboratory, London Regional Cancer Program, London Health Sciences Centre, Victoria Hospital (A3-123A), 800 Commissioners Rd E, London, ON, N6A 5W9, Canada. sdammak@uwo.ca.
Stephanie GulsteneDepartment of Radiation Oncology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
David A PalmaBaines Imaging Research Laboratory, London Regional Cancer Program, London Health Sciences Centre, Victoria Hospital (A3-123A), 800 Commissioners Rd E, London, ON, N6A 5W9, Canada.
Sarah A MattonenBaines Imaging Research Laboratory, London Regional Cancer Program, London Health Sciences Centre, Victoria Hospital (A3-123A), 800 Commissioners Rd E, London, ON, N6A 5W9, Canada.
Suresh SenanDepartment of Radiation Oncology, VU Amsterdam University Medical Centers, Amsterdam, The Netherlands.
Aaron D WardBaines Imaging Research Laboratory, London Regional Cancer Program, London Health Sciences Centre, Victoria Hospital (A3-123A), 800 Commissioners Rd E, London, ON, N6A 5W9, Canada.
London Health Sciences Centre · CAWestern University · CAVictoria Hospital · CAVrije Universiteit Amsterdam · NL

Funding

Government of Ontario, Canada Queen Elizabeth II Graduate ScholarshipNatural Sciences and Engineering Research Council of Canada Discovery Grant RGPIN-2019-06756Ontario Institute for Cancer Research Clinician-Scientist Operating Grant
6 · The paper itself

Abstract

Stereotactic ablative radiotherapy (SABR) is a highly effective treatment for patients with early-stage lung cancer who are inoperable. However, SABR causes benign radiation-induced lung injury (RILI) which appears as lesion growth on follow-up CT scans. This triggers the standard definition of progressive disease, yet cancer recurrence is not usually present, and distinguishing RILI from recurrence when a lesion appears to grow in size is critical but challenging. In this study, we developed a tool to do this using scans with apparent lesion growth after SABR from 68 patients. We performed bootstrapped experiments using radiomics and explored the use of multiple regions of interest (ROIs). The best model had an area under the receiver operating characteristic curve of 0.66 and used a sphere with a diameter equal to the lesion's longest axial measurement as the ROI. We also investigated the effect of using inter-feature and volume correlation filters and found that the former was detrimental to performance and that the latter had no effect. We also found that the radiomics features ranked as highly important by the model were significantly correlated with outcomes. These findings represent a key step in developing a tool that can help determine who would benefit from follow-up invasive interventions when a SABR-treated lesion increases in size, which could help provide better treatment for patients.

Indexed as

Carcinoma, Non-Small-Cell LungLung InjuryLung NeoplasmsRadiation InjuriesRadiosurgeryHumansNeoplasm Recurrence, LocalRadiomicsResponse Evaluation Criteria in Solid TumorsTomography, X-Ray Computed

Identifiers

PMID38355768
PMCPMC10866960
OpenAlexW4391810690

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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.