Evidence map›Paper›PMID 26835492›Full record

ArticleJournal of medical imaging (Bellingham, Wash.)2015

Imaging texture analysis for automated prediction of lung cancer recurrence after stereotactic radiotherapy.

Sarah A Mattonen, Shyama Tetar, David A Palma, Alexander V Louie, Suresh Senan, Aaron D Ward

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

Article in Journal of medical imaging (Bellingham, Wash.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
4.3field-weighted citation impact, top 6% 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

15 citing papers in PubMed, 38 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Delta-radiomics increases multicentre reproducibility: a phantom study.Medical oncology (Northwood, London, England) · 2020
    Article
  9. Article
  10. Article
  11. Technical Note: Identification of CT Texture Features Robust to Tumor Size Variations for Normal Lung Texture Analysis.International journal of medical physics, clinical engineering and radiation oncology · 2018
    Article
  12. Review
  13. Review
  14. Article
  15. 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 3 countries.

Sarah A MattonenThe University of Western Ontario , Department of Medical Biophysics, 1151 Richmond Street, London, Ontario N6A 3K7, Canada.
Shyama TetarVU University Medical Center , Department of Radiation Oncology, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands.
David A PalmaThe University of Western Ontario, Department of Medical Biophysics, 1151 Richmond Street, London, Ontario N6A 3K7, Canada; London Regional Cancer Program, Division of Radiation Oncology, 1151 Richmond Street, London, Ontario N6A 3K7, Canada.
Alexander V LouieLondon Regional Cancer Program , Division of Radiation Oncology, 1151 Richmond Street, London, Ontario N6A 3K7, Canada.
Suresh SenanVU University Medical Center , Department of Radiation Oncology, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands.
Aaron D WardThe University of Western Ontario , Department of Medical Biophysics, 1151 Richmond Street, London, Ontario N6A 3K7, Canada.
Amsterdam UMC Location VUmc · NLWestern University · CACancer Care Ontario · CAWestern University of Health Sciences · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Benign radiation-induced lung injury (RILI) is not uncommon following stereotactic ablative radiotherapy (SABR) for lung cancer and can be difficult to differentiate from tumor recurrence on follow-up imaging. We previously showed the ability of computed tomography (CT) texture analysis to predict recurrence. The aim of this study was to evaluate and compare the accuracy of recurrence prediction using manual region-of-interest segmentation to that of a semiautomatic approach. We analyzed 22 patients treated for 24 lesions (11 recurrences, 13 RILI). Consolidative and ground-glass opacity (GGO) regions were manually delineated. The longest axial diameter of the consolidative region on each post-SABR CT image was measured. This line segment is routinely obtained as part of the clinical imaging workflow and was used as input to automatically delineate the consolidative region and subsequently derive a periconsolidative region to sample GGO tissue. Texture features were calculated, and at two to five months post-SABR, the entropy texture measure within the semiautomatic segmentations showed prediction accuracies [areas under the receiver operating characteristic curve (AUC): 0.70 to 0.73] similar to those of manual GGO segmentations (AUC: 0.64). After integration into the clinical workflow, this decision support system has the potential to support earlier salvage for patients with recurrence and fewer investigations of benign RILI.

Indexed as

computed tomographylung cancerradiomicsrecurrencestereotactic radiation therapytexture analysis

Identifiers

PMID26835492
PMCPMC4718517
OpenAlexW2099719565

What OpenQuestion holds

Textmetadata
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.