Evidence map›Paper›PMID 34332857›Full record

ArticleRadiography (London, England : 1995)2022

A novel use for routine CBCT imaging during radiotherapy to detect COVID-19.

A Clough, J Sanders, K Banfill, C Faivre-Finn, G Price, C L Eccles, M C Aznar, M Van Herk

Open access · greenAbstract read
In one paragraph

Article in Radiography (London, England : 1995), 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
0.8field-weighted citation impact, top 27% 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, 6 citations in OpenAlex.

  1. Article
  2. COVID-19 identification in chest X-ray images using intelligent multi-level classification scenario.Computers & electrical engineering : an international journal · 2022
    Article
  3. 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

8 authors at 1 institution in 1 country.

A CloughThe Christie NHSFT, Manchester, United Kingdom. Electronic address: abigael.clough@nhs.net.
J SandersThe Christie NHSFT, Manchester, United Kingdom.
K BanfillThe Christie NHSFT, Manchester, United Kingdom; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.
C Faivre-FinnThe Christie NHSFT, Manchester, United Kingdom; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.
G PriceThe Christie NHSFT, Manchester, United Kingdom; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.
C L EcclesThe Christie NHSFT, Manchester, United Kingdom; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.
M C AznarDivision of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.
M Van HerkThe Christie NHSFT, Manchester, United Kingdom; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.
University of Manchester · GB

Funding

Cancer Research UK 28701
6 · The paper itself

Abstract

introductionThoracic CT is a useful tool in the early diagnosis of patients with COVID-19. Typical appearances include patchy ground glass shadowing. Thoracic radiotherapy uses daily cone beam CT imaging (CBCT) to check for changes in patient positioning and anatomy prior to treatment through a qualitative assessment of lung appearance by radiographers. Observation of changes related to COVID-19 infection during this process may facilitate earlier testing improving patient management and staff protection.

methodsA tool was developed to create overview reports for all CBCTs for each patient throughout their treatment. Reports contain coronal maximum intensity projection (MIP's) of all CBCTs and plots of lung density over time. A single therapeutic radiographer undertook a blinded off-line audit that reviewed 150 patient datasets for tool optimisation in which medical notes were compared to image findings. This cohort included 75 patients treated during the pandemic and 75 patients treated between 2014 and 2017. The process was repeated retrospectively on a subset of the 285 thoracic radiotherapy patients treated between January-June 2020 to assess the efficiency of the tool and process.

resultsThree patients in the n = 150 optimisation cohort had confirmed COVID-19 infections during their radiotherapy. Two of these were detected by the reported image assessment process. The third case was not detected on CBCT due to minimal density changes in the visible part of the lungs. Within the retrospective cohort four patients had confirmed COVID-19 based on RT-PCR tests, three of which were retrospectively detected by the reported process.

conclusionThe preliminary results indicate that the presence of COVID-19 can be detected on CBCT by therapeutic radiographers. IMPLICATIONS FOR PRACTICE: This process has now been extended to clinical service with daily assessments of all thoracic CBCTs. Changes noted are referred for oncologist review.

Indexed as

COVID-19Radiotherapy, Image-GuidedSpiral Cone-Beam Computed TomographyHumansRadiotherapy Planning, Computer-AssistedRetrospective StudiesSARS-CoV-2CBCTCone-beam CTCOVID-19 corona virusRadiotherapy

Identifiers

PMID34332857
PMCPMC8299223
OpenAlexW3184272816

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.