Evidence map›Paper›PMID 31855148›Full record

Trial reportHealth technology assessment (Winchester, England)2019

Whole-body MRI compared with standard pathways for staging metastatic disease in lung and colorectal cancer: the Streamline diagnostic accuracy studies.

Stuart A Taylor, Susan Mallett, Anne Miles, Stephen Morris, Laura Quinn, Caroline S Clarke, Sandy Beare, John Bridgewater, Vicky Goh, Sam Janes and 8 more

Open access · diamondAbstract readClinical TrialMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Health technology assessment (Winchester, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
5.1field-weighted citation impact, top 4% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 69 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Review
  5. Lung Cancer Staging: Imaging and Potential Pitfalls.Diagnostics (Basel, Switzerland) · 2023
    Review
  6. Article
  7. 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

18 authors at 9 institutions in 1 country.

Stuart A TaylorCentre for Medical Imaging, University College London, London, UK.ORCID 0000-0002-6765-8806
Susan MallettInstitute of Applied Health Research, NIHR Birmingham Biomedical Research Centre, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.ORCID 0000-0002-0596-8200
Anne MilesDepartment of Psychological Sciences, Birkbeck, University of London, London, UK.ORCID 0000-0003-3122-9900
Stephen MorrisApplied Health Research, University College London, London, UK.ORCID 0000-0002-5828-3563
Laura QuinnInstitute of Applied Health Research, NIHR Birmingham Biomedical Research Centre, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.ORCID 0000-0001-9660-4631
Caroline S ClarkeResearch Department of Primary Care and Population Health, and Priment Clinical Trials Unit, University College London, London, UK.ORCID 0000-0002-4676-1257
Sandy BeareCancer Research UK & UCL Cancer Trials Centre, University College London, London, UK.ORCID 0000-0002-9055-1804
John BridgewaterUCL Cancer Institute, University College London, London, UK.ORCID 0000-0001-9186-1604
Vicky GohDepartment of Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.ORCID 0000-0002-2321-8091
Sam JanesLungs for Living Research Centre, UCL Respiratory, University College London, London, UK.ORCID 0000-0002-6634-5939
Dow-Mu KohDepartment of Radiology, The Royal Marsden Hospital, Sutton, UK.ORCID 0000-0001-7654-8011
Alison Mortonc/o Centre for Medical Imaging, University College London, London, UK.ORCID 0000-0002-2089-5257
Neal NavaniLungs for Living Research Centre, UCL Respiratory, University College London, London, UK.ORCID 0000-0002-6412-7516
Alfred Oliverc/o Centre for Medical Imaging, University College London, London, UK.ORCID 0000-0002-7380-1074
Anwar PadhaniMount Vernon Centre for Cancer Treatment, Mount Vernon Hospital, Northwood, UK.ORCID 0000-0002-5830-5777
Shonit PunwaniCentre for Medical Imaging, University College London, London, UK.ORCID 0000-0002-1014-0870
Andrea RockallImaging Department, Imperial College Healthcare NHS Trust, London, UK.ORCID 0000-0001-8270-5597
Steve HalliganCentre for Medical Imaging, University College London, London, UK.ORCID 0000-0003-0632-5108
University College London · GBNIHR Birmingham Biomedical Research Centre · GBBirkbeck, University of London · GBCancer Research UK · GBCRUK Lung Cancer Centre of Excellence · GBImperial College Healthcare NHS Trust · GBKing's College London · GBMount Vernon Hospital · GBRoyal Marsden Hospital · GB

Funding

Cancer Research UK 16463Department of Health 10/68/01
6 · The paper itself

Abstract

backgroundWhole-body magnetic resonance imaging is advocated as an alternative to standard pathways for staging cancer.

objectivesThe objectives were to compare diagnostic accuracy, efficiency, patient acceptability, observer variability and cost-effectiveness of whole-body magnetic resonance imaging and standard pathways in staging newly diagnosed non-small-cell lung cancer (Streamline L) and colorectal cancer (Streamline C).

designThe design was a prospective multicentre cohort study.

settingThe setting was 16 NHS hospitals.

participantsConsecutive patients aged ≥ 18 years with histologically proven or suspected colorectal (Streamline C) or non-small-cell lung cancer (Streamline L).

interventionsWhole-body magnetic resonance imaging. Standard staging investigations (e.g. computed tomography and positron emission tomography-computed tomography). REFERENCE STANDARD: Consensus panel decision using 12-month follow-up data.

main outcome measuresThe primary outcome was per-patient sensitivity difference between whole-body magnetic resonance imaging and standard staging pathways for metastasis. Secondary outcomes included differences in specificity, the nature of the first major treatment decision, time and number of tests to complete staging, patient experience and cost-effectiveness.

resultsStreamline C - 299 participants were included. Per-patient sensitivity for metastatic disease was 67% (95% confidence interval 56% to 78%) and 63% (95% confidence interval 51% to 74%) for whole-body magnetic resonance imaging and standard pathways, respectively, a difference in sensitivity of 4% (95% confidence interval -5% to 13%; LIMITATIONS: Whole-body magnetic resonance imaging was interpreted by practitioners blinded to other clinical data, which may not fully reflect how it is used in clinical practice.

conclusionsIn colorectal and non-small-cell lung cancer, the whole-body magnetic resonance imaging staging pathway has similar accuracy to standard staging pathways, is generally preferred by patients, improves staging efficiency and has lower staging costs. Future work should address the utility of whole-body magnetic resonance imaging for treatment response assessment.

trial registrationCurrent Controlled Trials ISRCTN43958015 and ISRCTN50436483.

fundingThis project was funded by the NIHR Health Technology Assessment programme and will be published in full in

Indexed as

Whole Body ImagingAgedCarcinoma, Non-Small-Cell LungColorectal NeoplasmsEnglandFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedNeoplasm StagingPositron-Emission TomographyProspective StudiesSensitivity and SpecificityTomography, X-Ray ComputedCOLONIC NEOPLASMSCOST–BENEFIT ANALYSISLUNG NEOPLASMSMAGNETIC RESONANCE IMAGINGPROSPECTIVE STUDIESSENSITIVITY AND SPECIFICITYTECHNOLOGY ASSESSMENTWHOLE-BODY IMAGING

Identifiers

PMID31855148
PMCPMC6936168
OpenAlexW2995788072

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.