Evidence map›Paper›PMID 34476511›Full record

SynthesisNeuroradiology2021

Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review.

Hugh G Pemberton, Lara A M Zaki, Olivia Goodkin, Ravi K Das, Rebecca M E Steketee, Frederik Barkhof, Meike W Vernooij

Erratum issuedOpen access · hybridAbstract readSystematic Review
In one paragraph

Synthesis in Neuroradiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 48 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 1 pooled it
5.2field-weighted citation impact, top 1% 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

48 citing papers in PubMed, 1 synthesis or guideline pooled it, 80 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 2 countries.

Hugh G PembertonCentre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK. h.pemberton@ucl.ac.uk.ORCID https://orcid.org/0000-0001-8419-6423
Lara A M ZakiDepartment of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands.ORCID https://orcid.org/0000-0002-7050-4410
Olivia GoodkinCentre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.ORCID https://orcid.org/0000-0001-9283-9716
Ravi K DasClinical, Educational and Health Psychology, University College London, London, UK.ORCID https://orcid.org/0000-0003-0104-1544
Rebecca M E SteketeeDepartment of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands.ORCID https://orcid.org/0000-0003-2689-5490
Frederik BarkhofCentre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.ORCID https://orcid.org/0000-0003-3543-3706
Meike W VernooijDepartment of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands.ORCID https://orcid.org/0000-0003-4658-2176
Erasmus MC · NLNational Hospital for Neurology and Neurosurgery · GBQueen Mary University of London · GBUniversity College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Developments in neuroradiological MRI analysis offer promise in enhancing objectivity and consistency in dementia diagnosis through the use of quantitative volumetric reporting tools (QReports). Translation into clinical settings should follow a structured framework of development, including technical and clinical validation steps. However, published technical and clinical validation of the available commercial/proprietary tools is not always easy to find and pathways for successful integration into the clinical workflow are varied. The quantitative neuroradiology initiative (QNI) framework highlights six necessary steps for the development, validation and integration of quantitative tools in the clinic. In this paper, we reviewed the published evidence regarding regulatory-approved QReports for use in the memory clinic and to what extent this evidence fulfils the steps of the QNI framework. We summarize unbiased technical details of available products in order to increase the transparency of evidence and present the range of reporting tools on the market. Our intention is to assist neuroradiologists in making informed decisions regarding the adoption of these methods in the clinic. For the 17 products identified, 11 companies have published some form of technical validation on their methods, but only 4 have published clinical validation of their QReports in a dementia population. Upon systematically reviewing the published evidence for regulatory-approved QReports in dementia, we concluded that there is a significant evidence gap in the literature regarding clinical validation, workflow integration and in-use evaluation of these tools in dementia MRI diagnosis.

Indexed as

DementiaMagnetic Resonance ImagingHumansAIAtrophyDementia diagnosisNeuroradiologyQuantitative MRIVolumetric

Identifiers

PMID34476511
PMCPMC8528755
OpenAlexW3198344345

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.