Skip to main content
Top
Published in: Neuroradiology 11/2021

Open Access 01-11-2021 | Dementia | Review

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

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

Published in: Neuroradiology | Issue 11/2021

Login to get access

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.
Literature
3.
go back to reference Vernooij MW, Smits M (2012) Structural neuroimaging in aging and Alzheimer’s disease. Neuroimaging Clin N Am 22:33–55CrossRef Vernooij MW, Smits M (2012) Structural neuroimaging in aging and Alzheimer’s disease. Neuroimaging Clin N Am 22:33–55CrossRef
4.
go back to reference Scheltens P, Leys D, Barkhof F et al (1992) Atrophy of medial temporal lobes on MRI in "probable" Alzheimer’s disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry 55:967–972CrossRef Scheltens P, Leys D, Barkhof F et al (1992) Atrophy of medial temporal lobes on MRI in "probable" Alzheimer’s disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry 55:967–972CrossRef
7.
go back to reference ten Kate M, Barkhof F, Boccardi M, et al (2017) Clinical validity of medial temporal atrophy as a biomarker for Alzheimer’s disease in the context of a structured 5-phase development framework. Neurobiol Aging ten Kate M, Barkhof F, Boccardi M, et al (2017) Clinical validity of medial temporal atrophy as a biomarker for Alzheimer’s disease in the context of a structured 5-phase development framework. Neurobiol Aging
16.
go back to reference Duma C, Kopyov O, Kopyov A et al (2019) Human intracerebroventricular (ICV) injection of autologous, non-engineered, adipose-derived stromal vascular fraction (ADSVF) for neurodegenerative disorders: results of a 3-year phase 1 study of 113 injections in 31 patients. Mol Biol Rep 46:5257–5272. https://doi.org/10.1007/s11033-019-04983-5CrossRefPubMed Duma C, Kopyov O, Kopyov A et al (2019) Human intracerebroventricular (ICV) injection of autologous, non-engineered, adipose-derived stromal vascular fraction (ADSVF) for neurodegenerative disorders: results of a 3-year phase 1 study of 113 injections in 31 patients. Mol Biol Rep 46:5257–5272. https://​doi.​org/​10.​1007/​s11033-019-04983-5CrossRefPubMed
28.
29.
go back to reference Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 339:332–336CrossRef Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 339:332–336CrossRef
33.
go back to reference Smith SM, Jenkinson M, Woolrich MW, et al (2004) Advances in functional and structural MR image analysis and implementation as FSL. In: NeuroImage. Neuroimage Smith SM, Jenkinson M, Woolrich MW, et al (2004) Advances in functional and structural MR image analysis and implementation as FSL. In: NeuroImage. Neuroimage
49.
go back to reference Stelmokas J, Yassay L, Giordani B et al (2017) Translational MRI volumetry with NeuroQuant: effects of version and normative data on relationships with memory performance in healthy older adults and patients with mild cognitive impairment. J Alzheimer’s Dis 60:1499–1510. https://doi.org/10.3233/JAD-170306CrossRef Stelmokas J, Yassay L, Giordani B et al (2017) Translational MRI volumetry with NeuroQuant: effects of version and normative data on relationships with memory performance in healthy older adults and patients with mild cognitive impairment. J Alzheimer’s Dis 60:1499–1510. https://​doi.​org/​10.​3233/​JAD-170306CrossRef
76.
80.
go back to reference Suh CH, Shim WH, Kim SJ, et al (2020) Development and validation of a deep learning–based automatic brain segmentation and classification algorithm for Alzheimer disease using 3D T1-weighted volumetric images. Am J Neuroradiol. https://doi.org/10.3174/ajnr.a6848 Suh CH, Shim WH, Kim SJ, et al (2020) Development and validation of a deep learning–based automatic brain segmentation and classification algorithm for Alzheimer disease using 3D T1-weighted volumetric images. Am J Neuroradiol. https://​doi.​org/​10.​3174/​ajnr.​a6848
83.
go back to reference Cardoso MJ, Wolz R, Modat M, et al (2012) Geodesic information flows. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, pp 262–270 Cardoso MJ, Wolz R, Modat M, et al (2012) Geodesic information flows. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, pp 262–270
100.
go back to reference Granberg T, Uppman M, Hashim F, et al (2016) Clinical feasibility of synthetic MRI in multiple sclerosis: a diagnostic and volumetric validation study. In: Am J Neuroradiol, pp 1023–1029 Granberg T, Uppman M, Hashim F, et al (2016) Clinical feasibility of synthetic MRI in multiple sclerosis: a diagnostic and volumetric validation study. In: Am J Neuroradiol, pp 1023–1029
104.
go back to reference Scarpazza C, Ha M, Baecker L et al (2020) Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders. Transl Psychiatry 10:107CrossRef Scarpazza C, Ha M, Baecker L et al (2020) Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders. Transl Psychiatry 10:107CrossRef
105.
go back to reference Raji CA, Ly M, Benzinger TLS (2019) Overview of MR imaging volumetric quantification in neurocognitive disorders. Top Magn Reson Imaging 28:311–315CrossRef Raji CA, Ly M, Benzinger TLS (2019) Overview of MR imaging volumetric quantification in neurocognitive disorders. Top Magn Reson Imaging 28:311–315CrossRef
108.
go back to reference Liu CK, Miller BL, Cummings JL et al (1992) A quantitative MRI study of vascular dementia. Neurology 42:138–143CrossRef Liu CK, Miller BL, Cummings JL et al (1992) A quantitative MRI study of vascular dementia. Neurology 42:138–143CrossRef
119.
go back to reference Hosny A, Parmar C, Quackenbush J et al (2018) Artificial intelligence in radiology. Nat Rev Cancer 18:500–510CrossRef Hosny A, Parmar C, Quackenbush J et al (2018) Artificial intelligence in radiology. Nat Rev Cancer 18:500–510CrossRef
Metadata
Title
Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis—a systematic review
Authors
Hugh G. Pemberton
Lara A. M. Zaki
Olivia Goodkin
Ravi K. Das
Rebecca M. E. Steketee
Frederik Barkhof
Meike W. Vernooij
Publication date
01-11-2021
Publisher
Springer Berlin Heidelberg
Keywords
Dementia
Dementia
Published in
Neuroradiology / Issue 11/2021
Print ISSN: 0028-3940
Electronic ISSN: 1432-1920
DOI
https://doi.org/10.1007/s00234-021-02746-3

Other articles of this Issue 11/2021

Neuroradiology 11/2021 Go to the issue