Abstract
Purpose
To evaluate the diagnostic value of combined semiquantitative and quantitative assessment of brain atrophy in the diagnostic workup of the behavioural-variant of frontotemporal dementia (bvFTD).
Methods
Three neuroradiologists defined brain atrophy grading and identified atrophy pattern suggestive of bvFTD on 3D-T1 brain MRI of 112 subjects using a semiquantitative rating scale (Kipps’). A quantitative atrophy assessment was performed using two different automated software (Quantib® ND and Icometrix®). A combined semiquantitative and quantitative assessment of brain atrophy was made to evaluate the improvement in brain atrophy grading to identify probable bvFTD patients.
Results
Observers’ performances in the diagnosis of bvFTD were very good for Observer 1 (k value = 0.881) and 2 (k value = 0.867), substantial for Observer 3 (k value = 0.741). Semiquantitative atrophy grading of all the observers showed a moderate and a poor correlation with the volume values calculated by Icometrix® and by Quantib® ND, respectively. For the definition of neuroradiological signs presumptive of bvFTD, the use of Icometrix® software improved the diagnostic accuracy for Observer 1 resulting in an AUC of 0.974, and for Observer 3 resulting in a AUC of 0.971 (p-value < 0.001). The use of Quantib® ND software improved the diagnostic accuracy for Observer 1 resulting in an AUC of 0.974, and for Observer 3 resulting in a AUC of 0.977 (p-value < 0.001). No improvement was observed for Observer 2.
Conclusion
Combining semiquantitative and quantitative brain imaging evaluation allows to reduce discrepancies in the neuroradiological diagnostic workup of bvFTD by different readers.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Abbreviations
- FTD:
-
Frontotemporal dementia
- bvFTD:
-
Behavioural variant of frontotemporal dementia
- AD:
-
Alzheimer’s disease
- VBM:
-
Voxel-based morphometry
- MMSE:
-
Mini-Mental State Examination
- ICC:
-
Intraclass correlation coefficient
- ESNR:
-
European Society of Neuroradiology
- HC:
-
Healthy controls
References
Winblad B, Amouyel P, Andrieu S, Ballard C, Brayne C, Brodaty H et al (2016) Defeating Alzheimer’s disease and other dementias: a priority for European science and society. Lancet Neurol 15(5):455–532
Vernooij MW, van Buchem MA (2020) Neuroimaging in dementia. In: Hodler J, Kubik-Huch RA, von Schulthess GK (eds) Diseases of the brain, head and neck, spine 2020–2023: diagnostic imaging. Springer International Publishing, Cham, pp 131–142
Frisoni GB, Fox NC, Jack CR Jr, Scheltens P, Thompson PM (2010) The clinical use of structural MRI in Alzheimer disease. Nat Rev Neurol 6(2):67–77
Vernooij MW, Smits M (2012) Structural neuroimaging in aging and Alzheimer’s disease. Neuroimaging Clin N Am 22(1):33–55 (vii-viii)
Vernooij MW, Pizzini FB, Schmidt R, Smits M, Yousry TA, Bargallo N et al (2019) Dementia imaging in clinical practice: a European-wide survey of 193 centres and conclusions by the ESNR working group. Neuroradiol 61(6):633–642
Cajanus A, Hall A, Koikkalainen J, Solje E, Tolonen A, Urhemaa T et al (2018) Automatic MRI quantifying methods in behavioral-variant frontotemporal dementia diagnosis. Dement Geriatr Cogn Dis Extra 8(1):51–59
Young JJ, Lavakumar M, Tampi D, Balachandran S, Tampi RR (2018) Frontotemporal dementia: latest evidence and clinical implications. Ther Adv Psychopharmacol 8(1):33–48
Gossye H, Van Broeckhoven C, Engelborghs S (2019) The use of biomarkers and genetic screening to diagnose frontotemporal dementia: evidence and clinical implications. Front Neurosci 13:757
Rascovsky K, Hodges JR, Knopman D, Mendez MF et al (2011) Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 134(Pt 9):2456–2477
Vijverberg EG, Wattjes MP, Dols A, Krudop WA, Möller C, Peters A et al (2016) Diagnostic accuracy of MRI and additional [18F]FDG-PET for behavioral variant frontotemporal dementia in patients with late onset behavioral changes. J Alzheimers Dis 53(4):1287–1297
Harper L, Barkhof F, Fox NC, Schott JM (2015) Using visual rating to diagnose dementia: a critical evaluation of MRI atrophy scales. J Neurol Neurosurg Psychiatry 86(11):1225–1233
Kipps CM, Davies RR, Mitchell J, Kril JJ, Halliday GM, Hodges JR (2007) Clinical significance of lobar atrophy in frontotemporal dementia: application of an MRI visual rating scale. Dement Geriatr Cogn Disord 23(5):334–342
Vernooij MW, Jasperse B, Steketee R, Koek M, Vrooman H, Ikram MA et al (2018) Automatic normative quantification of brain tissue volume to support the diagnosis of dementia: a clinical evaluation of diagnostic accuracy. Neuroimage Clin 20:374–379
Hedderich DM, Dieckmeyer M, Andrisan T, Ortner M, Grundl L, Schön S et al (2020) Normative brain volume reports may improve differential diagnosis of dementing neurodegenerative diseases in clinical practice. Eur Radiol 30(5):2821–2829
Folstein MF, Folstein SE, McHugh PR (1975) Mini-mental state: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189–198
Broe M, Hodges JR, Schofield E, Shepherd CE, Kril JJ, Halliday GM (2003) Staging disease severity inpathologically confirmed cases of frontotemporal dementia. Neurol 60(6):1005–1011
Devenney E, Hornberger M, Irish M, Mioshi E, Burrell J, Tan R et al (2014) Frontotemporal dementia associated with the C9ORF72 mutation: a unique clinical profile. JAMA Neurol 71(3):331–339
Harper L, Fumagalli GG, Barkhof F, Scheltens P, O’Brien JT, Bouwman F et al (2016) MRI visual rating scales in the diagnosis of dementia: evaluation in 184 post-mortem confirmed cases. Brain 139(Pt 4):1211–1225
Klöppel S, Stonnington CM, Barnes J, Chen F, Chu C, Good CD et al (2008) Accuracy of dementia diagnosis: a direct comparison between radiologists and a computerized method. Brain 131(Pt 11):2969–2974
Liu S, Hou B, Zhang Y, Lin T, Fan X, You H et al (2020) Inter-scanner reproducibility of brain volumetry: influence of automated brain segmentation software. BMC Neurosci 21(1):35
Klöppel S, Stonnington CM, Chu C, Draganski B, Scahill RI, Rohrer JD et al (2008) Automatic classification of MR scans in Alzheimer’s disease. Brain 131(Pt 3):681–689
Pemberton HG, Goodkin O, Prados F, Das RK, Vos SB, Moggridge J et al (2021) Automated quantitative MRI volumetry reports support diagnostic interpretation in dementia: a multi-rater, clinical accuracy study. Eur Radiol 31(7):5312–5323
Yu Q, Mai Y, Ruan Y, Luo Y, Zhao L, Fang W et al (2021) An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer’s disease. Alzheimers Res Ther 13(1):23
Meyer S, Mueller K, Stuke K, Bisenius S, Diehl-Schmid J, Jessen F et al (2017) Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data. Neuroimage Clin 14:656–662
Zaki Lara AM, VernooijMeike W, Marion Smits (2022) Comparing two artificial intelligence software packages for normative brain volumetry in memory clinic imaging. Neuroradiol 64(7):1359–1366
Klöppel S, Yang S, Kellner E, Reisert M, Heimbach B, Urbach H et al (2018) Voxel-wise deviations from healthy aging for the detection of region-specific atrophy. Neuroimage Clin 20:851–860
Vemuri P, Gunter JL, Senjem ML, Whitwell JL, Kantarci K, Knopman DS et al (2008) Alzheimer’s disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 39(3):1186–1197
Davatzikos C, Bhatt P, Shaw LM, Batmanghelich KN, Trojanowski JQ (2011) Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol Aging 32(12):2322.e19–27
Marino S, Bonanno L, Lo Buono V, Ciurleo R, Corallo F, Morabito R et al (2019) Longitudinal analysis of brain atrophy in Alzheimer’s disease and frontotemporal dementia. J Int Med Res 47(10):5019–5027
Funding
Grants:
• European Research Council (StG-2016_714388_NeuroTRACK);
• Foundation Research on Alzheimer’s Disease.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors of this manuscript declare relationships with the following companies: Quantib B.V., Rotterdam, The Netherlands. One author of this manuscript (AvL) is a full-time paid employee of Quantib B.V.
Ethical approval
The local ethical standards committee on human experimentation approved the study protocol and all participants (or their caregivers) provided written informed consent prior to study inclusion.
Informed consent
Written informed consent was obtained from all subjects (patients) in this study. Standard protocol approvals, registrations, and patient consents.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Calloni Sonia Francesca and Vezzulli Paolo Quintiliano are equally contributing authors and share the first authorship.
Supplementary information
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Calloni, S.F., Vezzulli, P.Q., Castellano, A. et al. Combining semi-quantitative rating and automated brain volumetry in MRI evaluation of patients with probable behavioural variant of fronto-temporal dementia: an added value for clinical practise?. Neuroradiology 65, 1025–1035 (2023). https://doi.org/10.1007/s00234-023-03133-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00234-023-03133-w