Abstract
Objectives
Normative brain volume reports (NBVRs) are becoming more and more available for the workup of dementia patients in clinical routine. However, it is yet unknown how this information can be used in the radiological decision-making process. The present study investigates the diagnostic value of NBVRs for detection and differential diagnosis of distinct regional brain atrophy in several dementing neurodegenerative disorders.
Methods
NBVRs were obtained for 81 consecutive patients with distinct dementing neurodegenerative diseases and 13 healthy controls (HC). Forty Alzheimer’s disease (AD; 18 with dementia, 22 with mild cognitive impairment (MCI), 11 posterior cortical atrophy (PCA)), 20 frontotemporal dementia (FTD), and ten semantic dementia (SD) cases were analyzed, and reports were tested qualitatively for the representation of atrophy patterns. Gold standard diagnoses were based on the patients’ clinical course, FDG-PET imaging, and/or cerebrospinal fluid (CSF) biomarkers following established diagnostic criteria. Diagnostic accuracy of pattern representations was calculated.
Results
NBVRs improved the correct identification of patients vs. healthy controls based on structural MRI for rater 1 (p < 0.001) whereas the amount of correct classifications was rather unchanged for rater 2. Correct differential diagnosis of dementing neurodegenerative disorders was significantly improved for both rater 1 (p = 0.001) and rater 2 (p = 0.022). Furthermore, interrater reliability was improved from moderate to excellent for both detection and differential diagnosis of neurodegenerative diseases (κ = 0.556/0.894 and κ = 0.403/0.850, respectively).
Conclusion
NBVRs deliver valuable and observer-independent information, which can improve differential diagnosis of neurodegenerative diseases.
Key Points
• Normative brain volume reports increase detection of neurodegenerative atrophy patterns compared to visual reading alone.
• Differential diagnosis of regionally distinct atrophy patterns is improved.
• Agreement between radiologists is significantly improved from moderate to excellent when using normative brain volume reports.
![](https://cdn.statically.io/img/media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00330-019-06602-0/MediaObjects/330_2019_6602_Fig1_HTML.png)
![](https://cdn.statically.io/img/media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00330-019-06602-0/MediaObjects/330_2019_6602_Fig2_HTML.jpg)
Similar content being viewed by others
Abbreviations
- Aβ42:
-
Beta amyloid 1–42
- AD:
-
Alzheimer’s disease
- CDR:
-
Clinical Dementia Rating scale
- CDR-SOB:
-
Clinical Dementia Rating scale, Sum Of Boxes
- CERAD:
-
Consortium to Establish a Registry for Alzheimer’s Disease
- CSF:
-
Cerebrospinal fluid
- DLB:
-
Dementia with Lewy bodies
- EADC:
-
European Alzheimer’s Disease Consortium
- FDG-PET:
-
Fluorodeoxyglucose positron emission tomography
- FTD:
-
Frontotemporal dementia
- GCA:
-
Global cortical atrophy
- GM:
-
Gray matter
- HC:
-
Healthy controls
- LP:
-
Lumbar puncture
- MCI:
-
Mild cognitive impairment
- MMSE:
-
Mini-Mental State Examination
- MPRAGE:
-
Magnetization prepared rapid gradient echo
- MTA:
-
Mesial temporal atrophy
- NBVR:
-
Normative brain volume report
- NPV:
-
Negative predictive value
- PCA:
-
Posterior cortical atrophy
- PCC:
-
Posterior cingulate cortex
- PiB:
-
Pittsburgh compound B
- PPV:
-
Positive predictive value
- pTau:
-
Phosphorylated Tau 181
- SD:
-
Semantic dementia
- TE:
-
Time to echo
- TI:
-
Time to inversion
- TIV:
-
Total intracranial volume
- TPJ:
-
Temporoparietal junction
- TR:
-
Time to repetition
- tTau:
-
Total Tau
- WM:
-
White matter
References
Teipel S, Drzezga A, Grothe MJ et al (2015) Multimodal imaging in Alzheimer’s disease: validity and usefulness for early detection. Lancet Neurol 14:1037–1053
Frisoni GB, Fox NC, Jack CR Jr, Scheltens P, Thompson PM (2010) The clinical use of structural MRI in Alzheimer disease. Nat Rev Neurol 6:67–77
Teipel S, Kilimann I, Thyrian JR, Klöppel S, Hoffmann W (2017) Potential role of neuroimaging markers for early diagnosis of dementia in primary care. Curr Alzheimer Res 15:18–27
Harper L, Fumagalli GG, Barkhof F et al (2016) MRI visual rating scales in the diagnosis of dementia: evaluation in 184 post-mortem confirmed cases. Brain 139:1211–1225
Wahlund LO, Westman E, van Westen D et al (2017) Imaging biomarkers of dementia: recommended visual rating scales with teaching cases. Insights Imaging 8:79–90
Potvin O, Dieumegarde L, Duchesne S; Alzheimer's Disease Neuroimaging Initiative (2017) Normative morphometric data for cerebral cortical areas over the lifetime of the adult human brain. Neuroimage 156:315–339
Bruun M, Frederiksen KS, Rhodius-Meester HFM et al (2019) Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study. Alzheimers Res Ther 16:91–101
Minoshima S, Frey KA, Koeppe RA, Foster NL, Kuhl DE (1995) A diagnostic approach in Alzheimer’s disease using three-dimensional stereotactic surface projections of fluorine-18-FDG PET. J Nucl Med 36:1238–1248
Brown RKJ, Bohnen NI, Wong KK, Minoshima S, Frey KA (2014) Brain PET in suspected dementia: patterns of altered FDG metabolism. Radiographics 34:684–701
Albert MS, DeKosky ST, Dickson D et al (2011) The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7:270–279
McKhann GM, Knopman DS, Chertkow H et al (2011) The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7:263–269
Mendez MF, Ghajarania M, Perryman KM (2002) Posterior cortical atrophy: clinical characteristics and differences compared to Alzheimer’s disease. Dement Geriatr Cogn Disord 14:33–40
Neary D, Snowden JS, Gustafson L et al (1998) Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. Neurology 51:1546–1554
Savio A, Funger S, Tahmasian M et al (2017) Resting-state networks as simultaneously measured with functional MRI and PET. J Nucl Med 58:1314–1317
Chen W-P, Samuraki M, Yanase D et al (2008) Effect of sample size for normal database on diagnostic performance of brain FDG PET for the detection of Alzheimer’s disease using automated image analysis. Nucl Med Commun 29:270–276
Buchert R (2008) On the effect of sample size of the normal database on statistical power of single subject analysis. Nucl Med Commun 29:837
Huppertz H-J, Kroll-Seger J, Kloppel S, Ganz RE, Kassubek J (2010) Intra- and interscanner variability of automated voxel-based volumetry based on a 3D probabilistic atlas of human cerebral structures. Neuroimage 49:2216–2224
Opfer R, Suppa P, Kepp T, Spies L, Schippling S, Huppertz H-J (2016) Atlas based brain volumetry: how to distinguish regional volume changes due to biological or physiological effects from inherent noise of the methodology. Magn Reson Imaging 34:455–461
Malone IB, Leung KK, Clegg S et al (2015) Accurate automatic estimation of total intracranial volume: a nuisance variable with less nuisance. Neuroimage 104:366–372
Ashburner J, Friston KJ (2000) Voxel-based morphometry - the methods. Neuroimage 11:805–821
Muhlau M, Wohlschlager AM, Gaser C et al (2009) Voxel-based morphometry in individual patients: a pilot study in early Huntington disease. AJNR Am J Neuroradiol 30:539–543
Ashburner J (2007) A fast diffeomorphic image registration algorithm. Neuroimage 38:95–113
Forman SD, Cohen JD, Fitzgerald M, Eddy WF, Mintun MA, Noll DC (1995) Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): use of a cluster-size threshold. Magn Reson Med 33:636–647
Risacher S, Saykin A (2013) Neuroimaging biomarkers of neurodegenerative diseases and dementia. Semin Neurol 33:386–416
Whitwell JL, Jack CR Jr, Przybelski SA et al (2011) Temporoparietal atrophy: a marker of AD pathology independent of clinical diagnosis. Neurobiol Aging 32:1531–1541
Lehmann M, Crutch SJ, Ridgway GR et al (2011) Cortical thickness and voxel-based morphometry in posterior cortical atrophy and typical Alzheimer’s disease. Neurobiol Aging 32:1466–1476
Rohrer JD (2012) Structural brain imaging in frontotemporal dementia. Biochim Biophys Acta 1822:325–332
Rohrer JD, Warren JD, Modat M et al (2009) Patterns of cortical thinning in the language variants of frontotemporal lobar degeneration. Neurology 72:1562–1569
Lee G, Nho K, Kang B, Sohn KA, Kim D; for Alzheimer's Disease Neuroimaging Initiative (2019) Predicting Alzheimer’s disease progression using multi-modal deep learning approach. Sci Rep 9:1952
Basaia S, Agosta F, Wagner L et al (2019) Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage Clin 21:101645
Diehl-Schmid J, Onur OA, Kuhn J, Gruppe T, Drzezga A (2014) Imaging frontotemporal lobar degeneration. Curr Neurol Neurosci Rep 14:1–11
King RD, Brown B, Hwang M, Jeon T, George AT (2010) Fractal dimension analysis of the cortical ribbon in mild Alzheimer’s disease. Neuroimage 53:471–479
Cho Y, Seong J-K, Jeong Y, Shin SY (2012) Individual subject classification for Alzheimer’s disease based on incremental learning using a spatial frequency representation of cortical thickness data. Neuroimage 59:2217–2230
Klöppel S, Yang S, Kellner E et al (2018) Voxel-wise deviations from healthy aging for the detection of region-specific atrophy. NeuroImage Clin 20:851–860
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 55:967–972
Ferreira D, Cavallin L, Larsson EM et al (2015) Practical cut-offs for visual rating scales of medial temporal, frontal and posterior atrophy in Alzheimer’s disease and mild cognitive impairment. J Intern Med 278:277–290
Acknowledgments
We thank Dr. Lothar Spies and jung diagnostics GmbH for providing volumetric reports for the investigated MRI scans.
Funding
The authors state that this work has not received any funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Guarantor
The scientific guarantor of this publication is Timo Grimmer, MD.
Conflict of interest
The authors of this manuscript declare relationships with the following companies: jung diagnostics GmbH, Hamburg, Germany. Jung diagnostics GmbH provided the volumetric reports for the current study. No further relationships exist between the authors and jung diagnostics and no conflict of interest is present. Per Suppa, MD was an employee of jung diagnostics until November 2018.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Informed consent
Written informed consent was waived by the Institutional Review Board.
Ethical approval
Institutional Review Board approval was obtained (Reference # 176/18s).
Methodology
• Retrospective
• Diagnostic or prognostic study
• Performed at one institution
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(DOCX 252 kb)
Rights and permissions
About this article
Cite this article
Hedderich, D.M., Dieckmeyer, M., Andrisan, T. et al. Normative brain volume reports may improve differential diagnosis of dementing neurodegenerative diseases in clinical practice. Eur Radiol 30, 2821–2829 (2020). https://doi.org/10.1007/s00330-019-06602-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00330-019-06602-0