Top

Published in:

Open Access 01-08-2017 | Computer Applications

Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI

Authors: Esther E. Bron, Marion Smits, Janne M. Papma, Rebecca M. E. Steketee, Rozanna Meijboom, Marius de Groot, John C. van Swieten, Wiro J. Niessen, Stefan Klein

Published in: European Radiology | Issue 8/2017

Abstract

Objectives

To investigate the added diagnostic value of arterial spin labelling (ASL) and diffusion tensor imaging (DTI) to structural MRI for computer-aided classification of Alzheimer's disease (AD), frontotemporal dementia (FTD), and controls.

Methods

This retrospective study used MRI data from 24 early-onset AD and 33 early-onset FTD patients and 34 controls (CN). Classification was based on voxel-wise feature maps derived from structural MRI, ASL, and DTI. Support vector machines (SVMs) were trained to classify AD versus CN (AD-CN), FTD-CN, AD-FTD, and AD-FTD-CN (multi-class). Classification performance was assessed by the area under the receiver-operating-characteristic curve (AUC) and accuracy. Using SVM significance maps, we analysed contributions of brain regions.

Results

Combining ASL and DTI with structural MRI resulted in higher classification performance for differential diagnosis of AD and FTD (AUC = 84%; p = 0.05) than using structural MRI by itself (AUC = 72%). The performance of ASL and DTI themselves did not improve over structural MRI. The classifications were driven by different brain regions for ASL and DTI than for structural MRI, suggesting complementary information.

Conclusions

ASL and DTI are promising additions to structural MRI for classification of early-onset AD, early-onset FTD, and controls, and may improve the computer-aided differential diagnosis on a single-subject level.

Key points

• Multiparametric MRI is promising for computer-aided diagnosis of early-onset AD and FTD.

• Diagnosis is driven by different brain regions when using different MRI methods.

• Combining structural MRI, ASL, and DTI may improve differential diagnosis of dementia.

Available only for authorised users

Alzheimer’s Association (2015) 2015 Alzheimer’s disease facts and figures. Alzheimers Dement 11:332–384CrossRef

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 7:263–269CrossRefPubMedPubMedCentral

Rascovsky K, Hodges JR, Knopman D et al (2011) Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 134:2456–2477CrossRefPubMedPubMedCentral

Harris JM, Thompson JC, Gall C et al (2015) Do NIA-AA criteria distinguish Alzheimer’s disease from frontotemporal dementia? Alzheimers Dement 11:207–215CrossRefPubMed

Paquerault S (2012) Battle against Alzheimer’s disease: the scope and potential value of magnetic resonance imaging biomarkers. Acad Radiol 19:509–511CrossRefPubMed

Prince M, Bryce R, Ferri C (2011) World Alzheimer Report 2011, The benefits of early diagnosis and intervention. Alzheimer’s Disease International

Klöppel S, Abdulkadir A, Jack CR et al (2012) Diagnostic neuroimaging across diseases. Neuroimage 61:457–463CrossRefPubMed

Möller C, Pijnenburg YAL, Tijms B, Hafkemeijer A (2016) Alzheimer disease and behavioral variant frontotemporal dementia: automatic classification based on cortical atrophy for single-subject diagnosis. Radiology 1–11

Raamana PR, Rosen H, Miller B et al (2014) Three-class differential diagnosis among Alzheimer disease, frontotemporal dementia, and controls. Front Neurol 5:1–15

10.

Du A, Schuff N, Kramer J et al (2007) Different regional patterns of cortical thinning in Alzheimer’s disease and frontotemporal dementia. Brain 130:1159–1166CrossRefPubMedPubMedCentral

11.

Detre JA, Leigh JS, Williams DS, Koretsky AP (1992) Perfusion imaging. Magn Reson Med 23:37–45CrossRefPubMed

12.

Alsop DC, Detre JA, Golay X et al (2015) Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: a consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magn Reson Med 73:102–116CrossRefPubMed

13.

Du A, Jahng G, Hayasaka S, Kramer J (2006) Hypoperfusion in frontotemporal dementia and Alzheimer disease by arterial spin labeling MRI. Neurology 67:1215–1220CrossRefPubMedPubMedCentral

14.

Zhang Y, Schuff N, Ching C et al (2011) Joint assessment of structural, perfusion, and diffusion MRI in Alzheimer’s disease and frontotemporal dementia. Int J Alzheimer Dis 2011:1–11CrossRef

15.

Binnewijzend MA, Kuijer JPA, van der Flier WM et al (2014) Distinct perfusion patterns in Alzheimer’s disease, frontotemporal dementia and dementia with Lewy bodies. Eur Radiol 24:2326–2333CrossRefPubMed

16.

Steketee RME, Bron EE, Meijboom R, Houston GC, Klein S, Mutsaerts HJMM et al (2016) Early-stage differentiation between presenile Alzheimer’s disease and frontotemporal dementia using arterial spin labeling MRI. Eur Radiol 26(1):244–253CrossRefPubMed

17.

Dashjamts T, Yoshiura T, Hiwatashi A et al (2011) Simultaneous arterial spin labeling cerebral blood flow and morphological assessments for detection of Alzheimer’s Disease. Acad Radiol 18:1492–1499CrossRefPubMed

18.

Mak HK-F, Qian W, Ng KS et al (2014) Combination of MRI hippocampal volumetry and arterial spin labeling MR perfusion at 3-Tesla improves the efficacy in discriminating Alzheimer’s disease from cognitively normal elderly adults. J Alzheimer Dis 41:749–758

19.

Bron EE, Steketee RME, Houston GC, Oliver RA, Achterberg HC, Loog M et al (2014) Diagnostic classification of arterial spin labeling and structural MRI in presenile early stage dementia. Hum Brain Mapp 35(9):4916–4931CrossRefPubMed

20.

Zhang Y, Schuff N, Du A-T et al (2009) White matter damage in frontotemporal dementia and Alzheimer’s disease measured by diffusion MRI. Brain 132:2579–2592CrossRefPubMedPubMedCentral

21.

Lu PH, Lee GJ, Shapira J et al (2014) Regional differences in white matter breakdown between frontotemporal dementia and early-onset Alzheimer’s disease. J Alzheimer Dis 39:261–269

22.

Friese U, Meindl T, Herpertz SC et al (2010) Diagnostic utility of novel MRI-based biomarkers for Alzheimer’s disease: diffusion tensor imaging and deformation-based morphometry. J Alzheimer Dis 20:477–490CrossRef

23.

Haller S, Missonnier P, Herrmann FR et al (2013) Individual classification of mild cognitive impairment subtypes by support vector machine analysis of white matter DTI. Am J Neuroradiol 34:283–291CrossRefPubMed

24.

Besga A, Termenon M, Graña M et al (2012) Discovering Alzheimer’s disease and bipolar disorder white matter effects building computer aided diagnostic systems on brain diffusion tensor imaging features. Neurosci Lett 520:71–76CrossRefPubMed

25.

Cui Y, Wen W, Lipnicki DM et al (2012) Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: a combined spatial atrophy and white matter alteration approach. Neuroimage 59:1209–1217CrossRefPubMed

26.

O’Dwyer L, Lamberton F, Bokde ALW et al (2012) Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment. PLoS One 7:e32441–e32441CrossRefPubMedPubMedCentral

27.

McMillan CT, Avants BB, Cook P et al (2014) The power of neuroimaging biomarkers for screening frontotemporal dementia. Hum Brain Mapp 35:4827–4840CrossRefPubMedPubMedCentral

28.

Dyrba M, Grothe M, Kirste T, Teipel SJ (2015) Multimodal analysis of functional and structural disconnection in Alzheimer’s disease using multiple kernel SVM. Hum Brain Mapp 36:2118–2131CrossRefPubMed

29.

Gorno-Tempini ML, Hillis AE, Weintraub S et al (2011) Classification of primary progressive aphasia and its variants. Neurology 76:1006–1014CrossRefPubMedPubMedCentral

30.

Jones DK, Leemans A (2011) Diffusion tensor imaging. Meth Molec Biol 711:127–144CrossRef

31.

Chang C-C, Lin C-J (2011) LIBSVM: A library for support vector machines. ACM TIST 2:27–27

32.

Tax DMJ, Van Breukelen M, Duin RPW, Kittler J (2000) Combining multiple classifiers by averaging or by multiplying? Pattern Recognit 33:1475–1485CrossRef

33.

Hand DJ, Till RJ (2001) A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach Learn 45:171–186CrossRef

34.

Gaonkar B, Shinohara RT, Davatzikos C (2015) Interpreting support vector machine models for multivariate group wise analysis in neuroimaging. Med Image Anal 24:190–204CrossRefPubMedPubMedCentral

35.

Gaonkar B, Davatzikos C (2013) Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification. Neuroimage 78:270–283CrossRefPubMedPubMedCentral

36.

Bastos Leite A, Scheltens P, Barkhof F (2004) Pathological aging of the brain: an overview. Top Magn Reson Imaging 15:369–389CrossRefPubMed

37.

Frisoni G, Testa C, Zorzan A et al (2002) Detection of grey matter loss in mild Alzheimer’s disease with voxel based morphometry. J Neurol Neurosurg Psychiatry 73:657–664CrossRefPubMedPubMedCentral

38.

Chételat G, Desgranges B, De La Sayette V et al (2002) Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment. Neuroreport 13:1939–1943CrossRefPubMed

39.

Seelaar H, Rohrer JD, Pijnenburg YAL et al (2011) Clinical, genetic and pathological heterogeneity of frontotemporal dementia: a review. J Neurol Neurosurg Psychiatry 82:476–486CrossRefPubMed

40.

Hu WT, Wang Z, Lee V-Y et al (2010) Distinct cerebral perfusion patterns in FTLD and AD. Neurology 75:881–888CrossRefPubMedPubMedCentral

41.

Whitwell JL, Josephs KA, Rossor MN et al (2005) Magnetic resonance imaging signatures of tissue pathology in frontotemporal dementia. Arch Neurol 62:1402–1408CrossRefPubMed

42.

Jack CR, Barnes J, Bernstein MA et al (2015) Magnetic resonance imaging in Alzheimer’s Disease Neuroimaging Initiative 2. Alzheimers Dement 11:740–756CrossRefPubMedPubMedCentral

Title: Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI
Authors: Esther E. Bron
Marion Smits
Janne M. Papma
Rebecca M. E. Steketee
Rozanna Meijboom
Marius de Groot
John C. van Swieten
Wiro J. Niessen
Stefan Klein
Publication date: 01-08-2017
Publisher: Springer Berlin Heidelberg
Published in: European Radiology / Issue 8/2017
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-016-4691-x

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI

Abstract

Objectives

Methods

Results

Conclusions

Key points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key points

Please log in to get access to this content

Other articles of this Issue 8/2017

Attenuation-based kV pair selection in dual source dual energy computed tomography angiography of the chest: impact on radiation dose and image quality

Gadoxetic acid-enhanced magnetic resonance imaging characteristics of hepatocellular carcinoma occurring in liver transplants

Can lymphovascular invasion be predicted by preoperative multiphasic dynamic CT in patients with advanced gastric cancer?

A new technique for the diagnosis of acute appendicitis: abdominal CT with compression to the right lower quadrant

Technical success, technique efficacy and complications of minimally-invasive imaging-guided percutaneous ablation procedures of breast cancer: A systematic review and meta-analysis

MRI of endolymphatic hydrops in patients with Meniere’s disease: a case-controlled study with a simplified classification based on saccular morphology