Alzheimer's disease (AD) and frontotemporal dementia (FTD) require different treatments. Since clinical presentation can be nuanced, imaging biomarkers aid in diagnosis. Automated software such as Neuroreader (NR) provides volumetric imaging data, and indices between anterior and posterior brain areas have proven useful in distinguishing dementia subtypes in research cohorts. Existing indices are complex and require further validation in clinical settings.
Purpose
To provide initial validation for a simplified anterior-posterior index (API) from NR in distinguishing FTD and AD in a clinical cohort.
Material and Methods
A retrospective chart review was completed. We derived a simplified API: API = (logVA/VP-μ)/σ where is weighted volume of frontal and temporal lobes and of parietal and occipital lobes. and are the mean and standard deviation of logVA/VP computed for AD participants. Receiver operating characteristic (ROC) curves and regression analyses assessed the efficacy of the API versus brain areas in predicting diagnosis of AD versus FTD.
Results
A total of 39 participants with FTD and 78 participants with AD were included. The API had an excellent performance in distinguishing AD from FTD with an area under the ROC curve of 0.82 and a positive association with diagnostic classification on logistic regression analysis (B = 1.491, P < 0.001).
Conclusion
The API successfully distinguished AD and FTD with excellent performance. The results provide preliminary validation of the API in a clinical setting.
2023 Alzheimer’s disease facts and figures. Alzheimers Dement2023;19:1598–1695.
2.
ZhangXXTianYWangZT, et al.The epidemiology of Alzheimer’s disease modifiable risk factors and prevention. J Prev Alzheimers Dis2021;8:313–321.
3.
FerriCPPrinceMBrayneC, et al.Global prevalence of dementia: a Delphi consensus study. Lancet2005;366:2112–2117.
4.
Soria LopezJAGonzálezHMLégerGC. Alzheimer’s disease. Handb Clin Neurol2019;167:231–255.
5.
HebertLEBeckettLAScherrPA, et al.Annual incidence of Alzheimer disease in the United States projected to the years 2000 through 2050. Alzheimer Dis Assoc Disord2001;15:169–173.
6.
BreijyehZKaramanR. Comprehensive review on Alzheimer’s disease: causes and treatment. Molecules2020;25:2–14.
7.
Se ThoeEFauziATangYQ, et al.A review on advances of treatment modalities for Alzheimer’s disease. Life Sci2021;276:119129.
8.
American Psychiatric Association. Neurocognitive disorders. In: Diagnostic and statistical manual of mental disorders, 5th ed. Arlington, VA: American Psychiatric Association, 2014
9.
McKhannGMKnopmanDSChertkowH, et al.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 Dement2011;7:263–269.
10.
van DyckCHSwansonCJAisenP, et al.Lecanemab in early Alzheimer’s disease. N Engl J Med2023;388:9–21.
11.
HoySM. Lecanemab: first approval. Drugs2023;83:359–365.
12.
Budd HaeberleinSAisenPSBarkhofF, et al.Two randomized phase 3 studies of aducanumab in early Alzheimer's disease. J Prev Alzheimers Dis2022;9:197–210.
13.
KorhonenTKatiskoKCajanusA, et al.Comparison of prodromal symptoms of patients with behavioral variant frontotemporal dementia and Alzheimer disease. Dement Geriatr Cogn Disord2020;49:98–106.
ShajiKSSivakumarPTRaoGP, et al.Clinical practice guidelines for management of dementia. Indian J Psychiatry2018;60:S312–S328.
16.
Magrath GuimetNZapata-RestrepoLMMillerBL. Advances in treatment of frontotemporal dementia. J Neuropsychiatry Clin Neurosci2022;34:316–327.
17.
GambogiLBGuimarãesHCde SouzaLC, et al.Treatment of the behavioral variant of frontotemporal dementia: a narrative review. Dement Neuropsychol2021;15:331–338.
CummingsJRabinoviciGDAtriA, et al.Aducanumab: appropriate use recommendations update. J Prev Alzheimers Dis2022;9:221–230.
20.
CummingsJApostolovaLRabinoviciGD, et al.Lecanemab: appropriate use recommendations. J Prev Alzheimers Dis2023;10:362–377.
21.
JackCRBennettDABlennowK, et al.NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement2018;14:535–562.
22.
KhanSBarveKHKumarMS. Recent advancements in pathogenesis, diagnostics and treatment of Alzheimer’s disease. Curr Neuropharmacol2020;18:1106–1125.
23.
HanssonOEdelmayerRMBoxerAL, et al.The Alzheimer’s association appropriate use recommendations for blood biomarkers in Alzheimer’s disease. Alzheimers Dement2022;18:2669–2686.
24.
HanssonO. Biomarkers for neurodegenerative diseases. Nat Med2021;27:954–963.
25.
RajiCABenzingerTLS. The value of neuroimaging in dementia diagnosis. Continuum (Minneap Minn)2022;28:800–821.
26.
OntarioHQ. The appropriate use of neuroimaging in the diagnostic work-up of dementia: an evidence-based analysis. Ont Health Technol Assess Ser2014;14:1–64.
27.
Raposo RodríguezLTovar SalazarDJFernández GarcíaN, et al.Magnetic resonance imaging in dementia. Radiologia (Engl Ed)2018;60:476–484.
28.
VitaliPMigliaccioRAgostaF, et al.Neuroimaging in dementia. Semin Neurol2008;28:467–483.
29.
OldanJDJewellsVLPieperB, et al.Complete evaluation of dementia: PET and MRI correlation and diagnosis for the neuroradiologist. AJNR Am J Neuroradiol2021;42:998–1007.
30.
MinoshimaSMosciKCrossD, et al.Brain [F-18]FDG PET for clinical dementia workup: differential diagnosis of Alzheimer’s disease and other types of dementing disorders. Semin Nucl Med2021;51:230–240.
31.
SilvermanDHSmallGWPhelpsME. Clinical value of neuroimaging in the diagnosis of dementia. Sensitivity and specificity of regional cerebral metabolic and other parameters for early identification of Alzheimer’s disease. Clin Positron Imaging1999;2:119–130.
32.
WardJLyMRajiCA. Brain PET imaging: frontotemporal dementia. PET Clin2023;18:123–133.
33.
ValotassiouVMalamitsiJPapatriantafyllouJ, et al.SPECT and PET imaging in Alzheimer’s disease. Ann Nucl Med2018;32:583–593.
34.
YeoJMLimXKhanZ, et al.Systematic review of the diagnostic utility of SPECT imaging in dementia. Eur Arch Psychiatry Clin Neurosci2013;263:539–552.
35.
MoonisGSubramaniamRMTrofimovaA, et al.ACR Appropriateness Criteria® dementia. J Am Coll Radiol2020;17:S100–S112.
36.
FalgàsNBalasaMBargallóN, et al.Diagnostic accuracy of MRI visual rating scales in the diagnosis of early onset cognitive impairment. J Alzheimers Dis2020;73:1575–1583.
37.
YuQMaiYRuanY, et al.An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer’s disease. Alzheimers Res Ther2021;13:23.
38.
PembertonHGZakiLAMGoodkinO, et al.Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review. Neuroradiology2021;63:1773–1789.
39.
PerssonKBarcaMLCavallinL, et al.Comparison of automated volumetry of the hippocampus using NeuroQuant® and visual assessment of the medial temporal lobe in Alzheimer’s disease. Acta Radiol2018;59:997–1001.
40.
PerssonKSelbækGBrækhusA, et al.Fully automated structural MRI of the brain in clinical dementia workup. Acta Radiol2017;58:740–747.
41.
MinJMoonWJJeonJY, et al.Diagnostic efficacy of structural MRI in patients with mild-to-moderate Alzheimer disease: automated volumetric assessment versus visual assessment. AJR Am J Roentgenol2017;208:617–623.
42.
MorinASamper-GonzalezJBertrandA, et al.Accuracy of MRI classification algorithms in a tertiary memory center clinical routine cohort. J Alzheimers Dis2020;74:1157–1166.
43.
AbrigoJShiLLuoY, et al.Standardization of hippocampus volumetry using automated brain structure volumetry tool for an initial Alzheimer’s disease imaging biomarker. Acta Radiol2019;60:769–776.
44.
ZhaoLLiuWLuoY, et al.Visual rating and automated brain volumetry for medial temporal lobe atrophy in differential diagnosis and early detection of Alzheimer’s disease. Alzheimers Dement2020;16:e041374.
45.
ZhaoLLuoYLewD, et al.Risk estimation before progression to mild cognitive impairment and Alzheimer’s disease: an AD resemblance atrophy index. Aging (Albany NY)2019;11:6217–6236.
46.
MeysamiSRajiCAMendezMF. Quantified brain magnetic resonance imaging volumes differentiate behavioral variant frontotemporal dementia from early-onset Alzheimer’s disease. J Alzheimers Dis2022;87:453–461.
47.
BruunMKoikkalainenJRhodius-MeesterHFM, et al.Detecting frontotemporal dementia syndromes using MRI biomarkers. Neuroimage Clin2019;22:101711.
48.
BertelsonJAAjtaiB. Neuroimaging of dementia. Neurol Clin2014;32:59–93.
49.
AhdidanJRajiCADeYoeEA, et al.Quantitative neuroimaging software for clinical assessment of hippocampal volumes on MR imaging. J Alzheimers Dis2016;49:723–732.
50.
KohaneISChurchillSEMurphySN. A translational engine at the national scale: informatics for integrating biology and the bedside. J Am Med Inform Assoc2012;19:181–185.
51.
BradfordAKunikMESchulzP, et al.Missed and delayed diagnosis of dementia in primary care: prevalence and contributing factors. Alzheimer Dis Assoc Disord2009;23:306–314.
52.
Gorno-TempiniMLHillisAEWeintraubS, et al.Classification of primary progressive aphasia and its variants. Neurology2011;76:1006–1014.
53.
ArmstrongMJLitvanILangAE, et al.Criteria for the diagnosis of corticobasal degeneration. Neurology2013;80:496–503.
54.
AlbertMSDeKoskySTDicksonD, et al.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 Dement2011;7:270–279.
55.
RascovskyKHodgesJRKnopmanD, et al.Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain2011;134:2456–2477.
56.
HöglingerGURespondekGStamelouM, et al.Clinical diagnosis of progressive supranuclear palsy: the movement disorder society criteria. Mov Disord2017;32:853–864.
57.
BoeveBFBoxerALKumforF, et al.Advances and controversies in frontotemporal dementia: diagnosis, biomarkers, and therapeutic considerations. Lancet Neurol2022;21:258–272.
ErikssonHFereshtehnejadSMFalahatiF, et al.Differences in routine clinical practice between early and late onset Alzheimer’s disease: data from the Swedish dementia registry (SveDem). J Alzheimers Dis2014;41:411–419.
61.
McHughML. The chi-square test of independence. Biochem Med (Zagreb)2013;23:143–149.
62.
HazraAGogtayN. Biostatistics series module 3: comparing groups: numerical variables. Indian J Dermatol2016;61:251–260.
63.
StoltzfusJC. Logistic regression: a brief primer. Acad Emerg Med2011;18:1099–1104.
64.
Hajian-TilakiK. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J Intern Med2013;4:627–635.
65.
CurtinFSchulzP. Multiple correlations and Bonferroni's correction. Biol Psychiatry1998;44:775–777.
66.
MandrekarJN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol2010;5:1315–1316.
67.
HosmerDWLemeshowS. Assessing the fit of the model. In: Applied logistic regression. 2nd ed. New York, NY: John Wiley and Son, 2000:160–164.
68.
HarperLFumagalliGGBarkhofF, et al.MRI visual rating scales in the diagnosis of dementia: evaluation in 184 post-mortem confirmed cases. Brain2016;139:1211–1225.
WeintraubS. Neuropsychological assessment in dementia diagnosis. Continuum (Minneap Minn)2022;28:781–799.
73.
TsoiKKChanJYHiraiHW, et al.Cognitive tests to detect dementia: a systematic review and meta-analysis. JAMA Intern Med2015;175:1450–1458.
74.
LaaksoMPFrisoniGBKönönenM, et al.Hippocampus and entorhinal cortex in frontotemporal dementia and Alzheimer’s disease: a morphometric MRI study. Biol Psychiatry2000;47:1056–1063.
75.
LanFRoquetDDaltonMA, et al.Exploring graded profiles of hippocampal atrophy along the anterior-posterior axis in semantic dementia and Alzheimer’s disease. Neurobiol Aging2024;135:70–78.
76.
Dominguez PerezSPhillipsJSNoriseC, et al.Neuropsychological and neuroanatomical features of patients with behavioral/dysexecutive variant Alzheimer’s disease (AD): a comparison to behavioral variant frontotemporal dementia and amnestic AD groups. J Alzheimers Dis2022;89:641–658.
77.
GjerumLFrederiksenKSHenriksenOM, et al.Evaluating 2-[ 18 F]FDG-PET in differential diagnosis of dementia using a data-driven decision model. Neuroimage Clin2020;27:102267.
78.
ChenWSongXZhangY, et al.An MRI-based semiquantitative index for the evaluation of brain atrophy and lesions in Alzheimer's disease, mild cognitive impairment and normal aging. Dement Geriatr Cogn Disord2010;30:121–130.
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