FDG-PET improves accuracy in distinguishing
frontotemporal dementia and Alzheimer’s disease
Norman L.Foster,1Judith L. Heidebrink,2,4Christopher M.Clark,5William J. Jagust,7Steven E. Arnold,5,6
Nancy R. Barbas,2,4Charles S. DeCarli,8R. Scott T urner,2,4Robert A. Koeppe,3Roger Higdon9and
1Center for Alzheimer’s Care, Imaging and Research and Department of Neurology,University of Utah,2Department of
Neurology,University of Michigan,3Division of Nuclear Medicine,University of Michigan,4Ann Arbor Veterans
Administration Hospital,5Alzheimer’s Disease Center, Institute on Aging and Department of Neurology,University of
Pennsylvania,6Department of Psychiatry,University of Pennsylvania,7Department of Neuroscience,University of California,
8Department of Neurology,University of California at Davis,9BIATECH Institute and10Department of Radiology,University
Correspondence to: Norman L.Foster, MD, 650 Komas Drive, #106-A, Salt Lake City,UT 84108-1225,USA
Distinguishing Alzheimer’s disease (AD) and frontotemporal dementia (FTD) currently relies on a clinical
history and examination, but positron emission tomography with [18F] fluorodeoxyglucose (FDG-PET) shows
different patterns of hypometabolism in these disorders that might aid differential diagnosis. Six dementia
experts with variable FDG-PETexperience made independent, forced choice, diagnostic decisions in 45 patients
with pathologically confirmed AD (n=31) or FTD (n=14) using five separate methods: (1) review of clinical
summaries, (2) a diagnostic checklist alone, (3) summary and checklist, (4) transaxial FDG-PETscans and (5)
FDG-PET stereotactic surface projection (SSP) metabolic and statistical maps. In addition, we evaluated the
effect of the sequential review of a clinical summary followed by SSP.Visual interpretation of SSP images was
superior to clinical assessment and had the best inter-rater reliability (mean kappa=0.78) and diagnostic accu-
racy (89.6%). It also had the highest specificity (97.6%) and sensitivity (86%), and positive likelihood ratio for FTD
(36.5).The addition of FDG-PET to clinicalsummariesincreaseddiagnostic accuracy andconfidence forboth AD
and FTD. It was particularly helpful when raters were uncertain in their clinical diagnosis.Visual interpretation
of FDG-PETafter brief training is more reliable and accurate in distinguishing FTD from AD than clinical meth-
ods alone.FDG-PETaddsimportant informationthat appropriatelyincreases diagnostic confidence, even among
experienced dementia specialists.
Keywords: Alzheimer’s disease; PET; FDG; frontotemporal dementia
Abbreviations: AD=Alzheimer’s disease; FDG=fluorodeoxyglucose; FTD=frontotemporal dementia
Received February 21 , 2007 . Revised July 6, 2007 . Accepted July1 1 , 2007 . Advance Access publication August18, 2007
Identifying the specific cause of dementia is challenging and
increasingly important as effective, disease-specific treat-
ments have become available. Clinicians particularly need a
practical method to accurately differentiate frontotemporal
dementia (FTD) from Alzheimer’s disease (AD). The first
symptom of AD is typically memory loss, while the
hallmarks of FTD are behaviour and language disturbance
(Kertesz and Munoz, 1998). However, both disorders cause
an insidious, gradually progressive dementia that lacks
distinctive physical signs, and patients with FTD frequently
meet diagnostic criteria for AD (Varma et al., 1999). Thus,
it is not surprising that FTD is frequently misdiagnosed,
even in specialty clinics (Mendez et al., 1993).
It is important for physicians to determine whether AD
or FTD is the cause of dementia. FTD is a common cause
of early-onset dementia and in the 45 to 64-year age group,
AD and FTD have an equal prevalence of 15 per 100000
(Ratnavalli et al., 2002). A diagnosis of FTD can have
significant implications for family members. Approximately
one-third of patients with FTD have a family history of a
similar disorder, and relatives are at increased risk of
dementia at an earlier age than the general population
(Rosso, 2003). FTD and AD have different pathology and
prognosis.In FTD, causative
histology indicate a disturbance in the microtubule protein
tau or the trophic factor progranulin, instead of in the
doi:10.1093/brain/awm177 Brain (2007),130, 2616^2635
? The Author (2007).Publishedby Oxford University Pressonbehalfofthe Guarantorsof Brain. Allrightsreserved.For Permissions, please email: email@example.com
beta-amyloid, as is characteristic of AD (Hardy and Selkoe,
2002; Trojanowski and Mattson, 2003). FTD lacks the
cholinergic deficiency of AD and its distinctive symptoms
and clinical course often present different management
challenges (Procter et al., 1999; Foster, 2003a). These
discrepancies explain why the appropriate treatment of AD
and FTD differ and why these disorders need to be
Positron emission tomography with [18F] fluorodeox-
yglucose (FDG-PET) highlights the different distribution of
pathology in dementing disorders and might aid diagnosis.
Recognizing AD and FTD is a particularly promising
application for FDG-PET because of the sharp contrast in
their pattern of glucose hypometabolism. AD causes
hypometabolism predominantly in posterior regions: the
posterior temporoparietal association cortex and posterior
cingulate cortex (Minoshima et al., 1997). FTD causes
hypometabolism predominantly in anterior regions: the
frontallobes, anterior temporal
cingulate cortex (Ishii et al., 1998). Although FDG-PET
has been used to study neurodegenerative disease for over
two decades, its diagnostic potential has not been fully
exploited. Most studies have been designed to understand
the biology of dementia and are inadequate to assess
clinical utility (Gill et al., 2003). Evaluation of a diagnostic
test relies upon individual, rather than group differences
from a reference population and is assessed with statistical
measures such as sensitivity, specificity, predictive value and
likelihood ratio. These measures apply to a single diagnostic
comparison. A history suggesting cognitive impairment
confirmed on clinical examination is the best and least
costly way to distinguish between normal and demented
patients, but greater clinical experience and judgement are
needed to distinguish different kinds of dementing diseases.
Now that FDG-PET is becoming widely available, clinical
trials to evaluate whether FDG-PET has an important role
in the evaluation of dementia are timely and necessary.
We evaluated the utility of FDG-PET to distinguish AD and FTD
in individual patients whose diagnoses were known from
histopathological examinations using methods easily incorporated
in clinical practice. Initially we compared five separate diagnostic
procedures: three entailing review of only clinical information
and two involving review of only FDG-PET imaging (Table 1).
We then examined the sequential use of the most accurate clinical
method and most accurate imaging method. This permitted us to
determine whether FDG-PET provided any added benefit in a
dementia evaluation. Six raters independently used each diagnostic
approach to assign a diagnosis of AD or FTD. For each diagnostic
approach we determined its reliability, characteristics as a
diagnostic test and effect on diagnostic confidence.
We identified all patients with dementia who had an FDG-PET
scan at the University of Michigan between December 1984 and
July 1998 and subsequently received a post-mortem examination
documenting a histopathological diagnosis of AD or FTD,
uncomplicated by other pathology such as stroke or significant
numbers of cortical Lewy bodies. Only individuals with retrievable
parametric PET images that included most of the brain in the field
of view were considered. Of the total 48 individuals found in our
record review, three were excluded because their medical records
were not retrievable. Of the remaining 45 patients, 31 had definite
AD and 14 had FTD. AD patients met NIA–Reagan neuropatho-
logical criteria for either high (28 cases) or intermediate (3 cases)
likelihood of AD (NIA and Reagan Institute Working Group,
1997). We identified minor additional pathological abnormalities
in eight of these subjects: three with cortical Lewy bodies
insufficient to meet neuropathological criteria for dementia with
Lewy bodies (McKeith et al., 1996), and five with cortical
arteriolosclerosis, including two with subcortical lacunar infarc-
tions of indeterminate age. Two AD subjects, ages 34 and 35 years
at the time of their scans, had early-onset familial AD. Excluding
these two individuals, the mean age of AD subjects was 67.8?7.6
(age range 51–79 years).
Patients with FTD had several specific neuropathological
diagnoses generally recognized as causing the clinical syndrome
of frontotemporal dementia, including frontotemporal degenera-
tion without distinctive histopathology (five cases), Pick’s disease
(four cases), corticobasal degeneration (two cases), progressive
subcortical gliosis (one case), mesocorticolimbic degeneration
(one case) and frontotemporal dementia with parkinsonism linked
to chromosome 17 and a mutation in the TAU gene (FTDP-17T)
(one case). The presence or absence of progranulin mutations was
not assessed and we did not apply recently developed ultra-
sensitive ubiquitin antibodies in these cases.
Subjects were identified from autopsy results rather than clinical
diagnoses. Several patients with AD pathology had an atypical
presentation with prominent language or visual symptoms
(Table 2). One patient with AD had a particularly rapid course
and was clinically thought to have Creutzfeldt–Jakob disease.
Another was thought to have Parkinson’s disease with dementia.
Individuals with FTD pathology were not prospectively classified
into clinical subtypes because their initial evaluations occurred
between 1985 and 1998, and only two occurred after the first
clinical FTD criteria were published in 1994. As a result, except for
T able1 Diagnostic methods evaluated in this study
Independent diagnostic methods
Clinical data:1. Interpretation of
2. Symptom checklist score
3. Interpretation of clinical
scenario with symptom
1.Transaxial images of glucose
metabolism relative to pons
2. Stereotactic surface projection
(SSP) metabolic and
1. Interpretation of clinical scenario alone
2. Interpretation of clinical scenario
also considering SSP metabolic and statistical maps
FDG-PET in FTD and ADBrain (2007),130, 2616^26352617
one individual diagnosed with progressive supranuclear palsy, all
subjects with FTD histopathology received an initial clinical
diagnosis of AD. Nevertheless, medical records indicate seven
presented primarily with frontal symptoms of personality change
and behaviour disturbance and three presented with predominant
aphasia. A separate panel of six dementia specialists also provided
a consensus diagnosis based upon their retrospective review of the
clinical scenarios, knowing that pathology showed either AD or
FTD (Table 2). For cases diagnosed as FTD, they also provided a
subtype classification based upon published guidelines (Neary
et al., 1998). Several were difficult to classify into a single category
and had features of more than one subtype.
T able 2 Characteristics of individual study subjects
Age at Sx
Duration of Sx at
first visit (yrs)
to PET (yrs)
Clinical diagnosis and
AD, prominent aphasia
AD, prominent aphasia
AD, atypical slow course
AD, prominent aphasia
AD, atypical, possible CBD
AD, prominent visual
AD, prominent aphasia
AD, prominent aphasia
AD, atypical slow course
AD, prominent visual
AD, prominent aphasia
AD, prominent aphasia
AD, atypical frontal
AD, atypical frontal
AD, atypical frontal
AD, atypical frontal
AD, atypical frontal
AD, atypical frontal
AD, prominent aphasia
AD, atypical frontal
Note: AD: Alzheimer’s disease, FTD: frontotemporal dementia, PNFA: progressive non-fluent aphasia, PSP: progressive supranuclear palsy,
CBD: corticobasal degeneration.
2618Brain (2007),130, 2616^2635N. L.Foster et al.
AD and FTD subjects had similar demographic characteristics
(Table 3). Initial evaluations in our clinic occurred on average
4 years after symptom onset, although sometimes symptoms
reportedly had been present for a decade or more and dementia
was already severe. Two-thirds of subjects had their FDG-PET
scan within 1 year of their first visit.
We also identified 33 cognitively normal elderly individuals
of similar age to our study subjects who had received FDG-PET
scans as control subjects for previous research studies (Table 3).
We constructed a database of scans from these control subjects for
statistical comparisons with patient scans.
Six neurologists with 10 to 25 years of experience in dementia care
at three NIA-funded Alzheimer’s Disease Centers served as raters
(SA, NB, CC, CD, WJ and RST). FDG-PET research studies had
been conducted at all three Centers, but the raters themselves had
variable imaging experience; some were recognized experts in
FDG-PET imaging, others were novices. We selected two raters
from each Center so regional and institutional differences could be
examined. All raters were informed that study subjects had an
autopsy-confirmed diagnosis of either FTD or AD, but they did
not know the proportion of subjects with each diagnosis.
Institutional Review Boards at the University of Michigan and at
each of the investigator’s institutions approved this study.
We developed summaries extracted from all available medical
records of the clinical course of each patient. Often, serial
dementia clinic assessments performed over many years were
available, and many patients were followed until shortly before
death. A research assistant redacted all personal identifiers, clinical
diagnoses, the results of imaging studies, genetic analyses and
autopsy reports. All subjects received structural imaging studies,
either CT or MRI, as part of their clinical assessment. None
showed focal lesions. Structural imaging studies and detailed
neuropsychological data were redacted from the materials used to
generate the case scenarios to reduce bias. Many scans were not
retrievable for direct review and their methods and the quality of
the reports varied considerably. Likewise, neuropsychological
testing was inconsistent and therefore we only provided summary
scores. A single neurologist (JH), experienced in dementia
assessments and unaware of subject identity, diagnosis and
pathologic findings, reviewed the redacted medical records to
develop a chronological summary of the patient’s entire illness,
averaging 650 words in both AD and FTD subjects. These clinical
scenarios focused on the patient’s initial and most prominent
symptoms, and the results of mental status and neurological
examinations. They included illustrative examples of symptoms
and the results of neuropsychological testing when available
(see example, Appendix 1). They were similar in length and
content to summaries used in another study of diagnostic
reliability and validity in dementia, except they did not include
diagnostic imaging results (Blacker et al., 1994). Case scenarios
were assigned random numbers and sent to the raters for review.
Based solely upon the scenarios, raters were asked to make a
diagnosis of AD or FTD and indicate their degree of diagnostic
confidence—very confident, somewhat confident or uncertain.
Symptom checklists have been advocated as an aid in diagnostic
decision-making. After the raters used the clinical scenario to
reach a diagnosis, they were asked to use the clinical scenario to
complete and score a 26-item questionnaire developed by Barber
et al. (1995). This checklist identifies symptoms felt to be
characteristic of either AD or FTD based upon the timing of
their appearance in the course of disease. We followed the rules
outlined by Barber et al. to complete the questionnaire, but made
T able 3 Summary characteristics of study subjects
DiagnosisAD (n=31) FTD (n=14) All patients
Prevalence in this study
Age at scan
MMSE at scan
Time from symptom onset
to first clinic visit (years)
Time from first clinic visit
to PETscan (years)
Time from PETscan to death
Note:Values are mean?SD; N/A=not available; EXACT=Siemens/CTI Exact 47 scanner (xy pixel dimension1.91mm, in-plane?axial
resolution 8.0?5.0mm;TCC=The Cyclotron Corporation PCT 4600a scanner (xy pixel dimension 3.75mm, in-plane?axial resolution
12.0?9.5mm; ECAT=Siemens/CTI ECAT 931scanner (xy pixel dimension1.89mm, in-plane?axial resolution 8.0?7 .5mm.
FDG-PET in FTD and ADBrain (2007),130, 2616^2635 2619
minor formatting and grammar modifications to accommodate
our use of the questionnaire in a record review, rather than in its
original design as an informant interview. The rater divided the
patient’s clinical course into thirds and then determined whether
specific symptoms identified in the checklist were present or
absent. For example, in the first third of the illness a change in
personality increases the score to favour FTD, while geographic
disorientation and learning problems decreases the score to
favour AD. When raters thought information in the scenarios
was insufficient to assess a specific stage of the illness, the section
was omitted. However, even in those circumstances, the checklist
still provided a score that could be translated into a diagnosis
using the scoring rules (positive scores and zero indicated FTD,
negative scores indicated AD). The results of the checklist were
used in two ways. First, we simply recorded the diagnosis
indicated by the checklist score. Second, raters were asked to
make a diagnosis of either AD or FTD and indicate their degree of
confidence after completing the checklist and considering the
computed score along with the clinical scenario.
FDG-PET data were obtained from archived files. PET instru-
mentation and methods for reconstructing parametric images of
glucose metabolism have evolved rapidly over the years. Thus,
procedures for attenuation correction, scatter correction and
filtering were different depending on when the scan was
performed. We obtained FDG-PET scans from three PET
instruments with bismuth germanium oxide detectors. These
scanners have different technical specifications and resolutions and
use different data file formats. Fortunately, our archiving system
was able to retrieve and manipulate scan files from all these
instruments, despite the evolution in imaging acquisition that
occurred over the years. All scans in this study included the entire
brain including the brainstem, a requirement of our image
analysis software. Although it has a small axial field of view, this
was achieved in the TCC scanner using a series of 2–4 contiguous
and interleaved scans, each consisting of five transaxial images.
The use of multiple PET instruments could be a major concern in
research studies based upon quantitative image analysis. However,
we assumed that metabolic changes due to disease were likely
greater than those due to variations in FDG-PET data acquisition.
We provided raters with two different colour displays of
FDG-PET scan data—transaxial and stereotactic surface projection
(SSP) images. Because variations in FDG-PET data acquisition
may affect topographic patterns of glucose hypometabolism, we
wanted to investigate whether a voxel-wise method like SSP would
aid visual inspection of data from PET instruments with varying
physical specifications. Images had no personal identifiers or dates
and were labelled with randomly generated numbers. Transaxial
and SSP images and clinical scenarios were sent to raters on
separate dates and had different random number labels. Thus,
raters could not compare transaxial and SSP images from the
same subject or compare PET images to clinical scenarios.
Raters received all relevant transaxial images available for each
subject (15–47 per subject, depending upon the scanner) in a
standard format and orientation. Images were shown as relative
metabolic rates with the highest pixel value in the scan placed at
the highest value on the colour scale (Fig. 1A).
Traditionally, physicians have viewed FDG-PET scans as a series
of transaxial images. The first PET scanners produced only a single
‘slice’ image. Multi-slice PET instruments have higher spatial
resolution and provide more detailed information. The large
number of transaxial images these instruments generate (128 or
more in some current models) presents a challenge to the
clinician. The images must be mentally manipulated into a 3D
space to recognize, describe and interpret a metabolic pattern. SSP
is an automated analysis method that warps images into a uniform
stereotactic space and also permits statistical analysis of individual
scans. Each set of brain images is first oriented along a line passing
through the anterior and posterior commissures. Then, through a
series of automated steps, imaging data are interpolated to
establish a uniform image matrix and voxel size. Next, linear
scaling is used to correct for individual brain size and regional
anatomic differences with the Talairach atlas brain are minimized
with non-linear warping. This enables reliable pixel-by-pixel
comparisons of these anatomically standardized brain images.
SSP is designed to select data relevant for the interpretation of
scans in diseases primarily affecting the cerebral cortex and to
summarize this information in a series of 6 easily interpreted
surface projection maps (Minoshima et al., 1995b). To determine
projection map values, it uses a predetermined vector that is 6
pixels (13.5mm) long and oriented perpendicular to the outer and
medial surfaces of the right and left-brain hemispheres for each
surface pixel. The surface pixel is assigned the highest pixel value
found along this vector. Because SSP selects peak rather than
average for analysis it is relatively resistant to affects of atrophy
(Ishii et al., 2001). Previous studies suggest it may have higher
reliability for diagnostic decision-making than transaxial images
(Burdette et al., 1996). We normalized surface pixel values to the
pons, which is relatively preserved in AD (Minoshima et al.,
1995a). We determined pons activity by averaging the highest
300 pixels within the pontine region encompassed by the
anterior–posterior commissural line.
SSP results are displayed as true surface maps rather than a
transparent view of the brain surface often used in other
techniques. Raters received two complementary sets of SSP
images. The first, a metabolic map, shows values of glucose
metabolism relative to pons using the same colour scale as the
transaxial images. The second, a statistical map, shows surface
pixel-by-pixel z-scores derived from comparing an individual’s
scan with results in normal controls. The statistical map shows
only pixels with significant glucose hypometabolism compared to
the control population using a colour scale reflecting the degree of
significance. Figure 1B shows an example of both SSP image
displays for the same subject shown in Fig. 1A.
?36mm below the
Rater training for FDG-PET interpretation
FDG-PET images in this study were evaluated only after raters
completed a 2-h training session designed to reduce the impact of
their varied imaging expertise and to establish a uniform
approach to interpretation. For this training, we developed a set
of FDG-PET images not otherwise used in our study from
10 subjects with clinically diagnosed AD, 10 with clinically
diagnosed FTD and 5 normal elderly controls. We selected these
images to illustrate the full range of findings the raters might
expect to encounter in each diagnostic group. The images were
labelled only with a consecutive number and diagnosis, except for
normal subjects, where age also was provided. Raters were trained
via telephone while viewing images on their personal computers.
2620Brain (2007),130, 2616^2635 N. L.Foster et al.
The training session began with a review of PET methodology,
including technical issues that determine image quality, methods
of image display and factors that affect image appearance. Next,
imaging anatomy was reviewed with particular attention to
regions affected in FTD and AD. Most of the session was spent
reviewing and discussing images in the training set. Raters were
asked to evaluate training images using the same procedures they
would use later with study images. First, raters had to grade the
degree of overall scan abnormality as normal, uncertain abnormal,
somewhat abnormal or very abnormal. The rating of uncertain
abnormalities in the scan. Second, by focusing attention on the
areas most critical to the diagnosis of AD and FTD, they had to
decide whether metabolism was normal or abnormal. Five specific
brain regions were rated in each hemisphere so a total of
10 regions were assessed. These areas were the posterior
temporoparietal association cortex, posterior cingulate gyrus,
frontal association cortex, anterior temporal cortex and anterior
cingulate gyrus. Since rating the entire scan considered all regions,
and not just those rated individually, it was possible for the entire
encouragedratersto identifyeventhe mildest
Fig.1 Example of transaxial and SSP images from a patient with AD separately andindependently provided to raters. (A) The top six rows
of images are a set of 41transaxial images extending from the top (top left) to the bottom of the brain.The posterior part of the brain in
the last images is outside the scanner field of view. (B) The lower two rows show stereotactic surface projection (SSP) maps of glucose
metabolism relative to pons and pixels with significant z-scores compared to 33 elderly normal control subjects (last row). SSP maps pro-
vide six views of the brainçright andleftlateral, right andleftmedial, superior andinferior (in order fromleft to rightof the figure).Values
in all images are shown in a colour scale with values red > yellow > green > blue as indicated on the colour bar.Colour bars and labels are
provided for reference, but were not included in the images sent to the raters.
FDG-PET in FTD and ADBrain (2007),130, 2616^2635 2621
scan to be rated abnormal, even if all of these 10 regions were
considered normal. Third, raters had to decide whether there was
significant asymmetry in the degree of hypometabolism in left and
right cerebral hemispheres. Finally, raters had to make a diagnosis
of either FTD or AD and indicate their degree of diagnostic
confidence. Raters were instructed to use simple rules to assign a
diagnosis from the images. They were asked to interpret a scan as
AD when the degree of hypometabolism appeared greater in the
posterior association cortex and posterior cingulate gyrus than in
the anterior regions, and as FTD when hypometabolism appeared
greater in the frontal association cortex, anterior temporal cortex
and anterior cingulate gyrus than in the posterior regions. Raters
were forced to make a diagnosis of FTD or AD in each case, even
if they rated the scan as normal.
Sequential assessment of clinical scenarios and
Analysis of results of the three clinical and two FDG-PET rating
methods found that the diagnostic checklist had no appreciable
effect on diagnostic accuracy and that SSP was superior to
transaxial image display for FDG-PET. Consequently, after
completing these initial ratings, we used only clinical scenarios
and SSP FDG-PET images in a second round of ratings to evaluate
the potential value of adding FDG-PET to clinical evaluations. For
this part of the study, rating of the scenarios and SSP images was
accomplished using a web-based system that assured incremental
data presentation. No personal identifiers were included and we
used a random code number different from those used in previous
ratings. First, raters reviewed the case scenario and entered their
diagnosis and degree of diagnostic certainty. Only after this
response was confirmed and locked were raters allowed to view
the subject’s SSP images. To ensure that raters were appropriately
attentive to the scan, we again asked them for assessments of the
scan as a whole, the critical five brain regions in each hemisphere
relevant to our diagnostic rules, and the presence of hemispheric
asymmetry. After completing these assessments, raters again were
asked to assign a diagnosis of FTD or AD and indicate their
degree of confidence, this time considering both the FDG-PET
scan and scenario, which was still available for review on their
We compared the diagnostic judgements of raters to neuropatho-
logical diagnosis, which served as our reference standard. Since
six expert clinicians performed independent assessments on
45 subjects, there were 270 observations for each measure in the
study. Inter-rater reliability was assessed using rater agreement and
kappa statistics calculated for all possible rater pairs. Rater
consensus was evaluated using both unanimity and supermajority
(agreement of 5/6 raters) rules. The degree of agreement based
upon kappa statistics was rated as fair (kappa values 0.2–0.39),
moderate (kappa 0.4–0.59), substantial (0.6–0.79) or almost
perfect (0.8–1.0), according to convention (Landis and Koch,
1977). Diagnostic accuracy was assessed by computing rater
consensus, the proportion of ratings with correct diagnoses, and
standard methods for assessing a diagnostic test—sensitivity,
specificity, predictive values and likelihood ratios (Qizilbash,
2002). Because raters had only two diagnostic options, sensitivity
and specificity for FTD was equivalent to that for AD and positive
and negative predictive values were complementary. We analysed
rater performance by comparing diagnostic accuracy and con-
fidence and their change with addition of PET using graphic
displays and statistical tests for binary data. Logistic regression
models were fit to binary variables representing whether the
diagnosiswas correct (accuracy,
predictive value), whether the rater was ‘very confident’ in their
diagnosis, or whether a change in confidence was appropriate for a
given diagnosis. A different logistical model accounting for ratios
was appliedto determinewhether
differences in likelihood ratios. Some tests were conditional on
the diagnosis (FTD or AD) or whether a change in diagnosis or
confidence occurred. Since ratings of the same case by different
raters or the same rater using a different method and ratings of
different cases by the same rater are potentially correlated,
standard independence assumptions do not hold. Statistical tests
comparing different diagnostic methods adjusted standard errors
and hypothesis tests to account for correlations between cases and
raters. This adjustment used a robust variance estimate that
incorporates estimates of correlation from these two sources
(Andrews, 1991). We then used this adjusted variance estimate to
generate P-values based on Wald tests.
Raters found almost all scans abnormal (256/270, 95% for
transaxial; 267/270, 99% for SSP). In a small portion, the
degree of abnormality was mild and considered uncertain.
Most scans were rated as either somewhat or very abnormal
(84% of transaxial and 95% of SSP scans). Scans from AD
and FTD subjects had similar degrees of abnormality.
Hypometabolism was more frequent in frontal, anterior
cingulate and anterior temporal regions in FTD, and in
temporoparietal and posterior cingulate regions in AD,
consistent with the criteria used in this study to interpret
FDG-PET scans. However, each of these regions was
sometimes rated as hypometabolic in both AD and FTD
subjects. Likewise, none of the regions was rated as
abnormal in all patients with AD or in all patients with
FTD. Raters found posterior cingulate hypometabolism in a
much higher proportion of subjects with SSP than
transaxial images (71 versus 32% in the AD subjects).
Otherwise, the proportion of subjects with hypometabolism
in a particular region was similar with the two methods.
Significant hemispheric metabolic asymmetry was present
in approximately half of both AD and FTD cases, with very
similar results using either transaxial or SSP images.
A similar proportion of FTD cases had left or right-
hemispheric asymmetry, while the right hemisphere was
more often hypometabolic in the AD subjects with
significant asymmetry (34% rated predominantly right
hemisphere hypometabolism and 17% rated predominantly
left hemisphere hypometabolism).
The diagnostic agreement between raters was higher for
both FDG-PET methods than for any of the purely clinical
2622 Brain (2007),130, 2616^2635N. L.Foster et al.
methods (Table 4). Transaxial and SSP FDG-PET images
showed substantial inter-rater diagnostic agreement based
on mean kappa values, and agreement was slightly but not
significantly higher for SSP than for transaxial scans. The
review of clinical scenarios had moderate inter-rater
diagnostic agreement, and there was only fair inter-rater
agreement on the diagnosis based upon the symptom
checklist score. Although raters were asked to assign a
responses were rarely changed from those after the scenario
alone and inter-rater agreement therefore was similar.
Although diagnostic judgements in this study were made
on the basis of a global assessment of the pattern of
hypometabolism, an algorithmic approach using indepen-
dent judgements about specific brain region abnormalities
is a feasible alternative approach. Therefore, we examined
abnormality of individual regions. There was less inter-
rater agreement about whether a specific brain region was
hypometabolic than about diagnosis, although it was
consistently greater in every region with SSP than with
transaxial images. Mean kappa values for individual regions
ranged from 0.14 to 0.51 using transaxial images and from
0.36 to 0.74 using SSP images. Reliability was not
dependent upon the degree of reported hypometabolism.
SSP improved inter-rater reliability most in the posterior
cingulate cortex while increasing the proportion of AD
subjects with hypometabolism recognized in this region
(mean kappa 0.20 for transaxial images and 0.57 for SSP).
SSP also improved mean kappa scores for the anterior
temporal cortex as the proportion with hypometabolism in
this region declined. Raters had more agreement on the
significance and direction
asymmetry with transaxial images (mean kappa 0.54
Diagnosis was more accurate with both FDG-PET methods
then for any of the purely clinical methods (Table 5).
Overall accuracy was superior (P=0.02), as was specificity
for FTD (P=0.02). Diagnostic accuracy was consistently
better using SSP than transaxial FDG-PET images, with
89% of all ratings having the correct diagnosis with SSP,
but this did not reach statistical significance (P=0.2). The
completion of a diagnostic checklist did not improve
diagnosticaccuracy. Diagnostic accuracy
among raters with the symptom checklist and least with
clinical scenarios and SSP ratings. Diagnostic accuracy was
less in FTD than in AD subjects (Figs 2–4). This
discrepancy was present irrespective of the method used:
clinical scenarios (P=0.03), transaxial images (P=0.0003)
or SSP (P=0.004).
The usual methods for determining the value of a
diagnostic test showed that imaging generally outperformed
clinical measures. SSP achieved the highest sensitivities,
specificities, predictive values and likelihood ratios of all
four methods with the single exception of negative
predictive value for FTD/positive predictive value for AD,
where transaxial displays were slightly preferred (Tables 5
and 6). Of the clinical measures, the symptom checklist did
not have an appreciable effect on accuracy or reliability.
It provided a somewhat higher specificity for FTD, but it
had a lower specificity for AD. It improved the positive
likelihood ratio for FTD only slightly.
T able 4 Diagnostic inter-rater reliability of five independent methods to distinguish AD and FTD in 45 autopsy-confirmed
All raters agree on diagnosis
5/6 raters agree on diagnosis
Mean inter-rater kappa
(range of individual raters)
T able 5 Diagnostic accuracy, sensitivity and specificity of five independent diagnostic methods
Clinical scenario Symptom checklistScenario + checklist Transaxial FDG-PETSSP FDG-PET
All raters correct diagnosis
5/6 raters correct diagnosis
Mean diagnostic accuracy
(range of individual raters)
Mean FTD sensitivity/AD specificity
range of individual raters)
Mean FTD specificity/AD sensitivity
FDG-PET in FTD and ADBrain (2007),130, 2616^26352623
Raters often had limited confidence in their diagnosis,
particularly with clinical methods (Figs. 2 and 3). Although
completing a symptom checklist rarely altered a rater’s
diagnosis, it did tend to increase the rater’s diagnostic
confidence (data not shown). Raters might have inferred a
degree of confidence based upon the value of the checklist
score, but this was not the intent of the checklist authors or
our instruction. Consequently,
confidence from the checklist alone. Diagnostic confidence
appears to be a meaningful measure, because it appro-
priately reflected raters’ true diagnostic accuracy. Raters
were less confident about diagnosis when using transaxial
images than SSP images (P=0.02) and less accurate about
diagnosis when using scenarios than SSP images (P=0.02).
They were also both less confident and less accurate in FTD
cases than in AD cases. FDG-PET with SSP tended to
improve the raters’ diagnostic confidence more in AD than
in FTD (P=0.08).
Overall the performance of raters was remarkably similar.
Consistent with their status as dementia experts, the raters
had similarly high diagnostic accuracy and all pair-wise
inter-rater reliability comparisons were similar. All raters
had better diagnostic accuracy with imaging methods than
with the clinical scenario. All were more accurate with SSP
images than transaxial images (Fig. 4). Raters all found it
more difficult to accurately diagnose FTD than AD. The
reliability and accuracy of ratings was unaffected by the
rater’s institutional affiliation, so we did not detect any
geographical or practice variations.
Diagnostic accuracy of sequential ratings
The overall accuracy of initial clinical diagnosis of 79%
improved to 90% (P=0.03) after FDG-PET scans were
considered (Table 7). The addition of FDG-PET particularly
improved the diagnostic accuracy of FTD (P=0.01), but
improvements in the accuracy of AD did not reach
statistical significance (P=0.3). Positive predictive value
and positive likelihood ratio demonstrate that FDG-PET
improves the clinical accuracy of an FTD diagnosis more
than an AD diagnosis. Accuracy of an initial AD diagnosis
after clinical information alone was 85%, leaving relatively
Fig. 2 Diagnostic accuracy and confidence after review of case
scenario, and after review of FDG-PETscan displayed as stereo-
tactic surface projection maps. Each horizontal bar represents
the ratings in a single case.The length of the colour bar is
proportionate to the number ofratings with a particular response.
Vertical ticks represent the numbers of ratings, but not a
particular individual rater.Cases are ordered vertically based upon
the degree of diagnostic accuracy and confidence with SSP.Ratings
with correct diagnoses are ordered with increasing confidence
from the centre of the figure, while ratings with incorrect diag-
noses are arranged from the outside with decreasing confidence.
Ratings performed after review of the case scenario are shown in
the left half of the figure, and those performed after review of the
SSP FDG-PETscan are shown in the right half of the figure.Results
in subjects with a neuropathological diagnosis of FTD are shown in
the top of the figure, and of AD in the bottom of the figure.
Clinical diagnoses judged to be correct as compared to pathologi-
cal findings are shown in shades of red and incorrect diagnoses are
shown in shades of blue.The shading indicates the degree of
certainty with most intense shades indicating greater degree of
certainty.For example, the first row is an FTD case. All six raters
were highly confident in a correct diagnosis of FTD after review of
the SSP image.In contrast, after review of the clinical scenario, five
raters indicated the correct diagnosis of FTD, three were highly
confident and two were only somewhat confident.The sixth rater
was somewhat confident in what ultimately was an incorrect
diagnosis of AD.Overall diagnostic accuracy (P=0.02) and specifi-
city for FTD (P=0.02) were significantly better after review of the
SSP PET than after review of the case scenario.
2624 Brain (2007),130, 2616^2635 N. L.Foster et al.
little room for improvement. Regardless, the addition of
FDG-PET increased the sensitivity for AD to 98%, and for
4 of 6 raters, to 100%.
Scans changed the diagnosis in 42 (16%) of the
270 ratings;34 (81%)of which corrected an
initial misdiagnosis. In 11 of these ratings, the rater
changed their initial diagnosis of AD to FTD after review
of the scan, which corrected a misdiagnosis in 10/11 (91%)
cases. In 31 ratings, scan results led raters to change their
diagnosis from FTD to AD, which corrected a misdiagnosis
in 24/31 (77%) cases. The addition of FDG-PET caused
diagnostic errors in 8/270 (3%) ratings: one error changed
an initial AD diagnosis to FTD, and seven incorrectly
changed initial FTD diagnoses to AD.
The frequency at which individual raters changed their
diagnosis after viewing the FDG-PET scan was similar and
ranged from 13 to 20%. The likelihood that a rater would
change a diagnosis based upon FDG-PET scan results was
not related to the extent of previous imaging experience or
Diagnostic confidence in sequential ratings
Viewing the FDG-PET scan was significantly more likely to
increase diagnostic confidence than decrease it, even when
Fig. 3 Diagnostic accuracy and confidence with review of PET
scans displayed as transaxial images, and after review of FDG-PET
scans displayed as SSP images.The figureis constructedin the same
way as in Fig. 2.Cases are ordered vertically based upon the
degree of diagnostic accuracy and confidence with SSP. Although
overall diagnostic accuracy were similar with the two methods,
raters had significantly more confidence with SSP (difference in
percentage very confident: overall, P=0.02, AD only P=0.006,
FTD only 0.6; when restricted to cases where both diagnoses
were correct: overall P=0.006, AD only=0.005, FTD only
Fig. 4 Diagnostic accuracy by rater using three diagnostic meth-
odsçscenario alone, transaxial image alone and SSP image alone.
In A, ratings of all 45 subjects are shown. In B, only the 31AD
subjects are shown and in C, only the14 FTD subjects are shown.
The y axis indicates the number of subjects.The same scale is used
in all graphs with the dotted line showing the total possible
number of subjects in B and C.
FDG-PET in FTD and AD Brain (2007),130, 2616^26352625
diagnosis was unchanged (P=0.003, Table 8). Confidence
appropriately reflected diagnostic accuracy (Table 8, Fig. 5).
FDG-PET had most benefit on diagnostic accuracy when
raters were only somewhat confident or uncertain about
their initial diagnosis (Table 9). In these instances,
diagnostic accuracy increased from 71 to 90% (P=0.02).
Overall effect of the addition of FDG-PET
The addition of FDG-PET changed a diagnosis or changed
diagnostic confidence without changing the diagnosis in
53.7% of ratings (Fig. 6). We considered changes beneficial
if they corrected a misdiagnosis, increased confidence in a
correct diagnosis or decreased confidence in an incorrect
diagnosis. Other changes were considered adverse. Using
this guideline, the overall effect of adding FDG-PET on
ratings was beneficial in 42.2%, neutral in 46.3% and
adverse in 11.5%, and significantly more likely to be
beneficial (42.2%) than adverse (P=0.0001).
This study shows that FDG-PET is a reliable and valid
diagnostic test that can aid physicians in making the
sometimes difficult clinical distinction between AD and
FTD. We believe brain imaging is most helpful in answering
specific, narrowly framed, clinically relevant diagnostic
questions. Consequently, this study was designed to
evaluate only whether FDG-PET helped distinguish AD
and FTD, which characteristically have sharply contrasting
patterns of glucose hypometabolism. Our findings should
encourage subsequent studies addressing how imaging
practically can assist in answering the many other difficult
diagnostic and management questions that arise during
T able 6 Predictive values and likelihood ratios of five independent diagnostic methods
Mean PPV for FTD/NPV for AD
(range of individual raters)
Mean NPV for FTD/PPV for AD
(range of individual raters)
+ Likelihood ratio for FTD
+ Likelihood ratio for AD
? Likelihood ratio for FTD
? Likelihood ratio for AD
T able 7 Accuracy of rater diagnoses before and after
all cases (n=45)
FTD sensitivity/AD specificity
FTD specificity/AD sensitivity0.006
Positive predictive value for
value for AD
Negative predictive value for
value for AD
Positive likelihood ratio for
ratio for ADa
Positive likelihood ratio for
ratio for FTDa
Note:Values are the mean of all six raters.The range of individual
raters is shown in parentheses.
aA likelihood ratio of1indicates a testresult does not alter pretest
probability and has no value. A ratio of 2^5 indicates a small,
but sometimesimportant, test. A ratio >10 is generally considered
conclusive evidence that test performance changes pretest
probability (Quizilbash, 2002).
T able 8 Diagnostic confidence before and after considering
Diagnosis Diagnostic confidenceBefore
aP-values for the difference in the percentage of raters who were
very confident: overall=0.01, AD only=0.01, FTD only=0.02;
when analysis wasrestricted to cases where diagnosesbothbefore
and after FDG-PETwere correct, overall=0.001, AD only=0.03,
2626Brain (2007),130, 2616^2635 N. L.Foster et al.
Many previous FDG-PET studies have demonstrated that
these two disorders cause distinctive patterns of glucose
hypometabolism (Ishii et al., 1998; Foster et al., 1999;
Foster, 2003b). These and most studies of FDG-PET have
focused on group differences. Voxel-wise analysis methods
also have identified changes in groups of subjects with
mild cognitive impairment before the development of
frank dementia (Chetelat et al., 2003; Anchisi et al.,
2005). More stringent requirements must be met to
demonstratethat these differences
individuals and can be used in the practical care of
patients. Several studies are available showing that dement-
ing diseases cause metabolic changes that are sufficiently
robust to be identified with FDG-PET in single subjects
(Salmon, 2002). One large, multi-centre study of FDG-PET
used individual differences from a reference population to
distinguish individual normal control subjects and patients
with clinically diagnosed AD (Herholz et al., 2002).
Using an automated analysis method to quantify the sum
of t-score values across all abnormal voxels, the sensitivity
and specificity to discriminate between mild-moderate
AD and normal subjects were both 93%. Diagnostic
classification, however, was based upon a post hoc criterion
derived from the study data. Considerable evidence also has
accumulated demonstrating that visual interpretation of
FDG-PET has a high degree of diagnostic accuracy when
compared to neuropathological diagnosis (Mielke et al.,
1996; Hoffman et al., 2000; Silverman et al., 2001).
However, in these studies no comparison with clinical
information was possible. This study expands on previous
studies by showing that visual interpretation of FDG-PET
with predetermined diagnostic criteria can have high inter-
rater reliability, is superior to a detailed clinical summary,
and when added to other clinical information, can enhance
the accuracy and confidence of diagnosis.
Implications for clinical assessment
Imaging is unreliable as the sole basis of determining the
cause of dementia and only should be used as an adjunct to
other components of the diagnostic evaluation. A careful
consideration of the medical history and examination will
continue to be essential to dementia evaluations. However,
the recognition and interpretation of symptoms and disease
course are subjective, and the accuracy and confidence of
diagnosis using clinical methods alone can vary, depending
upon patient and physician characteristics and the amount
T able 9 Effect of initial diagnostic confidence on changes in
diagnosis and accuracy
115 (87%) 6 (5%)115 (87%)
98 (71%)36 (26%) 124 (90%)
Fig. 5 Diagnosis and diagnostic confidence before and after review
of FDG-PETscans.The figure is constructed in the same way as in
Fig. 2.Ratings with only a review of history and examination are
shown on the left of the figure, and ratings with FDG-PETadded
are on the right.Cases are ordered based upon the degree of
accuracy and confidence found after FDG-PETwas added to the
clinical scenario.Overall diagnostic accuracy (P=0.03) and specifi-
city for FTD (P=0.006) significantly improved when FDG-PETwas
added.Raters also had significantly greater diagnostic confidence
(difference in percentage very confident: overall, P=0.01, AD only,
P=0.01, FTD only P=0.02; restricted to cases where both diag-
noses were correct: overall, P=0.001, AD only, P=0.03, FTD only
FDG-PET in FTD and ADBrain (2007),130, 2616^2635 2627
and quality of information available. As our data illustrate,
even experts find it difficult to diagnose a specific
dementing disease in some cases. We asked our raters to
rely upon their clinical expertise rather than use explicit
diagnostic criteria. We chose this approach because it more
accurately reflected clinical practice, we were separately
evaluating a diagnostic checklist, and proposed diagnostic
criteria for FTD are still being refined and have not yet had
neuropathological validation (Neary et al., 1998; Rosen
et al., 2002). Furthermore, it was not our intention to
evaluate diagnostic criteria or propose new operational
procedures for criteria that often are subject to considerable
interpretation. Relying on our expert judgements appears
justified because there generally was high agreement about
diagnoses and we were unable to identify any significant
inter-rater or institutional differences. Furthermore, raters
achieved a sensitivity and specificity for AD based upon the
clinical scenarios well within the range observed in other
studies (Chui and Lee, 2002). While fewer studies of FTD
are available, they also achieved a sensitivity and specificity
for FTD based upon clinical scenario that is consistent with
other retrospective studies. For example, one study of eight
FTD patients found a mean sensitivity of 85%, a mean
specificity of 97% and a mean kappa value of 0.75 using
only first visit clinical data and Lund and Manchester
clinical criteria (Lopez et al., 1999). Another study of seven
patients with Pick’s disease that did not use clinical criteria
found a median sensitivity of 43%, a median specificity of
99% and a mean kappa value of 0.42 using only first visit
information (Litvan et al., 1997).
It has been argued that FDG-PET can add little to
dementia evaluations because of high accuracy in the
diagnosis of AD, at least in research centers studying
Alzheimer’s disease and when experts perform the evalua-
tions (Lopez et al., 1999; Holmes et al., 1999; Chui and Lee,
2002; Gill et al., 2003). However, this partly reflects the
high prior probability of AD in most clinical populations,
and does not necessarily imply a similar accuracy in clinical
situations where AD is less likely. In our study, adding
FDG-PET to clinical information increased the accuracy of
AD diagnosis from 86 to 97%. Diagnostic accuracy of FTD
using only clinical information is less (Litvan et al., 1997).
Recognizing the challenges of differentiating FTD and AD
diagnostic checklists have been proposed, each intended
to focus clinicians’ attention to symptoms felt to be
characteristic of FTD (Miller et al., 1997; Kertesz et al.,
2000). We chose to use the checklist of Barber et al.,
because it identifies symptoms characteristic of FTD
and AD to generate a score for diagnosis, was designed
for retrospective review of a patient’s entire clinical
course, and is the only checklist that has been validated
Fig. 6 The effect of FDG-PETon diagnostic accuracy and confidence.This flow diagram shows the overall effect of FDG-PETstudies on
diagnosis and diagnostic confidence.Outcomes that are beneficial are indicated in bold text.Outcomes that are either neutral or beneficial
are shaded. Appropriate changes in diagnosis after adding FDG-PETwere significantly greater than inappropriate changes (P=0.0001).
2628Brain (2007),130, 2616^2635 N. L.Foster et al.
Unfortunately, we found the checklist had poor reliability.
While the Barber checklist was intended for use with
reports of knowledgeable informants rather than review of
medical records, this does not seem to explain its
weaknesses in our study. Completing the diagnostic check-
list during review of the scenario did not improve the
diagnostic accuracy of our expert raters, although it might
benefit less experienced clinicians.
Interpretation of FDG-PETscans
Both diagnostic checklists and usual clinical methods
distinguish AD and FTD primarily by inferring sites of
pathology based on characteristic signs and symptoms.
FDG-PET provides a separate, simpler, more objective and
quantitative way to make this same judgement. Imaging
provides a wealth of data, which can be displayed in many
ways. Good reliabilityand
observed with traditional transaxial images, but even
better results were achieved when SSP maps were used to
display FDG-PET data. The advantage of SSP presumably is
due to its ability to summarize data from the cerebral
cortex in a few images and provide a visual comparison
with findings in normal control subjects. We found that
SSP was inferior to transaxial images only in its lesser
reliability in judging ‘significant’ hemispheric asymmetry, a
decision not aided by current SSP methods. Statistical maps
that highlight hemispheric differences likely would remedy
this weakness. There also are many potential ways to
analyse FDG-PET data. It is reassuring that we achieved
substantial levels of inter-rater reliability after a relatively
brief training using a simple diagnostic rule with the
clinically practical approach of visual interpretation. Visual
assessment of images has the advantage of utilizing all
available information and permits clinicians to simulta-
neously consider many factors that may have diagnostic
significance. Indeed, we have found in a separate study
using these same subjects that visual interpretation of scans
for diagnosis is superior to automated algorithms compar-
ing metabolic changes in specific brain regions (Higdon
et al., 2004). On the other hand, multivariate predictive
models using partial least-squares analysis based on the raw
data from the SSP images were able to achieve diagnostic
accuracy similar to that of our expert raters. Raters found
that some scans did not have clear-cut abnormalities,
particularly those with very mild deficits. Future studies
should evaluate strategies that could increase sensitivity
in detecting abnormalities in individual scans such as
multivariate statistical methods and using cohort-specific
normative brain atlases for statistical analyses. The lower
inter-rater reliability in judging abnormality in individual
brain regions suggests that reliability would be poor for an
algorithm derived from a combination of regions. Likewise,
visual assessment of metabolic asymmetry appears to be
insufficiently reliable as a basis for diagnostic decisions.
Our results demonstrate that FDG-PET increases diagnostic
confidence. Appropriately enhancing diagnostic confidence
could have a major benefit for patients. The uncertainty
that physicians feel in diagnosing dementing diseases has
been little studied, but likely is an important factor in the
quality of dementia care (Foster, 2001). Presumably,
physicians more confident in their diagnoses are more
likely to institute and sustain therapy, disclose a diagnosis
and fully discuss prognosis with patients and families. It is
difficult to know how diagnostic uncertainty in this study
might compare to the diagnostic confidence physicians
experience in clinical practice. Undoubtedly, individual
style and personality must heavily influence confidence.
We found that even highly experienced dementia experts
have considerable and remarkably similar degrees of
uncertainty in their diagnosis when it is based solely on
clinical data. It is noteworthy that our expert raters all
found the diagnosis obvious in only a few cases; in the vast
majority of cases, different raters expressed a range of
diagnostic confidence when provided with only clinical
information. We suspect that community physicians with
less experience in the diagnosis of dementing diseases
would express even greater uncertainty. This partly may
account for the significant underutilization of available
treatments in the community, even when dementia is
recognized (Magsi et al., 2005). Our study suggests that
diagnostic confidence is a valid reflection of diagnostic
considered as an outcome to assess the value of a diagnostic
test, because diagnostic confidence is likely to affect
physicians’ decisions about treatment. Rater uncertainty
varied considerably from case to case, and not surprisingly,
was greater for patients with FTD. FTD has many diverse
presentations that can be subtle or overlap with AD, and
since it is less common, clinicians also have less experience
to draw upon.
It is crucial to determine the cause of dementia early so that
treatments can begin. This study only included patients
with autopsy-confirmed diagnoses so we could use a
generally accepted gold standard for judging diagnostic
accuracy. Initial clinical evaluations were performed with-
out the benefit of our current understanding of dementing
diseases and often many years after symptom onset.
Our study provides critical information about histopatho-
logical diagnosis, which is difficult to obtain in a substantial
cohort of subjects who were scanned with very mild or pre-
clinical dementia. At least in the period represented in
this study, dementia evaluations were often delayed for
many years after symptom onset and few were scanned with
very mild or pre-clinical dementia.
artificial and autopsy confirmation required in this study
ofclinical summaries is
FDG-PET in FTD and ADBrain (2007),130, 2616^26352629
mean that the subjects were necessarily highly selected and
do not reflect a diverse population required for a Class I
study. Consequently, our results, including specificities and
sensitivities, may not be replicated in clinical practice.
Although a dementia expert extracted clinical information
most relevant to diagnostic decision-making, case scenarios
do not replicate the usual interactions between a physician,
patient and knowledgeable informants. On the other hand,
it is difficult to know whether our procedures would be
more or less likely to provide an accurate diagnosis than
usual clinical practice. Our use of dementia experts likely
increased the accuracy of diagnoses that were based solely
on clinical data. Clinical diagnosis is more often confirmed
at autopsywhen specialists
physicians perform dementia evaluations (Becker et al.,
1994; Holmes et al., 1999; Mendez et al., 1992). Our
summaries were derived from extensive medical records at
dementia research centres, rather than less extensive
assessments performed in the community. Furthermore,
our case summaries described the entire course of a
patient’s illness, information unavailable at an initial
diagnostic assessment. Although it is conceivable that
knowledge of symptoms occurring later in the illness
could be confusing and detrimental, previous studies have
shown that diagnostic accuracy improves when longitudinal
information is available (Becker et al., 1994; Litvan et al.,
1997). It also is important to recognize that patients in this
study may differ from those encountered in typical clinical
practice. Although some of our subjects were only mildly
impaired and others were severely demented when they
were scanned, the severity of their dementia in our study
population may not be representative of patients presenting
a similar diagnostic dilemma. We used the sequential
review of a clinical history and examination followed by a
review of imaging results to better reflect good medical
practice. However, a prospective trial of FDG-PET in the
evaluation of suspected FTD would complement this study
and could adequately address many of its limitations.
Our study forced raters to make a diagnostic decision,
even if they felt the FDG-PET scan was normal. The raters
were aware that all of the cases presented had dementia and
a diagnosis of ‘normal’ was not allowed. This encouraged
the raters to consider even minor variations in the scan.
Although it is not typical to force radiologists to make a
diagnosis without characteristic image findings, clinicians
often need to make definitive decisions, even when
confronted by an ambiguous history and examination.
Thus it seemed only fair to force the raters to always make
a specific diagnosis. It is unclear whether this would cause
any systematic bias, but the results appear to justify the
Several other methods also may help physicians distinguish
AD from FTD. We did not include structural brain imaging
in our study, but it is possible that CT or MRI also would
aid diagnostic accuracy. CT and MRI scans can reveal
regional atrophy, which might aid in diagnosis (Chan et al.,
2001; Boccardi et al., 2003). However, visual assessment of
hippocampal atrophy is not helpful in distinguishing AD
and FTD (Galton et al., 2001). We also did not explicitly
consider results of neuropsychological testing, although
pertinent findings were recorded in the summaries for
many of our cases. The possible contribution to diagnosis
of standardized psychological testing should be evaluated.
Many body fluid and other imaging biomarkers for AD and
FTD have been proposed and are under investigation
(Frank et al., 2003; Petrella et al., 2003; Klunk et al., 2004).
They may prove valuable in the future, but require
(The Ronald and Nancy Reagan Research Institute of the
Alzheimer’s Association and the National Institute on Aging
Working Group, 1998).
FDG-PETas a diagnostic biomarker
It is appropriate to consider the status of FDG-PET as
a diagnostic biomarker in light of the current results.
FDG-PET is clearly imperfect. Although raters knew that all
cases had an autopsy-confirmed dementing illness, 16% of
the transaxial images and 5% of the SSP images were rated
as normal or having uncertain abnormality and likely
would be considered non-diagnostic in a clinical setting.
The proportion of ratings indicating that scans did not
have clear-cut abnormalities was similar for both FTD and
AD subjects. On the other hand, this study substantially
adds to the increasing evidence that FDG-PET has utility
recommended by the Reagan Institute–NIA Work Group.
FDG-PET meets many of the proposed ideal characteristics.
It reflects a fundamental pathological feature of AD and
FTD, the selective regional loss of neurons and synapses in
the cerebral cortex. It has been validated using neuro-
pathologically confirmed cases in this and other studies
(Hoffman et al., 2000; Silverman et al., 2001). It also is
notable that in this study we have shown that FDG-PET is
reliable and can distinguish between two pathologically
distinct causes of dementia. Most studies of diagnostic
biomarkers only compare AD with normal subjects or with
non-demented controls, a much less clinically relevant
distinction. Our study included several individuals who
were scanned when they had mild dementia. Our results
confirm the observations of others that FDG-PET is able to
detect disease early in its course, as the biomarker
guidelines recommend (Minoshima et al., 1997; Berent
et al., 1999; Chetelat et al., 2003). Our study does not
address the remaining ideal characteristics of a diagnostic
biomarker. It will remain a subjective opinion whether
FDG-PET is sufficiently non-invasive, simple to perform
andinexpensive to meet
Widespread experience over the past several years has
as judgedby guidelines
2630Brain (2007),130, 2616^2635N. L.Foster et al.
demonstrated that FDG-PET can be practical in clinical
diagnosis, even if it is not ideal by these standards.
Most of the steps recommended by the Reagan Institute–
NIA Work Group for establishing FDG-PET as a biomarker
have been achieved. In our study, the traditional standards
of sensitivity, specificity and positive predictive value have
been achieved for FDG-PET using SSP, except for FTD
sensitivity and AD specificity, which are 73% rather than
80%. Positive likelihood ratio (+LR), a measure of how a
test alters pretest probability also should be considered
(Qizilbash, 2002). Tests with +LR values >10 generally are
considered to make conclusive changes to pretest prob-
ability. By this standard, our study finds FDG-PET has
great value for patients with FTD, but the pre-test
probability of AD is too high for a test to have much
effect on the +LR, even if it is highly accurate. There are
other ways to assess the value of FDG-PET, including its
effect on diagnostic confidence, treatment decisions and
health costs (Gill et al., 2003; McMahon et al., 2003).
Great care is needed when incorporating FDG-PET into the
diagnostic evaluation of patients with dementia. Imaging
cannot substitute for clinical information including a
detailed history, neurological and mental status examina-
tions. FDG-PET scan results cannot be considered con-
clusive and there is a risk of misinterpretation of scan
results. Pathological assessment still remains the only
certain way to confirm a clinical diagnosis. Physicians will
continue to face complexity when trying to distinguish AD
from FTD as illustrated by several recent examples in the
literature. A single case has been reported with frontal
predominant clinical features and AD pathology that
appeared to be more severe in the frontal cortex (Johnson
et al., 1999). This raises the possibility that AD might
sometimes cause frontal predominant hypometabolism.
In another report, a patient with clinical symptoms of
frontotemporal dementia was initially thought to have AD
because of a presenilin-1 gene mutation (PSEN1ins352).
Subsequently, this patient was found to have both FTD
ubiquitin inclusion pathology and a splice donor site
mutation in intron 1 of the progranulin gene. The
presenilin gene mutation in this case now appears to be a
non-pathological variant (Pickering-Brown et al., 2006). In
this case, clinical symptoms were more reliable than a
genetic test. Unfortunately, molecular imaging results are
unavailable for either of these cases, so we do not know
whether FDG-PET could have aided accurate diagnosis.
However, the report of an Italian family supports our
findings and found FDG-PET more diagnostically reliable
than clinical symptoms (Binetti et al., 2003). In affected
members of this family clinical history and symptom
presentation suggested a frontotemporal dementia and led
to an unsuccessful search for a mutation in the TAU gene,
Instead, FDG-PET revealed a clear-cut AD pattern of
hypometabolism and a novel T122R mutation in the
presenilin-2 gene (PSEN2) was subsequently discovered.
Our study addresses only one of many diagnostic
decision points physicians face in determining the cause
of dementia. FDG-PET scans may or may not be similarly
helpful when other disorders are suspected in addition to
FTD and AD. For example, in patients suspected to have
FTD, psychiatric illness often is a consideration and has not
been addressed in our study. Our subjects did not have
other neurological disorders and therefore a diagnosis
reasonably could be made considering only the FDG-PET
scan. In clinical practice, when complicating brain diseases
may be present, FDG-PET scans should only be interpreted
in conjunction with structural imaging studies. We decided
to compare the test characteristics of transaxial and SSP
imaging because of previous evidence that SSP was a
superior method for dementia diagnosis (Burdette et al.,
1996). This meant that raters were not able to directly
compare the two methods. Combining the two methods
may well have advantages. However, more information is
not always better for diagnostic interpretation and sum-
marizing imaging data may have its merits. Current PET
scanners often provide 128 or more transaxial images,
which can be challenging to mentally combine and
manipulate. Further research evaluating image interpreta-
tion is needed. Although we chose SSP because of our
familiarity and theoretical advantages, there are several
other software packages available that display images
topographically and provide statistical maps and may
provide similar benefits.
Training of raters was an important factor in achieving
the reliability and accuracy of FDG-PET interpretation in
this study. The experience of the physicians interpreting
clinical scans should be considered when ordering FDG-
PET studies. Since clinical use is a relatively recent
development, few physicians have been trained to evaluate
FDG-PET scans for the complex pattern of hypometabolism
seen in patients with dementia. Fortunately, our study
found that relatively brief, focused training given to
clinicians experienced in the evaluation of dementia is
adequate to provide good diagnostic precision in the
interpretation of FDG-PET scans. It is critical to attend
to technical issues involved with image acquisition and
processing. Errors in attenuation correction cause normal
scans to have the appearance of AD or FTD. Patient
behaviour during scanning also can lead to misinterpreta-
tion of results. Our subjects were all scanned at rest with
eyes open in a quiet, darkened room. Our rules for scan
interpretation may not apply to other protocols.
Diagnostic accuracy was enhanced most when the
clinician had limited diagnostic confidence after considering
only the results of a clinical history and examination.
This suggests that a targeted approach using FDG-PET
when there is diagnostic uncertainty would have greatest
consistently less in patients with FTD and FDG-PET often
FDG-PET in FTD and ADBrain (2007),130, 2616^2635 2631
improved accuracy and confidence. In some circumstances,
diagnostic confidence also may be low in some patients
with AD, particularly in those who have atypical presenta-
tions. Variations in the clinical presentation of AD are
being recognized increasingly (Caselli, 2000; Nestor et al.,
2003; Tang-Wai et al., 2004; Knibb et al., 2006). Enhancing
diagnostic accuracy and confidence in these situations will
have a favourable impact on patient management.
FDG-PET is a promising diagnostic biomarker in dement-
ing illness. We have shown that it is valuable in
differentiating AD and FTD, and particularly helpful
when findings in a clinical evaluation are not definitive
and physicians are not already highly confident in their
diagnosis. While additional research is needed to assure
that FDG-PET is used to greatest advantage in dementia
care, the visual interpretation of FDG-PET scans using
voxel-based analysis with our simple diagnostic rules to
distinguish AD and FTD provides a practical approach that
can be widely applied now to enhance diagnosis.
This study was supported by a pilot cooperative project
grant from the National Alzheimer’s Coordinating Center
(NIH Grant AG16976) and by the Michigan Alzheimer’s
Disease Research Center (NIH Grant AG08671), the
University of California at Davis Alzheimer’s Center (NIH
Grant AG10129) and the University of Pennsylvania
Alzheimer’s Center (NIH Grant AG10124). Co-author
Satoshi Minoshima developed and owns the copyright to
the Neurostat Neurological Statistical Imaging Software
used in this project. Dr Foster has received an honorarium
of less than $5000 for serving on the Scientific Advisory
Board of GE Healthcare. We thank Andrew P. Lieberman
for reviewing the neuropathology, and David E. Kuhl, Sid
Gilman, Gus Buchtel, David Knesper, R. Scott Turner and
Kirk Frey for making images from their research available
for this study. Members of the panel who provided a
retrospective consensus diagnosis for these subjects were
Howard Aizenstein, Bradley Boeve, James Leverenz, Elaine
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Appendix A.Clinical scenario no. 42
HPI: Sixty-six-year old right-handed woman with 2 years
of progressive decline in speech. Initial symptoms included
a significant decrease in sentence length and repetitive
FDG-PET in FTD and AD Brain (2007),130, 2616^26352633
performance of routine household chores, as though she
had forgotten having done them already. Whereas she was
previously an outgoing woman, she became reclusive,
she had little spontaneous verbal output. She received
speech therapy following a possible transient ischaemic
attack characterized by brief unresponsiveness, but derived
no benefit. She had a second episode in year 2 consisting of
staring followed by unconsciousness. She has stopped
cooking and is reluctant to eat unless food placed before
her. Tends to wear the same outfit repetitively, and
nolonger performs housework
When walking to a destination one-fourth mile away,
she wandered 3 miles off track.
SH: Prior homemaker and clerk, and married.
FH: Negative for dementia.
Mental status: Quiet with apathetic affect. Did not speak
unless specifically questioned. Oriented to city and year but
not month. Unable to perform any calculations but recalled
3 out of 3 objects at 5min after a prolonged registration
process. Extremely telegraphic speech. Significant naming
difficulties for simple items. Could perform two step and
crossed midline commands. Inaccurate when stating age.
Formal neuropsychological testing revealed verbal IQ 62,
performance IQ 72 and memory quotient 59.
Neuro exam: Possible right facial droop and slight
cogwheel rigidity of both upper extremities. Glabellar,
snout, grasp and palmomental reflexes all present. Unable
to learn tandem gait.
After 2.5 years of symptoms, she is able to say only
single words or brief phrases. She has moved to a new
home and is able to find her way around the new home.
Easily distracted during meals and seems more restless
overall. Dresses herself and walks independently, albeit
more slowly. Needs assistance with bathing and can help set
the table. No crying episodes. On exam, alert and coopera-
tive but does not respond verbally to any direct questions.
Able to write out short phrases. Written responses are
perseverative. She is able to identify some colours but
confabulates with other answers. Unable to point to named
body parts or mimic hand movements but can copy a cube.
Strong grasp reflexes bilaterally as well as palmomental,
glabellar and snout reflexes. Limb reflexes also increased.
After 3 years, she speaks at most one word with
prodding. Dresses herself and uses the bathroom appro-
priately and still assists with some household activities.
Walking 1mile per day. Able to write some notes, but
these are frequently meaningless. During the exam, she
will inappropriately arise and wander, even when spoken
to. Able to close eyes to command but not protrude
tongue to command. Does not verbalize during the
entire visit. Unable to write her son’s name. Slightly
stooped gait with decreased associated movements and
an extra step when turning. Limb reflexes mildly increased.
or usesthe phone.
After 3.5 years, she is able to recognize familiar
persons and cooperate with family members. Appetite
good but taking longer to eat. Able to walk within
neighbourhood without getting lost. Nocturnal inconti-
nence once per month. Selects own clothing but needs
supervision toprevent inappropriate
reminders for bathing. Sleeping well. On examination, she
is mute and does not respond to simple verbal commands.
Able to write her name with difficulty, but perseverates or
writes in neologisms to most questions. Unable to copy a
figure. Facial expression limited but occasionally smiles.
Slightly kyphotic posture. No tremor. Reflexes brisk with
palmomental reflexes,increased jaw
sustained glabellar reflexes.
After 4 years, she wanders frequently within the home.
Incontinent of urine at night. Able to assist with dressing.
Clenches or sits on hands. Has had two additional episodes
of ‘passing out’. Good appetite but occasionally chokes
when putting too much food in her mouth or chewing
inadequately. On examination, she remains mute and does
not respond to any commands. Fails to make eye contact
and is not socially appropriate. She arises spontaneously
with a mildly stooped gait and has a lack of facial
expression. Grasp and palmomental reflexes prominent
bilaterally. Glabellar and jaw jerk also present.
After 4.5 years, she needs occasional assistance with
eating but sleeps well. Became lost when walking with her
husband outside the home. Frequently incontinent. Rocks
back and forth in a chair while seated. Family feels she can
still recognize individuals, and she laughs appropriately at
jokes. On exam, she remains mute and can follow limited
gestured commands. Mild facial masking with cogwheel
rigidity and limb hypereflexia. Dramatic grasp reflexes,
prominent snout and glabellar reflexes. Clasps hands in
front or behind her when walking and has a moderate
kyphosis with slow turns.
After 5 years her husband is dressing and feeding her, but
she remains cooperative.
After 6 years, she resides in a nursing home and is walking
only with assistance and requires tube feeding. Does not react
to other persons. Incontinent. Generally sits with eyes closed.
After 7 years, she requires total care, including for
transfers. Opens eyes when name called or shoulder
touched but otherwise keeps eyes closed.
She died after 7 years of symptoms.
jerk, snout and
(adapted from Barber et al.)
Scenario Number _____________ or Autopsy Number
Site: (circle) UCDU Penn
2634 Brain (2007),130, 2616^2635N. L.Foster et al.
Symptom Checklist p. 2 Download full-text
or Autopsy Number ___________
Scenario Number __________
Onset of symptom in first third of typical course of progressive dementia
(Circle if symptom present)
Any memory impairment
Recent memory loss
New learning difficulties
Regularly loses objects
Disorientation to place
Any topographic impairment
Navigation difficulties in new environments
Navigation difficulties in neighborhood
T otal this column (range +1to ?9)
Impaired object manipulation
Any change in personality
Loss of empathy
Distress about handicaps
Anxiety about handicaps
Loss of confidence
T otal this column (range +4 to ?6)
T otal this section (range +5 to ?15)
Onset of symptom in second third of typical course of progressive dementia
(Circle if symptom present, cross section out if scenario doesn’t include second third of illness)
Navigation difficulties within home
Impaired facial recognition
Impaired object recognition
Impaired object location
çç T otal this section (range 0 to ?6)
Symptoms absent even in last third of the typical course of progressive dementia
(Circle if symptom never observed, cross section out if scenario doesn’t include last third of illness)
Does not regularly lose objects
No navigation difficulties in neighborhood
No navigation difficulties in home
No impaired object recognition
No impaired object location
No loss of empathy
No inappropriate affect
T otal this column (range +6 to ?3)
No distress about handicaps
No anxiety about handicaps
No loss of confidence
T otal this column (range +3 to ?4)
T otal this section (range +9 to ?7)
T otal Symptom Score (range +14 to ?28)
FDG-PET in FTD and ADBrain (2007),130, 2616^26352635