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. 2024 May 20;19(5):e0303111. doi: 10.1371/journal.pone.0303111

Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET

Hanneke F M Rhodius-Meester 1,2,3,4,*, Ingrid S van Maurik 1,2,5,6, Lyduine E Collij 7, Aniek M van Gils 1,2, Juha Koikkalainen 8, Antti Tolonen 8, Yolande A L Pijnenburg 1,2, Johannes Berkhof 5,6, Frederik Barkhof 7,9, Elsmarieke van de Giessen 1,2,7, Jyrki Lötjönen 8, Wiesje M van der Flier 1,2,5,6
Editor: Wataru Araki10
PMCID: PMC11104589  PMID: 38768188

Abstract

Background

The use of amyloid-PET in dementia workup is upcoming. At the same time, amyloid-PET is costly and limitedly available. While the appropriate use criteria (AUC) aim for optimal use of amyloid-PET, their limited sensitivity hinders the translation to clinical practice. Therefore, there is a need for tools that guide selection of patients for whom amyloid-PET has the most clinical utility. We aimed to develop a computerized decision support approach to select patients for amyloid-PET.

Methods

We included 286 subjects (135 controls, 108 Alzheimer’s disease dementia, 33 frontotemporal lobe dementia, and 10 vascular dementia) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [18F]florbetaben amyloid-PET. In our computerized decision support approach, using supervised machine learning based on the DSI classifier, we first classified the subjects using only neuropsychology, APOE, and quantified MRI. Then, for subjects with uncertain classification (probability of correct class (PCC) < 0.75) we enriched classification by adding (hypothetical) amyloid positive (AD-like) and negative (normal) PET visual read results and assessed whether the diagnosis became more certain in at least one scenario (PPC≥0.75). If this was the case, the actual visual read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient certainty in the computerized approach with three scenarios: 1) without amyloid-PET, 2) amyloid-PET according to the AUC, and 3) amyloid-PET for all patients.

Results

The computerized approach advised PET in n = 60(21%) patients, leading to a diagnosis with sufficient certainty in n = 188(66%) patients. This approach was more efficient than the other three scenarios: 1) without amyloid-PET, diagnostic classification was obtained in n = 155(54%), 2) applying the AUC resulted in amyloid-PET in n = 113(40%) and diagnostic classification in n = 156(55%), and 3) performing amyloid-PET in all resulted in diagnostic classification in n = 154(54%).

Conclusion

Our computerized data-driven approach selected 21% of memory clinic patients for amyloid-PET, without compromising diagnostic performance. Our work contributes to a cost-effective implementation and could support clinicians in making a balanced decision in ordering additional amyloid PET during the dementia workup.

Introduction

The neuropathological hallmark of Alzheimer’s disease (AD), amyloid-beta, can be visualized via amyloid positron emission tomography (PET) [1–3]. After having shown clinical impact in memory clinic patients, the use of amyloid-PET in daily clinical practice is upcoming, both for accurate and etiological diagnosis and to initiate disease-modifying treatment (DMT) [4–8]. At the same time, amyloid-PET is costly and limitedly available outside tertiary memory clinics. There is a need for tools that can aid clinicians in identifying which patient would benefit from amyloid-PET to ensure an accurate etiological diagnosis, whilst remaining efficient [9–12].

The Amyloid Imaging Task Force (AIT) has developed appropriate use criteria (AUC) based on expert opinion, to foster the optimal use of amyloid-PET [13]. Amyloid-PET is deemed appropriate in patients with possible AD ‘for whom substantial uncertainty exists and for whom greater confidence would result from determining whether amyloid pathology is present or not’. Also, amyloid-PET may be performed in young-onset dementia to increase diagnostic confidence [13]. Despite the efforts of the AIT, the AUC are not sufficiently able to discriminate between patients who would benefit from amyloid-PET and those who would not [14–16]. For example, we showed that in an unselected memory clinic cohort, patients not fulfilling the AUC also benefited from amyloid-PET [14]. Translation of the AUC to clinical practice is thus challenging, hampering successful implementation of this expensive test in memory clinicians [17]. Studies have repeatedly shown that amyloid-PET increases diagnostic confidence. Nonetheless, it is likely that in some patients diagnostic confidence was already high enough before amyloid-PET, whilst in others confidence may remain low, even after amyloid PET. Knowledge on how amyloid status would impact etiological diagnosis in individual patients would help clinicians to decide which patient should undergo an amyloid-PET and which patient should not.

We previously developed a computerized decision support approach to support clinicians in identifying patients most likely to benefit from cerebrospinal fluid (CSF) biomarkers [18]. This data-driven approach restricted CSF testing to 26% of cases without compromising diagnostic accuracy. In this work, we took a similar data-driven approach to predict which patients would benefit from amyloid-PET testing. More specifically, we tested whether this approach may help to answer the following question: if a clinician already has detailed information on neuropsychological tests, APOE and brain imaging, would additional amyloid-PET contribute to a more certain etiological diagnosis?

Material and methods

Subjects

We retrospectively included 286 memory clinic subjects who visited our memory clinic seeking medical help between January 2015 and December 2016 (the Amsterdam Dementia Cohort) with a diagnosis of Alzheimer’s dementia (AD), frontotemporal lobe dementia (FTD), vascular dementia (VaD), or subjective cognitive decline (SCD) [19, 20]. As part of the ABIDE (Alzheimer Biomarkers in Daily practice) project [5, 21], [18F]florbetaben PET was offered for clinical care to all consecutive memory clinic patients between January 2015 and December 2016. Subjects who had both amyloid-PET and brain MRI results available were included.

All subjects received a standardized work-up at baseline to come to a diagnosis, including medical history, physical, neurological and neuropsychological assessment, MRI, laboratory tests, and amyloid-PET. A diagnosis of SCD was made when the cognitive complaints could not be confirmed by cognitive testing and criteria for mild cognitive impairment (MCI) or dementia were not met. Subjects with SCD served as controls. Probable AD was diagnosed using the core clinical criteria of the NIA-AA [22]. Probable FTD (including the behavioural variant of FTD, progressive non-fluent aphasia, and semantic dementia) was diagnosed using the criteria from Rasckovsky and Gorno-Tempini, respectively [23, 24]. VaD was diagnosed using the NINDS-AIREN criteria [25].Since the classifier we used for this study (see below for detailed description) is currently only able to classify controls, patients with AD, FTD and VAD, subjects with other diagnoses, such as dementia with Lewy bodies (DLB) were not included.

The data in this study were collected during routine care and retrieved retrospectively. The Daily Board of the Medical Ethical Committee (METc) of the VUmc Medical Center provided an exemption to seek formal approval. All patients provided written informed consent for their data to be used for research purposes. The authors had no access to information that could identify individual participants during or after data collection.

Neuropsychology testing

Cognitive functions were assessed with a brief standardized test battery, including widely used tests. We used the Mini-Mental State Examination (MMSE) for global cognitive functioning [26]. For memory, we applied the Rey auditory verbal learning task (RAVLT) [26]. To measure mental speed and executive functioning, we included Trail Making Tests A and B (TMT-A, TMT-B) [27]. Language and executive functioning were tested by category fluency (animals) [28]. Finally, for behavioral symptoms, we used the Neuropsychiatric Inventory (NPI) [29]. Missing data ranged from n = 3 (1%) (MMSE) to n = 75 (26%) (NPI).

APOE genotype

Apolipoprotein E (APOE) genotype was determined with the light cycler APOE mutation detection method (Roche diagnostics GmbH, Mannheim, Germany). Patients were dichotomized into APOE e4 carriers (hetero- and homozygous) and non-carriers. APOE data were available in 283 (99%) subjects.

Imaging markers

MRI images were acquired using 1.5 T or 3 T scanners including 3D isotropic T1 and 2D or 3D FLAIR sequences. We extracted six imaging markers using the cNeuro® cMRI quantification tool as described in [18]:

  • Computed medial temporal lobe atrophy (cMTA) was computed for the left and right hemispheres from the volumes of the hippocampus and inferior lateral ventricle as described in [30, 31]. The volumes were obtained from a multi-atlas segmentation algorithm [32].

  • Computed global cortical atrophy (cGCA) measured the gray matter concentration based on the voxel-based morphometry (VBM) analysis [30, 31].

  • AD similarity scale was computed by representing the patient image as a linear combination of regional volumes from a database of previously diagnosed patients [17, 38]. The AD similarity scale was defined as the share of the weights from the linear model having the diagnostic label AD.

  • Anterior-posterior index was defined as a ratio of the cortical volumes at frontal and temporal lobe regions to those at parietal and occipital lobe regions [33].

  • The volume of white matter hyperintensities (WMH) was extracted from FLAIR images [31, 34].

Amyloid-PET

Procedures for amyloid-PET using [18F]florbetaben have been described in detail elsewhere [5, 21]. Per standard protocol, 20-minute scans consisting of 4x5 minute frames were collected 90–110 minutes post-injection of approximately 300 MBq±20% [18F]florbetaben (Neuraceq™, Life Molecular Imaging, Berlin, Germany). We used visual reads and repeated the analyses using Centiloids. Visual reads were available in all subjects, Centiloids in 248 (87%).

PET scans were visually assessed by a certified and experienced nuclear physician blinded to clinical diagnosis. Images were scaled based on the total white matter signal and grey color scaling. Transverse, sagittal, and coronal views were displayed using the software package Vinci 2.56. Images were rated as either positive (binding in one or more cortical brain regions unilaterally) or negative (predominantly white matter uptake) according to criteria defined in the label by the manufacturer (Life Molecular Imaging).

For Centiloid quantification, all scans were pre-processed using a validated standard Centiloid pipeline and converted to the Centiloid scale[34]. Briefly, the four frames from the PET images were first averaged and co-registered to the corresponding T1-weighted scans. Then, the T1- weighted MRI scans were warped to standard space; the same warp was applied to warp the co-registered PET image. These procedures were performed in SPM12. PET images were intensity normalized using the whole cerebellum as the reference region using the mask provided by the Centiloid method [34] (http://www.gaain.org/centiloid-project). Global cortical Centiloid values were calculated using the standard GAAIN target region Centiloid. Centiloid calibration has been previously described [35].

Disease State Index classifier and probability of correct class

The Disease State Index (DSI) classifier was previously developed and validated in the European FP7 PredictND project [36, 37]. The DSI is a simple, supervised, and data-driven machine learning method that compares different diagnostic groups with each other; in this work controls, AD, FTD, and VaD. There is no need to impute data or exclude cases with incomplete data, as the classifier can handle missing data. The classifier is based on a training set with diagnosed patients [36]. For each single test (e.g. neuropsychological, APOE-status, MRI), the similarities of each patient’s data to the distributions of the diagnostic groups in the training set are computed. When single tests are combined, the tests with higher classification accuracy are weighted more. First, a DSI value is calculated for each pair-wise comparison (AD-controls, AD-FTD, AD-VaD, FTD-controls, FTD-VaD, VaD-controls). Then, the final DSI-value for each diagnostic group (controls-AD-FTD-VaD) is calculated by averaging the corresponding DSI-values, e.g AD-controls, AD-FTD and AD-VaD for AD etc, as described in [18]. As a result, a DSI value (continuous value between zero and one) is given for each diagnostic group (controls-AD-FTD-VaD), estimating the likelihood of the specific diagnosis.

This study was performed using five-fold cross-validation, i.e., 80% of the dataset was used as the training set in the DSI classifier when classifying the remaining 20% of the patients. This was repeated five times so that each patient was classified once.

A high DSI value or a big difference in DSI values between the two most likely diagnostic groups provides more certainty in making a diagnosis than a low value or a small difference [18]. The probability that the diagnostic group of the highest DSI value is correct is defined as the probability of correct class (PCC). The diagnosis suggested is compared with the ground truth diagnosis for the cases with comparable highest DSI value and the difference between two highest DSI values in a reference database and the share of correct diagnoses is calculated. The reference database used consisted of 770 memory clinic patients (Amsterdam dementia cohort and PredictND) diagnosed with the same guidelines as used in the current study [20, 37]. That dataset consisted of 308 controls, 338 ADs, 89 FTDs, and 35 VaDs. The mean age was 65.8 ± 8.7 years, and 54% were females.

In this study, patients were considered as having a diagnosis with sufficient certainty if PCC was ≥0.75. This selection was a compromise between the number of diagnosed patients and accuracy. In clinical practice, the clinician can adjust the applied PCC cutoff depending on the pre-test probability.

Diagnostic scenario’s to select patients for amyloid-PET

We applied four diagnostic scenarios (Fig 1) in which patients were considered as having a diagnosis with sufficient certainty if the PCC was ≥0.75. As amyloid-PET measures, we used visual reads and repeated our analyses using Centiloid quantification.

Fig 1. Flow chart for the four diagnostic approaches, using amyloid-PET visual read, summarizing the results in the last column.

Fig 1

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AUC: appropriate use criteria, AUC+: patients fulfilling appropriate use criteria according to [13], operationalized as described in [14], PCC: probability of correct class, NP: neuropsychology, MRI: magnetic resonance imaging, Sim: simulate, FU: follow-up. Numbers in circles denote groups described in Table 2.

  • Scenario A: In this Computer-supported decision approach, we performed amyloid-PET only when predicted to change the certainty in diagnosis based on our data-driven method (Fig 1A). Patients were regarded as sufficient certain cases if PCC was ≥0.75 based on APOE, neuropsychology, and MRI (step one). When PCC was <0.75, the computer tool added both (hypothetical) positive and negative amyloid PET. Hypothetical PCC’s were computed for both positive amyloid-PET and negative amyloid-PET values (step two). If either of these hypothetical PCC values reached >0.75, the actually observed amyloid-PET values were added, after which DSI and PCC, were computed (step three). On repeating this scenario using Centiloid values, we took the mean Centiloid value for AD patients (69.40 ± 39.3) and the mean Centiloid value for controls (11.95 ± 24.31) as hypothetical values in step two.

We compared this approach, with the following three ‘control scenario’s:

  • Scenario B: In the No amyloid-PET approach, we calculated DSI and PPC for each patient using only APOE, neuropsychology and MRI, excluding amyloid-PET (Fig 1B).

  • Scenario C: In the AUC scenario, we performed amyloid-PET based on the AUC criteria (Fig 1C). We classified patients as AUC-positive (AUC+) and AUC-negative (AUC-), according to [14]. In this paper patients were classified during pre-PET multidisciplinary meetings as AUC+ when they either i) had AD as diagnostic possibility (≥15%) but with a confidence <85% in AD as diagnosis, or ii) had a young-onset dementia (<65 years old. All other patients were classified as AUC-[38]. In patients classified AUC+, we calculated PCC by adding amyloid-PET (step two). For AUC- patients, no amyloid-PET was added.

  • Scenario D: In the All amyloid-PET approach (Fig 1D)., we calculated DSI and PCC for each patient using APOE, neuropsychology, MRI, and amyloid-PET.

For all four approaches, we reported the number of patients diagnosed with sufficient certainty and the number of patients in which PET was performed.

Statistical analyses

Further scrutinizing the data of scenario A, we tested differences in baseline characteristics, diagnosis, and DSI between patients with sufficiently certain diagnosis based directly on neuropsychology and MRI (step one in computerized decision support approach, group 1 in Fig 1A), patients not eligible for amyloid-PET testing (step two, group 2) and patients with actual amyloid-PET testing (step three, groups 3 and 4).

Lastly, we visualized the impact of different PCC cut-offs for the proportion of patients diagnosed (percentage of patients above PCC cutoff) and the proportion of patients with amyloid-PET measurement for all four diagnostic approaches described above.

MRI markers were normalized for age, sex and head size [39]. Statistical analyses were performed using SPSS version 22 (IBM, Armonk, NY, USA), STATA version 14.1, and R version 3.5.3. A MATLAB toolbox created by [40] was used in the DSI analyses. The analyses were performed in MATLAB version R2018b (MathWorks, Natick, MA, USA).

Results

Baseline characteristics

In the study sample, the mean age was 64±8 and 129 (45%) were females. Table 1 shows details of the baseline characteristic of this sample, stratified per diagnostic group.

Table 1. Baseline characteristics according to baseline diagnosis.

n = Control n = 135 AD n = 108 FTD n = 33 VaD n = 10
Female, n(%) 286 58 (43) 56 (52) 13 (39) 2 (20)
Age, in years 286 60 ± 8 66 ± 7 66 ± 7 72 ± 6
APOE e4 carrier, n(%) 283 53 (39) 74 (69) 11 (33) 2 (20)
Neuropsychology
MMSE 283 28 ± 2 22 ± 4 24 ± 5 24 ± 4
RAVLT learning 275 39 ± 10 22 ± 7 26 ± 11 23 ± 9
RAVLT recall 275 8 ± 3 2 ± 2 4 ± 3 3 ± 3
Animal fluency 269 22 ± 6 14 ± 5 11 ± 6 12 ± 5
TMT-A, in seconds 273 39 ± 22 92 ± 84 70 ± 55 70 ± 24
TMT-B, in seconds 273 100 ± 65 294 ± 252 204 ± 171 219 ± 69
NPI, total score 211 14 ± 16 9 ± 9 17 ± 15 19 ± 21
MRI
cMTA score right 283 0.31 ± 0.51 1.37 ± 0.94 1.54 ± 1.20 1.34 ± 1.10
cMTA score left 283 0.31 ± 0.58 1.49 ± 1.07 1.86 ± 1.48 1.99 ± 1.30
cGCA score 283 0.39 ± 0.58 1.52 ± 0.86 1.89 ± 0.95 2.15 ± 0.81
Anterior Posterior index 283 -0.25 ± 1.23 0.03 ± 1.76 -1.84 ± 2.79 -0.44 ± 1.53
AD similarity scale 283 0.47 ± 0.11 0.63 ± 0.09 0.57 ± 0.10 0.61 ± 0.07
WMH volume 279 6.00 ± 5.33 9.90 ± 13.1 11.08 ± 14.06 21.01 ± 18.34
Amyloid-PET
Visual Read (neg/pos) 286 107 / 28 5 / 103 31 / 2 6 / 4
Centiloids; mean ± SD 248 11.95 ± 24.31 69.40 ± 39.3 7.52 ± 21.28 15.93 ± 22.87
AUC +, n(%) 286 6 (4) 81 (75) 20 (61) 6 (60)
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AD: Alzheimer’s disease, FTD: Frontotemporal dementia, VAD: Vascular dementia, MMSE: Mini-Mental state Examination, RAVLT: Rey Auditory Verbal Learning Test, TMT: Trail Making Test, NPI: Neuropsychiatric Inventory score, cMTA: computed medial temporal lobe atrophy scale (0–4), derived from volume of hippocampus and volume of inferor lateral ventricle, cGCA: computed global cortical atrophy scale (0–3), derived from concentration of cortical grey matter using voxel based morphometry, AD similarity scale: based on hippocampus ROI, Anterior posterior index: weighted ratio of volumes of the frontal/temporal lobes and parietal/occipital lobes WMH: volume of white matter hyperintensities. MRI volumes are adjusted for head size, AUC +: number of patients fulfilling appropriate use criteria according to [13], operationalized as described in [14].

Diagnostic approaches to select patients for amyloid-PET

In our search for the optimal approach to select patients for amyloid-PET, we applied four diagnostic approaches. Fig 1 shows the flowchart of these four approaches and summarizes the number of patients with sufficient certain diagnoses (PCC≥0.75), and the number of patients selected for amyloid-PET. In these results, the amyloid-PET biomarker was the visual read. First, we applied the computerized decision support approach (scenario A). Using demographics, APOE, neuropsychology and MRI only, diagnostic prediction was sufficiently certain (PCC ≥0.75) in 155 (54%) cases. In the 131 (46%) remaining cases, hypothetical positive and negative amyloid-PET values were added (step 1), and this led to an increase of PCC to ≥0.75 in 60 (46%) cases, thus advising performing an amyloid-PET scan (step 2). When real amyloid-PET values were actually added to the model, we observed a PCC≥0.75 in 33 (55%) patients (step 3). Overall, the computerized approach led to a diagnosis with sufficient confidence in 188 (66%) patients by performing PET in 60 (21%) patients, with correct classification of 152 patients.

We compared our data-driven approach to three control scenario’s. In scenario B, the scenario without amyloid- PET, we used demographics, APOE, neuropsychology, and MRI only, and found a diagnosis with sufficient confidence in 155 patients (54%) from which 125 were correctly classified. Scenario C, applying amyloid-PET based on the AUC, led to amyloid-PET in a larger group of 113 (40%) patients, yet not to a higher proportion of patients with a certain diagnosis, 156 (55%), and correctly classifying 138 patients. In scenario D, performing amyloid-PET in all patients, again did not lead to more diagnoses with sufficient confidence, namely 154 (54%), and correct classification in 142 patients.

Using Centiloid values instead of visual reads yielded similar results (see S1 Fig).

Differences in patients groups using computerized decision support approach

Following the flowchart of the computerized decision support approach in Fig 1A, four distinct groups can be separated in the three steps, marked with 1-2-3-4 in the figure and summarized in Table 2. In the first group are those patients with a diagnosis with sufficient certainty, using only demographics, APOE, neuropsychology, and MRI. This group was the most extensive (n = 155) and contained patients with all types of diagnoses. These patients had the largest difference in DSI value between the first and second suggested diagnoses. Presumably, this group had a clear, distinct profile, both clinically and on imaging, and little co-morbidity. This group contrasts with the second group, containing the patients in which adding hypothetical amyloid-PET values did not increase diagnostic certainty (n = 71). Here, the difference between first and the second DSI was the smallest, indicating co-morbid neuropathology or neuropsychological profiles that are hard to distinguish from each other. This group could not be certainly diagnosed neither with nor without amyloid PET, and follow-up or other testing is advised. The third group included those patients in whom the computerized approach suggested amyloid-PET according to hypothetical +/- amyloid-PET results (n = 33). After adding actual PET values, the PCC increased to ≥0.75. In this group, the amyloid-PET scan was often positive (64%) and contained mainly patients with AD (23/33). The final group consisted of the patients for whom, despite performing amyloid-PET scan, the diagnosis remained unclear (n = 27).

Table 2. Comparison of different patient groups deriving from the computerized decision support approach using visual reads; matching Fig 1A.

1. Direct sufficient certain diagnosis 2. PET not useful 3. PET helpful to establish diagnosis 4. Not diagnosed
n = 155 n = 71 n = 33 n = 27
Female, n(%) 68 (44) 30 (42) 14 (42) 17 (63)
Age, in years 62 ± 8 64 ± 8 67 ± 8 66 ± 6
APOE e4 carrier, n(%) 76 (49) 33 (46) 17 (52) 14 (56)
MMSE 26 ± 4 26 ± 3 22 ± 5 23 ± 4
Amyloid-PET, visual read
Negative 87 (56) 44 (62) 12 (36) 6 (22)
Positive 68 (44) 27 (38) 21 (64) 21 (78)
Clinical diagnosis
Control 87 (64) 41 (30) 3 (2) 4 (3)
AD 47 (44) 20 (19) 23 (21) 18 (17)
FTD 17 (52) 8 (24) 5 (15) 3 (9)
VaD 4 (40) 2 (20) 2 (20) 2 (20)
Difference with second DSI 0.33 ± 0.09 0.08 ± 0.05 0.10 ± 0.06 0.11 ± 0.06
AUC +, n(%) 57 (37) 24 (34) 20 (61) 16 (59)
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MMSE: Mini-Mental state Examination, AD: Alzheimer´s disease, FTD: Frontotemporal dementia, VAD: Vascular dementia, DSI: Disease State Index, Difference with second DSI: DSI based on demographics, APOE, neuropsychology and MRI, AUC +: number of patients fulfilling appropriate use criteria according to [13], operationalized as described in [14]

Effect of different PCC cutoffs on diagnosis and amyloid-PET

For the results described above, we used PCC≥0.75 to define which diagnostic prediction is accurate and has sufficient certainty. Of note, this is an arbitrary choice. To study the effect of the PCC cutoff, we repeated all our analyses for different PCC cutoffs ranging from 0.5 to 1.0. In Fig 2, we compared all four approaches based on the proportion of patients diagnosed with sufficient certainty (Fig 2A) and the number of performed amyloid-PET scans (Fig 2B) using different PCC cutoffs. As expected, the share of patients with certain diagnosis declined with increasing PCC, independent of the scenario used. Overall the proportion of diagnosed patients was largest when using the computerized decision support approach and the lowest when performing no PET, independent of the PCC cutoff.

Fig 2. Visualization of the share of patients diagnosed (blue, 2A) and the share of patients with amyloid-PET performed (red, 2B) for different probability of correct class cutoffs, comparing computerized decision support, no amyloid-PET, AUC, and amyloid-PET for all patients.

Fig 2

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Blue: proportion of patients diagnosed, Red: proportion of patients with amyloid-PET taken, PCC: probability of correct class. Solid lines show results for the computerized decision support (Fig 1A), dotted lines show results for using no amyloid-PET, but only demographics, APOE, neuropsychology and MRI (Fig 1B), dashed dotted lines show results for AUC (Fig 1C) and dashed lines using all data (Fig 1D).

Example of visualization of computerized decision support in clinical practice

How the computerized decision support approach could be used in clinical practice is visualized in Fig 3. Case A, for example, is a 65-year-old female, who experiences memory problems, but also scores low on fluency and high on NPI, while MRI showed hardly atrophy. Based on demographics, neuropsychology, and MRI, the classifier suggested an FTD diagnosis (DSI 0.72) yet with a minimal difference to the next most probable diagnosis AD (DSI 0.71). Therefore, the probability of correct class (PCC) is low (0.51). When the tool adds hypothetical positive and negative amyloid-PET scans, the clinician can see that both a positive and a negative amyloid-PET result, would influence the diagnostic certainty (PCC > 0.75 in both situations). The lower panel shows results after addition of the actual amyloid PET scan, which was positive in this case, leading to a high PCC (0.78) for AD diagnosis (DSI 0.81). In case B, a 71–year-old female who has trouble performing the cognitive tests due to impaired understanding, yet surprisingly does perform TMT-A relatively fast, whereas MRI showed mild bitemporal atrophy. The classifier showed a low PCC (0.50) using only demographics, neuropsychology, and MRI, with equal DSI for both AD and FTD diagnosis (DSI 0.63). In this case, adding a hypothetical positive or negative amyloid-PET changed the PCC to >0.75 for a negative amyloid PET scan (albeit not for a positive PET scan). Based on an increase to >0.75 in one of the scenario’s, the clinician is advised to embark on ordering an amyloid-PET scan, which in this case was negative. A clinical diagnosis of probable FTD was confirmed.

Fig 3. Examples of visualization of the computerized decision approach for clinical use, applying hypothetical positive and negative amyloid-PET scan, based on visual reads.

Fig 3

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NP: neuropsychology, MRI: magnetic resonance imaging, PCC: probability of correct class, AD: Alzheimer’s disease, FTD: Frontotemporal dementia, VAD: Vascular dementia, CN: control.

Discussion

In this study, a data-driven approach in which diagnostics classification is enriched by adding (hypothetical) amyloid positive (AD-like) and negative (normal) PET to aid the clinician in deciding whether performing an actual amyloid-PET scan contributed to a more certain diagnosis. Our computerized decision support approach advised performing an amyloid-PET scan in 21% of the patients without compromising proportion of correctly classified cases. Our approach was thus more efficient than the other scenario’s, where we would have performed PET in all patients, in none, or according to the appropriate use criteria (AUC). When implemented in a computer tool, this approach can support clinicians in making a balanced decision in ordering additional (expensive) amyloid-PET testing using personalized patient data.

Approaches such as the data-driven approach we demonstrated in this study, can aid in translating appropriate use criteria (AUC) to clinical practice. The AUC state that amyloid-PET is deemed appropriate in patients with possible AD ‘for whom substantial uncertainty exists and for whom greater confidence would result from determining whether amyloid pathology is present or not’, and in young-onset dementia to increase diagnostic confidence [13]. How to operationalize these criteria is not clear. Severable studies have shown that the current AUC advises amyloid-PET both too few and too many patients [4, 14, 16, 41]. Even in our study, 40% of the patients would require amyloid-PET, according to the AUC, without leading to higher proportion of patients with certain diagnosis.

Several prediction models predicting positive or negative amyloid-PET scans have been developed [42–44]. We add to this literature by developing a data-driven method with a different starting point. Namely, what happens to the diagnosis if an amyloid-PET is normal or abnormal? This approach follows the way clinicians think more naturally; ‘would ordering an amyloid-PET scan help me in gaining a clearer and more certain diagnosis?’. We simulated a positive (AD-like) and negative (normal) amyloid-PET to estimate whether knowledge of amyloid status might impact (confidence in) diagnosis in an unselected memory clinic population, including controls, AD, FTD, VaD patients. Our computerized approach(scenario A) led to 152 (53%) correctly classified subjects while performing amyloid-PET in only 60 (21%) subjects. Performing PET in all (scenario D) led to 142 (49%) correctly classified subjects, yet by performing amyloid-PET in 286 (100%) subjects. As can be seen in Fig 1, accuracy is slightly higher in D, since overall less patients received a diagnosis in this approach. One can imagine that in case of multiple pathologies or borderline amyloid-PET results, adding amyloid-PET only confuses and leads thus to a lower number of certainly diagnosed patients. These findings show that it is possible to think of scenario’s where expensive diagnostic tests are used only when they are likely to increase diagnostic certainty, which is in line with appropriate use criteria stating that an additional test should only be performed when it will increase the confidence of the clinician in a certain diagnosis.

As the prevalence of dementia increases and new disease-modifying therapies (DMTs) entering, there is an increasing need for precise etiological diagnosis, while the diagnostic work-up needs to remain efficient [45]. To initiate treatment with DMTs, an accurate etiological diagnosis is crucial [46].In this future clinical practice, where DMTs are widely available [47], a data-driven approach will serve as a valuable tool for narrowing the target population for treatment. Our study presents a data-driven approach that is aimed at achieving diagnosis in the most efficient manner without compromising diagnostic performance. In addition such approach may streamline clinical decision-making pipelines with blood-based biomarkers in the future by limiting the number of patients that require confirmatory testing [48]. However, detecting underlying (AD) pathology marks only the beginning. Once the diagnose is made, the subsequent step will be to define eligibility. In a new EU-project, we will further develop this stepwise approach to identify potential eligible patients [49]. This approach will encompass other patients as well, such as those with mild cognitive impairment (MCI) and DLB, to address the relevant question whether they have underlying AD or not. Novel decision models have to be developed to aid in this classification, of which work is ongoing.

The classifier used in this study, is based on simple supervised machine learning and is thus able to deal with missing data. Providing visualization of the approach, with a PCC cut-off that clinicians can alter, as in Fig 3, helps clinicians to understand what the tool ‘thinks’, as opposed to a black box [9]. Visualization is also helpful in shared decision-making, guiding clinician and patient in discussing whether to perform amyloid-PET [50]. The visualization we have shown here can be further optimized in co-creation with end-users and usability testing in clinical practice. The cut-off of 0.75 for the PCC was selected for this manuscript to demonstrate how the proposed computerized decision support algorithm typically performs. If the clinician prefers higher classification accuracy with less patients diagnosed, a higher cut-off should be used, and vice versa. To date this method is not yet available for clinical use, but the previously developed data driven approach to select the optimal patient for CSF [18] is available via the cNeuro® tool.

Finally, the strengths of our study are the use of an unselected memory clinic cohort, consisting of controls and patients with AD, FTD and VaD, reflecting clinical practice [51].

There are also limitations to discuss. First, we were not able to include other neurodegenerative diseases, such as DLB, or those with MCI, since the used classifier to date does not include these patients. Tauopathies mimicking AD, such as argyrophillic grain disease (AGD) are not in our database, and could therefore not be included. To reflect even better ordinary clinical practice, development of the classifier is ongoing to include more diagnostic groups [52]. However, this study was set up to address the use of amyloid PET in differential diagnosis of a number of common differential diagnostic dilemma’s, in particular AD versus FTD versus VaD. Second, we included only patients from a tertiary memory clinic, which may hamper generalizability, yet also reflects daily practice since amyloid-PET is mainly ordered in tertiary memory clinics. Third, we classified patients as having one diagnosis, while patients seldom have one type of neurodegeneration but often comorbid pathology. Yet, the DSI classifier provides room for comorbid pathology by providing a DSI for each diagnosis, where co-existing pathologies would lead to multiple diagnoses with comparable DSI, while pure disease would results in one DSI standing out compared to the others. Also, in this cohort, comorbid pathology was present, given the often small differences in DSI between the first and second suggested diagnosis. In addition, amyloid-PET is not as specific as tau-PET, leading to more frequent discordance with the clinical diagnosis, which was also the case in our cohort. Fourth, while all patients received a standardized workup and were scanned with the same PET scanner, the MRI scanners differed. Yet we know from previous studies that our MRI quantification tool can deal with different scanners and field strengths [32]. Finally, we performed our analyses with visual readings being a dichotomize measure, which could be a disadvantage. However, visual readings are most often used in clinical practice and thus are easy for clinicians to understand. In addition, repeating our analyses with continuous values, namely Centiloids, a linear transformation off SUV, showed comparable results. This shows the face validity of our results.

Conclusion

With the current difficulties in selecting those who might benefit from amyloid-PET and the future challenges with increasing need for biomarker confirmation, for example in the context of initiating disease modifying treatment, smart tools are needed to efficiently use resources and keep healthcare affordable. We developed a data-driven approach using patient’s data and show that restricting the ordering of amyloid-PET to 21% of patients without compromising diagnostic performance. Future studies focusing on implementing tools like this into clinical practice, to efficiently guide stepwise diagnostic testing, are the next step [49].

Supporting information

S1 Fig. Flow chart for the four diagnostic approaches, using Centiloid values, summarizing the results in the last column.

AUC: appropriate use criteria, AUC+: patients fulfilling appropriate use criteria according to [13], operationalized as described in [14], PCC: probability of correct class, NP: neuropsychology, MRI: magnetic resonance imaging, Sim: simulate, FU: follow-up.

(PPTX)

pone.0303111.s001.pptx (49.7KB, pptx)

Acknowledgments

Research of the Alzheimer’s Center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. We thank Mahnaz Shekari for her significant contribution to the processing of the amyloid-PET images.

Data Availability

The data from this study are available upon request. Subjects included in this study only consented to their data being shared with third parties when it is used to gain more knowledge about Alzheimer’s disease and dementia. Therefore, the data may only be shared via a data-request procedure. The data underlying the results presented in this study are therefore available upon request from the Alzheimer Center Amsterdam, via wm.vdflier@amsterdamumc.nl or metc@vumc.nl. More information on the Cohort can be found at JPND, Amsterdam dementia cohort - JPND Neurodegenerative Disease Research (neurodegenerationresearch.eu).

Funding Statement

The Vrije Universiteit Medical Center Alzheimer Center is supported by the Stichting Alzheimer Nederland and Stichting Vrije Universiteit Medical Center Fonds. The clinical database structure was developed with funding from Stichting Dioraphte. For development of the PredictAD tool, VTT Technical Research Centre of Finland has received funding from European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreements 601055 (VPH-DARE@IT) ), 224328 (PredictAD), and 611005 (PredictND). The collaboration project DAILY (project number LSHM19123-HSGF) is co-funded by the PPP Allowance made available by Health-Holland, Top Sector Life Sciences & Health, to stimulate public-private partnerships. The ABIDE clinical utility study is funded by the PPP Allowance made available by Health-Holland, Top Sector Life Sciences & Health, to stimulate public-private partnerships and co-funded by Life Molecular Imaging GmbH (grant no. LSHM18075). HR is the recipient of the Memorable Dementia Fellowship 2021 (ZonMw project number 10510022110004) and Alzheimer Nederland InterACT grant (project number WE.08-2022-06). LC is supported by the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement no. 115952. This Joint Undertaking receives the support from the European Union’s Horizon 2020 research and innovation program and EFPIA. FB is supported by the NIHR biomedical research centre at UCLH. The chair of WF is supported by the Pasman Stichting. WF is recipient of ABOARD, which is a public–private partnership receiving funding from ZonMW (number 73305095007) and Health-Holland, Top Sector Life Sciences and Health (public–private partnership allowance; number LSHM2010). HR and WF are recipients of Prominent. The Prominent project is supported by the Innovative Health Initiative Joint Undertaking (JU) under grant agreement no. 101112145. The JU receives support from the European Union’s Horizon Europe research and innovation programme and COCIR, EFPIA, EuropaBio MedTech Europe, Vaccines Europe, BioArctic AB and Combinostics Oy. Views and opinions expressed are those of the authors and do not necessarily reflect those of the aforementioned parties. Neither of the aforementioned parties can be held responsible for them.

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PONE-D-23-25666Computerized decision support is an effective approach to select memory clinic patients for amyloid-PETPLOS ONE

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"Research of the Alzheimer’s Center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. The Vrije Universiteit Medical Center Alzheimer Center is supported by the Stichting Alzheimer Nederland and Stichting Vrije Universiteit Medical Center Fonds. The clinical database structure was developed with funding from Stichting Dioraphte. For development of the PredictAD tool, VTT Technical Research Centre of Finland has received funding from European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreements601055 (VPH-DARE@IT) ), 224328 (PredictAD), and 611005 (PredictND). The collaboration project DAILY (project number LSHM19123-HSGF) is co-funded by the PPP Allowance made available by Health-Holland, Top Sector Life Sciences & Health, to stimulate public-private partnerships. The ABIDE clinical utility study is funded by the PPP Allowance made available by health-Holland, Top Sector Life Sciences & Health, to stimulate public-private partnerships and co-funded by Life Molecular Imaging GmbH (grant no.: LSHM18075). HR is recipient of the Memorabel Dementia Fellowship 2021 (ZonMw projectnumber 10510022110004) and Alzheimer Nederland InterACT grant (projectnumber WE.08-2022-06). LC is supported by the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115952. This Joint Undertaking receives the support from the European Union’s Horizon 2020 research and innovation program and EFPIA. FB is supported by the NIHR biomedical research centre at UCLH. The chair of WF is supported by the Pasman Stichting. WF is recipient of ABOARD, which is a public–private partnership receiving funding from ZonMW (number 73305095007) and Health-Holland, Top Sector Life Sciences and Health (public–private partnership allowance; number LSHM20106), WF and HR are recipient of the Horizon 2022 project PROMINENT (project number 101112145). We thank Mahnaz Shekari for her significant contribution to the processing of the amyloid-PET images."

We note that you have provided additional information within the Acknowledgements Section that is not currently declared in your Funding Statement. Please note that funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form.

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"We have read the journal's policy and the authors of this manuscript have the following competing interests: Hanneke FM Rhodius- Meester performs contract research for Combinostics, all funding is paid to her institution. Ingrid van Maurik received a consultancy fee (paid to the university) from Roche. Lyduine E Collij has received consultancy fees from GE Healthcare, all funding is paid to her institution. Aniek M van Gils reports no disclosures. Juha Koikkalainen and Jyrki Lötjönen report that VTT Technical Research Centre of Finland owns the following IPR related to the paper: 1. J. Koikkalainen and J. Lotjonen. A method for inferring the state of a system, US7,840,510 B2, PCT/FI2007/050277. 2. J. Lotjonen, J. Koikkalainen and J. Mattila.State Inference in a heterogeneous system, PCT/FI2010/050545. FI20125177. Koikkalainen and Lötjönen are shareholders in Combinostics Oy. Antti Tolonen reports no disclosures. Yolande AL Pijnenburg has received funding from Dioraphte Foundation, Zabawas Foundation, JPND, ZonMW, NWO, Team Alzheimer and the Dutch Brain Foundation. Johannes Berkhof reports no disclosures. Frederik Barkhof is member of the Steering committee or Data Safety Monitoring Board member for Biogen, Merck, ATRI/ACTC and Prothena. FB is consultant for Roche, Celltrion, Rewind Therapeutics, Merck, IXICO, Jansen, Combinostics. FB has research agreements with Merck, Biogen, GE Healthcare, Roche. Co-founder and shareholder of Queen Square Analytics LTD. Elsmarieke van de Giessen has received research support from NWO, ZonMw, Hersenstichting and KWF. EvdG has performed contract research for Heuron Inc., Roche and 1st Biotherapeutics. EvdG has a consultancy agreement with IXICO for the reading of PET scans. Wiesje M van der Flier performs contract research for Biogen. Research programs of WF have been funded by ZonMW, NWO, EU-FP7, EU-JPND, Alzheimer Nederland, CardioVascular Onderzoek Nederland, Health~Holland, Topsector Life Sciences & Health, stichting Dioraphte, Gieskes-Strijbis fonds, stichting Equilibrio, Pasman stichting, stichting Alzheimer & NeuroPsychiatry Foundation, Philips, Biogen MA Inc, Novartis-NL, Life-MI, AVID, Roche BV, Fujifilm, Combinostics. WF has performed contract research for Biogen MA Inc, and Boehringer Ingelheim. WF has been an invited speaker at Boehringer Ingelheim, Biogen MA Inc, Danone, Eisai, WebMD Neurology (Medscape), Springer Healthcare. WF is consultant to Oxford Health Policy Forum CIC, Roche, and Biogen MA Inc. WF participated in advisory boards of Biogen MA Inc and Roche. All funding is paid to her institution. WF was associate editor of Alzheimer, Research & Therapy in 2020/2021. WF is associate editor at Brain"

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Additional Editor Comments :

In addition to the reviewers’ comments, this editor considers that several parts in the manuscript need to be clarified or explained in more details, as follows.

1 p8 line 230-233 They cite ref13 but citing this ref. is insufficient. Please explain about AUC in more details.

2 Subjects in page 4 and Discussion

They included patients with AD, FTD, VaD, and SCD. Please explain why they did not include those with other dementia disorders such as DLB and AGD. In ordinary clinical practice, these non-AD dementia disorders are relatively common and should not be neglected. Clearly, this is a limitation of this study and should also be discussed.

3 Page 7, Ref 37 A correct ref should be described.

4 Fig.2 This figure should be corrected as it does not cover the whole graphs. Fig 3 Is it possible to change the background color from blue to white?

5 Discussion page 14 line 403-405 “Our computerized approach even outranked performing 404 amyloid-PET in all patients” Please explain about the reasons for this result more clearly.

6. Discussion page 15 line 418-431

This editor judges that this part is not a balanced view and should be shortened or more carefully revised. It can only be said that a data-driven approach will be a supportive tool in the future clinical practice in which disease-modifying treatments become widely available. In addition, in the clinical practice, differential diagnosis of MCI patients (MCI due to AD or MCI not due to AD) is also important but this issue is not discussed in the manuscript. Why not include some discussion about this point?

7 Abstract line 48 “AUC” Please include statement about what AUC means.

8 Abstract the last sentence “ supports clinicians in making a balanced decision in ordering additional amyloid PET during the dementia workup” This does not reflect the real situation, and should be corrected.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

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Reviewer #1: Partly

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: With the insurance coverage of Lecanemab and other therapeutic agents against amyloid-β, there is growing interest in testing for amyloid-β detection. As the authors state in their introduction, PET scans are too expensive to be used for screening, raising the question of which patients should be aggressively screened for PET.

The purpose of this study is to "develop a computerized decision support approach to select patients for amyloid PET," but the study targets AD, FTLD, and VaD, and is more focused on disease type diagnosis rather than DMT target selection. Therefore, the study is disappointing to those who expect it to be a supportive approach to narrow down the target population for treatment of amyloid-β.

At the method of study, the following details should be considered.

1. the final classification result (likelihood of diagnosis) seems to be obtained by combining binary classification by DSI, but the rationale for "we considered that patients had a sufficiently reliable diagnosis if the PCC was 0.75 or higher" is not provided. Presumably, there is a valid basis, but it is not clear how many false positives or false negatives this may include.

2. it says that DSI "can handle missing data," but it is not clear how much missingness is actually allowed or how much it affects accuracy.

3. Figure 3 is helpful in understanding this study. For example, in Patient B, FTD is suspected and an amyloid PET scan is recommended as a result. However, without presentation of the present illness, symptoms, results of psychological testing, and MRI findings, the usefulness of DSI cannot be realized for readers. Wouldn't ordinary clinical diagnostic methods have been sufficient to suspect FTD?

4. it is stated that "the strength of this study is the use of a control group and an unselected memory clinic cohort consisting of AD, FTD, and VaD patients, which reflects clinical practice," but it is not stated how PD, epilepsy, and other illness were excluded to get there.

First of all, I would like to express my utmost respect for the results of this study. In general, the study would have been more interesting if it had been conducted with DMT in mind. In understanding the utility of DSI, which requires the input of a variety of clinical data, what is its advantage over the diagnoses we clinicians make in our daily clinical practice? If we could understand how DSI differs from and is superior to the diagnosis that we clinicians usually make in daily clinical practice, it would be more convincing.

Reviewer #2: The article of Hanneke et al. affirms that a computerized decision support approach to select patients from the Memory Clinic for amyloid-PET increase the diagnostic certainty, being useful for the doctor requesting. The approach applies The Disease State Index (DSI) classifier, already tested with proven high diagnostic accuracy [references 35 and 36 of the authors] . The DSI suggests a diagnosis with a probability of correct class (PCC), based on demographics, neuropsychology, and MRI data. Thereafter, the hypothetical positive and negative amyloid-PET PCC’s values are calculated to evaluate the influence in the diagnostic certainty. The authors point out that this starting point makes the difference to other models that predict positive or negative amyloid-PET [references 40-42 of the authors]. Afterwards, an amyloid PET scan is order only if an increase to >0.75 of the PCC. Finally, the real amyloid-PET values are added to the model and PCC is evaluated.

This approach is compared with three control scenarios: without amyloid- PET using demographics, APOE, neuropsychology, and MRI; applying amyloid-PET based on The Appropriate Use Criteria (AUC) [reference 12 of the authors]; and performing amyloid-PET in all patients. Using visual PET reads and Centiloid values submit similar results to support the affirmation as a meaningful conclusion.

This proposed tool is quite relevant for the clinics, as the authors justify well in the introduction and discussion, considering nowadays there is conclusive evidence on the clinical usefulness of amyloid-PET, in the in vivo diagnosis of Alzheimer's disease (AD), as shown the results of two large international series [the American project IDEAS: reference 4 of the authors; and the European AMYPAD: reference 7 of the authors and Altomare et al. 2023]. Furthermore, even if the amyloid-PET is prescribed regarding the AUC there are other situations in which the added certainty of amyloid-PET could be helpful [reference 13 of the authors and Altomare et al. 2018]. Moreover, an increasing demand [Verger et al. 2023] is predictably because of its contribution to AD diagnosis, AD treatment (the search and introduction of possible modifying therapies [Garnier-Crussard A et al. 2023] and is the gold standard for investigating disease mechanisms [Bao et al. 2021]) and to screening improvement for clinical trials [Rabinovici et al. 2019].In words of the authors: amyloid-PET is costly and limitedly available, so intelligent and efficient use of our resources is already needed.

The statistics, with a large sample size (N=286) including controls (N=135), and other analyses (v.g. amyloid-PET procedure, Centiloid), are well conducted with a high technical standard and are described to enable reproduce. Results are exposed with sufficient detail (including mean and standard deviation) represented with clear figures and tables. The manuscript is properly presented and is written in standard English. The research meets all applicable experimentation ethics standards and integrity (blinding etc.). The article adheres to appropriate reporting guidelines and community standards for data availability.

In this work, a real-life cohort is studied (recruiting in a tertiary memory clinic) in contradistinction to the computerized decision support approach applied by the author of correspondence et al. in the cohort of Alzheimer’s Disease Neuroimaging Initiative (ADNI2) that can be consulted at Alzheimer’s Dement. 2020;16(Suppl. 5):e042687).Therefore, it cannot be considered as a replication study. The data were collected during routine care and the amyloid-PET is interpreted visually by an imaging specialist from the local hospital setting, as often do in practice and in the IDEAS project [reference 4 of the authors], and no by centralizing reading. Agreeing to the authors, this naturalistic setting features, far from being a limitation, represent an advantage due to its immediate applicability in clinical practice in the dementia workup. The strengths and limitations are well exposed.

Some aspects that may weaken the quality of the manuscript and that the authors could clarify so readers better understand could be: DSI is not included in the abstract, the data of the patients correctly classified presented in the flow chart (Figure 1) seems no be present or explained in the text. Also review: line 283 doubt about the 180 patients (66%)¿is it maybe 188 (66%)?, line 293-297 review typographical errors and also doubt about 1654 (54%).%)¿is it maybe 154(54%)?, line 638: 37. !!! INVALID CITATION !!! [20, 36]. Please, take as a kind suggestion the possibility of including the refrence of AMYPAD 2023 in the introduction and update the reference 44. Maybe will be interesting a brief mention about the availability of the computerized decision support approach created by the authors for use in other centers.

References:

Altomare D, Barkhof F, Caprioglio C, Collij LE, Scheltens P, Lopes Alves I, et al. Clinical effect of early vs late amyloid positron emission tomography in Memory Clinic patients: The AMYPAD-DPMS randomized clinical trial. JAMA Neurology [Internet]. 2023 May 8 Available from: https://jamanetwork.com/journals/jamaneurology/fullarticle/2804755

Altomare D, Ferrari C, Festari C, Guerra UP, Muscio C, Padovani A, et al. Quantitative appraisal of the Amyloid Imaging Taskforce Appropriate Use Criteria for amyloid‐PET. Alzheimers Dement. 2018;14(8):1088-98.

Verger A, Yakushev I, Albert NL, Van Berckel B, Brendel M, Cecchin D, et al. FDA approval of lecanemab: the real start of widespread amyloid PET use? — the EANM Neuroimaging Committee perspective. Eur J Nucl Med Mol Imaging. 2023;50(6):1553-5.

Garnier-Crussard A, Flaus A. Positive opinion of the French National Authority for Health on the reimbursement of amyloid tracer (Flutemetamol). Eur J Nucl Med Mol Imaging. 2023;50(2):253-4.

Bao W, Xie F, Zuo C, Guan Y, Huang YH. PET Neuroimaging of Alzheimer’s Disease: radiotracers and their utility in clinical research. Front Aging Neurosci. 2021; 13:624330.

Rabinovici GD, Gatsonis C, Apgar C, Chaudhary K, Gareen I, Hanna L, et al. Association of amyloid positron emission tomography with subsequent change in clinical management among Medicare beneficiaries with mild cognitive impairment or dementia. JAMA. 2019;321(13):1286.

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Reviewer #2: Yes: RAQUEL SÁNCHEZ VAÑÓ

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PLoS One. 2024 May 20;19(5):e0303111. doi: 10.1371/journal.pone.0303111.r002

Author response to Decision Letter 0

26 Jan 2024

Amsterdam, January 26th 2024

Regarding PONE-D-23-25666

Dear dr Wataru Araki, Academic Editor, and esteemed Reviewers of PlosOne.

Please find uploaded our revised manuscript for publication in PLOS ONE ‘Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET’.

We thank the editor and reviewers for their careful reading, thoughtful comments, and recommendation for revision. Please find below our responses to the comments in a point-by-point fashion. We have highlighted changes (via track&change) in response to the reviewers’ comments in the manuscript.

We hope the rebuttal adequately addresses the points raised during the review process,

Kind regards, also on behalf of the co-authors,

Hanneke Rhodius- Meester

==================================================================================

Journal Requirements:

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- Reply: To our knowledge our manuscript meets the style requirements, but we are happy to make changes if needed.

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- Reply: We are not sure to which code you are referring in our manuscript. Syntaxes used have been created in MatLab and have been described in previous publication (Cluitmans et al 2013 A MATLAB toolbox for classification and visualization of heterogenous multi-scale human data using the Disease State Fingerprint method - PubMed (nih.gov)), as stated in the methods section (L265-266).

3. Thank you for stating the following in the Acknowledgments Section of your manuscript:

"Research of the Alzheimer’s Center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. The Vrije Universiteit Medical Center Alzheimer Center is supported by the Stichting Alzheimer Nederland and Stichting Vrije Universiteit Medical Center Fonds. The clinical database structure was developed with funding from Stichting Dioraphte. For development of the PredictAD tool, VTT Technical Research Centre of Finland has received funding from European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreements601055 (VPH-DARE@IT) ), 224328 (PredictAD), and 611005 (PredictND). The collaboration project DAILY (project number LSHM19123-HSGF) is co-funded by the PPP Allowance made available by Health-Holland, Top Sector Life Sciences & Health, to stimulate public-private partnerships. The ABIDE clinical utility study is funded by the PPP Allowance made available by health-Holland, Top Sector Life Sciences & Health, to stimulate public-private partnerships and co-funded by Life Molecular Imaging GmbH (grant no.: LSHM18075). HR is recipient of the Memorabel Dementia Fellowship 2021 (ZonMw projectnumber 10510022110004) and Alzheimer Nederland InterACT grant (projectnumber WE.08-2022-06). LC is supported by the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115952. This Joint Undertaking receives the support from the European Union’s Horizon 2020 research and innovation program and EFPIA. FB is supported by the NIHR biomedical research centre at UCLH. The chair of WF is supported by the Pasman Stichting. WF is recipient of ABOARD, which is a public–private partnership receiving funding from ZonMW (number 73305095007) and Health-Holland, Top Sector Life Sciences and Health (public–private partnership allowance; number LSHM20106), WF and HR are recipient of the Horizon 2022 project PROMINENT (project number 101112145). We thank Mahnaz Shekari for her significant contribution to the processing of the amyloid-PET images."

We note that you have provided additional information within the Acknowledgements Section that is not currently declared in your Funding Statement. Please note that funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form.

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

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Please include your amended statements within your cover letter; we will change the online submission form on your behalf:

- Reply: Apologies for misunderstanding this section, we have added amended statements to our cover letter:

Acknowledgment Section: "Research of the Alzheimer’s Center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. The Vrije Universiteit Medical Center Alzheimer Center is supported by the Stichting Alzheimer Nederland and Stichting Vrije Universiteit Medical Center Fonds. The clinical database structure was developed with funding from Stichting Dioraphte. We thank Mahnaz Shekari for her significant contribution to the processing of the amyloid-PET images.

Funding Statement: “For development of the PredictAD tool, VTT Technical Research Centre of Finland has received funding from European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreements601055 (VPH-DARE@IT) ), 224328 (PredictAD), and 611005 (PredictND). The collaboration project DAILY (project number LSHM19123-HSGF) is co-funded by the PPP Allowance made available by Health-Holland, Top Sector Life Sciences & Health, to stimulate public-private partnerships. The ABIDE clinical utility study is funded by the PPP Allowance made available by health-Holland, Top Sector Life Sciences & Health, to stimulate public-private partnerships and co-funded by Life Molecular Imaging GmbH (grant no.: LSHM18075). HR is recipient of the Memorabel Dementia Fellowship 2021 (ZonMw projectnumber 10510022110004) and Alzheimer Nederland InterACT grant (projectnumber WE.08-2022-06). LC is supported by the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115952. This Joint Undertaking receives the support from the European Union’s Horizon 2020 research and innovation program and EFPIA. FB is supported by the NIHR biomedical research centre at UCLH. The chair of WF is supported by the Pasman Stichting. WF is recipient of ABOARD, which is a public–private partnership receiving funding from ZonMW (number 73305095007) and Health-Holland, Top Sector Life Sciences and Health (public–private partnership allowance; number LSHM20106), WF and HR are recipient of the Horizon 2022 project PROMINENT (project number 101112145).”

Thank you for changing the online submission form.

4. Thank you for stating the following in the Competing Interests section:

"We have read the journal's policy and the authors of this manuscript have the following competing interests: Hanneke FM Rhodius- Meester performs contract research for Combinostics, all funding is paid to her institution. Ingrid van Maurik received a consultancy fee (paid to the university) from Roche. Lyduine E Collij has received consultancy fees from GE Healthcare, all funding is paid to her institution. Aniek M van Gils reports no disclosures. Juha Koikkalainen and Jyrki Lötjönen report that Combinostics Oy owns the following IPR related to the paper: 1. J. Koikkalainen and J. Lotjonen. A method for inferring the state of a system, US7,840,510 B2, PCT/FI2007/050277. 2. J. Lotjonen, J. Koikkalainen and J. Mattila.State Inference in a heterogeneous system, PCT/FI2010/050545. FI20125177. Koikkalainen and Lötjönen are shareholders in Combinostics Oy. Antti Tolonen reports no disclosures. Yolande AL Pijnenburg has received funding from Dioraphte Foundation, Zabawas Foundation, JPND, ZonMW, NWO, Team Alzheimer and the Dutch Brain Foundation. Johannes Berkhof reports no disclosures. Frederik Barkhof is member of the Steering committee or Data Safety Monitoring Board member for Biogen, Merck, ATRI/ACTC and Prothena. FB is consultant for Roche, Celltrion, Rewind Therapeutics, Merck, IXICO, Jansen, Combinostics. FB has research agreements with Merck, Biogen, GE Healthcare, Roche. Co-founder and shareholder of Queen Square Analytics LTD. Elsmarieke van de Giessen has received research support from NWO, ZonMw, Hersenstichting and KWF. EvdG has performed contract research for Heuron Inc., Roche and 1st Biotherapeutics. EvdG has a consultancy agreement with IXICO for the reading of PET scans. Wiesje M van der Flier performs contract research for Biogen. Research programs of WF have been funded by ZonMW, NWO, EU-FP7, EU-JPND, Alzheimer Nederland, CardioVascular Onderzoek Nederland, Health~Holland, Topsector Life Sciences & Health, stichting Dioraphte, Gieskes-Strijbis fonds, stichting Equilibrio, Pasman stichting, stichting Alzheimer & NeuroPsychiatry Foundation, Philips, Biogen MA Inc, Novartis-NL, Life-MI, AVID, Roche BV, Fujifilm, Combinostics. WF has performed contract research for Biogen MA Inc, and Boehringer Ingelheim. WF has been an invited speaker at Boehringer Ingelheim, Biogen MA Inc, Danone, Eisai, WebMD Neurology (Medscape), Springer Healthcare. WF is consultant to Oxford Health Policy Forum CIC, Roche, and Biogen MA Inc. WF participated in advisory boards of Biogen MA Inc and Roche. All funding is paid to her institution. WF was associate editor of Alzheimer, Research & Therapy in 2020/2021. WF is associate editor at Brain"

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- Reply: We added the requested information on the consent procedure to the paragraph ‘Subjects’ in the Material and Methods section (L121-124).

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We will update your Data Availability statement on your behalf to reflect the information you provide.

- Reply: Thank you for addressing this important point. We are afraid there are indeed restrictions to data-sharing, since the data contains potentially sensitive information (namely medical data). In the patient information folder (PIF), that subjects read before giving written informed consent, it is therefore stated specifically that their data is only shared with third parties when that data is used to gain more knowledge on Alzheimer’s disease and dementia. We can only guarantee this, via a data-request form and checking if the intended use of data aligns with the consent given by subjects. The data underlying the results presented in this study are therefore available upon request from the Data access team of the Alzheimer Center Amsterdam, via wm.vdflier@amsterdamumc.nl. We added this statement to our cover letter.

7. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.

- Reply: Apologies to this misunderstanding. The data used in this study is part of the Amsterdam Dementia Cohort, of the Alzheimer Center Amsterdam, and can be retrieved from there via a data request. More information on the ADC can be found at JPND, Amsterdam dementia cohort - JPND Neurodegenerative Disease Research (neurodegenerationresearch.eu), unfortunately without a DOI. We have updated our Data Availability statement in the cover later. Thank you for updating our statement.

==================================================================================

Additional Editor Comments :

In addition to the reviewers’ comments, this editor considers that several parts in the manuscript need to be clarified or explained in more details, as follows.

1 p8 line 230-233 They cite ref13 but citing this ref. is insufficient. Please explain about AUC in more details.

- Reply: Thank you for the opportunity to explain how we defined patients to be AUC positive or negative. De Wilde et al (ref 13) previously classified each included patient. In short, clinical syndrome (dementia, MCI, or SCD), suspected etiology (AD, vascular pathology, frontotemporal dementia, Lewy body dementia, other neurodegenerative disease, or non-neurodegenerative disease), and level of diagnostic confidence in suspected etiology (visual analog scale, 0–100%) were determined during pre-PET multidisciplinary meetings. After that patients were classified as AUC+ when they either i) had AD as diagnostic possibility (≥15%) but with a confidence <85% in AD as diagnosis, or ii) had a young-onset dementia (<65 years old. All other patients were classified as AUC-.This method was based on Altomare et al in 2018 10.1016/j.jalz.2018.02.022. We added details on this in the Material and Methods section (L243-246) and the Abstract (L34-36).

2 Subjects in page 4 and Discussion

They included patients with AD, FTD, VaD, and SCD. Please explain why they did not include those with other dementia disorders such as DLB and AGD. In ordinary clinical practice, these non-AD dementia disorders are relatively common and should not be neglected. Clearly, this is a limitation of this study and should also be discussed.

- Reply: Unfortunately we were not able to include other neurodegenerative diseases, such as dementia with Lewy bodies, or MCI (

Attachment

Submitted filename: Response to reviewers.docx

pone.0303111.s002.docx (35.7KB, docx)

Decision Letter 1

Wataru Araki

5 Mar 2024

PONE-D-23-25666R1Computerized decision support is an effective approach to select memory clinic patients for amyloid-PETPLOS ONE

Dear Dr. Rhodius-Meester,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

This editor judges that some parts of the revised manuscript still need to be clarified, as noted in the comments of the editor. 

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We look forward to receiving your revised manuscript.

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Wataru Araki

Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

In the revised manuscript, the authors have addressed almost all the criticisms of the reviewers. However, this editor thinks that some parts are not well explained or clarified and still need to be amended as indicated below. Especially, the authors should be careful to make the manuscript more comprehensible in general.

1) Response to Editor comment 5

L405-406, L428-430? L523?

These revisions are not enough to clarify the question raised by the editor.

2) Response to Reviewer #1 comment 4 and Editor comment 2

L119 is not enough. Please explain about the reason for this point.

3) Discussion L427-429

As such, -----testing [45]. This sentence seems to be an overstatement. Please change the expression.

Discussion L431-438 It is better to move this paragraph after the first paragraph in Discussion.

Discussion L455-463 This paragraph is too superficial and may be omitted.

Discussion L478-500 This paragraph should be revised so that readers can understand what the limitations of this work are. Please list the limitations in order (First, second, third ----)

Line 482 a MCI should be MCI

Line 483-485 Recruiting in ----- value. This sentence is not understandable and should be rephrased.

Conclusion Line 510-511 “to funnel patient through the diagnostic pathway,” This expression is not grammatically correct and should be corrected.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

********** 

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

********** 

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

********** 

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Reviewer #2: Yes

********** 

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Reviewer #2: Yes

********** 

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Reviewer #2: (No Response)

********** 

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Reviewer #2: Yes: RAQUEL SÁNCHEZ VAÑÓ

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PLoS One. 2024 May 20;19(5):e0303111. doi: 10.1371/journal.pone.0303111.r004

Author response to Decision Letter 1

15 Apr 2024

Additional Editor Comments:

In the revised manuscript, the authors have addressed almost all the criticisms of the reviewers. However, this editor thinks that some parts are not well explained or clarified and still need to be amended as indicated below. Especially, the authors should be careful to make the manuscript more comprehensible in general.

- Reply: We thank the editor for these suggestion and have changed the manuscript based on the comments below, and in general.

1) Response to Editor comment 5. L405-406, L428-430? L523? These revisions are not enough to clarify the question raised by the editor.

- Reply: The editor refers to the sentence in the original manuscript “Our computerized approach even outranked performing 404 amyloid-PET in all patients” where he during the first revision asked to explain the reasons for this result more clearly. In the revised version we rephrased this to “ Our approach was thus more efficient than the other scenario’s, where we would have performed PET in all patients, in none, or according to the appropriate use criteria (AUC).” in L405-407.

We are not sure what the editor seeks to clarify. The stepwise approach (scenario A) leads to 152 (53%) correctly classified subjects performing only PET in 60 (21%) subjects. Performing PET in all (scenario D) leads to 142 (49%) correctly classified subjects, yet with PET in 286 (100%) subjects. As can be seen in Fig 1, the overall accuracy is slightly higher in D, since overall less patients received a diagnosis in the approach performing PET in all. This is why we rephrased the original sentence, replacing ‘outperformed’ to ‘more efficient’. We have now rephrased this in the abstract, throughout the entire discussion and added more explanation.

Perhaps the editor questions why performing all tests in all is not superior to a stepwise approach? We feel our findings here are in line with several appropriate use criteria stating that an additional test should only be performed when it will increase the confidence of the clinician in a certain diagnosis. This was also added to the discussion. We hope this answer clarify the editor’s question. (L53-54, L59-60, L406, L425, L434-447, L464-465, L537, and we deleted sentence L538-539)

2) Response to Reviewer #1 comment 4 and Editor comment 2. L119 is not enough. Please explain about the reason for this point.

- Reply: The editor refers to his comment on “why they did not include those with other dementia disorders such as DLB and AGD. In ordinary clinical practice, these non-AD dementia disorders are relatively common and should not be neglected”, and the reviewer comment on “how PD, epilepsy, and other illness were excluded to get there.”

We totally agree that adding DLB would be useful. As explained in the methods, we were unfortunately not able to include other neurodegenerative diseases (such as DLB and AGD) or MCI, since the used classifier to date does not include these categories and therefore cannot classify them. And, though DLB is a common diagnosis in a memory clinic setting, the other diagnoses mentioned here are in fact less relevant. AGD, PD, epilepsy can sometimes occur in a memory clinic, yet are not common diagnoses (we unfortunately don’t even have AGD patients in our database) and do not pose a clinical dilemma very often. This study was set up to address the use of amyloid PET in differential diagnosis of a number of common differential diagnostic dilemma’s, in particular AD vs FTD vs VaD. We added more explanation about this to the methods section and to limitations in the discussion. We hope this explains our reason sufficiently. (L102, L118-120, L502-509)

3) Discussion L427-429. As such, -----testing [45]. This sentence seems to be an overstatement. Please change the expression.

-Reply: We apologize for this statement, which we added as a response to reviewer 1. We have now rephrased the sentence to ’ In addition, such approach may streamline clinical decision-making pipelines with blood-based biomarkers in the future by limiting the number of patients that require confirmatory testing [49]’. (L465-468).

4) Discussion L431-438 It is better to move this paragraph after the first paragraph in Discussion.

- Reply: We thank you for this suggestion. We have now moved this paragraph after the first paragraph, and deleted 3 sentences there to prevent doubling text. (L412-425)

5) Discussion L455-463 This paragraph is too superficial and may be omitted.

- Reply: We have now omitted this paragraph and changed the first sentence after this paragraph to allow for better flow. (L477-485, L487)

6) Discussion L478-500 This paragraph should be revised so that readers can understand what the limitations of this work are. Please list the limitations in order (First, second, third ----)

- Reply: We thank the editor for this suggestion, and have rewritten the limitations sections. (L500-523)

7) Line 482 a MCI should be MCI

-- Reply: Apologies, we changed this into ‘MCI’. (L504)

8) Line 483-485 Recruiting in ----- value. This sentence is not understandable and should be rephrased.

- Reply: We rephrased the sentence to ‘Second, we included only patients from a tertiary memory clinic , which may hamper generalizability, yet also reflects daily practice since amyloid-PET is mainly ordered in tertiary memory clinics’. (L509-512)

9)Conclusion Line 510-511 “to funnel patient through the diagnostic pathway,” This expression is not grammatically correct and should be corrected.

- Reply: We rephrased this final sentence to ‘ to efficiently guide stepwise diagnostic testing’. (L540-541)

Reviewers' comments:

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

6. Review Comments to the Author

Reviewer #2: (No Response)

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

Reviewer #2: Yes: RAQUEL SÁNCHEZ VAÑÓ

Reply: we thank the reviewer for their efforts and are glad all comments have been addressed.

Attachment

Submitted filename: Response to reviewers.docx

pone.0303111.s003.docx (18.1KB, docx)

Decision Letter 2

Wataru Araki

19 Apr 2024

Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET

PONE-D-23-25666R2

Dear Dr. Rhodius-Meester,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Wataru Araki

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The authors have responded to all the criticisms very appropriately.

Reviewers' comments:

Acceptance letter

Wataru Araki

8 May 2024

PONE-D-23-25666R2

PLOS ONE

Dear Dr. Rhodius-Meester,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

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on behalf of

Dr. Wataru Araki

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Flow chart for the four diagnostic approaches, using Centiloid values, summarizing the results in the last column.

    AUC: appropriate use criteria, AUC+: patients fulfilling appropriate use criteria according to [13], operationalized as described in [14], PCC: probability of correct class, NP: neuropsychology, MRI: magnetic resonance imaging, Sim: simulate, FU: follow-up.

    (PPTX)

    pone.0303111.s001.pptx (49.7KB, pptx)
    Attachment

    Submitted filename: Response to reviewers.docx

    pone.0303111.s002.docx (35.7KB, docx)
    Attachment

    Submitted filename: Response to reviewers.docx

    pone.0303111.s003.docx (18.1KB, docx)

    Data Availability Statement

    The data from this study are available upon request. Subjects included in this study only consented to their data being shared with third parties when it is used to gain more knowledge about Alzheimer’s disease and dementia. Therefore, the data may only be shared via a data-request procedure. The data underlying the results presented in this study are therefore available upon request from the Alzheimer Center Amsterdam, via wm.vdflier@amsterdamumc.nl or metc@vumc.nl. More information on the Cohort can be found at JPND, Amsterdam dementia cohort - JPND Neurodegenerative Disease Research (neurodegenerationresearch.eu).

    Articles from PLOS ONE are provided here courtesy of PLOS

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