Participants
We adopted FBB PET as Aβ-PET for this experiment and retrospectively recruited subjects who visited the Department of Neurology and Nuclear Medicine of the Dong-A University Hospital (DAUH) and underwent dual-phase FBB from November 2015 to June 2020. The total number of subjects was 264, consisting of 74 cognitive normal (CN) and 190 AD. Detailed demographic data of the participants are presented in Figure 1. All CN cases had normal age-, gender-, and education-adjusted performance on standardized cognitive tests. The AD participants met the following inclusion criteria: 1) criteria for dementia according to the Diagnostic and Statistical Manual of Mental Disorders 4th Edition (DSM-IV-TR)15, and 2) the criteria for probable AD according to the NIA-AA core clinical criteria16. The individual FBB PET imaging for Aβ load was visually evaluated by brain Aβ plaque load (BAPL) scoring system, which defines a BAPL score of 1 (no Aβ load), 2 (minor Aβ load), and 3 (significant Aβ load)17. Dong-A University Hospital Institutional Review Board (DAUHIRB) reviewed this study with the member who participated in Institutional Review Board Membership List Ⅲ and finally approved this study protocol (DAUHIRB-17-108). All procedures for data acquisition were in accordance with the ethical standards of DAUHIRB with 1964 Helsinki declaration and its later amendments or comparable ethical standards. We guarantee that informed consent was obtained from all participants for this study.
PET acquisition
All FBB PET imaging for this experiment was performed using a Biograph 40mCT Flow PET/CT scanner (Siemens Healthcare, Knoxville, TN, USA) and reconstructed through UltraHD-PET (TrueX-TOF). An FBB dose of 300 MBq was administered to the participants as an intravenous bolus injection from 0 to 20 min and from 90 to 120 min post-injection after helical CT with a 0.5 sec rotation time at 100 kVp and 228 mAs. The image acquisition time for dual-phase FBB PET was determined by related studies to sufficiently include the peak of Aβ uptake for early phase FBB PET (eFBB) and the manufacturer’s recommendations and for delay-phase FBB PET (dFBB)10, 17, 18. The acquired dynamic eFBB and static dFBB was 27 frames of 128 × 128 × 110 (3.19 mm × 3.19 mm × 1.5 mm) resliced from a field of view of 408 mm × 408 mm × 165 mm, and one frame of 400 × 400 × 110 (1.02 mm × 1.02 mm × 1.5 mm) resliced from a field of view of 408 mm × 408 mm × 165 mm, respectively. Static eFBB to evaluate potential perfusion was made by averaging the frames corresponding to 2–7mins from dynamic eFBB. The optimal time period required to obtain static eFBB was internally determined using the approach in Reference10.
Pre-processing
The pre-processing procedures applied to extract regional mean SUVr for dynamic eFBB or static eFBB/dFBB, respectively, were as follows. For the spatial normalization of all PET images, we used an in-house eFBB PET template19, which averaged 8 CN and 8 AD randomly selected from the collective spatially normalized FBB data pool in Montreal Neurological Institute (MNI) space20. Each static eFBB was spatially non-linearly registered to the template space. For dynamic eFBB, the deformation field corresponding to the template space from the mean of the total number of frames was used. The deformation fields for a dFBB were identical to those estimated in the spatial normalization process of the matched static eFBB21. As a result, the spatially registered imaging was in a voxel space of 95 × 79 × 68 (height × width × depth). We merged Hammers atlas22 into nine representative regions (frontal lobe, temporal lobe, parietal lobe, occipital lobe, posterior cingulate, caudate, putamen, and thalamus) for the reference region for count normalization and volume of interest for estimating the mean SUVr. After spatial normalization, the intensities of each image were normalized with respect to the mean uptake of the whole cerebellar region as a reference region. Finally, for dynamic eFBB and static eFBB/dFBB, regional mean SUVr of 8 × 1 and regional time-activity curve (TAC) data of 8 × 27 (number of target regions × temporal length) were obtained, respectively.
Calculation of AD positivity score based on brain blood flow and amyloid-β plaque
To calculate the AD positivity score, we aimed to build a proper machine learning-based classification model to predict the probability of whether the given regional TAC or mean SUVr data belong to the CN or AD distribution. Fig. 1 shows the structure of the adopted models for this study. Fig. 1a is a combined model that predicts AD using both dynamic eFBB and dFBB extracted from dual-phase FBB, and Fig. 1b shows the structure of the model using only one of either eFBB or dFBB (single-phase FBB). The combined model (NNaggregated) in Fig. 1a consists of long short-term memory (LSTMeFBB) model, which is used to extract temporal features such as dynamic eFBB, feedforward neural network (NNdFBB) for dFBB, and last NN to make a final diagnosis decision (NNDx) using the concatenated features (eFBB and dFBB) delivered from the preceding layers. Each LSTMeFBB and NNdFBB and NNDx have an output layer with two nodes leading to the softmax function to interpret the model output as the probability for diagnostic label, and their model parameters were trained to minimize the cross-entropy loss between the predicted probability and one-hot encoded actual label. To train the weights of each internal model (LSTMeFBB, NNdFBB, and NNDx) constituting the combined model, we alternately trained the internal models in a 1:1:2 ratio of epochs. The models depicted in Fig. 1b are a logistic regression (LR) and support vector machine (SVM), or an NN-based model, and were used as a baseline to evaluate the feasibility of the combined model that interprets dual-phase FBB.
Detailed parameters for model selection and model evaluation
For the purpose of this experiment, we focused on showing that the model with dual-phase FBB is more useful for estimating AD positivity than a model with only dFBB. Therefore, we tried to simplify and unify the model structure and detailed parameters of each model as much as possible. NN-based models, including LSTM, have two hidden layers, eight nodes of each hidden layer, and L2 regularization was applied with a weight of 0.01. The learning curves of all models were set to be trained up to 10000 epochs but were stopped if the validation loss was not updated more than 200 times. The learning rate was 0.0005, and the Adam optimizer23 was used for each setting. If the validation loss was not updated more than 100 times at a point, 0.001 of decay rate was applied to the learning rate of the point.
The SVM used in the experiment used a linear kernel as a kernel function. A radial basis function or polynomial kernel was also tested in an internal experiment but no meaningful difference was observed, and a simpler model was finally adopted to prevent overfitting.
The software used in this experiment was the SPM12 library and MATLAB R2020a for the data pre-processing, including spatial normalization, count normalization, and for calculating regional mean SUVr based on the Hammers atlas22. Keras 2.2.4 library and Python 3.6.9 were used to select and evaluate a model for estimating AD positivity. The experimental tool was implemented and tested on Linux Ubuntu 16.04 LTS with an Intel Core i7-6800K CPU and two GPUs (NVIDIA GeForce GTX 1080).
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)
For model selection and evaluation, we used a nested cross-validation framework24, 25. In situations where there is a limited dataset, nested cross-validation could be an appropriate alternative when it is difficult to divide the dataset into training, validation, and testing sets. Its inner loop achieves model selection through a predefined hyper-parameter search space and wrapper function, and the outer loop is used to measure the generalization performance of the selected model. Although we used 4-fold cross-validation, instead of using the wrapper function, with the dataset corresponding to the inner loop, we manually tried to find a unified hyper-parameter that the NN-based model does not underfit or overfit. We assign the index to the total dataset for each fold to simulate nested cross-validation and to maintain the reproducibility of our experiment.
Statistical Analysis
The experimental data included only cases that underwent dual-phase FBB imaging obtained at the Department of Neurology or Nuclear Medicine of the DAUH, excluding cases with other types of dementia or inconsistent imaging protocols. As a result, a total of 264 cases, including 74 CN and 190 AD, were included in this analysis. We used independent-sample t-tests for numerical variables such as age, and education, and Pearson’s Chi-square test for categorical variables such as sex, FBB reading, K-MMSE, CDR, and GDS to determine whether the characteristics of subjects in our experimental dataset are biased according to the diagnostic label. For the demographic analysis, we used IBM SPSS statistics version 23. In order to evaluate the classification performance of trained models, we calculated the accuracy (ACC) and area under the receiver operating characteristic curve (AUROC) for AD detection using DeLong’s method26 and Spearman correlation between predicted AD positivity scores and neuropsychological tests/actual diagnostic label. for these processes, we used MedCalc version 18.9.1 (MedCalc Software). In all tests, the statistical significance level was set at p<0.001 with a two-sided test.