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      Classification of 18F-Flutemetamol scans in cognitively normal older adults using machine learning trained with neuropathology as ground truth

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          Abstract

          Purpose

          End-of-life studies have validated the binary visual reads of 18F-labeled amyloid PET tracers as an accurate tool for the presence or absence of increased neuritic amyloid plaque density. In this study, the performance of a support vector machine (SVM)-based classifier will be tested against pathological ground truths and its performance determined in cognitively healthy older adults.

          Methods

          We applied SVM with a linear kernel to an 18F-Flutemetamol end-of-life dataset to determine the regions with the highest feature weights in a data-driven manner and to compare between two different pathological ground truths: based on neuritic amyloid plaque density or on amyloid phases, respectively. We also trained and tested classifiers based on the 10% voxels with the highest amplitudes of feature weights for each of the two neuropathological ground truths. Next, we tested the classifiers’ diagnostic performance in the asymptomatic Alzheimer’s disease (AD) phase, a phase of interest for future drug development, in an independent dataset of cognitively intact older adults, the Flemish Prevent AD Cohort-KU Leuven (F-PACK). A regression analysis was conducted between the Centiloid (CL) value in a composite volume of interest (VOI), as index for amyloid load, and the distance to the hyperplane for each of the two classifiers, based on the two pathological ground truths. A receiver operating characteristic analysis was also performed to determine the CL threshold that optimally discriminates between neuritic amyloid plaque positivity versus negativity, or amyloid phase positivity versus negativity, within F-PACK.

          Results

          The classifiers yielded adequate specificity and sensitivity within the end-of-life dataset (neuritic amyloid plaque density classifier: specificity of 90.2% and sensitivity of 83.7%; amyloid phase classifier: specificity of 98.4% and sensitivity of 84.0%). The regions with the highest feature weights corresponded to precuneus, caudate, anteromedial prefrontal, and also posterior inferior temporal and inferior parietal cortex. In the cognitively normal cohort, the correlation coefficient between CL and distance to the hyperplane was −0.66 for the classifier trained with neuritic amyloid plaque density, and −0.88 for the classifier trained with amyloid phases. This difference was significant. The optimal CL cut-off for discriminating positive versus negative scans was CL = 48–51 for the different classifiers (area under the curve ( AUC) = 99.9%), except for the classifier trained with amyloid phases and based on the 10% voxels with highest feature weights. There the cut-off was CL = 26 ( AUC = 99.5%), which closely matched the CL threshold for discriminating phases 0–2 from 3–5 based on the end-of-life dataset and the neuropathological ground truth.

          Discussion

          Among a set of neuropathologically validated classifiers trained with end-of-life cases, transfer to a cognitively normal population works best for a classifier trained with amyloid phases and using only voxels with the highest amplitudes of feature weights.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s00259-022-05808-7.

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          Most cited references31

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          pROC: an open-source package for R and S+ to analyze and compare ROC curves

          Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.
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            Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

            An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.
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              Neuropathological stageing of Alzheimer-related changes

              Eighty-three brains obtained at autopsy from nondemented and demented individuals were examined for extracellular amyloid deposits and intraneuronal neurofibrillary changes. The distribution pattern and packing density of amyloid deposits turned out to be of limited significance for differentiation of neuropathological stages. Neurofibrillary changes occurred in the form of neuritic plaques, neurofibrillary tangles and neuropil threads. The distribution of neuritic plaques varied widely not only within architectonic units but also from one individual to another. Neurofibrillary tangles and neuropil threads, in contrast, exhibited a characteristic distribution pattern permitting the differentiation of six stages. The first two stages were characterized by an either mild or severe alteration of the transentorhinal layer Pre-alpha (transentorhinal stages I-II). The two forms of limbic stages (stages III-IV) were marked by a conspicuous affection of layer Pre-alpha in both transentorhinal region and proper entorhinal cortex. In addition, there was mild involvement of the first Ammon's horn sector. The hallmark of the two isocortical stages (stages V-VI) was the destruction of virtually all isocortical association areas. The investigation showed that recognition of the six stages required qualitative evaluation of only a few key preparations.
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                Author and article information

                Contributors
                rik.vandenberghe@uzleuven.be
                Journal
                Eur J Nucl Med Mol Imaging
                Eur J Nucl Med Mol Imaging
                European Journal of Nuclear Medicine and Molecular Imaging
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1619-7070
                1619-7089
                6 May 2022
                6 May 2022
                2022
                : 49
                : 11
                : 3772-3786
                Affiliations
                [1 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Laboratory for Cognitive Neurology, , KU Leuven, ; Leuven, Belgium
                [2 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Alzheimer Research Centre KU Leuven, Leuven Brain Institute, ; Leuven, Belgium
                [3 ]GRID grid.430790.9, ISNI 0000 0004 0602 1531, Bioclinica, ; Newark, CA 94560 USA
                [4 ]GRID grid.410569.f, ISNI 0000 0004 0626 3338, Department of Pathology, , UZ Leuven, ; Leuven, Belgium
                [5 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Laboratory of Neuropathology, , KU Leuven, ; Leuven, Belgium
                [6 ]GRID grid.410569.f, ISNI 0000 0004 0626 3338, Division of Nuclear Medicine, , UZ Leuven, ; Leuven, Belgium
                [7 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, , KU Leuven, ; Leuven, Belgium
                [8 ]GRID grid.410569.f, ISNI 0000 0004 0626 3338, Neurology Department, , University Hospitals Leuven, ; Herestraat 49, 3000 Leuven, Belgium
                Author information
                http://orcid.org/0000-0001-7964-7963
                http://orcid.org/0000-0003-1777-4742
                http://orcid.org/0000-0003-1980-2540
                http://orcid.org/0000-0001-6237-2502
                Article
                5808
                10.1007/s00259-022-05808-7
                9399207
                35522322
                6ecb179f-7b05-42c6-85f5-86c888a1a58b
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 20 December 2021
                : 19 April 2022
                Funding
                Funded by: Foundation for Alzheimer Research SAO-FRMA
                Award ID: 09013
                Award ID: 11020
                Award ID: 13007
                Award Recipient :
                Funded by: Research Foundation Flanders
                Award ID: G094418N
                Award ID: G0F8516N
                Award ID: G0G1519N
                Award Recipient :
                Funded by: KU Leuven
                Award ID: C14/17/108
                Award ID: C14/21/109
                Award Recipient :
                Funded by: Vlaams Agentschap voor Innovatie en Onderzoek
                Award ID: HBC.2019.2523
                Award Recipient :
                Categories
                Original Article
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2022

                Radiology & Imaging
                positron emission tomography (pet),18f-flutemetamol,alzheimer’s disease (ad),neuropathology,classification,support vector machine (svm)

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