Dataset Information

Prediction of amyloid ? PET positivity using machine learning in patients with suspected cerebral amyloid angiopathy markers.

ABSTRACT: Amyloid-?(A?) PET positivity in patients with suspected cerebral amyloid angiopathy (CAA) MRI markers is predictive of a worse cognitive trajectory, and it provides insights into the underlying vascular pathology (CAA vs. hypertensive angiopathy) to facilitate prognostic prediction and appropriate treatment decisions. In this study, we applied two interpretable machine learning algorithms, gradient boosting machine (GBM) and random forest (RF), to predict A? PET positivity in patients with CAA MRI markers. In the GBM algorithm, the number of lobar cerebral microbleeds (CMBs), deep CMBs, lacunes, CMBs in dentate nuclei, and age were ranked as the most influential to predict A? positivity. In the RF algorithm, the absence of diabetes was additionally chosen. Cut-off values of the above variables predictive of A? positivity were as follows: (1) the number of lobar CMBs?>?16.4(GBM)/14.3(RF), (2) no deep CMBs(GBM/RF), (3) the number of lacunes?>?7.4(GBM/RF), (4) age?>?74.3(GBM)/64(RF), (5) no CMBs in dentate nucleus(GBM/RF). The classification performances based on the area under the receiver operating characteristic curve were 0.83 in GBM and 0.80 in RF. Our study demonstrates the utility of interpretable machine learning in the clinical setting by quantifying the relative importance and cutoff values of predictive variables for A? positivity in patients with suspected CAA markers.

SUBMITTER: Jung YH

PROVIDER: S-EPMC7608617 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction of amyloid β PET positivity using machine learning in patients with suspected cerebral amyloid angiopathy markers.

Jung Young Hee YH Lee Hyejoo H Kim Hee Jin HJ Na Duk L DL Han Hyun Jeong HJ Jang Hyemin H Seo Sang Won SW

Scientific reports 20201102 1

Amyloid-β(Aβ) PET positivity in patients with suspected cerebral amyloid angiopathy (CAA) MRI markers is predictive of a worse cognitive trajectory, and it provides insights into the underlying vascular pathology (CAA vs. hypertensive angiopathy) to facilitate prognostic prediction and appropriate treatment decisions. In this study, we applied two interpretable machine learning algorithms, gradient boosting machine (GBM) and random forest (RF), to predict Aβ PET positivity in patients with CAA M ...[more]

PMID: 33139780

Similar Datasets

Project description:Traditionally, analysis of neuropathological markers in neurodegenerative diseases has relied on visual assessments of stained sections. Resulting semiquantitative scores often vary between individual raters and research centers, limiting statistical approaches. To overcome these issues, we have developed six deep learning-based models, that identify some of the most characteristic markers of Alzheimer's disease (AD) and cerebral amyloid angiopathy (CAA). The deep learning-based models are trained to differentially detect parenchymal amyloid β (Aβ)-plaques, vascular Aβ-deposition, iron and calcium deposition, reactive astrocytes, microglia, as well as fibrin extravasation. The models were trained on digitized histopathological slides from brains of patients with AD and CAA, using a workflow that allows neuropathology experts to train convolutional neural networks (CNNs) on a cloud-based graphical interface. Validation of all models indicated a very good to excellent performance compared to three independent expert human raters. Furthermore, the Aβ and iron models were consistent with previously acquired semiquantitative scores in the same dataset and allowed the use of more complex statistical approaches. For example, linear mixed effects models could be used to confirm the previously described relationship between leptomeningeal CAA severity and cortical iron accumulation. A similar approach enabled us to explore the association between neuroinflammation and disparate Aβ pathologies. The presented workflow is easy for researchers with pathological expertise to implement and is customizable for additional histopathological markers. The implementation of deep learning-assisted analyses of histopathological slides is likely to promote standardization of the assessment of neuropathological markers across research centers, which will allow specific pathophysiological questions in neurodegenerative disease to be addressed in a harmonized way and on a larger scale.

Project description:Cerebral amyloid angiopathy (CAA) involves cerebrovascular amyloid deposition and is classified into several types according to the amyloid protein involved. Of these, sporadic amyloid β-protein (Aβ)-type CAA is most commonly found in older individuals and in patients with Alzheimer's disease (AD). Cerebrovascular Aβ deposits accompany functional and pathological changes in cerebral blood vessels (CAA-associated vasculopathies). CAA-associated vasculopathies lead to development of hemorrhagic lesions [lobar intracerebral macrohemorrhage, cortical microhemorrhage, and cortical superficial siderosis (cSS)/focal convexity subarachnoid hemorrhage (SAH)], ischemic lesions (cortical infarction and ischemic changes of the white matter), and encephalopathies that include subacute leukoencephalopathy caused by CAA-associated inflammation/angiitis. Thus, CAA is related to dementia, stroke, and encephalopathies. Recent advances in diagnostic procedures, particularly neuroimaging, have enabled us to establish a clinical diagnosis of CAA without brain biopsies. Sensitive magnetic resonance imaging (MRI) methods, such as gradient-echo T2(*) imaging and susceptibility-weighted imaging, are useful for detecting cortical microhemorrhages and cSS. Amyloid imaging with amyloid-binding positron emission tomography (PET) ligands, such as Pittsburgh Compound B, can detect CAA, although they cannot discriminate vascular from parenchymal amyloid deposits. In addition, cerebrospinal fluid markers may be useful, including levels of Aβ40 for CAA and anti-Aβ antibody for CAA-related inflammation. Moreover, cSS is closely associated with transient focal neurological episodes (TFNE). CAA-related inflammation/angiitis shares pathophysiology with amyloid-related imaging abnormalities (ARIA) induced by Aβ immunotherapies in AD patients. This article reviews CAA and CAA-related disorders with respect to their epidemiology, pathology, pathophysiology, clinical features, biomarkers, diagnosis, treatment, risk factors, and future perspectives.

Dataset Information

Prediction of amyloid ? PET positivity using machine learning in patients with suspected cerebral amyloid angiopathy markers.

Publications

Prediction of amyloid β PET positivity using machine learning in patients with suspected cerebral amyloid angiopathy markers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets