Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles.

Autor: Vizcarra JC; The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Dr NW, Atlanta, GA, 30332, USA., Pearce TM; Department of Pathology, Division of Neuropathology, University of Pittsburgh Medical Center, Room S701 Scaife Hall 3550 Terrace Street, Pittsburgh, PA, 15261, USA., Dugger BN; Department of Pathology and Laboratory Medicine, University of California-Davis School of Medicine, 3400A Research Building III Sacramento, Davis, CA, 95817, USA., Keiser MJ; Department of Pharmaceutical Chemistry, Department of Bioengineering and Therapeutic Sciences, Institute for Neurodegenerative Diseases, Kavli Institute for Fundamental Neuroscience, and Bakar Computational Health Sciences Institute, University of California, 675 Nelson Rising Ln, Box 0518, San Francisco, CA, 94143, USA., Gearing M; Department of Neurology, Emory University School of Medicine, 12 Executive Park Dr NE, Atlanta, GA, 30322, USA.; Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 1364 Clifton Rd, Atlanta, GA, 30322, USA., Crary JF; Departments of Pathology, Neuroscience, and Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.; Neuropathology Brain Bank and Research Core, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.; Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA.; Department of Pathology, Icahn School of Medicine at Mount Sinai, Icahn Building 9th Floor, Room 20A, 1425 Madison Avenue, New York, NY, 10029, USA., Kiely EJ; Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 1364 Clifton Rd, Atlanta, GA, 30322, USA., Morris M; Department of Pathology, Johns Hopkins School of Medicine, Baltimore, MD, 21218, USA., White B; Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA, 02215, USA., Glass JD; Department of Neurology, Emory University School of Medicine, 12 Executive Park Dr NE, Atlanta, GA, 30322, USA.; Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 1364 Clifton Rd, Atlanta, GA, 30322, USA.; Center for Neurodegenerative Disease, Emory University School of Medicine, Whitehead Biomedical Research Building, 615 Michael Street, 5th Floor, Suite 500, Atlanta, GA, 30322, USA., Farrell K; Departments of Pathology, Neuroscience, and Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.; Neuropathology Brain Bank and Research Core, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.; Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA.; Department of Pathology, Icahn School of Medicine at Mount Sinai, Icahn Building 9th Floor, L9-02C, 1425 Madison, Avenue, New York, NY, USA., Gutman DA; Department of Pathology and Laboratory Medicine, Emory University School of Medicine, 1364 Clifton Rd, Atlanta, GA, 30322, USA. dgutman@emory.edu.
Jazyk: angličtina
Zdroj: Acta neuropathologica communications [Acta Neuropathol Commun] 2023 Dec 18; Vol. 11 (1), pp. 202. Date of Electronic Publication: 2023 Dec 18.
DOI: 10.1186/s40478-023-01691-x
Abstrakt: Machine learning (ML) has increasingly been used to assist and expand current practices in neuropathology. However, generating large imaging datasets with quality labels is challenging in fields which demand high levels of expertise. Further complicating matters is the often seen disagreement between experts in neuropathology-related tasks, both at the case level and at a more granular level. Neurofibrillary tangles (NFTs) are a hallmark pathological feature of Alzheimer disease, and are associated with disease progression which warrants further investigation and granular quantification at a scale not currently accessible in routine human assessment. In this work, we first provide a baseline of annotator/rater agreement for the tasks of Braak NFT staging between experts and NFT detection using both experts and novices in neuropathology. We use a whole-slide-image (WSI) cohort of neuropathology cases from Emory University Hospital immunohistochemically stained for Tau. We develop a workflow for gathering annotations of the early stage formation of NFTs (Pre-NFTs) and mature intracellular (iNFTs) and show ML models can be trained to learn annotator nuances for the task of NFT detection in WSIs. We utilize a model-assisted-labeling approach and demonstrate ML models can be used to aid in labeling large datasets efficiently. We also show these models can be used to extract case-level features, which predict Braak NFT stages comparable to expert human raters, and do so at scale. This study provides a generalizable workflow for various pathology and related fields, and also provides a technique for accomplishing a high-level neuropathology task with limited human annotations.
(© 2023. The Author(s).)
Databáze: MEDLINE
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