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Tang, X.* ; Kusmartseva, I.* ; Kulkarni, S.* ; Posgai, A.* ; Speier, S. ; Schatz, D.A.* ; Haller, M.J.* ; Campbell-Thompson, M.* ; Wasserfall, C.H.* ; Roep, B.O.* ; Kaddis, J,S.* ; Atkinson, M.A.*

Image-based machine learning algorithms for disease characterization in the human type 1 diabetes pancreas.

Am. J. Pathol. 191, 454-462 (2021)
Verlagsversion DOI PMC
Closed
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Emerging data suggest that type 1 diabetes affects not only the β-cell-containing islets of Langerhans, but also the surrounding exocrine compartment. Using digital pathology, machine learning algorithms were applied to provide high-resolution, whole-slide images of human pancreata to determine if the tissue composition in individuals with or at risk for type 1 diabetes differs from those without diabetes. Transplant-grade pancreata from organ donors were evaluated from 16 nondiabetic autoantibody negative controls, 8 nondiabetic autoantibody positive subjects who have increased type 1 diabetes risk, and 19 persons with type 1 diabetes (0 to 12 years' duration). HALO image analysis algorithms were implemented to compare architecture of the main pancreatic duct as well as cell size, density, and area of acinar, endocrine, ductal, and other nonendocrine, nonexocrine tissues. Type 1 diabetes was found to affect exocrine area, acinar cell density, and size, whereas the type of difference correlated with the presence or absence of insulin-positive cells remaining in the pancreas. These changes were not observed before disease onset, as indicated by modeling cross-sectional data from pancreata of autoantibody positive subjects and those diagnosed with type 1 diabetes. These data provide novel insights into anatomic differences in type 1 diabetes pancreata and demonstrate that machine learning can be adapted for the evaluation of disease processes from cross-sectional data sets.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Sprache englisch
Veröffentlichungsjahr 2021
Prepublished im Jahr 2020
HGF-Berichtsjahr 2020
ISSN (print) / ISBN 0002-9440
e-ISSN 1525-2191
Zeitschrift American Journal of Pathology, The
Quellenangaben Band: 191, Heft: 3, Seiten: 454-462 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Ste 800, 230 Park Ave, New York, Ny 10169 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Pancreatic Islet Research (IPI)
POF Topic(s) 90000 - German Center for Diabetes Research
Forschungsfeld(er) Helmholtz Diabetes Center
PSP-Element(e) G-502600-005
Förderungen Juvenile Diabetes Research Foundation
Leona M. & Harry B. Helmsley Charitable Trust
IH National Institute of Diabetes and Digestive and Kidney Diseasesesupported Human Islet Research Network
NIH National Institute of Allergy and Infectious Diseases program project
NIH Common Fund supported Stimulating Peripheral Activity to Relieve Conditions program
NIH National Institute of Diabetes and Digestive and Kidney Diseases
Network for Pancreatic Organ Donors with Diabetes (nPOD)
DOI 10.1016/j.ajpath.2020.11.010
WOS ID WOS:000632503000004
WOS ID WOS:000633052800005
Scopus ID 85100982437
PubMed ID 33307036
Erfassungsdatum 2021-02-08
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