Differentiation of lymphocytic-plasmacytic enteropathy and small cell lymphoma in cats using histology-guided mass spectrometry.

HGMS MALDI mass spectrometry PARR PCR for antigen receptor rearrangements chronic enteropathy clonality testing feline inflammatory bowel disease inflammatory enteropathy small cell lymphoma

Journal

Journal of veterinary internal medicine
ISSN: 1939-1676
Titre abrégé: J Vet Intern Med
Pays: United States
ID NLM: 8708660

Informations de publication

Date de publication:
Mar 2020
Historique:
received: 31 10 2019
accepted: 14 02 2020
pubmed: 27 2 2020
medline: 15 12 2020
entrez: 27 2 2020
Statut: ppublish

Résumé

Differentiation of lymphocytic-plasmacytic enteropathy (LPE) from small cell lymphoma (SCL) in cats can be challenging. Histology-guided mass spectrometry (HGMS) is a suitable method for the differentiation of LPE from SCL in cats. Forty-one cats with LPE and 52 cats with SCL. This is a retrospective clinicopathologic study. Duodenal tissue samples of 17 cats with LPE and 22 cats with SCL were subjected to HGMS, and the acquired data were used to develop a linear discriminate analysis (LDA) machine learning algorithm. The algorithm was subsequently validated using a separate set of 24 cats with LPE and 30 cats with SCL. Cases were classified as LPE or SCL based on a consensus by an expert panel consisting of 5-7 board-certified veterinary specialists. Histopathology, immunohistochemistry, and clonality testing were available for all cats. The panel consensus classification served as a reference for the calculation of test performance parameters. Relative sensitivity, specificity, and accuracy of HGMS were 86.7% (95% confidence interval [CI]: 74.5%-98.8%), 91.7% (95% CI: 80.6%-100%), and 88.9% (95% CI: 80.5%-97.3%), respectively. Comparatively, the clonality testing had a sensitivity, specificity, and accuracy of 85.7% (95% CI: 72.8%-98.7%), 33.3% (95% CI: 14.5%-52.2%), and 61.5% (95% CI: 48.3%-74.8%) relative to the panel decision. Histology-guided mass spectrometry was a reliable technique for the differentiation of LPE from SCL in duodenal formalin-fixed paraffin-embedded samples of cats and might have advantages over tests currently considered state of the art.

Sections du résumé

BACKGROUND BACKGROUND
Differentiation of lymphocytic-plasmacytic enteropathy (LPE) from small cell lymphoma (SCL) in cats can be challenging.
HYPOTHESIS/OBJECTIVE OBJECTIVE
Histology-guided mass spectrometry (HGMS) is a suitable method for the differentiation of LPE from SCL in cats.
ANIMALS METHODS
Forty-one cats with LPE and 52 cats with SCL.
METHODS METHODS
This is a retrospective clinicopathologic study. Duodenal tissue samples of 17 cats with LPE and 22 cats with SCL were subjected to HGMS, and the acquired data were used to develop a linear discriminate analysis (LDA) machine learning algorithm. The algorithm was subsequently validated using a separate set of 24 cats with LPE and 30 cats with SCL. Cases were classified as LPE or SCL based on a consensus by an expert panel consisting of 5-7 board-certified veterinary specialists. Histopathology, immunohistochemistry, and clonality testing were available for all cats. The panel consensus classification served as a reference for the calculation of test performance parameters.
RESULTS RESULTS
Relative sensitivity, specificity, and accuracy of HGMS were 86.7% (95% confidence interval [CI]: 74.5%-98.8%), 91.7% (95% CI: 80.6%-100%), and 88.9% (95% CI: 80.5%-97.3%), respectively. Comparatively, the clonality testing had a sensitivity, specificity, and accuracy of 85.7% (95% CI: 72.8%-98.7%), 33.3% (95% CI: 14.5%-52.2%), and 61.5% (95% CI: 48.3%-74.8%) relative to the panel decision.
CONCLUSIONS AND CLINICAL IMPORTANCE CONCLUSIONS
Histology-guided mass spectrometry was a reliable technique for the differentiation of LPE from SCL in duodenal formalin-fixed paraffin-embedded samples of cats and might have advantages over tests currently considered state of the art.

Identifiants

pubmed: 32100916
doi: 10.1111/jvim.15742
pmc: PMC7096630
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

669-677

Subventions

Organisme : New River VDL, LLC

Informations de copyright

© 2020 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

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Auteurs

Sina Marsilio (S)

Department of Veterinary Medicine and Epidemiology, UC Davis School of Veterinary Medicine, Davis, California.
Gastrointestinal Laboratory, Department of Small Animal Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas.

Shelley J Newman (SJ)

Long Island University CVM, Brookville, New York.

James Scot Estep (JS)

Texas Veterinary Pathology, LLC, San Antonio, Texas.

Paula R Giaretta (PR)

Department of Veterinary Pathobiology, Texas A&M University, College Station, Texas.

Jonathan A Lidbury (JA)

Gastrointestinal Laboratory, Department of Small Animal Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas.

Emma Warry (E)

Department of Small Animal Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas.

Andi Flory (A)

Veterinary Specialty Hospital, San Diego, California.

Paul S Morley (PS)

Veterinary Education, Research, and Outreach Center, Texas A&M University, Canyon, Texas.

Katy Smoot (K)

New River VDL, LLC, Morgantown, West Virginia, USA.

Erin H Seeley (EH)

New River VDL, LLC, Morgantown, West Virginia, USA.

Matthew J Powell (MJ)

New River VDL, LLC, Morgantown, West Virginia, USA.

Jan S Suchodolski (JS)

Gastrointestinal Laboratory, Department of Small Animal Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas.

Jörg M Steiner (JM)

Gastrointestinal Laboratory, Department of Small Animal Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas.

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