Differentiation of lymphocytic‐plasmacytic enteropathy and small cell lymphoma in cats using histology‐guided mass spectrometry. (26th February 2020) - Marsilio, Sina; Newman, Shelley J.; Estep, James Scot; Giaretta, Paula R.; Lidbury, Jonathan A.; Warry, Emma; Flory, Andi; Morley, Paul S.; Smoot, Katy; Seeley, Erin H.; Powell, Matthew J.; Suchodolski, Jan S.; et al.
Abstract: Background: Differentiation of lymphocytic‐plasmacytic enteropathy (LPE) from small cell lymphoma (SCL) in cats can be challenging. Hypothesis/Objective: Histology‐guided mass spectrometry (HGMS) is a suitable method for the differentiation of LPE from SCL in cats. Animals: Forty‐one cats with LPE and 52 cats with SCL. 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: 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: 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.