Differentiating intestinal T-cell lymphoma from chronic enteropathy (CE) in endoscopic
samples is often challenging. In the present study, automated machine learning systems
were developed to distinguish between the two diseases, predict clonality, and detect
prognostic factors of intestinal lymphoma in cats. Four models were created for four
experimental conditions: experiment 1 to distinguish between intestinal T-cell lymphoma
and CE; experiment 2 to distinguish large cell lymphoma, small cell lymphoma, and CE;
experiment 3 to distinguish granzyme B+ lymphoma, granzyme B- lymphoma, and CE; and
experiment 4 to distinguish between T-cell receptor (TCR) clonal population and TCR
polyclonal population. After each experiment, a pathologist reviewed the test images and
scored for lymphocytic infiltration, epitheliotropism, and epithelial injury. The models
of experiments 1–4 achieved area under the receiver operating characteristic curve scores
of 0.943 (precision, 87.59%; recall, 87.59%), 0.962 (precision, 86.30%; recall, 86.30%),
0.904 (precision, 82.86%; recall, 80%), and 0.904 (precision, 81.25%; recall, 81.25%),
respectively. The images predicted as intestinal T-cell lymphoma showed significant
infiltration of lymphocytes and epitheliotropism than CE. These models can provide
evaluation tools to assist pathologists with differentiating between intestinal T-cell
lymphoma and CE.