Identifying free-text features to improve automated classification ofstructured histopathology reports for feline small intestinaldisease

摘要

The histologic evaluation of gastrointestinal (GI) biopsies is the standard for diagnosis of a variety of GI diseases (e.g., inflammatory bowel disease [IBD] and alimentary lymphoma [ALA]). The World Small Animal Veterinary Association (WSAVA) Gastrointestinal International Standardization Group proposed a reporting standard for GI biopsies consisting of a defined set of microscopic features. We compared the machine classification accuracy of free-text microscopic findings with those represented in the WSAVA format with a diagnosis of IBD and ALA. Unstructured free-text duodenal biopsy pathology reports from cats (n = 60) with a diagnosis of IBD (n = 20), ALA (n = 20), or normal (n = 20) were identified. Biopsy samples from these cases were then scored following the WSAVA guidelines to create a set of structured reports. Three supervised machine-learning algorithms were trained using the structured and then the unstructured reports. Diagnosis classification accuracy for the 3 algorithms was compared using the structured and unstructured reports. Using naive Bayes and neural networks, unstructured information-based models achieved higher diagnostic accuracy (0.90 and 0.88, respectively) compared to the structuredinformation-based models (0.74 and 0.72, respectively). Results suggest thatdiscriminating diagnostic information was lost using current WSAVA microscopicguideline features. Addition of free-text features (number of plasma cells)increased WSAVA auto-classification performance. The methodologies reported inour study represent a way of identifying candidate microscopic features for usein structured histopathology reports.