A text-mining based analysis of 100,000 tumours affecting dogs and cats in the United Kingdom


Rodriguez, Jose, Killick, David R ORCID: 0000-0002-8787-7651, Ressel, Lorenzo ORCID: 0000-0002-6614-1223, Espinosa de los Monteros, Antonio, Santana, Angelo, Beck, Samuel, Cian, Francesco, McKay, Jenny S, Noble, PJ, Pinchbeck, Gina L ORCID: 0000-0002-5671-8623 et al (show 2 more authors) (2021) A text-mining based analysis of 100,000 tumours affecting dogs and cats in the United Kingdom. SCIENTIFIC DATA, 8 (1). 266-.

Access the full-text of this item by clicking on the Open Access link.

Abstract

Cancer is a major reason for veterinary consultation, especially in companion animals. Cancer surveillance plays a key role in prevention but opportunities for such surveillance in companion animals are limited by the lack of suitable veterinary population health infrastructures. In this paper we describe a pathology-based animal tumour registry (PTR) developed within the Small Animal Veterinary Surveillance Network (SAVSNET) built from electronic pathology records (EPR) submitted to this network. From an original collection of 180232 free text (non-structured) EPRs reported between April 2018 and June 2019, we used specific text-mining methodologies to identify 109895 neoplasias. These data were normalized to describe both the tumour (type and location) and the animal (breed, neutering status and veterinary practice postcode). The resulting PTR, the largest of its kind for companion animals to date, is an important research resource being able to facilitate a wide array of research in areas including surveillance, clinical decision making and comparative cancer biology.

Item Type: Article
Uncontrolled Keywords: Animals, Dogs, Cats, Neoplasms, Cat Diseases, Dog Diseases, Data Mining, United Kingdom
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Infection, Veterinary and Ecological Sciences
Depositing User: Symplectic Admin
Date Deposited: 02 Nov 2021 13:40
Last Modified: 18 Jan 2023 21:25
DOI: 10.1038/s41597-021-01039-x
Open Access URL: https://doi.org/10.1038/s41597-021-01039-x
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3142511
Dimensions
Altmetric
CORE (COnnecting REpositories)