Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/112534
Title: A text-mining based analysis of 100,000 tumours affecting dogs and cats in the United Kingdom
Authors: Rodríguez Torres, José 
Killick, David R.
Ressel, Lorenzo
Espinosa De Los Monteros Y Zayas, Antonio 
Santana Del Pino, Ángelo 
Beck, Samuel
Cian, Francesco
McKay, Jenny S.
Noble, P. J.
Pinchbeck, Gina L.
Singleton, David A.
Radford, Alan D.
UNESCO Clasification: 310904 Medicina interna
320713 Oncología
Keywords: Cancer epidemiology
Epidemiology
Issue Date: 2021
Journal: Scientific data 
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.
URI: http://hdl.handle.net/10553/112534
ISSN: E2052-4463
DOI: 10.1038/s41597-021-01039-x
Source: Scientific Data [EISSN 2052-4463], v. 8 (1), 266, (Diciembre 2021)
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