Web Application for the Automated Extraction of Diagnosis and Site From Pathology Reports for Keratinocyte Cancers.

Autor:	Thompson BS; Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane Queensland, Australia., Hardy S; Otso, Brisbane, Queensland, Australia., Pandeya N; Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane Queensland, Australia.; School of Public Health, University of Queensland, Brisbane, Queensland, Australia., Dusingize JC; Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane Queensland, Australia., Green AC; Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane Queensland, Australia.; Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom., Millane A; School of Public Health, University of Queensland, Brisbane, Queensland, Australia., Bourke D; Max Kelsen, Brisbane, Queensland, Australia., Grande R; Max Kelsen, Brisbane, Queensland, Australia., Bean CD; Max Kelsen, Brisbane, Queensland, Australia., Olsen CM; Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane Queensland, Australia.; Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia., Whiteman DC; Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane Queensland, Australia.; Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia.
Jazyk:	angličtina
Zdroj:	JCO clinical cancer informatics [JCO Clin Cancer Inform] 2020 Aug; Vol. 4, pp. 711-723.
DOI:	10.1200/CCI.19.00152
Abstrakt:	Purpose: Keratinocyte cancers are exceedingly common in high-risk populations, but accurate measures of incidence are seldom derived because the burden of manually reviewing pathology reports to extract relevant diagnostic information is excessive. Thus, we sought to develop supervised learning algorithms for classifying basal and squamous cell carcinomas and other diagnoses, as well as disease site, and incorporate these into a Web application capable of processing large numbers of pathology reports. Methods: Participants in the QSkin study were recruited in 2011 and comprised men and women age 40-69 years at baseline (N = 43,794) who were randomly selected from a population register in Queensland, Australia. Histologic data were manually extracted from free-text pathology reports for participants with histologically confirmed keratinocyte cancers for whom a pathology report was available (n = 25,786 reports). This provided a training data set for the development of algorithms capable of deriving diagnosis and site from free-text pathology reports. We calculated agreement statistics between algorithm-derived classifications and 3 independent validation data sets of manually abstracted pathology reports. Results: The agreement for classifications of basal cell carcinoma (κ = 0.97 and κ = 0.96) and squamous cell carcinoma (κ = 0.93 for both) was almost perfect in 2 validation data sets but was slightly lower for a third (κ = 0.82 and κ = 0.90, respectively). Agreement for total counts of specific diagnoses was also high (κ > 0.8). Similar levels of agreement between algorithm-derived and manually extracted data were observed for classifications of keratoacanthoma and intraepidermal carcinoma. Conclusion: Supervised learning methods were used to develop a Web application capable of accurately and rapidly classifying large numbers of pathology reports for keratinocyte cancers and related diagnoses. Such tools may provide the means to accurately measure subtype-specific skin cancer incidence.
Databáze:	MEDLINE
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