Development and Validation of an Algorithm to Identify Patients with Advanced Cutaneous Squamous Cell Carcinoma from Pathology Reports.

Autor:	Eggermont C; Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands., Wakkee M; Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands., Bruggink A; Nationwide Network and Registry of Histo- and Cytopathology (PALGA), Houten, The Netherlands., Voorham Q; Nationwide Network and Registry of Histo- and Cytopathology (PALGA), Houten, The Netherlands., Schreuder K; Department of Research and Development, Netherlands Comprehensive Cancer Organization (IKNL), Utrecht, The Netherlands., Louwman M; Department of Research and Development, Netherlands Comprehensive Cancer Organization (IKNL), Utrecht, The Netherlands., Mooyaart A; Department of Pathology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands., Hollestein L; Department of Dermatology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands; Department of Research and Development, Netherlands Comprehensive Cancer Organization (IKNL), Utrecht, The Netherlands. Electronic address: l.hollestein@erasmusmc.nl.
Jazyk:	angličtina
Zdroj:	The Journal of investigative dermatology [J Invest Dermatol] 2023 Jan; Vol. 143 (1), pp. 98-104.e5. Date of Electronic Publication: 2022 Aug 01.
DOI:	10.1016/j.jid.2022.07.008
Abstrakt:	To facilitate nationwide epidemiological research on advanced cutaneous squamous cell carcinoma (cSCC), that is, locally advanced, recurrent, or metastatic cSCC, we sought to develop and validate a rule-based algorithm that identifies advanced cSCC from pathology reports. The algorithm was based on both hierarchical histopathological codes and free text from pathology reports recorded in the National Pathology Registry. Medical files from the Erasmus Medical Center of 186 patients with stage III/IV/recurrent cSCC and 184 patients with stage I/II cSCC were selected and served as the gold standard to assess the performance of the algorithm. The rule-based algorithm showed a sensitivity of 91.9% (95% confidence interval = 88.0‒95.9), a specificity of 96.7% (95% confidence interval = 94‒2-99.3), and a positive predictive value of 78.5% (95% confidence interval = 74.2‒82.8) for all advanced cSCC combined. The sensitivity was lower per subgroup: locally advanced (52.3‒86.2%), recurrent cSCC (23.3%), and metastatic cSCC (70.0%). The specificity per subgroup was above 97%, and the positive predictive value was above 78%, with the exception of metastatic cSCC, which had a positive predictive value of 62%. This algorithm can be used to identify advanced patients with cSCC from pathology reports and will facilitate large-scale epidemiological studies of advanced cSCC in the Netherlands and internationally after external validation. (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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