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Quality control on digital cancer registration
Population-based cancer registration methods are subject to internationally-established rules. To ensure efficient and effective case recording, population-based cancer registries widely adopt digital processing (DP) methods. At the Veneto Tumor Registry (RTV), about 50% of all digitally-identified...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778557/ https://www.ncbi.nlm.nih.gov/pubmed/36548228 http://dx.doi.org/10.1371/journal.pone.0279415 |
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author | Guzzinati, Stefano Battagello, Jessica Bovo, Emanuela Baracco, Maddalena Baracco, Susanna Carpin, Eva Dal Cin, Antonella Fiore, Anna Rita Greco, Alessandra Martin, Giancarla memo, Laura Monetti, Daniele Rizzato, Silvia Stocco, Carmen Zamberlan, Sara Zorzi, Manuel Rugge, Massimo |
author_facet | Guzzinati, Stefano Battagello, Jessica Bovo, Emanuela Baracco, Maddalena Baracco, Susanna Carpin, Eva Dal Cin, Antonella Fiore, Anna Rita Greco, Alessandra Martin, Giancarla memo, Laura Monetti, Daniele Rizzato, Silvia Stocco, Carmen Zamberlan, Sara Zorzi, Manuel Rugge, Massimo |
author_sort | Guzzinati, Stefano |
collection | PubMed |
description | Population-based cancer registration methods are subject to internationally-established rules. To ensure efficient and effective case recording, population-based cancer registries widely adopt digital processing (DP) methods. At the Veneto Tumor Registry (RTV), about 50% of all digitally-identified (putative) cases of cancer are further profiled by means of registrars’ assessments (RAs). Taking these RAs for reference, the present study examines how well the registry’s DP performs. A series of 1,801 (putative) incident and prevalent cancers identified using DP methods were randomly assigned to two experienced registrars (blinded to the DP output), who independently re-assessed every case. This study focuses on the concordance between the DP output and the RAs as concerns cancer status (incident versus prevalent), topography, and morphology. The RAs confirmed the cancer status emerging from DP for 1,266/1,317 incident cancers (positive predictive value [PPV] = 96.1%) and 460/472 prevalent cancers (PPV = 97.5%). This level of concordance ranks as “optimal”, with a Cohen’s K value of 0.91. The overall prevalence of false-positive cancer cases identified by DP was 2.9%, and was affected by the number of digital variables available. DP and the RAs were consistent in identifying cancer topography in 88.7% of cases; differences concerned different sites within the same anatomo-functional district (according to the International Agency for Research on Cancer [IARC]) in 9.6% of cases. In short, using DP for cancer case registration suffers from only trivial inconsistencies. The efficiency and reliability of digital cancer registration is influenced by the availability of good-quality clinical information, and the regular interdisciplinary monitoring of a registry’s DP performance. |
format | Online Article Text |
id | pubmed-9778557 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-97785572022-12-23 Quality control on digital cancer registration Guzzinati, Stefano Battagello, Jessica Bovo, Emanuela Baracco, Maddalena Baracco, Susanna Carpin, Eva Dal Cin, Antonella Fiore, Anna Rita Greco, Alessandra Martin, Giancarla memo, Laura Monetti, Daniele Rizzato, Silvia Stocco, Carmen Zamberlan, Sara Zorzi, Manuel Rugge, Massimo PLoS One Research Article Population-based cancer registration methods are subject to internationally-established rules. To ensure efficient and effective case recording, population-based cancer registries widely adopt digital processing (DP) methods. At the Veneto Tumor Registry (RTV), about 50% of all digitally-identified (putative) cases of cancer are further profiled by means of registrars’ assessments (RAs). Taking these RAs for reference, the present study examines how well the registry’s DP performs. A series of 1,801 (putative) incident and prevalent cancers identified using DP methods were randomly assigned to two experienced registrars (blinded to the DP output), who independently re-assessed every case. This study focuses on the concordance between the DP output and the RAs as concerns cancer status (incident versus prevalent), topography, and morphology. The RAs confirmed the cancer status emerging from DP for 1,266/1,317 incident cancers (positive predictive value [PPV] = 96.1%) and 460/472 prevalent cancers (PPV = 97.5%). This level of concordance ranks as “optimal”, with a Cohen’s K value of 0.91. The overall prevalence of false-positive cancer cases identified by DP was 2.9%, and was affected by the number of digital variables available. DP and the RAs were consistent in identifying cancer topography in 88.7% of cases; differences concerned different sites within the same anatomo-functional district (according to the International Agency for Research on Cancer [IARC]) in 9.6% of cases. In short, using DP for cancer case registration suffers from only trivial inconsistencies. The efficiency and reliability of digital cancer registration is influenced by the availability of good-quality clinical information, and the regular interdisciplinary monitoring of a registry’s DP performance. Public Library of Science 2022-12-22 /pmc/articles/PMC9778557/ /pubmed/36548228 http://dx.doi.org/10.1371/journal.pone.0279415 Text en © 2022 Guzzinati et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Guzzinati, Stefano Battagello, Jessica Bovo, Emanuela Baracco, Maddalena Baracco, Susanna Carpin, Eva Dal Cin, Antonella Fiore, Anna Rita Greco, Alessandra Martin, Giancarla memo, Laura Monetti, Daniele Rizzato, Silvia Stocco, Carmen Zamberlan, Sara Zorzi, Manuel Rugge, Massimo Quality control on digital cancer registration |
title | Quality control on digital cancer registration |
title_full | Quality control on digital cancer registration |
title_fullStr | Quality control on digital cancer registration |
title_full_unstemmed | Quality control on digital cancer registration |
title_short | Quality control on digital cancer registration |
title_sort | quality control on digital cancer registration |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778557/ https://www.ncbi.nlm.nih.gov/pubmed/36548228 http://dx.doi.org/10.1371/journal.pone.0279415 |
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