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The natural history of ductal carcinoma in situ (DCIS) in simulation models: A systematic review
OBJECTIVE: Assumptions on the natural history of ductal carcinoma in situ (DCIS) are necessary to accurately model it and estimate overdiagnosis. To improve current estimates of overdiagnosis (0–91%), the purpose of this review was to identify and analyse assumptions made in modelling studies on the...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10412870/ https://www.ncbi.nlm.nih.gov/pubmed/37541171 http://dx.doi.org/10.1016/j.breast.2023.07.012 |
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author | Poelhekken, Keris Lin, Yixuan Greuter, Marcel J.W. van der Vegt, Bert Dorrius, Monique de Bock, Geertruida H. |
author_facet | Poelhekken, Keris Lin, Yixuan Greuter, Marcel J.W. van der Vegt, Bert Dorrius, Monique de Bock, Geertruida H. |
author_sort | Poelhekken, Keris |
collection | PubMed |
description | OBJECTIVE: Assumptions on the natural history of ductal carcinoma in situ (DCIS) are necessary to accurately model it and estimate overdiagnosis. To improve current estimates of overdiagnosis (0–91%), the purpose of this review was to identify and analyse assumptions made in modelling studies on the natural history of DCIS in women. Methods: A systematic review of English full-text articles using PubMed, Embase, and Web of Science was conducted up to February 6, 2023. Eligibility and all assessments were done independently by two reviewers. Risk of bias and quality assessments were performed. Discrepancies were resolved by consensus. Reader agreement was quantified with Cohen's kappa. Data extraction was performed with three forms on study characteristics, model assessment, and tumour progression. RESULTS: Thirty models were distinguished. The most important assumptions regarding the natural history of DCIS were addition of non-progressive DCIS of 20–100%, classification of DCIS into three grades, where high grade DCIS had an increased chance of progression to invasive breast cancer (IBC), and regression possibilities of 1–4%, depending on age and grade. Other identified risk factors of progression of DCIS to IBC were younger age, birth cohort, larger tumour size, and individual risk. CONCLUSION: To accurately model the natural history of DCIS, aspects to consider are DCIS grades, non-progressive DCIS (9–80%), regression from DCIS to no cancer (below 10%), and use of well-established risk factors for progression probabilities (age). Improved knowledge on key factors to consider when studying DCIS can improve estimates of overdiagnosis and optimization of screening. |
format | Online Article Text |
id | pubmed-10412870 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-104128702023-08-11 The natural history of ductal carcinoma in situ (DCIS) in simulation models: A systematic review Poelhekken, Keris Lin, Yixuan Greuter, Marcel J.W. van der Vegt, Bert Dorrius, Monique de Bock, Geertruida H. Breast Review OBJECTIVE: Assumptions on the natural history of ductal carcinoma in situ (DCIS) are necessary to accurately model it and estimate overdiagnosis. To improve current estimates of overdiagnosis (0–91%), the purpose of this review was to identify and analyse assumptions made in modelling studies on the natural history of DCIS in women. Methods: A systematic review of English full-text articles using PubMed, Embase, and Web of Science was conducted up to February 6, 2023. Eligibility and all assessments were done independently by two reviewers. Risk of bias and quality assessments were performed. Discrepancies were resolved by consensus. Reader agreement was quantified with Cohen's kappa. Data extraction was performed with three forms on study characteristics, model assessment, and tumour progression. RESULTS: Thirty models were distinguished. The most important assumptions regarding the natural history of DCIS were addition of non-progressive DCIS of 20–100%, classification of DCIS into three grades, where high grade DCIS had an increased chance of progression to invasive breast cancer (IBC), and regression possibilities of 1–4%, depending on age and grade. Other identified risk factors of progression of DCIS to IBC were younger age, birth cohort, larger tumour size, and individual risk. CONCLUSION: To accurately model the natural history of DCIS, aspects to consider are DCIS grades, non-progressive DCIS (9–80%), regression from DCIS to no cancer (below 10%), and use of well-established risk factors for progression probabilities (age). Improved knowledge on key factors to consider when studying DCIS can improve estimates of overdiagnosis and optimization of screening. Elsevier 2023-07-27 /pmc/articles/PMC10412870/ /pubmed/37541171 http://dx.doi.org/10.1016/j.breast.2023.07.012 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Poelhekken, Keris Lin, Yixuan Greuter, Marcel J.W. van der Vegt, Bert Dorrius, Monique de Bock, Geertruida H. The natural history of ductal carcinoma in situ (DCIS) in simulation models: A systematic review |
title | The natural history of ductal carcinoma in situ (DCIS) in simulation models: A systematic review |
title_full | The natural history of ductal carcinoma in situ (DCIS) in simulation models: A systematic review |
title_fullStr | The natural history of ductal carcinoma in situ (DCIS) in simulation models: A systematic review |
title_full_unstemmed | The natural history of ductal carcinoma in situ (DCIS) in simulation models: A systematic review |
title_short | The natural history of ductal carcinoma in situ (DCIS) in simulation models: A systematic review |
title_sort | natural history of ductal carcinoma in situ (dcis) in simulation models: a systematic review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10412870/ https://www.ncbi.nlm.nih.gov/pubmed/37541171 http://dx.doi.org/10.1016/j.breast.2023.07.012 |
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