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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...

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Autores principales: Poelhekken, Keris, Lin, Yixuan, Greuter, Marcel J.W., van der Vegt, Bert, Dorrius, Monique, de Bock, Geertruida H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
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.
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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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