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Modeling the natural history of ductal carcinoma in situ based on population data

BACKGROUND: The incidence of ductal carcinoma in situ (DCIS) has increased substantially since the introduction of mammography screening. Nevertheless, little is known about the natural history of preclinical DCIS in the absence of biopsy or complete excision. METHODS: Two well-established populatio...

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Autores principales: Chootipongchaivat, Sarocha, van Ravesteyn, Nicolien T., Li, Xiaoxue, Huang, Hui, Weedon-Fekjær, Harald, Ryser, Marc D., Weaver, Donald L., Burnside, Elizabeth S., Heckman-Stoddard, Brandy M., de Koning, Harry J., Lee, Sandra J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7251719/
https://www.ncbi.nlm.nih.gov/pubmed/32460821
http://dx.doi.org/10.1186/s13058-020-01287-6
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author Chootipongchaivat, Sarocha
van Ravesteyn, Nicolien T.
Li, Xiaoxue
Huang, Hui
Weedon-Fekjær, Harald
Ryser, Marc D.
Weaver, Donald L.
Burnside, Elizabeth S.
Heckman-Stoddard, Brandy M.
de Koning, Harry J.
Lee, Sandra J.
author_facet Chootipongchaivat, Sarocha
van Ravesteyn, Nicolien T.
Li, Xiaoxue
Huang, Hui
Weedon-Fekjær, Harald
Ryser, Marc D.
Weaver, Donald L.
Burnside, Elizabeth S.
Heckman-Stoddard, Brandy M.
de Koning, Harry J.
Lee, Sandra J.
author_sort Chootipongchaivat, Sarocha
collection PubMed
description BACKGROUND: The incidence of ductal carcinoma in situ (DCIS) has increased substantially since the introduction of mammography screening. Nevertheless, little is known about the natural history of preclinical DCIS in the absence of biopsy or complete excision. METHODS: Two well-established population models evaluated six possible DCIS natural history submodels. The submodels assumed 30%, 50%, or 80% of breast lesions progress from undetectable DCIS to preclinical screen-detectable DCIS; each model additionally allowed or prohibited DCIS regression. Preclinical screen-detectable DCIS could also progress to clinical DCIS or invasive breast cancer (IBC). Applying US population screening dissemination patterns, the models projected age-specific DCIS and IBC incidence that were compared to Surveillance, Epidemiology, and End Results data. Models estimated mean sojourn time (MST) in the preclinical screen-detectable DCIS state, overdiagnosis, and the risk of progression from preclinical screen-detectable DCIS. RESULTS: Without biopsy and surgical excision, the majority of DCIS (64–100%) in the preclinical screen-detectable state progressed to IBC in submodels assuming no DCIS regression (36–100% in submodels allowing for DCIS regression). DCIS overdiagnosis differed substantially between models and submodels, 3.1–65.8%. IBC overdiagnosis ranged 1.3–2.4%. Submodels assuming DCIS regression resulted in a higher DCIS overdiagnosis than submodels without DCIS regression. MST for progressive DCIS varied between 0.2 and 2.5 years. CONCLUSIONS: Our findings suggest that the majority of screen-detectable but unbiopsied preclinical DCIS lesions progress to IBC and that the MST is relatively short. Nevertheless, due to the heterogeneity of DCIS, more research is needed to understand the progression of DCIS by grades and molecular subtypes.
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spelling pubmed-72517192020-06-04 Modeling the natural history of ductal carcinoma in situ based on population data Chootipongchaivat, Sarocha van Ravesteyn, Nicolien T. Li, Xiaoxue Huang, Hui Weedon-Fekjær, Harald Ryser, Marc D. Weaver, Donald L. Burnside, Elizabeth S. Heckman-Stoddard, Brandy M. de Koning, Harry J. Lee, Sandra J. Breast Cancer Res Research Article BACKGROUND: The incidence of ductal carcinoma in situ (DCIS) has increased substantially since the introduction of mammography screening. Nevertheless, little is known about the natural history of preclinical DCIS in the absence of biopsy or complete excision. METHODS: Two well-established population models evaluated six possible DCIS natural history submodels. The submodels assumed 30%, 50%, or 80% of breast lesions progress from undetectable DCIS to preclinical screen-detectable DCIS; each model additionally allowed or prohibited DCIS regression. Preclinical screen-detectable DCIS could also progress to clinical DCIS or invasive breast cancer (IBC). Applying US population screening dissemination patterns, the models projected age-specific DCIS and IBC incidence that were compared to Surveillance, Epidemiology, and End Results data. Models estimated mean sojourn time (MST) in the preclinical screen-detectable DCIS state, overdiagnosis, and the risk of progression from preclinical screen-detectable DCIS. RESULTS: Without biopsy and surgical excision, the majority of DCIS (64–100%) in the preclinical screen-detectable state progressed to IBC in submodels assuming no DCIS regression (36–100% in submodels allowing for DCIS regression). DCIS overdiagnosis differed substantially between models and submodels, 3.1–65.8%. IBC overdiagnosis ranged 1.3–2.4%. Submodels assuming DCIS regression resulted in a higher DCIS overdiagnosis than submodels without DCIS regression. MST for progressive DCIS varied between 0.2 and 2.5 years. CONCLUSIONS: Our findings suggest that the majority of screen-detectable but unbiopsied preclinical DCIS lesions progress to IBC and that the MST is relatively short. Nevertheless, due to the heterogeneity of DCIS, more research is needed to understand the progression of DCIS by grades and molecular subtypes. BioMed Central 2020-05-27 2020 /pmc/articles/PMC7251719/ /pubmed/32460821 http://dx.doi.org/10.1186/s13058-020-01287-6 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Chootipongchaivat, Sarocha
van Ravesteyn, Nicolien T.
Li, Xiaoxue
Huang, Hui
Weedon-Fekjær, Harald
Ryser, Marc D.
Weaver, Donald L.
Burnside, Elizabeth S.
Heckman-Stoddard, Brandy M.
de Koning, Harry J.
Lee, Sandra J.
Modeling the natural history of ductal carcinoma in situ based on population data
title Modeling the natural history of ductal carcinoma in situ based on population data
title_full Modeling the natural history of ductal carcinoma in situ based on population data
title_fullStr Modeling the natural history of ductal carcinoma in situ based on population data
title_full_unstemmed Modeling the natural history of ductal carcinoma in situ based on population data
title_short Modeling the natural history of ductal carcinoma in situ based on population data
title_sort modeling the natural history of ductal carcinoma in situ based on population data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7251719/
https://www.ncbi.nlm.nih.gov/pubmed/32460821
http://dx.doi.org/10.1186/s13058-020-01287-6
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