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

Publication ,  Journal Article
Chootipongchaivat, S; van Ravesteyn, NT; Li, X; Huang, H; Weedon-Fekjær, H; Ryser, MD; Weaver, DL; Burnside, ES; Heckman-Stoddard, BM; Lee, SJ ...
Published in: Breast Cancer Res
May 27, 2020

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.

Duke Scholars

Published In

Breast Cancer Res

DOI

EISSN

1465-542X

Publication Date

May 27, 2020

Volume

22

Issue

1

Start / End Page

53

Location

England

Related Subject Headings

  • United States
  • SEER Program
  • Prognosis
  • Oncology & Carcinogenesis
  • Models, Statistical
  • Middle Aged
  • Medical Overuse
  • Incidence
  • Humans
  • Follow-Up Studies
 

Citation

APA
Chicago
ICMJE
MLA
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Chootipongchaivat, S., van Ravesteyn, N. T., Li, X., Huang, H., Weedon-Fekjær, H., Ryser, M. D., … Lee, S. J. (2020). Modeling the natural history of ductal carcinoma in situ based on population data. Breast Cancer Res, 22(1), 53. https://doi.org/10.1186/s13058-020-01287-6
Chootipongchaivat, Sarocha, Nicolien T. van Ravesteyn, Xiaoxue Li, Hui Huang, Harald Weedon-Fekjær, Marc D. Ryser, Donald L. Weaver, et al. “Modeling the natural history of ductal carcinoma in situ based on population data.Breast Cancer Res 22, no. 1 (May 27, 2020): 53. https://doi.org/10.1186/s13058-020-01287-6.
Chootipongchaivat S, van Ravesteyn NT, Li X, Huang H, Weedon-Fekjær H, Ryser MD, et al. Modeling the natural history of ductal carcinoma in situ based on population data. Breast Cancer Res. 2020 May 27;22(1):53.
Chootipongchaivat, Sarocha, et al. “Modeling the natural history of ductal carcinoma in situ based on population data.Breast Cancer Res, vol. 22, no. 1, May 2020, p. 53. Pubmed, doi:10.1186/s13058-020-01287-6.
Chootipongchaivat S, van Ravesteyn NT, Li X, Huang H, Weedon-Fekjær H, Ryser MD, Weaver DL, Burnside ES, Heckman-Stoddard BM, de Koning HJ, Lee SJ. Modeling the natural history of ductal carcinoma in situ based on population data. Breast Cancer Res. 2020 May 27;22(1):53.

Published In

Breast Cancer Res

DOI

EISSN

1465-542X

Publication Date

May 27, 2020

Volume

22

Issue

1

Start / End Page

53

Location

England

Related Subject Headings

  • United States
  • SEER Program
  • Prognosis
  • Oncology & Carcinogenesis
  • Models, Statistical
  • Middle Aged
  • Medical Overuse
  • Incidence
  • Humans
  • Follow-Up Studies