The natural history of ductal carcinoma in situ (DCIS) in simulation models: A systematic review

Keris Poelhekken; Yixuan Lin; Marcel J W Greuter; Bert van der Vegt; Monique Dorrius; Geertruida H de Bock

doi:10.1016/j.breast.2023.07.012

The natural history of ductal carcinoma in situ (DCIS) in simulation models: A systematic review

Breast. 2023 Oct:71:74-81. doi: 10.1016/j.breast.2023.07.012. Epub 2023 Jul 27.

Authors

Keris Poelhekken¹, Yixuan Lin², Marcel J W Greuter³, Bert van der Vegt⁴, Monique Dorrius³, Geertruida H de Bock²

Affiliations

¹ University of Groningen, University Medical Center Groningen, Groningen, Department of Epidemiology, P.O. Box 30 001, FA40, 9700, RB, Groningen, the Netherlands; University of Groningen, University Medical Center Groningen, Groningen, Department of Radiology, PO Box 30.001, EB44, 9700, RB, Groningen, the Netherlands. Electronic address: k.poelhekken@umcg.nl.
² University of Groningen, University Medical Center Groningen, Groningen, Department of Epidemiology, P.O. Box 30 001, FA40, 9700, RB, Groningen, the Netherlands.
³ University of Groningen, University Medical Center Groningen, Groningen, Department of Radiology, PO Box 30.001, EB44, 9700, RB, Groningen, the Netherlands.
⁴ University of Groningen, University Medical Center Groningen, Groningen, Department of Pathology and Medical Biology, PO Box 30.001, 9700, RB, Groningen, the Netherlands.

Abstract

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.

Keywords: Breast carcinoma in situ; Breast neoplasms; Computational modelling; Disease progression; Early detection of cancer.

Publication types

Systematic Review
Review

MeSH terms

Breast Neoplasms* / pathology
Carcinoma, Ductal, Breast* / pathology
Carcinoma, Intraductal, Noninfiltrating* / pathology
Computer Simulation
Disease Progression
Early Detection of Cancer
Female
Humans