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

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

Keris Poelhekken  1 , Yixuan Lin  2 , Marcel J W Greuter  3 , Bert van der Vegt  4 , Monique Dorrius  3 , Geertruida H de Bock  2
Affiliations

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

Keris Poelhekken et al. Breast. 2023 Oct.

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.

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Conflict of interest statement

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:BvdV declares advisory board/consultancy (on request) for Visiopharm, Philips, MSD /Merck, Daiichi-Sankyo/AstraZenica and speaker's fee from Visiopharm, Diaceutics, MSD /Merck. All honoraria to UMCG. All unrelated to the current manuscript.

Figures

Fig. 1
Fig. 1
Flow diagram of identification of eligible studies.
Fig. 2
Fig. 2
Schematic overview of the natural history of DCIS transitions found in the models. Arrows indicate a pathway for progression (black), or regression (gray). From a state of no breast cancer progression can occur either to DCIS, to an undetectable state, or directly to IBC. DCIS can progress to a general state of IBC or to a specific stage. Models included a non-progressive fraction of DCIS or grades of DCIS. Models often separated pre-clinical stages, before detection, and clinical stages, after screen- or clinical detection. Regression was found only from DCIS stage to no breast cancer or an undetectable state.
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