Overdiagnosis of invasive breast cancer (BC) is a contentious issue. The aim of this paper is to estimate the overdiagnosis rate of invasive BC in an organised BC screening program and to evaluate the impact of age and follow-up time. The micro-simulation model SiMRiSc was calibrated and validated for BC screening in Flanders, where women are screened biennially from age 50 to 69. Overdiagnosis rate was defined as the number of invasive BC that would not have been diagnosed in the absence of screening per 100,000 screened women during the screening period plus follow-up time (which was set at 5 years and varied from 2 to 15 years). Overdiagnosis rate was calculated overall and stratified by age. The overall overdiagnosis rate for women screened biennially from 50 to 69 was 20.1 (95%CI: 16.9-23.2) per 100,000 women screened at 5-year follow-up from stopping screening. Overdiagnosis at 5-year follow-up time was 12.9 (95%CI: 4.6-21.1) and 74.2 (95%CI: 50.9-97.5) per 100,000 women screened for women who started screening at age 50 and 68, respectively. At 2- and 15-year follow-up time, overdiagnosis rate was 98.5 (95%CI: 75.8-121.3) and 13.4 (95%CI: 4.9-21.9), respectively, for women starting at age 50, and 297.0 (95%CI: 264.5-329.4) and 34.2 (95%CI: 17.5-50.8), respectively, for those starting at age 68. Sufficient follow-up time (≥10 years) after screening stops is key to obtaining unbiased estimates of overdiagnosis. Overdiagnosis of invasive BC is a larger problem in older compared to younger women.

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. 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. 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.

Purpose: To develop a novel simulation model for ductal carcinoma in situ (DCIS), fully validate it, and provide new estimates for DCIS in the setting of population-based biennial screening. Methods: A micro-simulation Markov model for DCIS (SimDCIS) was developed. Input parameters were independently derived from the literature and transition parameters were age- and grade-dependent. The model was applied to the Dutch biennial screening program. SimDCIS was internally, cross, and externally validated by comparison of the model output to data from the Netherlands Cancer Registry, a modelling study on the United Kingdom Frequency Trial, and the United Kingdom screening program, respectively. Univariate and probabilistic sensitivity analyses were performed to estimate uncertainty. DCIS regression, progression to invasive breast cancer (IBC), clinical detection, and screen-detection were estimated in Dutch screening setting. Results: SimDCIS matched observed data in internal, external, and cross-validation. The model was most sensitive to DCIS onset probability, and the maximum variation in screen-detection rate was 11%. In Dutch screening setting, DCIS regression, progression to IBC, clinical detection, and screen-detection were estimated at 8% (0–14%), 19% (16–24%), 8% (0–13%), and 61% (56–65%), respectively. Grade distribution was 20% grade 1, 38% grade 2, and 42% grade 3. Conclusion: SimDCIS provides strong accuracy across validation methods and is particularly sensitive to DCIS onset probability. Most DCIS will be found through screening, of which less than 50% of DCIS will be grade 3, less than 1 in 10 will regress, and 1 out of 5 DCIS will progress to IBC in biennial screening setting.

Due to COVID-19, the Dutch breast cancer screening program was interrupted for three months with uncertain long-term effects. The aim of this study was to estimate the long-term impact of this interruption on delay in detection, tumour size of screen-detected breast cancers, and interval cancer rate. After validation, the micro-simulation model SiMRiSc was used to calculate the effects of interruption of the breast cancer screening program for three months and for hypothetical interruptions of six and twelve months. A scenario without interruption was used as reference. Outcomes considered were tumour size of screen-detected breast cancers and interval cancer rate. Women of 55–59 and 60–64 years old at time of interruption were considered. Uncertainties were estimated using a sensitivity analysis. The three-month interruption had no clinically relevant long-term effect on the tumour size of screen-detected breast cancers. A 19% increase in interval cancer rate was found between last screening before and first screening after interruption compared to no interruption. Hypothetical interruptions of six and twelve months resulted in larger increases in interval cancer rate of 38% and 78% between last screening before and first screening after interruption, respectively, and an increase in middle-sized tumours in first screening after interruption of 26% and 47%, respectively. In conclusion, the interruption of the Dutch screening program is not expected to result in a long-term delay in detection or clinically relevant change in tumour size of screen-detected cancers, but only affects the interval cancer rate between last screening before and first screening after interruption.

To estimate ductal carcinoma in situ (DCIS) overdiagnosis overall and by grade in population-based screening and to determine the variation in overdiagnosis estimates by definition. Using a fully validated micro-simulation Markov model for DCIS (SimDCIS), the number, rate, and proportion of DCIS overdiagnoses were estimated overall and by grade. Overdiagnoses comprised excess DCIS cases in the screened versus the unscreened population; overdiagnosis rate equaled the number of DCIS overdiagnoses per 100,000 screened women; and DCIS overdiagnosis proportion equaled overdiagnosed DCIS divided by total diagnosed DCIS in the screened population. Base estimates for overdiagnosed DCIS were from a population perspective (ages 50-100 years) and included screen-detected, clinically detected, or progressed DCIS (i.e., invasive breast cancer with DCIS precursor). Overdiagnosis was also estimated for alternative definitions and perspectives. Univariate and probabilistic sensitivity analyses were performed to estimate uncertainty. Base definitions yielded an overdiagnosis rate of 38.1 (range, 25.7-58.7) per 100,000 screened women and a proportion of 20 % (range 13 %-30 %). Stratification by grade showed 24 %, 20 %, and 18 % proportion overdiagnosis for grades 1, 2, and 3, respectively. Varying the definition led to overdiagnosis estimates from 18 % to 94 %; these overdiagnosis estimates increased by 36 %-49 % when excluding invasive breast cancer and by 54 %-71 % when including only screen-detected DCIS. Individual perspective estimates were 12 % higher than population perspective estimates. In biennial screening, approximately 1 in 5 DCIS is overdiagnosed, but with minimal variation between grades. A consensus definition and perspective for overdiagnosis would reduce the observed variation in DCIS overdiagnosis estimates.

Lung cancer screening in high-risk populations with low-dose computed tomography (LDCT) reduces lung cancer mortality by detecting cancer at early stage. Immunotherapy improves survival in these patients. This study evaluates the cost-effectiveness and budget impact of LDCT screening extending immunotherapy to early-stage patients. A micro-simulation model Simulation Model on Radiation Risk and cancer Screening (SiMRiSc, validated against NELSON results) was used to a Dutch heavy smokers cohort. Average cost-effectiveness ratio (ACER), lung cancer mortality reduction, and budget impact of biennial screening for heavy smokers aged 55-74 (adapted from the UK strategy) were evaluated in context of immunotherapy, compared with no-screening. A cost-effectiveness threshold was set at 60 k€ per life year gained (LYG). Compared with no-screening, limiting immunotherapy to advanced-stage patients, screening resulted in ACERs of 4.7 k€/LYG for males and 6.6 k€/LYG for females, reducing lung cancer mortality by 18.1 % and 17.1 %, respectively. Extending immunotherapy to all stages increased ACERs to 5.2 k€/LYG for males and 7.1 k€/LYG for females, with lung cancer mortality reduction of 21.1 % and 20.1 %, respectively. Budget impact analysis shows screening saved 35-52 million€ when immunotherapy restricted to advanced-stage, and 24-39 million€ for all stages immunotherapy over three-screening rounds compared with no-screening, primarily due to a 53 % reduction for advanced-stage cases. Lung cancer screening in high-risk population remains cost-effective when immunotherapy is offered to all stages. By shifting diagnoses from advanced to early stages, screening yielding substantial savings. These findings support LDCT screening implementation even in healthcare systems broadly using immunotherapy.