Long-term effects of the interruption of the Dutch breast cancer screening program due to COVID-19:A modelling study

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

Overdiagnosis of invasive breast cancer in population-based breast cancer screening:A short- and long-term perspective

BACKGROUND: Overdiagnosis of invasive breast cancer (BC) is a contentious issue. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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

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

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

Fully automated quantification method (FQM) of coronary calcium in an anthropomorphic phantom

Objective: Coronary artery calcium (CAC) score is a strong predictor for future adverse cardiovascular events. Anthropomorphic phantoms are often used for CAC studies on computed tomography (CT) to allow for evaluation or variation of scanning or reconstruction parameters within or across scanners against a reference standard. This often results in large number of datasets. Manual assessment of these large datasets is time consuming and cumbersome. Therefore, this study aimed to develop and validate a fully automated, open-source quantification method (FQM) for coronary calcium in a standardized phantom. Materials and Methods: A standard, commercially available anthropomorphic thorax phantom was used with an insert containing nine calcifications with different sizes and densities. To simulate two different patient sizes, an extension ring was used. Image data were acquired with four state-of-the-art CT systems using routine CAC scoring acquisition protocols. For interscan variability, each acquisition was repeated five times with small translations and/or rotations. Vendor-specific CAC scores (Agatston, volume, and mass) were calculated as reference scores using vendor-specific software. Both the international standard CAC quantification methods as well as vendor-specific adjustments were implemented in FQM. Reference and FQM scores were compared using Bland-Altman analysis, intraclass correlation coefficients, risk reclassifications, and Cohen’s kappa. Also, robustness of FQM was assessed using varied acquisitions and reconstruction settings and validation on a dynamic phantom. Further, image quality metrics were implemented: noise power spectrum, task transfer function, and contrast- and signal-to-noise ratio among others. Results were validated using imQuest software. Results: Three parameters in CAC scoring methods varied among the different vendor-specific software packages: the Hounsfield unit (HU) threshold, the minimum area used to designate a group of voxels as calcium, and the usage of isotropic voxels for the volume score. The FQM was in high agreement with vendor-specific scores and ICC’s (median [95% CI]) were excellent (1.000 [0.999-1.000] to 1.000 [1.000-1.000]). An excellent interplatform reliability of κ = 0.969 and κ = 0.973 was found. TTF results gave a maximum deviation of 3.8% and NPS results were comparable to imQuest. Conclusions: We developed a fully automated, open-source, robust method to quantify CAC on CT scans in a commercially available phantom. Also, the automated algorithm contains image quality assessment for fast comparison of differences in acquisition and reconstruction parameters.</p