Estimating Distributions of Breast Cancer Onset and Growth in a Swedish Mammography Screening Cohort

Strandberg, Rickard; Czene, Kamila; Eriksson, Mikael; Hall, Per; Humphreys, Keith

doi:10.1158/1055-9965.epi-21-1011

Cited by 10 publications

(8 citation statements)

References 27 publications

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“…In a previous study 43 we estimated the association between PD and STS to have a coefficient of −2.09 (95% CI −2.93, −1.26), which is noticeably smaller in magnitude than what we estimate in this study. One reason is the inclusion of the other screening variables.…”

Section: Discussioncontrasting

confidence: 95%

Influence of mammographic density and compressed breast thickness on true mammographic sensitivity: a cohort study

Strandberg,

Illipse,

Czene

et al. 2023

Sci Rep

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Understanding the detectability of breast cancer using mammography is important when considering nation-wide screening programmes. Although the role of imaging settings on image quality has been studied extensively, their role in detectability of cancer at a population level is less well studied. We wish to quantify the association between mammographic screening sensitivity and various imaging parameters. Using a novel approach applied to a population-based breast cancer screening cohort, we specifically focus on sensitivity as defined in the classical diagnostic testing literature, as opposed to the screen-detected cancer rate, which is often used as a measure of sensitivity for monitoring and evaluating breast cancer screening. We use a natural history approach to model the presence and size of latent tumors at risk of detection at mammography screening, and the screening sensitivity is modeled as a logistic function of tumor size. With this approach we study the influence of compressed breast thickness, x-ray exposure, and compression pressure, in addition to (percent) breast density, on the screening test sensitivity. When adjusting for all screening parameters in addition to latent tumor size, we find that percent breast density and compressed breast thickness are statistically significant factors for the detectability of breast cancer. A change in breast density from 6.6 to 33.5% (the inter-quartile range) reduced the odds of detection by 61% (95% CI 48–71). Similarly, a change in compressed breast thickness from 46 to 66 mm reduced the odds by 42% (95% CI 21–57). The true sensitivity of mammography, defined as the probability that an examination leads to a positive result if a tumour is present in the breast, is associated with compressed breast thickness after accounting for mammographic density and tumour size. This can be used to guide studies of setups aimed at improving lesion detection. Compressed breast thickness—in addition to breast density—should be considered when assigning complementary screening modalities and personalized screening intervals.

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Section: Discussioncontrasting

confidence: 95%

Influence of mammographic density and compressed breast thickness on true mammographic sensitivity: a cohort study

Strandberg,

Illipse,

Czene

et al. 2023

Sci Rep

Self Cite

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“…The exponential growth assumption has been shown in vivo to hold reasonably well for breast cancer compared to alternatives. 49,50 Also, the tumor volume doubling times estimated in a previous study 36 are similar to those estimated in vivo. 50,51 The MVK model is based on the Knudson two-hit hypothesis, 28 and thus has a cancer biology connection.…”

Section: Discussionsupporting

confidence: 81%

“…For training the model, that is, estimating the parameters in our onset, growth and detection submodels, we used follow‐up data until August 31, 2017. We note that this is the same end of follow‐up date as we used in a previous study based on KARMA, 36 before we obtained a more recent data update. The validation set for our current study used data from September 1, 2017 until October 31, 2019.…”

Section: Risk Prediction In a Swedish Mammography Screening Cohortmentioning

confidence: 96%

Novel predictions of invasive breast cancer risk in mammography screening cohorts

Strandberg

Czene

Hall

et al. 2023

Statistics in Medicine

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Mammography screening programs are aimed at reducing mortality due to breast cancer by detecting tumors at an early stage. There is currently interest in moving away from the age‐based screening programs, and toward personalized screening based on individual risk factors. To accomplish this, risk prediction models for breast cancer are needed to determine who should be screened, and when. We develop a novel approach using a (random effects) continuous growth model, which we apply to a large population‐based, Swedish screening cohort. Unlike existing breast cancer prediction models, this approach explicitly incorporates each woman's individual screening visits in the prediction. It jointly models invasive breast cancer tumor onset, tumor growth rate, symptomatic detection rate, and screening sensitivity. In addition to predicting the overall risk of invasive breast cancer, this model can make separate predictions regarding specific tumor sizes, and the mode of detection (eg, detected at screening, or through symptoms between screenings). It can also predict how these risks change depending on whether or not a woman will attend her next screening. In our study, we predict, given a future diagnosis, that the probability of having a tumor less than (as opposed to greater than) 10‐mm diameter, at detection, will be, on average, 2.6 times higher if a woman in the cohort attends their next screening. This indicates that the model can be used to evaluate the short‐term benefit of screening attendance, at an individual level.

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“…The decrease may be related to longer wait times for diagnostic bilateral mammograms following an abnormal screening mammogram. Breast cancers are frequently slow growing, 29 , 30 , 31 so any delays in diagnosis may not lead to increases in cancer stage. Colorectal cancer incidence also decreased at the start of the pandemic in all regions except Northern.…”

Section: Discussionmentioning

confidence: 99%

Cancer incidence during the COVID‐19 pandemic by region of residence in Manitoba, Canada: A cancer registry‐based interrupted time series study

Decker,

Feely,

Bucher

et al. 2023

Cancer Medicine

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IntroductionHealth care in Manitoba, Canada is divided into five regions, each with unique geographies, demographics, health care access, and health status. COVID‐19‐related restrictions and subsequent responses also differed by region. To understand the impact of the pandemic on cancer incidence in the context of these differences, we examined age‐standardized cancer incidence rates by region over time before and after the COVID‐19 pandemic.MethodsWe used a population‐based quasi‐experimental study design, population‐based data, and an interrupted time series analysis to examine the rate of new cancer diagnoses before (January 2015 until December 2019) and after the start of COVID‐19 and the interventions implemented to mitigate its impact (April 2020 until December 2021) by region.ResultsOverall cancer incidence differed by region and remained lower than expected in Winnipeg (4.6% deficit, 447 cases), Prairie Mountain (6.9% deficit, 125 cases), and Southern (13.0% deficit, 238 cases). Southern was the only region that had a significantly higher deficit in cases compared to Manitoba (ratio 0.92, 95% CI 0.86, 0.99). Breast and colorectal cancer incidence decreased at the start of the pandemic in all regions except Northern. Lung cancer incidence decreased in the Interlake‐Eastern region and increased in the Northern region. Prostate cancer incidence increased in Interlake‐Eastern.ConclusionsThe impact of the COVID‐19 pandemic on cancer incidence differed by region. The deficit in the number of cases was largest in the southern region and was highest for breast and prostate cancers. Cancer incidence did not significantly decrease in the most northern, remote region.

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Estimating Distributions of Breast Cancer Onset and Growth in a Swedish Mammography Screening Cohort

Cited by 10 publications

References 27 publications

Influence of mammographic density and compressed breast thickness on true mammographic sensitivity: a cohort study

Influence of mammographic density and compressed breast thickness on true mammographic sensitivity: a cohort study

Novel predictions of invasive breast cancer risk in mammography screening cohorts

Cancer incidence during the COVID‐19 pandemic by region of residence in Manitoba, Canada: A cancer registry‐based interrupted time series study

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