Statistical models of tumour onset and growth for modern breast cancer screening cohorts

Strandberg, James; Humphreys, Keith

doi:10.1016/j.mbs.2019.108270

Cited by 8 publications

(17 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This particularly highlights the importance of considering individual screening histories and behaviors, as is incorporated into our modeling strategy (see section 4 of ref. 11 ).…”

Section: Resultsmentioning

confidence: 99%

“…This does not necessarily translate to an equivalent relationship between age at onset (carcinogenesis) and tumor growth rate, as has been demonstrated in ref. 11 . Here, we study directly the relationship between these latent processes.…”

Section: Introductionmentioning

confidence: 99%

“…As well as using our model for the first time on empirical data, we also present an extension to the model described in ref. 11 , which allows the tumor growth rate to depend on the age at onset. We study the dependency between the two unobserved processes of onset and growth, and we show that, under modeling assumptions, it can be quantified on the basis of observational data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Strandberg

Czene

Eriksson

et al. 2022

Cancer Epidemiology, Biomarkers &Amp; Prevention

Self Cite

View full text Add to dashboard Cite

Background: In recent years, biologically motivated continuous tumor growth models have been introduced for breast cancer screening data. These provide a novel framework from which mammography screening effectiveness can be studied. Methods:We use a newly developed natural history model, which is unique in that it includes a carcinogenesis model for tumor onset, to analyze data from a large Swedish mammography cohort consisting of 65,536 participants, followed for periods of up to 6.5 years. Using patient data on age at diagnosis, tumor size, mode of detection, as well as screening histories, we estimate distributions of patient's age at onset, (inverse) tumor growth rates, symptomatic detection rates, and screening sensitivities. We also allow the growth rate distribution to depend on the age at onset. Results:We estimate that by the age of 75, 13.4% of women have experienced onset. Based on a model which accounts for the role of mammographic density in screening sensitivity, we estimated median tumor doubling times of 167 days for tumors with onset occurring at age 40, and 207 days for tumors with onset occurring at age 60. Conclusions:With breast cancer natural history models and population screening data, we can estimate latent processes of tumor onset, tumor growth, and mammography screening sensitivity. We can also study the relationship between the age at onset and tumor growth rates.Impact: Quantifying the underlying processes of breast cancer progression is important in the era of individualized screening.

show abstract

“…This particularly highlights the importance of considering individual screening histories and behaviors, as is incorporated into our modeling strategy (see section 4 of ref. 11 ).…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Strandberg

Czene

Eriksson

et al. 2022

Cancer Epidemiology, Biomarkers &Amp; Prevention

Self Cite

View full text Add to dashboard Cite

show abstract

“…In future work, we aim to adapt this model to the settings of modern breast cancer screening cohorts. 37 An alternative approach for studying the recurrence of breast cancer, in terms of time to distant metastasis, is to use multi-state models. Mariotto et al 6 recently described such an approach, which they specify as a mixture cure model.…”

Section: Discussionmentioning

confidence: 99%

A natural history and copula-based joint model for regional and distant breast cancer metastasis

Gasparini

Humphreys

2022

Stat Methods Med Res

Self Cite

View full text Add to dashboard Cite

The few existing statistical models of breast cancer recurrence and progression to distant metastasis are predominantly based on multi-state modelling. While useful for summarising the risk of recurrence, these provide limited insight into the underlying biological mechanisms and have limited use for understanding the implications of population-level interventions. We develop an alternative, novel, and parsimonious approach for modelling latent tumour growth and spread to local and distant metastasis, based on a natural history model with biologically inspired components. We include marginal sub-models for local and distant breast cancer metastasis, jointly modelled using a copula function. Different formulations (and correlation shapes) are allowed, thus we can incorporate and directly model the correlation between local and distant metastasis flexibly and efficiently. Submodels for the latent cancer growth, the detection process, and screening sensitivity, together with random effects to account for between-patients heterogeneity, are included. Although relying on several parametric assumptions, the joint copula model can be useful for understanding – potentially latent – disease dynamics, obtaining patient-specific, model-based predictions, and studying interventions at a population level, for example, using microsimulation. We illustrate this approach using data from a Swedish population-based case-control study of postmenopausal breast cancer, including examples of useful model-based predictions.

show abstract

“…Models can then be trained using maximum likelihood estimation. It can be shown 24 that a breast cancer case, with diagnosis of a tumor of volume v, at age x, has an individual likelihood contribution…”

Section: Training the Risk Prediction Modelmentioning

confidence: 99%

Novel predictions of invasive breast cancer risk in mammography screening cohorts

Strandberg

Czene

Hall

et al. 2023

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Statistical models of tumour onset and growth for modern breast cancer screening cohorts

Cited by 8 publications

References 31 publications

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

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

A natural history and copula-based joint model for regional and distant breast cancer metastasis

Novel predictions of invasive breast cancer risk in mammography screening cohorts

Contact Info

Product

Resources

About