Quantifying the natural history of breast cancer

Abstract

Background:Natural history models of breast cancer progression provide an opportunity to evaluate and identify optimal screening scenarios. This paper describes a detailed Markov model characterising breast cancer tumour progression.Methods:Breast cancer is modelled by a 13-state continuous-time Markov model. The model differentiates between indolent and aggressive ductal carcinomas in situ tumours, and aggressive tumours of different sizes. We compared such aggressive cancers, that is, which are non-indolent, to those which are non-growing and regressing. Model input parameters and structure were informed by the 1978–1984 Ostergotland county breast screening randomised controlled trial. Overlaid on the natural history model is the effect of screening on diagnosis. Parameters were estimated using Bayesian methods. Markov chain Monte Carlo integration was used to sample the resulting posterior distribution.Results:The breast cancer incidence rate in the Ostergotland population was 21 (95% CI: 17–25) per 10 000 woman-years. Accounting for length-biased sampling, an estimated 91% (95% CI: 85–97%) of breast cancers were aggressive. Larger tumours, 21–50 mm, had an average sojourn of 6 years (95% CI: 3–16 years), whereas aggressive ductal carcinomas in situ took around half a month (95% CI: 0–1 month) to progress to the invasive ⩽10 mm state.Conclusion:These tumour progression rate estimates may facilitate future work analysing cost-effectiveness and quality-adjusted life years for various screening strategies.

Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study

“…It was assumed that indolent DCIS will never progress but aggressive DCIS will progress through a defined natural history model. The probability of aggressive DCIS was informed by Tan et al 310 …”

“…The approach presented in Tan et al 310 was taken to estimate this parameter in which tumour growth is defined as a series of events classified by the size in millimeters. Each of the sojourn times was taken from Tan et al 310 and used to estimate individual growth rates for each participant. Each sojourn time was assumed to follow an exponential distribution.…”

Continuous tumour growth models, lead time estimation and length bias in breast cancer screening studies

“…To show that our lead time bias correction works, we compare the 'true' survival times in the presence of a screening programme, s pres , (measured from when symptomatic detection would have occurred) to lead time corrected survival times, s à pres . We use parameter values ¼ 6Á10 À4 and c ¼ 4Á10 À3 in equation (10). At this point, we include only screening cases so that there is no length bias (here, we are not comparing nonexchangeable groups, such as screening to interval cases).…”

“…In our simulation, subgroups are defined on screening/interval status, which is defined under our simulated scenario of screening, although when plotting survival times we measure survival from diagnosis both under screening and under the counterfactual scenario of no screening. Screening cases will have a screen and a symptomatic diagnosis and, after symptomatic diagnosis, will have different survival times under the presence and absence of screening, since in the presence of screening their survival times are generated using a different tumour size than in the absence of screening, see equation (10). We recall that overdiagnosed cancers are uncommon in our simulated data and that all women attend screening.…”

“…Multi-state Markov models have long been the main modelling framework in the breast cancer screening literature to assess mammography screening sensitivity and sojourn time. [8][9][10][11][12][13][14] Although lead times have been estimated based on other approaches, using Bayesian methodology, 15,16 model-based studies of lead time, length bias and overdiagnosis have predominantly used multi-state Markov models with exponentially distributed sojourn times. 7,12,17,18 The sojourn time represents the period during which a tumour is asymptomatic but screen-detectable.…”