Quantifying the natural history of breast cancer

• Published in: British journal of cancer Vol. 109; no. 8; pp. 2035 - 2043
• Main Authors: Tan, K H X, Simonella, L, Wee, H L, Roellin, A, Lim, Y-W, Lim, W-Y, Chia, K S, Hartman, M, Cook, A R
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.10.2013; Nature Publishing Group
• Subjects: 692/699/67/1347; 692/699/67/2322; 692/699/67/70; Adult; Aged; Biological and medical sciences; Biomedical and Life Sciences; Biomedicine; Breast cancer; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - epidemiology; Breast Neoplasms - pathology; Cancer Research; Carcinoma in Situ - diagnostic imaging; Carcinoma in Situ - epidemiology; Carcinoma in Situ - pathology; Carcinoma, Ductal, Breast - diagnostic imaging; Carcinoma, Ductal, Breast - epidemiology; Carcinoma, Ductal, Breast - pathology; Clinical Study; Disease Progression; Drug Resistance; Early Detection of Cancer - methods; Early Detection of Cancer - statistics & numerical data; Epidemiology; Female; Gynecology. Andrology. Obstetrics; Humans; Mammary gland diseases; Mammography; Markov Chains; Medical sciences; Medicin och hälsovetenskap; Middle Aged; Models, Biological; Molecular Medicine; Monte Carlo Method; Multiple tumors. Solid tumors. Tumors in childhood (general aspects); Oncology; Randomized Controlled Trials as Topic; Reproducibility of Results; Sweden - epidemiology; Tumors; tumour size; indolent ductal carcinoma; breast cancer; Markov model; modelling; Ductal carcinoma; Breast disease; Carcinoma in situ; Pathogenesis; Breast cancer; Malignant tumor; Mammary gland diseases; Cancerology; indolent ductal carcinoma in situ; Cancer; Quantitative analysis
• ISSN: 0007-0920; 1532-1827; 1532-1827
• DOI: 10.1038/bjc.2013.471

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.