%0 Journal Article
%T Breast cancer tumor growth estimated through mammography screening data
%A Harald Weedon-Fekj£¿r
%A Bo H Lindqvist
%A Lars J Vatten
%A Odd O Aalen
%A Steinar Tretli
%J Breast Cancer Research
%D 2008
%I BioMed Central
%R 10.1186/bcr2092
%X A likelihood-based estimating procedure was used, where both tumor growth and the screen test sensitivity were modeled as continuously increasing functions of tumor size. The method was applied to cancer incidence and tumor measurement data from 395,188 women aged 50 to 69 years.Tumor growth varied considerably between subjects, with 5% of tumors taking less than 1.2 months to grow from 10 mm to 20 mm in diameter, and another 5% taking more than 6.3 years. The mean time a tumor needed to grow from 10 mm to 20 mm in diameter was estimated as 1.7 years, increasing with age. The screen test sensitivity was estimated to increase sharply with tumor size, rising from 26% at 5 mm to 91% at 10 mm. Compared with previously used Markov models for tumor progression, the applied model gave considerably higher model fit (85% increased predictive power) and provided estimates directly linked to tumor size.Screening data with tumor measurements can provide population-based estimates of tumor growth and screen test sensitivity directly linked to tumor size. There is a large variation in breast cancer tumor growth, with faster growth among younger women.Mammography screening is now an established part of the health service in developed countries. There is, however, still an ongoing discussion related to optimizing mammography screening, including determining optimal time intervals between screenings and which age groups to invite. For these decisions, adequate estimates of breast cancer tumor growth and screening test sensitivity (STS) are crucial. In addition, better knowledge of tumor growth will benefit the evaluation of screening programs [1], as well as the interpretation of clinical trials and epidemiological studies. There are some observational studies of patients that were initially overlooked at earlier mammograms [2-4] or were refused treatment [2,3], but these studies are small and are probably influenced by length of time bias, since slow-growing tumors spend relatively
%U http://breast-cancer-research.com/content/10/3/R41