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Abstract

A key component of breast cancer screening program is the collection of data on symptoms at the time of screening visit. In many cases, however, the data are not subsequently analyzed for relationships between symptoms and screening program performance. It is a unique study that analyzes the role of symptoms and its relation with screening program performance in a longitudinal outlook. The screening dataset consists of the total number of visits (4.5 million screening visits) made by screening age women since the start of the program and followed for more than 20 years (until 2012). Key symptom variableslump, retraction, secretion were analyzed for their role with program performance indicators- cancer detection rate, attendance rate, recall rate, etc. in a longitudinal outlook. Various innovative methodological approach are used to better fit the screening data of a repeated (women invited every two years) mammography screening program. Marginal and conditional probability models were developed to calculate the cumulative probability of any or first false positives and cancer detection in those who reported symptoms compared to those with no symptoms. The results show a promising role that symptoms can contribute to a population-based screening program in addition to mammography screening. The implication of the results can be more favorable in a setting, with no repeated screening program at a population level, where clinical breast examination (CBE) is feasible provided that adequate diagnostic services are available.

Biography

Email: deependra.singh@cancer.fi, deependra.singh@uta.fi