Tumour Growth and Spread Models for Evaluation of Early Detection Methodologies and Hormone Therapy Effectiveness in Breast Cancer

We will develop and use novel, biologically-motivated statistical models of tumor progression to elucidate mechanisms of breast cancer progression and to predict the risk of (in particular, aggressive) breast cancer.

The models will be estimated using data from Swedish and Norwegian studies which represent some of the world’s most detailed population-based studies of breast cancer and breast cancer screening, combining longitudinal register, questionnaire, image and molecular data.

The models are extensions of prior methods that we have developed that separate out the roles of factors in screening and symptomatic detection of breast cancer, and tumor onset, growth and spread. The information we create is vital for planning and evaluating approaches to (secondary) prevention of breast cancer.

Our risk prediction models are novel, and rigorously incorporate screening information.We will also study in detail the background of false-positive mammography results – i.e. recalls that do not lead to a diagnosis, but can be a considerable psychological burden for women.

Moreover, we will use simulation-based approaches to evaluate the performance of new (personalised) screening strategies.Finally, we will use our novel statistical approaches with detailed prescription data to study the efficacy of hormone therapy in estrogen receptor-positive patients.