Prediction of lymph node metastasis in primary breast cancer

Breast cancer is the most common malignant tumor in females in the Western world. Nowadays the short-term prognosis is excellent due to early detection within public screening programs and adjuvant therapies. Thus, de-escalation of surgical interventions and adjuvant therapies are focus for ongoing research. The surgical interventions have evolved from excision of the entire breast to breast-conserving strategies and from axillary clearance in the axilla for nodal staging to sentinel node biopsy. In the field of axillary nodal staging, omission of surgical staging is a current research topic while only around 20% of patients present with nodal metastasis and yet all undergo a surgical staging procedures. Prediction models using clinical, tumor biological and imaging data are thus evolving trying to predict healthy nodes ahead of surgery with high accuracy.

The PhD project is concerned with various datasets and prediction models for non-invasive nodal staging. The prediction models include metadata on clinical characteristics, gene expression profiles and imaging features. The hypothesis is that a comprehensive prediction model including these features will have superior accuracy compared to a more simple model. To achieve this, advanced biostatistical and bioinformatics methods have to be applied.