Background
Neoadjuvant Chemotherapy (NACT) is increasingly used for pre-operative treatment of breast cancer patients. Successful application of NACT, resulting in a substantial or complete reduction in tumor volume, enables breast-conserving surgery in a higher number of cases. In addition, NACT allows assessment of tumor sensitivity to chemotherapy. The most reliable measure of NACT effectiveness is quantification of post-operative residual disease via histology, which has been shown to be a strong indicator of long-term prognosis. Although many patients have indeed substantial benefit from neoadjuvant treatment, there is a large group of patients not responding while still experiencing the toxic side effects. To date, it is impossible to predict upfront whether a patient will respond to NACT.
Aim
The aim of PROACTING (PRedicting neOAdjuvant Chemotherapy Treatment response with deep learnING) is to explore the feasibility of a predictive model of NACT response based on the analysis of H&E-stained digital pathology images. This project will run for a period of 2 years as a collaboration between the Netherlands Cancer Institute (NKI) and the Computational Pathology group of the Radboud University Medical Center (Radboudumc).
