PROACTING

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).

Funding

People

Francesco Ciompi

Francesco Ciompi

Assistant Professor

Jeroen van der Laak

Jeroen van der Laak

Associate Professor

Witali Aswolinskiy

Witali Aswolinskiy

Postdoctoral Researcher

 Esther Lips

Esther Lips

Jelle Wesseling

Jelle Wesseling

Pathologist

Pathology, NKI