Predict neoadjuvant chemotherapy response in breast cancer histopathology from a panel of immunohistochemical markers

Predict neoadjuvant chemotherapy response in breast cancer histopathology from a panel of immunohistochemical markers

Background

Invasive breast cancer is increasingly treated with neoadjuvant (i.e., pre-operative) chemotherapy. However, it is effective only for some patients.

Project goal

Develop biomarkers based on the joint analysis of multiple digital pathology whole-slide images of pre-operative breast cancer biopsies stained with hematoxylin and eosin (H&E) and a panel of immunohistochemical (IHC) markers to predict treatment response.

Tasks

  • Develop deep learning systems to quantify biomarkers in IHC slides
  • Apply existing deep learning models for the extraction of some biomarkers
  • Align extracted features via registration across multiple slides
  • Use extracted features to build a prediction model of treatment response

Requirements

  • Students with a major in computer science, biomedical engineering, artificial intelligence, physics, or a related area in the final stage of master level studies are invited to apply.
  • Affinity with programming in Python
  • Interest in deep learning and medical image analysis

Information

People

Witali Aswolinskiy

Witali Aswolinskiy

Postdoctoral Researcher

Francesco Ciompi

Francesco Ciompi

Assistant Professor