In short, Heike Grabsch's research entails the following:
Artificial intelligence for high-throughput histological characterisation of the host anti-tumour immune response in regional lymph nodes from oesophageal cancer patients
'Oesophageal cancer (OeC) patients are currently treated based on the disease stage. There is increasing evidence that the host anti-tumour immune response plays a key role in eliminating tumour cells. The local anti-tumour immune response (immune cells infiltrating the primary tumour) depends on the interaction with the host’s immune cells in the tumour draining lymph nodes (LNs). The clinical importance of the LN-based host anti-tumour immune response has not been investigated in detail, mainly due to lack of objective high-throughput LN characterisation methodology. In this project, we aim to develop a Deep Learning (DL)-based approach to automatically find LN containing digital slides, segment LNs and characterise their microarchitecture in OeCpatients from several clinical trials. We expect to (1) expand our basic understanding of the systemic host anti-tumour immune response including changes induced by conventional chemotherapy and (2) to discover and validate clinically useful LN-based biomarker for OeC patients to determine individual patient follow-up strategy or need for additional treatment.'
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