DIGITAL PATHOLOGY

DETECTION OF METASTASIS ON LYMPH NODES

Detection of metastases in hematoxylin and eosin stained slides of lymph node sections is of high clinical relevance but requires large amounts of reading time from histopathologists, whose numbers are inadequate to meet the demand at a global scale and has a major contribution to diagnostic bottlenecks all over the world. In the UK this translates to more than 13,000 people waiting for more than 2 months to start treatment at any given time. We are currently developing a deep learning powered system for automatic detection of metastatic regions on lymph-node sections. This system will serve as an augmentation tool for pathologists increasing diagnostic speed and accuracy.

In December 2017 we received funding from the NHS Small Business Research Initiative to develop a proof-of-concept system for detecting metastatic regions on lymph node Hematoxylin & Eosin stained Whole Slide Images from breast cancer patients.
Our α-prototype reached an average of 97% accuracy on blind samples and achieved 53% decrease of diagnostic time (2.1 times faster, p-value=0.01318) and a concurrent 4.6% increase in diagnostic accuracy coming from detection of previously missed micrometastases, in a pilot clinical evaluation carried out by six experienced histopathologists from the University of Athens Medical School (June 2018). Having completed the feasibility stage of our project we quickly move forward to produce predictive models for melanoma, as well as lung, colon and prostate cancers which, along with breast account for more than 50% of all cancers globally. Upon completion, our metastasis detection system will be embedded in a modular deep-learning powered computer aided diagnosis tool for digital pathology ready to be deployed in hospitals worldwide. The metastasis detection module along with other deep learning powered modules will automate a big range of the histopathologists' time-consuming and laborious daily diagnostic routine and will also provide novel tools for predicting therapeutic responses and recurrence probability.