We aim to build imaging instruments that can help clinicians detect cancer non invasively in live tissue using machine learning. We believe that by combining optical coherence tomography (OCT) with machine learning we can create a virtual biopsy tool that can provide intuitive images for clinical diagnosis at a single cell resolution.

One in four people worldwide will ultimately be affected by cancer. Surgical removal is the main treatment for most solid cancers. The surgeon is tasked with the delicate balancing act of excising enough tissue to avoid leaving behind residual cancer cells while not removing too much tissue, which can harm organ function. This is particularly important for brain tumors, the most common type of solid tumor in children and the leading cause of pediatric cancer mortality.

The gold standard for detecting most solid cancers and confirming tumor margins is hematoxylin and eosin (H&E) stained tissue sections, which require an invasive biopsy procedure. Unfortunately, current non-invasive in-vivo imaging modalities do not produce images of comparable usefulness.

We developed a novel imaging modality called a “virtual H&E biopsy” that generates H&E-like images of skin tissue in real-time, non-invasively, up to 1mm into the tissue. This imaging modality provides real-time diagnosis of tumor margins and invasiveness by scanning a large tissue area for residual cancer cells. Such information would guide treatment decisions for diseases such as brain and skin cancer. Beyond its clinical benefits, this technology can also be used for research into tumor development and tumor responses to treatment by providing in-vivo H&E-like images of healthy and tumorous tissue microstructures changing over time, allowing physicians to watch tumor growth.

The images below show a traditional histology image compared to virtual histology, scale bar is 100 microns.