OCT as a Platform for Non-Invasive Virtual H&E Biopsy

The broad objective of this project is to develop imaging instrumentation and algorithmic technology to perform non-invasive, real-time, in-vivo, 3D, virtual H&E biopsies. 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 propose a novel imaging modality called a "virtual H&E biopsy'' that would generate H&E-like images of living tissue in real time. non-invasively up to 1 mm into the tissue. This imaging modality would be able to provide 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. To generate virtual H&E images, we will optimize a new imaging instrument we have developed based on optical coherence tomography (OCT) and image translation by a generative adversarial neural network (GAN). The key breakthrough enabling us to train a GAN to generate virtual H&E images is a technique called optical barcoding, which we used to obtain a dataset of OCT images and corresponding real H&E images aligned to single-cell precision. We have demonstrated this virtual H&E system with ex-vivo human skin tissue samples. For the proposed project, we will first train a GAN to generate virtual H&E images of healthy mouse brain tissue and glioblastoma mouse brain tissue ex-vivo (Aim 1 ). Second, we will use transfer learning to retrain the GAN to generate virtual H&E images of mouse brain tissue of in-vivo OCT scan (Aim 2a), and track for the first time how H&E images change as a mouse glioblastoma tumor develops (Aim 2b}. Finally, we will assess whether the GAN can be retrained across species by applying transfer learning on the mouse-brain trained GAN and use it to generate a virtual H&E biopsy of ex-vivo low-grade human glioma (Aim 3). To the best of our knowledge, this will be the first time transfer learning has been applied across species for biomedical images. Such transfer learning can accelerate virtual biopsy research since mouse samples are significantly easier to obtain and handle, thereby opening up applications in locations where acquiring a human dataset for training a virtual biopsy GAN would be difficult or impossible to achieve (e.g., the retina).