506 Deep Learning-Based Image-to-Image Translation to Identify Macrophage Infiltration in High-Grade Glioma Using Label-Free Stimulated Raman Histology

INTRODUCTION: 5-Aminolevulinic acid (5-ALA) is the most widely used fluorophore in image-guided glioma surgery, and previous work at our institution demonstrated that 5-ALA highlights tumor-associated macrophages (TAMs) in two-photon microscopy images of brain tumor tissue. Using a unique, paired dataset of stimulated Raman histology (SRH) and two-photon images that share one-to-one spatial resolution, we propose a deep-learning approach to identify macrophages from intraoperative SRH images without requiring fluorescent labels. METHODS: We compiled a dataset of 9,554 non-overlapping, 300-by-300 pixel fields of view from paired SRH and two-photon images representing 40 cases of high-grade glioma. A deep generative adversarial network (pix2pix), consisting of a U-Net generator and PatchGAN discriminator, was trained to generate synthetic two-photon images from each SRH patch. The model was trained for 200 epochs, and similarity between the synthetic and real distributions was assessed qualitatively while training and quantitatively using FrÉchet inception distance (FID). RESULTS: Our model was successfully trained to generate synthetic two-photon images nearly indistinguishable from ground truth examples. The FID between a held-out test set of real and synthetic images was 8.58, compared to a mean FID of 9.26 ± 0.24 between randomly sampled sets of real images. Our model consistently inferred the location of brightly fluorescing TAMs in held-out, previously unseen test images. CONCLUSIONS: We used deep learning to visualize TAMs in label-free SRH images. Analysis of TAM infiltration has the potential to identify patients most likely to benefit from immunotherapy clinical trials. Ongoing work will leverage additional deep-learning approaches to automatically identify and quantify TAM infiltration with SRH, enabling rapid patient-specific analysis of the glioma immune microenvironment.