TMIC-84. A DEEP LEARNING-BASED METHOD FOR RAPID, PATIENT-SPECIFIC ASSAY OF MACROPHAGE INFILTRATION IN HIGH-GRADE GLIOMA USING LABEL-FREE STIMULATED RAMAN HISTOLOGY

Abstract BACKGROUND 5-Aminolevulinic acid (5-ALA), the most widely used fluorophore in image-guided glioma surgery, identifies tumor-associated macrophages (TAM) in high-grade glioma (HGG) tissue. Infiltrating macrophages, the predominant immune cells in glioma, are highly implicated in tumor progression, recurrence, and treatment response – particularly in immunotherapy. Using a unique, paired dataset of label-free stimulated Raman histology (SRH) and two-photon microscopy (TPEF) images sharing one-to-one spatial resolution, we developed a deep-learning approach to automatically quantify TAM infiltration from intraoperatively acquired SRH images. METHODS We compiled a dataset of 906 paired whole-slide SRH/TPEF images from 79 patients with HGG. We first trained a pix2pix generative adversarial network to convert raw, label-free SRH into synthetic TPEF with identifiable macrophages. Next, we trained a MaskR-CNN model to locate and segment TAMs. The pix2pix network was trained using 5,531 hand-picked 300-by-300-pixel fields-of-view (FOV) best exemplifying TAMs. We used a human-in-the-loop approach to train the segmentation network on 1,000 hand-labeled FOVs. RESULTS Macrophage segmentation from purely histologic data was near-identical to the fluorescence-based ground truth, with a mean dice score of 90.3%. Predicted TAM density was highly correlated with r=0.735. Subgroup analyses of TAM density in 346,836 non-overlapping FOVs revealed significantly lower TAM density in IDH-mutant (p< 0.01) and MGMT-hypermethylated (p=0.03) tumors. Our end-to-end algorithm uses fresh, unlabeled tissue specimens in the operating room and takes just three minutes to analyze whole-slide SRH images. CONCLUSION We demonstrate how deep neural networks can be used to rapidly and quantitatively evaluate macrophage infiltration in HGG in the operating room. Our software enables analysis of glioma patients’ tumor immune environment without immunohistochemistry or fluorescent labels, and may be leveraged to study the effects of immune-modulating therapies in the tumor microenvironment. Future work will focus on evaluating macrophage density and distribution as biomarkers for response to immunotherapies showing efficacy in glioma patients.