Abstract 455: Automating integration of histomorphologic and immunohistochemistry data using computer vision tools

Abstract Background: Modern diagnostic pathology workflows involve the integration of histomorphologic, immunohistochemical (IHC), and molecular data to reach a final diagnosis. Recently, advances in deep learning have revolutionized pathology by providing the prospect for expert-level autonomous image analysis tools. Despite recent innovations in deep learning, integrating histomorphologic and molecular information found on respective H&E- and IHC-stained tissue sections still remains a challenge. Here, we aim to address this issue by incorporating computer vision tools, including deep learning and scale-invariant feature transform (SIFT), to align H&E-stained sections with accompanying IHC studies for automated subclassification of gliomas. Methods: To test the workflow, we trained the publicly available VGG19 convolutional neural network (CNN) using patches of pathologist-annotated H&E-stained WSIs to recognize histological patterns of 16 common tissue and brain tumor classes, including diffuse glioma, meningioma, metastatic carcinoma, and schwannoma. To complement the histomorphologic analysis, we optimized several deep learning classifiers, including Mask R-CNN, to recognize various IHC markers, such as Ki-67, IDH1-R132H and ATRX, relevant for molecular subclassification of gliomas. For the integrated analysis, we employed SIFT to align lesional regions of H&E and IHC images. Results: The histomorphologic classifier excelled at classification with accuracies of 100% for glioma, meningioma and metastatic carcinoma, and 93% for schwannoma (n = 125). The Mask R-CNN was tested on 147 images generated from 34 brain tumor Ki-67 WSIs and showed a high concordance with aggregate pathologists’ estimates (n = 3 assessors; y = 0.9712x -1.945, r = 0.9750). Using the deep learning classifiers and SIFT, we were able to identify tumor regions from the H&E images and align them to their corresponding IHC images. This resulted in significant improvement of ATRX and IDH1-R132H quantification compared to unaligned WSIs. Finally, SIFT and partially sampling the lesional regions decreased computational time significantly without compromising accuracy. Conclusion: SIFT can work in concert with deep learning tools to help examine the histomorphologic and molecular patterns of various brain tumors and provide a framework for integrating deep learning tools to provide automated diagnoses. Citation Format: Michael K. Lee, Kevin Faust, Madhumitha Rabindranath, Phedias Diamandis. Automating integration of histomorphologic and immunohistochemistry data using computer vision tools [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 455.