{"id":846,"date":"2021-03-11T00:00:00","date_gmt":"2021-03-11T00:00:00","guid":{"rendered":"https:\/\/www.lunit.io\/media-hub\/lunit-presents-ai-powered-pathologic-classification-of-immune-phenotype-that-predicts-response-to-immunotherapy-at-uscap-2021\/"},"modified":"2021-03-11T00:00:00","modified_gmt":"2021-03-11T00:00:00","slug":"lunit-presents-ai-powered-pathologic-classification-of-immune-phenotype-that-predicts-response-to-immunotherapy-at-uscap-2021","status":"publish","type":"media-hub","link":"https:\/\/www.lunit.io\/en\/media-hub\/lunit-presents-ai-powered-pathologic-classification-of-immune-phenotype-that-predicts-response-to-immunotherapy-at-uscap-2021\/","title":{"rendered":"Lunit Presents AI-powered Pathologic Classification of Immune Phenotype that Predicts Response to Immunotherapy at USCAP 2021"},"content":{"rendered":"<ul>\n<li>\n<p>Lunit SCOPE IO, an AI therapeutic biomarker platform, can analyze immune phenotype of NSCLC patients at a higher accuracy level compared to pathologists<\/p>\n<\/li>\n<li>\n<p>With pathological consensus, AI-based analysis can be utilized as a novel biomarker of immunotherapy in the digital pathology era<\/p>\n<\/li>\n<li>\n<p>Findings to be presented at USCAP online poster presentation, Monday, March 15<\/p>\n<\/li>\n<\/ul>\n<p><\/p>\n<p><\/p>\n<p><\/p>\n<p><\/p>\n<p><\/p>\n<p>Lunit today announced an upcoming abstract presentation featuring its AI-based predictive analysis of immune phenotype at the 2021 United States and Canadian Academy of Pathology (USCAP) Annual Meeting, to be held online on March 13-18.<\/p>\n<p><\/p>\n<p><\/p>\n<p>Lunit, a leading medical AI startup, focuses on developing AI biomarkers that accurately predict cancer patients\u2019 response to immunotherapy, based on digital analysis of histopathological whole slide images. With its proprietary deep learning algorithm, Lunit is developing an AI therapeutic biomarker platform called \u2018Lunit SCOPE IO\u2019.<\/p>\n<p><\/p>\n<p><\/p>\n<p>In the abstract, Lunit presents the performance of Lunit SCOPE IO in the analysis of whole-slide images of non-small cell lung cancer (NSCLC) patients. The abstract, titled, \u2018Development and validation of deep learning-based pathologic classification of immune phenotype in non-small cell lung cancer,\u2019 will be presented during the poster session on Monday, March 15, 2:15 p.m. Pacific Time.<\/p>\n<p><\/p>\n<p><\/p>\n<p>According to recent studies, Lunit SCOPE IO showed remarkable performance in classifying the immune phenotypes of NSCLC patients, by analyzing their H&amp;E whole slide images. In the upcoming USCAP abstract presentation, the researchers share the findings from further validation of the AI\u2019s performance, by directly comparing the AI detection results with that of pathologists\u2019 diagnostic results.<\/p>\n<p><\/p>\n<p><\/p>\n<p>By analyzing H&amp;E whole slide images of NSCLC patients, the AI software accurately predicted three immune phenotypes (3-IP) by 92.4%. This showed a higher rate compared to the 3-IP annotation made by independent pathologists with the same validation set, which showed an 80.5% average concordance rate.<\/p>\n<p><\/p>\n<p><\/p><figcaption>\n<p><img decoding=\"async\" src=\"\/en\/wp-content\/uploads\/EDITOR\/ed97ff53-5a34-446e-b242-9e825d5912bf.jpg#1038\" alt=\"\u25b2 \uc554\uc870\uc9c1 \uc885\uc591\uce68\uc724\ub9bc\ud504\uad6c \ubd84\ud3ec\uc5d0 \ub530\ub77c \uc138 \uac00\uc9c0 \uba74\uc5ed\ud559\uc801 \ud615\uc9c8(3-IP)\ub85c \ubd84\ub958\ub41c \ubaa8\uc2b5 - (\uc704\uc5d0\uc11c\ubd80\ud130) \uba74\uc5ed \ud65c\uc131, \uba74\uc5ed \uc81c\uc678, \uba74\uc5ed \uacb0\ud54d: \uba74\uc5ed\ud559\uc801 \ud615\uc9c8\uc5d0 \ub530\ub77c \ud658\uc790\uc758 \uba74\uc5ed\ud56d\uc554\uc81c \ubc18\uc751 \ubc0f \uc0dd\uc874\uc728\uc774 \ub2ec\ub77c\uc9c8 \uc218 \uc788\ub2e4.\">Representative images of three immune phenotypes (3-IP). Left, H&amp;E, center, deep-learning detected histologic features, right, overlaid.<\/p>\n<\/figcaption><p><\/p>\n<p><\/p>\n<p><\/p>\n<p><\/p>\n<p>\u201cDevelopment of consensus and utilizing AI-based analysis to classify immune phenotypes are of high interest due to the lack of pathologic consensus for the classification of tumor infiltrating lymphocyte(TIL) distribution, which is the key factor in prediction of response to immunotherapy,\u201d said Dr. Chan-Young Ock, Chief Medical Officer of Oncology at Lunit, who led the study. \u201cAs each immune phenotype leads to different survival rates and response to immune checkpoint inhibitors, it was important to validate that our AI algorithm can accurately predict such classification.\u201d<\/p>\n<p><\/p>\n<p><\/p><figcaption>\n<p><img decoding=\"async\" src=\"\/en\/wp-content\/uploads\/EDITOR\/e229f2c7-b5e6-4b40-8f9f-165d95ac06ef.jpg#1039\" alt=\"\u25b2 \ub8e8\ub2db \uc2a4\ucf54\ud504 IO (Lunit SCOPE IO) \uc774\ubbf8\uc9c0\">Lunit SCOPE IO<\/p>\n<\/figcaption><p><\/p>\n<p><\/p>\n<p><\/p>\n<p>\u201cAlthough more and more cancer patients are treated with immune checkpoint inhibitors, prediction of response to them is limited. Novel biomarkers are needed to identify more patients that respond well to this powerful treatment,\u201d said Brandon Suh, CEO of Lunit. \u201cThe value that this study presents is in the accuracy of AI-based pathologic classification of immune phenotype, demonstrating the possibility of Lunit SCOPE IO to be utilized as a novel biomarker of immunotherapy in the digital pathology era.\u201d<\/p>\n<p><\/p>\n<p><\/p>\n<p>Based on the company\u2019s commitment in clinical research and studies, Lunit\u2019s findings have also been presented in ASCO and AACR since 2019, acknowledged for its potential value. \u201cWe will continue our development as we plan to expand the scope of our analysis and further train the algorithm into a reliable product that can help improve patient outcome,\u201d added Suh.<\/p>\n<p><\/p>\n<\/p>\n<p><strong>Abstract Information<\/strong><\/p>\n<p>Abstract #: 780<\/p>\n<p>Abstract Title: Development and Validation of Deep Learning-Based Pathologic Classification of Immune Phenotype in Non-Small Cell Lung Cancer<\/p>\n<p>Presentation Details: Poster Session on Monday, March 15, 2021 from 2:15 PM - 3:15 PM Pacific Time<\/p>\n<p>USCAP website: <a href=\"https:\/\/www.uscap.org\/uscap-annual-meeting\/\" target=\"_blank\"><u>https:\/\/www.uscap.org\/uscap-annual-meeting\/<\/u><\/a><\/p>\n<p><\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"template":"","media-hub-type":[35],"class_list":["post-846","media-hub","type-media-hub","status-publish","hentry","media-hub-type-precision-oncology"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Lunit Presents AI-powered Pathologic Classification of Immune Phenotype that Predicts Response to Immunotherapy at USCAP 2021 - Lunit<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.lunit.io\/en\/media-hub\/lunit-presents-ai-powered-pathologic-classification-of-immune-phenotype-that-predicts-response-to-immunotherapy-at-uscap-2021\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Lunit Presents AI-powered Pathologic Classification of Immune Phenotype that Predicts Response to Immunotherapy at USCAP 2021 - 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