{"id":1765,"date":"2025-05-28T13:46:07","date_gmt":"2025-05-28T13:46:07","guid":{"rendered":"https:\/\/www.lunit.io\/publication\/use-of-artificial-intelligence-powered-spatial-analysis-of-tumor-microenvironment-to-predict-the-prognosis-in-resected-gallbladder-cancer\/"},"modified":"2025-11-01T08:44:30","modified_gmt":"2025-11-01T08:44:30","slug":"use-of-artificial-intelligence-powered-spatial-analysis-of-tumor-microenvironment-to-predict-the-prognosis-in-resected-gallbladder-cancer","status":"publish","type":"publication","link":"https:\/\/www.lunit.io\/en\/publication\/use-of-artificial-intelligence-powered-spatial-analysis-of-tumor-microenvironment-to-predict-the-prognosis-in-resected-gallbladder-cancer\/","title":{"rendered":"Use of artificial intelligence-powered spatial analysis of tumor microenvironment to predict the prognosis in resected gallbladder cancer"},"content":{"rendered":"<h3>Use of artificial intelligence-powered spatial analysis of tumor microenvironment to predict the prognosis in resected gallbladder cancer<\/h3>\n<p>Young Hoon Choi, Hyemin Kim, So Jeong Yoon, Yeong Hak Bang, Kee-Taek Jang, Changhoon Yoo, Chang Ho Ahn, Soohyun Hwang, Sangwon Shin, Sang Hyun Shin, In Woong Han, Jin Seok Heo, Kwang Hyuck Lee, Jong Kyun Lee, Se-Hoon Lee, Kyu Taek Lee, Hongbeom Kim, Joo Kyung Park<\/p>\n<p><strong>ASCO, 2025<\/strong><\/p>\n<p><strong>Background:<\/strong><br \/>\nGallbladder cancer (GBC) is a highly lethal disease with a lack of reliable biomarkers. The tumor microenvironment (TME) is closely associated with prognosis, but its clinical application as a prognostic marker is limited by evaluation challenges. This study assessed the prognostic significance of AI-powered TME analysis in resected GBC patients.<br \/>\n<strong>Methods:<\/strong><br \/>\nA total of 225 GBC patients with an R0 resection were enrolled, and their hematoxylin &amp; eosin (H&amp;E)-stained GBC sections were analyzed using Lunit SCOPE IO, an artificial intelligence (AI)-powered whole-slide image (WSI) analyze, to evaluate TME-related features, including tumor-infiltrating lymphocyte (TIL) density, fibroblast (FB) density, and tertiary lymphoid structure (TLS) counts. Risk stratification was based on TME-related risk factors (low TIL, high FB, low TLS), and survival outcomes were assessed. External validation was conducted using 146 biliary tract cancer patients.<br \/>\n<strong>Results:<\/strong><br \/>\nOverall survival (OS) and disease-free survival (DFS) declined as the number of TME-related risk factors increased. Patients with three risk factors had the poorest outcomes (median OS: 17.7 months [reference]; median DFS: 12.7 months [reference]), followed by those with two risk factors (median OS: 115.9 months, HR = 0.40, 95% CI: 0.19\u20130.85; median DFS: 57.8 months, HR = 0.37, 95% CI: 0.18\u20130.74) and one risk factor (median OS: 126.5 months, HR = 0.34, 95% CI: 0.16\u20130.74; median DFS: 117.2 months, HR = 0.30, 95% CI: 0.15\u20130.62). Patients with no risk factors had the best survival (median OS: not reached, HR = 0.20, 95% CI: 0.06\u20130.67; median DFS: not reached, HR = 0.13, 95% CI: 0.04\u20130.41). External validation confirmed consistent trends across all risk groups.<br \/>\n<strong>Conclusions:<\/strong><br \/>\nAI-powered TME analysis shows promise as a practical tool for identifying TME-related risk factors using H&amp;E-stained WSI, providing valuable prognostic information for resected GBC patients.<\/p>\n<p style=\"text-align: center;\"><a href=\"https:\/\/meetings.asco.org\/abstracts-presentations\/249742\"><strong>View abstract<\/strong><\/a><\/p>\n","protected":false},"featured_media":0,"template":"","publication-oncology":[95,79,133,77,93],"publication-region":[],"publication-type":[],"radiology":[],"class_list":["post-1765","publication","type-publication","status-publish","hentry","publication-oncology-conference-posters","publication-oncology-gi","publication-oncology-lunit-scope-io","publication-oncology-tumor-type","publication-oncology-type-of-evidence"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Use of artificial intelligence-powered spatial analysis of tumor microenvironment to predict the prognosis in resected gallbladder cancer - 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\/publication\/use-of-artificial-intelligence-powered-spatial-analysis-of-tumor-microenvironment-to-predict-the-prognosis-in-resected-gallbladder-cancer\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Use of artificial intelligence-powered spatial analysis of tumor microenvironment to predict the prognosis in resected gallbladder cancer - Lunit\" \/>\n<meta property=\"og:description\" content=\"Use of artificial intelligence-powered spatial analysis of tumor microenvironment to predict the prognosis in resected gallbladder cancer Young Hoon Choi, Hyemin Kim, So Jeong Yoon, Yeong Hak Bang, Kee-Taek Jang, Changhoon Yoo, Chang Ho Ahn, Soohyun Hwang, Sangwon Shin, Sang Hyun Shin, In Woong Han, Jin Seok Heo, Kwang Hyuck Lee, Jong Kyun Lee, Se-Hoon [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.lunit.io\/en\/publication\/use-of-artificial-intelligence-powered-spatial-analysis-of-tumor-microenvironment-to-predict-the-prognosis-in-resected-gallbladder-cancer\/\" \/>\n<meta property=\"og:site_name\" content=\"Lunit\" \/>\n<meta property=\"article:modified_time\" content=\"2025-11-01T08:44:30+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@lunit_ai\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/publication\\\/use-of-artificial-intelligence-powered-spatial-analysis-of-tumor-microenvironment-to-predict-the-prognosis-in-resected-gallbladder-cancer\\\/\",\"url\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/publication\\\/use-of-artificial-intelligence-powered-spatial-analysis-of-tumor-microenvironment-to-predict-the-prognosis-in-resected-gallbladder-cancer\\\/\",\"name\":\"Use of artificial intelligence-powered spatial analysis of tumor microenvironment to predict the prognosis in resected gallbladder cancer - 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