{"id":5239,"date":"2026-07-07T15:00:58","date_gmt":"2026-07-07T06:00:58","guid":{"rendered":"https:\/\/www.lunit.io\/en\/?post_type=publication&#038;p=5239"},"modified":"2026-07-07T15:00:58","modified_gmt":"2026-07-07T06:00:58","slug":"artificial-intelligence-guided-analysis-of-the-tumor-microenvironment-predicts-response-to-pembrolizumab-in-rare-tumors","status":"publish","type":"publication","link":"https:\/\/www.lunit.io\/ko\/publication\/artificial-intelligence-guided-analysis-of-the-tumor-microenvironment-predicts-response-to-pembrolizumab-in-rare-tumors\/","title":{"rendered":"Artificial intelligence-guided analysis of the tumor microenvironment predicts response to pembrolizumab in rare tumors"},"content":{"rendered":"<h2>Abstract<\/h2>\n<div id=\"sec-1\" class=\"subsection\">\n<p id=\"p-2\"><strong>Background<\/strong>\u00a0Pathologic tumor response and changes in the tumor microenvironment (TME) predict outcomes to immune checkpoint inhibitors, but are understudied in rare tumors. We investigated whether artificial intelligence (AI)-powered analyses of pretreatment and on-treatment biopsies may inform treatment outcomes to pembrolizumab.<\/p>\n<\/div>\n<div id=\"sec-2\" class=\"subsection\">\n<p id=\"p-3\"><strong>Methods<\/strong>\u00a0We evaluated 256 baseline and 248 on-treatment biopsies from 84 patients with rare tumors (10 cohorts) in a phase II pembrolizumab trial. Intratumoral tumor-infiltrating lymphocyte (iTIL) density and tumor content (TC) were assessed on H&amp;E-stained slides using a deep learning\u2013based analyzer (Lunit SCOPE IO). Baseline iTIL and changes in iTIL and TC were correlated with progression-free survival (PFS) and overall survival (OS). Multiplex immunofluorescence was performed in 27 paired samples to assess TME changes.<\/p>\n<\/div>\n<div id=\"sec-3\" class=\"subsection\">\n<p id=\"p-4\"><strong>Results<\/strong>\u00a0In the high-iTIL tumor group, a baseline iTIL of \u226560 cells\/mm<sup>2<\/sup>\u00a0was associated with favorable PFS (HR 0.49, 95%\u2009CI 0.25 to 0.99, p=0.046) and higher CD8<sup>+<\/sup>\u00a0and CD8<sup>+<\/sup>PD-1<sup>+<\/sup>\u00a0and lower FoxP3<sup>+<\/sup>CD8<sup>+<\/sup>PD-1<sup>+<\/sup>\u00a0T-cell density. However, this association with PFS was not seen in the overall cohort (HR 0.62, 95%\u2009CI 0.37 to 1.06, p=0.082). In paired biopsies, on-treatment increase in iTIL showed a trend toward improved PFS (HR 0.64, 95%\u2009CI 0.40 to 1.06, p=0.084) and was significantly associated with improved OS (HR 0.55, 95%\u2009CI 0.35 to 1.01, p=0.037). This increase was also associated with reduced spatial distance between CD8<sup>+<\/sup>\u00a0immune and tumor cells. Decreased TC during treatment was significantly associated with prolonged PFS and OS (PFS: HR 0.51, p=0.019; OS: HR 0.54, p=0.042). The combination of increased iTIL and decreased TC was significantly associated with better PFS (HR 0.36, p=0.009) and OS (HR 0.36, p=0.029).<\/p>\n<\/div>\n<div id=\"sec-4\" class=\"subsection\">\n<p id=\"p-5\" style=\"text-align: left;\"><strong>Conclusion<\/strong>\u00a0AI-powered assessment of the TME before and during treatment may help inform treatment outcomes to pembrolizumab in patients with rare tumors.<\/p>\n<p style=\"text-align: center;\"><a href=\"https:\/\/jitc.bmj.com\/content\/14\/6\/e014768\"><b>View Abstract<\/b><\/a><\/p>\n<\/div>\n","protected":false},"featured_media":0,"template":"","publication-oncology":[133,85,94],"publication-region":[87,91],"publication-type":[],"radiology":[],"class_list":["post-5239","publication","type-publication","status-publish","hentry","publication-oncology-lunit-scope-io","publication-oncology-pan-cancer","publication-oncology-peer-reviewed-clinical-papers","publication-region-asia","publication-region-north-america"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Artificial intelligence-guided analysis of the tumor microenvironment predicts response to pembrolizumab in rare tumors - 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\/artificial-intelligence-guided-analysis-of-the-tumor-microenvironment-predicts-response-to-pembrolizumab-in-rare-tumors\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Artificial intelligence-guided analysis of the tumor microenvironment predicts response to pembrolizumab in rare tumors - Lunit\" \/>\n<meta property=\"og:description\" content=\"Abstract Background\u00a0Pathologic tumor response and changes in the tumor microenvironment (TME) predict outcomes to immune checkpoint inhibitors, but are understudied in rare tumors. We investigated whether artificial intelligence (AI)-powered analyses of pretreatment and on-treatment biopsies may inform treatment outcomes to pembrolizumab. Methods\u00a0We evaluated 256 baseline and 248 on-treatment biopsies from 84 patients with rare tumors [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.lunit.io\/en\/publication\/artificial-intelligence-guided-analysis-of-the-tumor-microenvironment-predicts-response-to-pembrolizumab-in-rare-tumors\/\" \/>\n<meta property=\"og:site_name\" content=\"Lunit\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@lunit_ai\" \/>\n<meta name=\"twitter:label1\" content=\"\uc608\uc0c1 \ub418\ub294 \ud310\ub3c5 \uc2dc\uac04\" \/>\n\t<meta name=\"twitter:data1\" content=\"2\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/publication\\\/artificial-intelligence-guided-analysis-of-the-tumor-microenvironment-predicts-response-to-pembrolizumab-in-rare-tumors\\\/\",\"url\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/publication\\\/artificial-intelligence-guided-analysis-of-the-tumor-microenvironment-predicts-response-to-pembrolizumab-in-rare-tumors\\\/\",\"name\":\"Artificial intelligence-guided analysis of the tumor microenvironment predicts response to pembrolizumab in rare tumors - Lunit\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/#website\"},\"datePublished\":\"2026-07-07T06:00:58+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/publication\\\/artificial-intelligence-guided-analysis-of-the-tumor-microenvironment-predicts-response-to-pembrolizumab-in-rare-tumors\\\/#breadcrumb\"},\"inLanguage\":\"ko-KR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.lunit.io\\\/en\\\/publication\\\/artificial-intelligence-guided-analysis-of-the-tumor-microenvironment-predicts-response-to-pembrolizumab-in-rare-tumors\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/publication\\\/artificial-intelligence-guided-analysis-of-the-tumor-microenvironment-predicts-response-to-pembrolizumab-in-rare-tumors\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Artificial intelligence-guided analysis of the tumor microenvironment predicts response to pembrolizumab in rare tumors\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/#website\",\"url\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/\",\"name\":\"Lunit\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"ko-KR\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/#organization\",\"name\":\"Lunit\",\"url\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"ko-KR\",\"@id\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/wp-content\\\/uploads\\\/2025\\\/10\\\/Logo-black.svg\",\"contentUrl\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/wp-content\\\/uploads\\\/2025\\\/10\\\/Logo-black.svg\",\"width\":189,\"height\":52,\"caption\":\"Lunit\"},\"image\":{\"@id\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/x.com\\\/lunit_ai\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/lunit-inc\",\"https:\\\/\\\/x.com\\\/lunitoncology\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Artificial intelligence-guided analysis of the tumor microenvironment predicts response to pembrolizumab in rare tumors - Lunit","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.lunit.io\/en\/publication\/artificial-intelligence-guided-analysis-of-the-tumor-microenvironment-predicts-response-to-pembrolizumab-in-rare-tumors\/","og_locale":"ko_KR","og_type":"article","og_title":"Artificial intelligence-guided analysis of the tumor microenvironment predicts response to pembrolizumab in rare tumors - Lunit","og_description":"Abstract Background\u00a0Pathologic tumor response and changes in the tumor microenvironment (TME) predict outcomes to immune checkpoint inhibitors, but are understudied in rare tumors. 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