{"id":1717,"date":"2024-05-09T13:45:57","date_gmt":"2024-05-09T13:45:57","guid":{"rendered":"https:\/\/www.lunit.io\/publication\/clinical-validation-of-artificial-intelligence-powered-pd-l1-tumor-proportion-score-interpretation-for-immune-checkpoint-inhibitor-response-prediction-in-non-small-cell-lung-cancer\/"},"modified":"2025-11-01T15:53:23","modified_gmt":"2025-11-01T15:53:23","slug":"clinical-validation-of-artificial-intelligence-powered-pd-l1-tumor-proportion-score-interpretation-for-immune-checkpoint-inhibitor-response-prediction-in-non-small-cell-lung-cancer","status":"publish","type":"publication","link":"https:\/\/lunit.supremeclients.com\/en\/publication\/clinical-validation-of-artificial-intelligence-powered-pd-l1-tumor-proportion-score-interpretation-for-immune-checkpoint-inhibitor-response-prediction-in-non-small-cell-lung-cancer\/","title":{"rendered":"Clinical Validation of Artificial Intelligence\u2013Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non\u2013Small Cell Lung Cancer"},"content":{"rendered":"<h3>Clinical Validation of Artificial Intelligence\u2013Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non\u2013Small Cell Lung Cancer<\/h3>\n<p>Hyojin Kim, Seokhwi Kim, Sangjoon Choi, Changhee Park, Seonwook Park, Sergio Pereira, Minuk Ma, Donggeun Yoo, Kyunghyun Paeng, Wonkyung Jung, Sehhoon Park, Se-Hoon Lee, Yoon-La Choi, Jin-Haeng Chung, Tony S. Mok, and Chan-Young Ock<\/p>\n<p><strong>JCO Precision Oncology, 2024<\/strong><\/p>\n<p><strong>Abstract<\/strong><br \/>\n<strong>Purpose<\/strong><br \/>\nEvaluation of PD-L1 tumor proportion score (TPS) by pathologists has been very impactful but is limited by factors such as intraobserver\/interobserver bias and intratumor heterogeneity. We developed an artificial intelligence (AI)\u2013powered analyzer to assess TPS for the prediction of immune checkpoint inhibitor (ICI) response in advanced non\u2013small cell lung cancer (NSCLC).<\/p>\n<p><strong>Materials and Methods<\/strong><br \/>\nThe AI analyzer was trained with 393,565 tumor cells annotated by board-certified pathologists for PD-L1 expression in 802 whole-slide images (WSIs) stained by 22C3 pharmDx immunohistochemistry. The clinical performance of the analyzer was validated in an external cohort of 430 WSIs from patients with NSCLC. Three pathologists performed annotations of this external cohort, and their consensus TPS was compared with AI-based TPS.<\/p>\n<p><strong>Results<\/strong><br \/>\nIn comparing PD-L1 TPS assessed by AI analyzer and by pathologists, a significant positive correlation was observed (Spearman coefficient = 0.925; P &lt; .001). The concordance of TPS between AI analyzer and pathologists according to TPS \u226550%, 1%-49%, and &lt;1% was 85.7%, 89.3%, and 52.4%, respectively. In median progression-free survival (PFS), AI-based TPS predicted prognosis in the TPS 1%-49% or TPS &lt;1% group better than the pathologist's reading, with the TPS \u226550% group as a reference (hazard ratio [HR], 1.49 [95% CI, 1.19 to 1.86] v HR, 1.36 [95% CI, 1.08 to 1.71] for TPS 1%-49% group, and HR, 2.38 [95% CI, 1.69 to 3.35] v HR, 1.62 [95% CI, 1.23 to 2.13] for TPS &lt;1% group).<\/p>\n<p><strong>Conclusion<\/strong><br \/>\nPD-L1 TPS assessed by AI analyzer correlates with that of pathologists, with clinical performance also being comparable when referenced to PFS. The AI model can accurately predict tumor response and PFS of ICI in advanced NSCLC via assessment of PD-L1 TPS.<\/p>\n<p style=\"text-align: center;\"><a href=\"https:\/\/ascopubs.org\/doi\/abs\/10.1200\/PO.23.00556\"><strong>Read the full paper<\/strong><\/a><\/p>\n","protected":false},"featured_media":0,"template":"","publication-oncology":[78,135,94,70,77,93],"publication-region":[],"publication-type":[],"radiology":[],"class_list":["post-1717","publication","type-publication","status-publish","hentry","publication-oncology-lung-cancer","publication-oncology-lunit-scope-pd-l1","publication-oncology-peer-reviewed-clinical-papers","publication-oncology-product","publication-oncology-tumor-type","publication-oncology-type-of-evidence"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Clinical Validation of Artificial Intelligence\u2013Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non\u2013Small Cell Lung 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:\/\/lunit.supremeclients.com\/publication\/clinical-validation-of-artificial-intelligence-powered-pd-l1-tumor-proportion-score-interpretation-for-immune-checkpoint-inhibitor-response-prediction-in-non-small-cell-lung-cancer\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Clinical Validation of Artificial Intelligence\u2013Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non\u2013Small Cell Lung Cancer - Lunit\" \/>\n<meta property=\"og:description\" content=\"Clinical Validation of Artificial Intelligence\u2013Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non\u2013Small Cell Lung Cancer Hyojin Kim, Seokhwi Kim, Sangjoon Choi, Changhee Park, Seonwook Park, Sergio Pereira, Minuk Ma, Donggeun Yoo, Kyunghyun Paeng, Wonkyung Jung, Sehhoon Park, Se-Hoon Lee, Yoon-La Choi, Jin-Haeng Chung, Tony S. 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