دورية أكاديمية
Clinical Validation of Artificial Intelligence-Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non-Small Cell Lung Cancer.
| العنوان: | Clinical Validation of Artificial Intelligence-Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non-Small Cell Lung Cancer. |
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| المؤلفون: | Kim H; Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea., Kim S; Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea., Choi S; Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea., Park C; Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea., Park S; Lunit Inc., Seoul, Republic of Korea., Pereira S; Lunit Inc., Seoul, Republic of Korea., Ma M; Lunit Inc., Seoul, Republic of Korea., Yoo D; Lunit Inc., Seoul, Republic of Korea., Paeng K; Lunit Inc., Seoul, Republic of Korea., Jung W; Lunit Inc., Seoul, Republic of Korea., Park S; Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea., Ock CY; Lunit Inc., Seoul, Republic of Korea., Lee SH; Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea., Choi YL; Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea., Chung JH; Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea. |
| المصدر: | JCO precision oncology [JCO Precis Oncol] 2024 May; Vol. 8, pp. e2300556. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't; Validation Study |
| اللغة: | English |
| بيانات الدورية: | Publisher: American Society of Clinical Oncology Country of Publication: United States NLM ID: 101705370 Publication Model: Print Cited Medium: Internet ISSN: 2473-4284 (Electronic) Linking ISSN: 24734284 NLM ISO Abbreviation: JCO Precis Oncol Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Alexandria, VA : American Society of Clinical Oncology, [2017]- |
| مواضيع طبية MeSH: | Carcinoma, Non-Small-Cell Lung*/drug therapy , Carcinoma, Non-Small-Cell Lung*/pathology , Immune Checkpoint Inhibitors*/therapeutic use , Lung Neoplasms*/drug therapy , Lung Neoplasms*/pathology , B7-H1 Antigen*/analysis , Artificial Intelligence*, Humans ; Male ; Female ; Aged ; Middle Aged ; Adult ; Aged, 80 and over |
| مستخلص: | Purpose: Evaluation 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)-powered analyzer to assess TPS for the prediction of immune checkpoint inhibitor (ICI) response in advanced non-small cell lung cancer (NSCLC). Materials and Methods: The 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. Results: In comparing PD-L1 TPS assessed by AI analyzer and by pathologists, a significant positive correlation was observed (Spearman coefficient = 0.925; P < .001). The concordance of TPS between AI analyzer and pathologists according to TPS ≥50%, 1%-49%, and <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 <1% group better than the pathologist's reading, with the TPS ≥50% 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 <1% group). Conclusion: PD-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. |
| المشرفين على المادة: | 0 (Immune Checkpoint Inhibitors) 0 (B7-H1 Antigen) 0 (CD274 protein, human) |
| تواريخ الأحداث: | Date Created: 20240509 Date Completed: 20240509 Latest Revision: 20240513 |
| رمز التحديث: | 20240514 |
| DOI: | 10.1200/PO.23.00556 |
| PMID: | 38723233 |
| قاعدة البيانات: | MEDLINE |
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