Lunit to Showcase 7 Studies at ASCO 2024, Including AI Innovations in HER2 Quantification, and Multimodal Predictive Models for Immunotherapy Response
Lunit to Showcase 7 Studies at ASCO 2024, Including AI Innovations in HER2 Quantification, and Multimodal Predictive Models for Immunotherapy Response
- Lunit's ASCO 2024 presentations to highlight advances including HER2 ultra-low detection and AI-powered ICI response prediction models for NSCLC, demonstrating the impact of Lunit SCOPE suite on precision oncology
-Lunit 的 ASCO 2024 演講重點介紹了 HER2 超低檢測和 AI 驅動的 NSCLC ICI 反應預測模型等進展，展示了 Lunit SCOPE 套件對精準腫瘤學的影響
SEOUL, South Korea, May 24, 2024 /PRNewswire/ -- Lunit (KRX:328130.KQ), a leading provider of AI-powered solutions for cancer diagnostics and therapeutics, today announced the presentation of seven studies at the American Society of Clinical Oncology (ASCO) 2024 Annual Meeting in Chicago, from May 31 to June 4. Lunit will present detailed findings on several innovative studies, including the identification of HER2 ultra-low expression in breast cancer using AI-based quantification, and a deep learning-based model integrating chest CT and histopathology analysis for predicting immunotherapy response in non-small cell lung cancer (NSCLC).
韓國首爾，2024年5月24日 /PRNewswire/ — 人工智能癌症診斷和治療解決方案的領先提供商Lunit（KRX: 328130.KQ）今天宣佈在5月31日至6月4日在芝加哥舉行的美國臨床腫瘤學會（ASCO）2024年年會上發表七項研究。Lunit將介紹幾項創新研究的詳細發現，包括使用基於人工智能的量化方法識別乳腺癌中HER2的超低表達，以及基於深度學習的模型，該模型整合了胸部CT和組織病理學分析，用於預測非小細胞肺癌（NSCLC）的免疫療法反應。
In a poster presentation, Lunit's AI-powered HER2 analyzer, Lunit SCOPE HER2, demonstrated the ability to identify HER2 ultra-low expression and differentiate it from true HER2-negative cases in breast cancer patients using continuous subcellular quantification from HER2 immunohistochemistry (IHC) images.
在海報展示中，Lunit的人工智能驅動的HER2分析儀Lunit SCOPE HER2展示了使用HER2免疫組織化學（IHC）圖像的持續亞細胞定量來識別HER2超低表達並將其與乳腺癌患者中真正的HER2陰性病例區分開來的能力。
According to findings presented at ASCO 2022, HER2-targeted antibody-drug conjugates (ADCs) can effectively target tumor cells even in HER2-low breast cancers. This highlights the importance of accurately identifying HER2-low and HER2 ultra-low expression in breast cancer, especially for patients previously classified as HER2-negative. In response, Lunit developed an AI-based whole-slide image (WSI) analyzer for IHC-stained slides to differentiate between true HER2-negative and HER2 ultra-low cases. The AI model evaluated over 67 million tumor cells and 119 million non-tumor cells from 401 WSIs, identifying a significant proportion of HER2 ultra-low cases among pathologist-assessed HER2 score 0 cases. This AI-powered analysis could expand and refine treatment options for patients with HER2-targeted therapies, as demonstrated by the 23.6% of HER2 score 0 cases identified as HER2 ultra-low by AI, and the 51.9% of HER2 score 1+ cases classified as HER2 low by AI, comparable to the 52.3% objective response rate to a HER2-targeted ADC observed in another clinical trial.
根據在ASCO 2022上發表的研究結果，即使在HER2低乳腺癌中，HER2靶向抗體藥物偶聯物（ADC）也可以有效靶向腫瘤細胞。這凸顯了準確識別乳腺癌中HER2低表達和HER2超低表達的重要性，特別是對於以前被歸類爲HER2陰性的患者。作爲回應，Lunit開發了一種基於人工智能的全玻片圖像（WSI）分析儀，用於IHC染色的幻燈片，以區分真正的HER2陰性和HER2超低病例。該人工智能模型評估了來自401個WSI的超過6700萬個腫瘤細胞和1.19億個非腫瘤細胞，在病理學家評估的HER2評分爲0的病例中，確定了HER2超低病例的很大一部分。這種基於人工智能的分析可以擴展和完善HER2靶向療法患者的治療選擇，人工智能確定爲HER2評分0的病例中有23.6％被AI確定爲超低HER2分數的病例，以及51.9％的HER2評分1+病例被AI歸類爲低HER2的病例，與另一項臨床試驗中觀察到的HER2靶向ADC的52.3％的客觀反應率相當。
In another study, Lunit developed and validated an AI model that analyzes patients' chest CT images alone and in combination with pathology images to predict Immune Checkpoint Inhibitor (ICI) response in NSCLC patients. Lunit's deep learning-based chest CT prediction model, developed using data from 1,876 NSCLC patients treated with ICIs, predicted treatment response based on pre-treatment chest CT scans, along with PD-L1 status and immune phenotype. The model demonstrated significant predictive power as an independent biomarker. Patients predicted as responders by the AI model showed significantly longer median time to the next treatment (TTNT; 7 months vs. 2.5 months) and a longer overall survival (OS; 16.5 months vs. 7.6 months) compared to patients predicted as non-responders. Combining the AI CT model with histopathologic biomarkers such as PD-L1 expression and tumor-infiltrating lymphocytes (TILs) further enhanced prediction accuracy, highlighting the complementary strengths of imaging and pathology data in improving predictive models for ICI response.
在另一項研究中，Lunit開發並驗證了一種人工智能模型，該模型可單獨分析患者的胸部CT圖像，並結合病理圖像來預測非小細胞肺癌患者的免疫檢查點抑制劑（ICI）反應。Lunit 基於深度學習的胸部 CT 預測模型是使用來自 1,876 名 ICI 治療的 NSCLC 患者的數據開發的，該模型根據治療前胸部 CT 掃描以及 PD-L1 狀態和免疫表型預測了治療反應。該模型顯示出作爲獨立生物標誌物的強大預測能力。與預測爲無反應的患者相比，人工智能模型預測爲反應者的患者顯示出下次治療的中位時間（TTNT；7個月對2.5個月）和更長的總存活期（OS；16.5個月對7.6個月）。將 AI CT 模型與 PD-L1 表達和腫瘤浸潤淋巴細胞 (TIL) 等組織病理學生物標誌物相結合，進一步提高了預測準確性，突顯了成像和病理學數據在改進 ICI 反應預測模型方面的互補優勢。
A collaborative study with Stanford University School of Medicine examined the association of immune phenotypes with outcomes after immunotherapy in metastatic melanoma, highlighting the heterogeneity of immune phenotypes across melanoma subtypes.
一項與斯坦福大學醫學院的合作研究調查了免疫表型與轉移性黑色素瘤免疫治療後結局的關係，突顯了黑色素瘤亞型之間免疫表型的異質性。
Another study with Northwestern University utilized AI-powered analysis of tertiary lymphoid structures (TLS) in H&E whole-slide images to predict immunotherapy response in NSCLC patients. This demonstrated AI's potential in identifying predictive biomarkers for survival outcomes.
西北大學的另一項研究利用人工智能對H＆E全幻燈片圖像中的三級淋巴結構（TLS）進行分析，來預測非小細胞肺癌患者的免疫療法反應。這表明了人工智能在識別存活結果的預測性生物標誌物方面的潛力。
"At ASCO 2024, Lunit proudly presents seven groundbreaking studies that illustrate our pioneering role in AI-driven precision oncology," said Brandon Suh, CEO of Lunit. "From HER2 quantification to predictive models for immunotherapy response, our work is transforming oncology by making cancer treatment not just personalized but predictive, ensuring the best possible outcomes for patients worldwide."
Lunit首席執行官Brandon Suh表示：“在2024年的ASCO，Lunit自豪地展示了七項開創性的研究，這些研究說明了我們在人工智能驅動的精準腫瘤學中的開創性作用。”“從HER2量化到免疫療法反應的預測模型，我們的工作正在改變腫瘤學，使癌症治療不僅個性化，而且具有預測性，從而確保全球患者獲得最佳療效。”
In addition to the studies above, Lunit will present three more studies at this year's ASCO, demonstrating the diverse capabilities of the Lunit SCOPE suite. The studies include comprehensive histopathomic prediction models for early breast cancer, and hypothetical test-and-control group generation for treatment selection in TPS-high NSCLC.
除了上述研究外，Lunit還將在今年的ASCO上再介紹三項研究，展示Lunit SCOPE套件的多樣化功能。這些研究包括早期乳腺癌的全面組織病理學預測模型，以及針對TPS-High NSCLC治療選擇的假設測試和對照組生成。
Visit Lunit at booth IH22 to discover how the Lunit SCOPE suite is revolutionizing oncology research and clinical practice.
參觀位於IH22展位的Lunit，了解Lunit SCOPE套件如何革新腫瘤學研究和臨床實踐。
Presentations at ASCO 2024 featuring Lunit SCOPE include:
在 ASCO 2024 上以 Lunit SCOPE 爲主題的演講包括：
- "Identification of HER2 ultra-low based on an artificial intelligence (AI)-powered HER2 subcellular quantification from HER2 immunohistochemistry images" (1115, Poster Board #93)
- "Deep learning–based chest CT model to predict treatment response to immune checkpoint inhibitors in non-small cell lung cancer independently and additively to histopathological biomarkers" (8536, Poster Board #400)
- "Artificial intelligence (AI) –powered H&E whole-slide image (WSI) analysis to predict recurrence in hormone receptor positive (HR+) early breast cancer (EBC)" (571, Poster Board #163)
- "Immune phenotype profiling based on anatomic origin of melanoma and impact on clinical outcomes of immune checkpoint inhibitor treatment" (9569, Poster Board #353)
- "Artificial intelligence (AI) -powered H&E whole-slide image (WSI) analysis of tertiary lymphoid structure (TLS) to predict response to immunotherapy in non-small cell lung cancer (NSCLC)" (3135, Poster Board #280)
- "Updated safety, efficacy, pharmacokinetics, and biomarkers from the phase 1 study of IMC-002, a novel anti-CD47 monoclonal antibody, in patients with advanced solid tumors" (2642, Poster Board #121)
- "Relationship between immune phenotype and treatment selection of Chemo-IO vs. IO-only in TPS-high NSCLC using hypothetical test-and-control group generation based on survival data extracted from phase III trials" (e13569)
- “基於人工智能 (AI) 驅動的 HER2 亞細胞定量分析，從 HER2 免疫組織化學圖像中鑑定 HER2 超低” (1115，海報板 #93)
- “基於深度學習的胸部 CT 模型，可獨立預測非小細胞肺癌中對免疫檢查點抑制劑的治療反應，並補充組織病理學生物標誌物”（8536，海報板 #400）
- “由人工智能 (AI) 驅動的 H&E 全幻燈片圖像 (WSI) 分析，可預測激素受體陽性 (HR+) 早期乳腺癌 (EBC) 的復發” (571，海報板 #163)
- “基於黑色素瘤解剖學起源的免疫表型分析以及對免疫檢查點抑制劑治療臨床結果的影響”（9569，海報板 #353）
- “基於人工智能 (AI) 的 H&E 全幻燈片圖像 (WSI) 分析三級淋巴結構 (TLS)，預測非小細胞肺癌 (NSCLC) 對免疫療法的反應” (3135，海報板 #280)
- “針對晚期實體瘤患者的新型抗 CD47 單克隆抗體 IMC-002 的 1 期研究的最新安全性、有效性、藥代動力學和生物標誌物”（2642，海報板 #121）
- “根據從三期試驗中提取的存活數據生成假設的測試和對照組，在高TPS非小細胞肺癌中，免疫表型與Chemo-io與僅限IO的治療選擇之間的關係”（e13569）
About Lunit
關於 Lunit
Founded in 2013, Lunit is a medical AI company on a mission to conquer cancer. We harness AI-powered medical image analytics and AI biomarkers to ensure accurate diagnosis and optimal treatment for each cancer patient. Our FDA-cleared Lunit INSIGHT suite for cancer screening serves over 3,000 hospitals and medical institutions across 40+ countries.
Lunit 成立於 2013 年，是一家醫療人工智能公司，其使命是戰勝癌症。我們利用人工智能驅動的醫學圖像分析和人工智能生物標誌物，確保爲每位癌症患者提供準確的診斷和最佳治療。我們經美國食品藥品管理局批准的Lunit INSIGHT癌症篩查套件爲40多個國家的3,000多家醫院和醫療機構提供服務。
Our clinical findings are featured in top journals, including the Journal of Clinical Oncology and the Lancet Digital Health, and presented at global conferences such as the ASCO and RSNA.
我們的臨床發現發表在《臨床腫瘤學雜誌》和《柳葉刀數字健康》等頂級期刊上，並在ASCO和RSNA等全球會議上發表。
In 2024, Lunit acquired Volpara Health Technologies, setting the stage for unparalleled synergy and accuracy, particularly in breast health and screening technologies.
2024年，Lunit收購了Volpara Health Technologies，爲無與倫比的協同作用和準確性奠定了基礎，尤其是在乳房健康和篩查技術方面。
Headquartered in Seoul, South Korea, with a global network of offices, Lunit leads in medical AI innovation. Discover more at lunit.io.
Lunit總部位於韓國首爾，擁有全球辦事處網絡，在醫療人工智能創新方面處於領先地位。在以下網址了解更多 lunit.io。
SOURCE Lunit
來源 Lunit
譯文內容由第三人軟體翻譯。