- 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 oncologySEOUL, 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的ASCO 2024演讲重点介绍了包括HER2超低检测和人工智能支持的非小细胞肺癌ICI反应预测模型在内的进展，展示了Lunit SCOPE suite对精准肿瘤学的影响，韩国，2024年5月24日/PRNewswire/--Lunit（KRX:328130.KQ），一家领先的癌症诊断和治疗人工智能解决方案提供商，今天宣布在5月31日至6月4日于芝加哥举行的美国临床肿瘤学会（ASCO）2024年年会上介绍了七项研究。

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)..

Lunit将介绍几项创新研究的详细结果，包括使用基于AI的定量方法鉴定乳腺癌中HER2的超低表达，以及基于深度学习的模型，该模型整合了胸部CT和组织病理学分析，用于预测非小细胞肺癌（NSCLC）的免疫治疗反应。。

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Visit Lunit at booth IH22 to discover how the Lunit SCOPE suite is revolutionizing oncology research and clinical practice.

参观位于IH22展位的Lunit，了解Lunit SCOPE suite如何彻底改变肿瘤学研究和临床实践。

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..

在海报演示中，Lunit的AI-powered HER2分析仪Lunit SCOPE HER2展示了使用HER2免疫组织化学（IHC）图像的连续亚细胞定量来识别HER2超低表达并将其与乳腺癌患者中真正的HER2阴性病例区分开的能力。。

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.

根据ASCO 2022的研究结果，即使在HER2低乳腺癌中，HER2靶向抗体-药物偶联物（ADC）也可以有效靶向肿瘤细胞。这突出了准确鉴定乳腺癌中HER2低表达和HER2超低表达的重要性，特别是对于先前被归类为HER2阴性的患者。

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.

作为回应，Lunit开发了一种基于AI的全幻灯片图像（WSI）分析仪，用于IHC染色的幻灯片，以区分真正的HER2阴性和HER2超低病例。AI模型评估了来自401个WSI的6700多万个肿瘤细胞和1.19亿个非肿瘤细胞，确定了病理学家评估的HER2评分为0的病例中HER2超低病例的显着比例。

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..

这种人工智能支持的分析可以扩大和改进HER2靶向治疗患者的治疗选择，如人工智能确定的HER2评分0病例中有23.6%为HER2超低，而HER2评分1+病例中有51.9%被AI归类为HER2低，与另一项临床试验中观察到的HER2靶向ADC的52.3%客观缓解率相当。。

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.

在另一项研究中，Lunit开发并验证了一种AI模型，该模型可以单独分析患者的胸部CT图像，并结合病理图像来预测NSCLC患者的免疫检查点抑制剂（ICI）反应。Lunit基于深度学习的胸部CT预测模型是使用1876名接受ICI治疗的NSCLC患者的数据开发的，该模型基于治疗前胸部CT扫描以及PD-L1状态和免疫表型预测治疗反应。

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.

该模型作为独立的生物标志物显示出显着的预测能力。与预测为无应答者的患者相比，AI模型预测为应答者的患者显示出下一次治疗的中位时间显着更长（TTNT；7个月比2.5个月）和更长的总生存期（OS；16.5个月比7.6个月）。

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.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.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.

将AI CT模型与组织病理学生物标志物（如PD-L1表达和肿瘤浸润淋巴细胞（TIL））相结合，进一步提高了预测准确性，突出了成像和病理数据在改善ICI反应预测模型方面的互补优势。与斯坦福大学医学院的一项合作研究检查了免疫表型与转移性黑色素瘤免疫治疗后结局的关系，突出了黑色素瘤亚型免疫表型的异质性。西北大学的另一项研究利用人工智能对H＆E全幻灯片图像中的三级淋巴结构（TLS）进行分析，以预测NSCLC患者的免疫治疗反应。

This demonstrated AI's potential in identifying predictive biomarkers for survival outcomes.'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 .

这证明了人工智能在确定生存结果的预测性生物标志物方面的潜力。”Lunit首席执行官布兰登·苏（BrandonSuh）说，在2024年ASCO大会上，Lunit骄傲地介绍了七项开创性的研究，这些研究说明了我们在人工智能驱动的精准肿瘤学中的开创性作用从HER2定量到。

'Identification of HER2 ultra-low based on an artificial intelligence (AI)-powered HER2 subcellular quantification from HER2 immunohistochemistry images' (1115, Poster Board #93)

“基于人工智能（AI）的HER2免疫组织化学图像的HER2亚细胞定量鉴定HER2超低”（1115，海报板#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)

“基于深度学习的胸部CT模型，可独立预测非小细胞肺癌对免疫检查点抑制剂的治疗反应，并与组织病理学生物标志物相加”（8536，海报板#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)

“人工智能（AI）-支持H＆E全幻灯片图像（WSI）分析，以预测激素受体阳性（HR+）早期乳腺癌（EBC）的复发”（571，海报板#163）

'Immune phenotype profiling based on anatomic origin of melanoma and impact on clinical outcomes of immune checkpoint inhibitor treatment' (9569, Poster Board #353)

“基于黑色素瘤解剖起源的免疫表型分析以及对免疫检查点抑制剂治疗临床结果的影响”（9569，海报板#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)

“人工智能（AI）支持的三级淋巴结构（TLS）的H＆E全幻灯片图像（WSI）分析，以预测非小细胞肺癌（NSCLC）对免疫治疗的反应”（3135，海报板#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)

“新型抗CD47单克隆抗体IMC-002在晚期实体瘤患者中的第一阶段研究的最新安全性，有效性，药代动力学和生物标志物”（2642，海报板#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)

“基于从III期试验中提取的生存数据，使用假设测试和对照组生成，仅在TPS高NSCLC中免疫表型与化疗IO与IO的治疗选择之间的关系”（e13569）

About LunitFounded 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.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.In 2024, Lunit acquired Volpara Health Technologies, setting the stage for unparalleled synergy and accuracy, particularly in breast health and screening technologies.Headquartered in Seoul, South Korea, with a global network of offices, Lunit leads in medical AI innovation.

关于Lunit成立于2013年，Lunit是一家致力于征服癌症的医疗AI公司。我们利用人工智能支持的医学图像分析和人工智能生物标志物，确保每位癌症患者的准确诊断和最佳治疗。我们经FDA批准的Lunit INSIGHT癌症筛查套件为40多个国家的3000多家医院和医疗机构提供服务。我们的临床发现在顶级期刊上发表，包括《临床肿瘤学杂志》和《柳叶刀数字健康》，并在ASCO和RSNA等全球会议上发表。2024年，Lunit收购了Volpara Health Technologies，为无与伦比的协同作用和准确性奠定了基础，特别是在乳房健康和筛查技术方面。Lunit总部位于韩国首尔，拥有全球办公室网络，在医疗AI创新方面处于领先地位。

Discover more at lunit.io.SOURCE Lunit.

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