- 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
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).
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.
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.
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.
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. 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 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."
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.
Visit Lunit at booth IH22 to discover how the Lunit SCOPE suite
is revolutionizing oncology research and clinical practice.
Presentations at ASCO 2024 featuring Lunit SCOPE
include:
- "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)
About 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.
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. Discover more at lunit.io.
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