We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
Werfen

Download Mobile App




AI Identifies Advanced Lung Cancer Patients Who Respond to Immunotherapy

By LabMedica International staff writers
Posted on 10 Oct 2023

Lung cancer treatment planning is often complex due to the variations in evaluating immune biomarkers. More...

In a new study, researchers utilized artificial intelligence (AI) and digital pathology techniques to improve the accuracy of such evaluations.

The study by scientists at the Yale School of Medicine (New Haven, CT, USA) focused on how AI-based digital assessment could fare against traditional manual methods in scoring the PD-L1 immune biomarker. The goal was to see if a novel immunotherapy treatment called atezolizumab could be beneficial for patients suffering from advanced non-small cell lung cancer (NSCLC). To undertake this research, they drew upon data from the IMpower 110 phase III trial, which examined the effectiveness of atezolizumab against chemotherapy for treating advanced NSCLC. Through both manual and AI-guided evaluations of tumor cells, the team discovered that the AI system was more efficient at identifying patients as PD-L1 positive than manual methods.

Moreover, the study found that both AI-based and traditional manual scoring techniques were equally competent at predicting patient results, including how long patients lived and how long it took before the cancer progressed. Additionally, the AI system aided in confirming that for patients with a particular subtype of NSCLC known as squamous histology, the existence of PD-L1+ lymphocytes was linked to better outcomes in terms of slowing down disease progression when treated with atezolizumab.

“Our study suggests that artificial intelligence has the ability to improve the identification of PD-L1 positive patients by providing a predictive accuracy that was better than manual scoring,” said Roy S. Herbst, lead study author and deputy director of Yale Cancer Center. “The research underscores the potential of digital pathology and AI tools in enhancing PD-L1 scoring accuracy for both clinical practice and clinical trials.”

“The insights gained with AI and digital scoring could make diagnosing and choosing the right treatment easier,” added Herbst. “Our data shows that this AI technology can help refine strategies for treating advanced non-small cell lung cancer.”

Related Links:
Yale School of Medicine 


Gold Member
Automatic Hematology Analyzer
DH-800 Series
Gold Member
Fibrinolysis Assay
HemosIL Fibrinolysis Assay Panel
Capillary Blood Collection Tube
IMPROMINI M3
8-Channel Pipette
SAPPHIRE 20–300 µL
Read the full article by registering today, it's FREE! It's Free!
Register now for FREE to LabMedica.com and get access to news and events that shape the world of Clinical Laboratory Medicine.
  • Free digital version edition of LabMedica International sent by email on regular basis
  • Free print version of LabMedica International magazine (available only outside USA and Canada).
  • Free and unlimited access to back issues of LabMedica International in digital format
  • Free LabMedica International Newsletter sent every week containing the latest news
  • Free breaking news sent via email
  • Free access to Events Calendar
  • Free access to LinkXpress new product services
  • REGISTRATION IS FREE AND EASY!
Click here to Register








Channels

Hematology

view channel
Image: Research has linked platelet aggregation in midlife blood samples to early brain markers of Alzheimer’s (Photo courtesy of Shutterstock)

Platelet Activity Blood Test in Middle Age Could Identify Early Alzheimer’s Risk

Early detection of Alzheimer’s disease remains one of the biggest unmet needs in neurology, particularly because the biological changes underlying the disorder begin decades before memory symptoms appear.... Read more

Microbiology

view channel
Image: The SMART-ID Assay delivers broad pathogen detection without the need for culture (Photo courtesy of Scanogen)

Rapid Assay Identifies Bloodstream Infection Pathogens Directly from Patient Samples

Bloodstream infections in sepsis progress quickly and demand rapid, precise diagnosis. Current blood-culture methods often take one to five days to identify the pathogen, leaving clinicians to treat blindly... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.