Treatment planning for lung cancer can often be complex due to variations in assessing immune biomarkers. In a new study, Yale Cancer Center researchers at Yale School of Medicine used artificial intelligence (AI) tools and digital pathology to improve the accuracy of this process.
Researchers compared AI-powered digital scoring with traditional manual scoring of the PD-L1 immune biomarker to determine if a new immunotherapy treatment, atezolizumab, could benefit patients with advanced non-small cell lung cancer. PD-L1 expression is considered the best biomarker to predict responsiveness to immune-checkpoint inhibitors.
Roy S. Herbst, lead study author and deputy director of Yale Cancer Center, assistant dean of translational research, Yale School of Medicine.
To conduct this study, researchers used data from the phase III trial IMpower 110, which tested the effectiveness of atezolizumab compared to chemotherapy as a first-line treatment for advanced non-small cell lung cancer (NSCLC). Using both manual and AI-powered tumor cell scoring, researchers found that the AI model was able to identify more patients as PD-L1 positive compared to the conventional manual scoring.
The study also demonstrated that both manual and digital scoring methods were equally adept at predicting patient outcomes, including overall survival and progression-free survival. The AI model also helped conclude that among patients with squamous histology (a specific subtype of NSCLC), the presence of PD-L1+ lymphocytes correlated with improved progression-free survival when treatment included atezolizumab. (VKR/Newswise)
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