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.
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.