Is machine learning-based assessment of tumor-infiltrating lymphocytes on standard histologic images associated with outcomes of immunotherapy in patients with NSCLC?

Recent advances in artificial intelligence and slide scanning technology have enabled big data analysis of pathology tissue images.We read with great interest the article by Rakaee and colleagues (1), who developed a machine learning (ML)-based method to count tumor-infiltrating lymphocytes (TILs) on hematoxylin-eosin-stained standard pathologic images of primary or metastatic tumors.The authors demonstrated an association between TIL status based on ML-based assessment and outcomes of immune checkpoint inhibitor (ICI) therapy in patients with non-small cell lung cancer (NSCLC).Specifically, in retrospective cohorts of patients with NSCLC who underwent anti-programmed death-ligand 1 (PD-L1) or anti-programmed death-1 (PD-1) (i.e., anti-CD274 or anti-PDCD1) monotherapy, high-TIL (≥250 cells/mm 2 ) tumors were associated with more prolonged survival as compared with low-TIL (<250 cells/mm 2 ) tumors.Particularly for PD-L1-negative tumors, TIL assessment showed good performance in predicting ICI response.Very few patients with PD-L1-negative and low-TIL tumors responded to