Byoung Chul Cho

TL;DR: In this paper, a machine learning-based clinical decision support algorithm was proposed to predict the anti-programmed death (PD)-1 response by comprehensively combining the clinical information, including patient characteristics, mutations and laboratory findings, from the electronic medical records of 142 patients with non-small-cell lung cancer treated with anti-PD-1 therapy.

TL;DR: It is demonstrated that whole tumorTLG could be an independent prognostic factor and high-dose radiotherapy could improve outcomes for NPC showing high whole tumor TLG.

TL;DR: In this article , the authors report 5-year results from the phase III KEYNOTE-042 study (ClinicalTrials.gov identifier: NCT02220894), where patients with locally advanced/metastatic non-small-cell lung cancer (NSCLC) without EGFR/ALK alterations and with programmed death ligand-1 (PD-L1) tumor proportion score (TPS) ≥ 1% received pembrolizumab 200 mg once every 3 weeks for 35 cycles or chemotherapy (carboplatin + paclitaxel or pemetrexed) for 4-6 cycles.