Deep learning score predicts PD-L1 status among patients with non-small cell lung cancer

A deep learning score accurately predicted PD-L1 expression among a cohort of patients with non-small cell lung cancer who underwent PET/CT scans, according to study findings published in Journal for ImmunoTherapy of Cancer. "This study is important, as it is the single largest multi-institutional radiomic study population of [patients with NSCLC] to date treated with immunotherapy who had PET/CT scans that were used to predict PD-L1 status and subsequent treatment response," Robert J. Gillies, PhD, chair of cancer physiology and vice chair of radiology research at Moffitt Cancer Center, said in a press release. "Because images are routinely obtained and are not subject to sampling bias per se, we propose that the individualized risk assessment information provided by these analyses may be useful as a future clinical decision support tool pending larger prospective trials." Gillies and colleagues developed a deep learning score to predict PD-L1 expression, durable clinical benefit, PFS and OS among 697 patients with NSCLC treated with immune checkpoint inhibitors across three institutions. According to study results, the score enabled researchers to distinguish between patients with PD-L1-positive and PD-L1-negative status.