Development and validation of a supervised deep learning algorithm for automated whole‐slide programmed death‐ligand 1 tumour proportion score assessment in non‐small cell lung cancer
Immunohistochemical programmed death‐ligand 1 (PD‐L1) staining to predict responsiveness to immunotherapy in patients with advanced non‐small cell lung cancer (NSCLC) has several drawbacks: a robust gold standard is lacking, and there is substantial interobserver and intraobserver variance, with up to 20% discordance around cutoff points. The aim of this study was to develop a new deep learning‐based PD‐L1 tumour proportion score (TPS) algorithm, trained and validated on a routine diagnostic dataset of digitised PD‐L1 (22C3, laboratory‐developed test)‐stained samples.
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Development and validation of a supervised deep learning algorithm for automated whole‐slide programmed death‐ligand 1 tumour proportion score assessment in non‐small cell lung cancer
Semantic Scholar · Medicine · 2021
Abstract
Immunohistochemical programmed death‐ligand 1 (PD‐L1) staining to predict responsiveness to immunotherapy in patients with advanced non‐small cell lung cancer (NSCLC) has several drawbacks: a robust gold standard is lacking, and there is substantial interobserver and intraobserver variance, with up to 20% discordance around cutoff points. The aim of this study was to develop a new deep learning‐based PD‐L1 tumour proportion score (TPS) algorithm, trained and validated on a routine diagnostic dataset of digitised PD‐L1 (22C3, laboratory‐developed test)‐stained samples.
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