Artificial intelligence for diagnosis and predictive biomarkers in Non-Small cell lung cancer Patients: New promises but also new hurdles for the pathologist

Abstract

The rapid development of artificial intelligence (AI) based tools in pathology laboratories has brought forward unlimited opportunities for pathologists. Promising AI applications used for accomplishing diagnostic, prognostic and predictive tasks are being developed at a high pace. This is notably true in thoracic oncology, given the significant and rapid therapeutic progress made recently for lung cancer patients. Advances have been based on drugs targeting molecular alterations, immunotherapies, and, more recently antibody-drug conjugates which are soon to be introduced. For over a decade, many proof-of-concept studies have explored the use of AI algorithms in thoracic oncology to improve lung cancer patient care. However, despite the enthusiasm in this domain, the set-up and use of AI algorithms in daily practice of thoracic pathologists has not been operative until now, due to several constraints. The purpose of this review is to describe the potential but also the current barriers of AI applications in routine thoracic pathology for non-small cell lung cancer patient care and to suggest practical solutions for rapid future implementation.