Precision histology: how deep learning is poised to revitalize histomorphology for personalized cancer care

Abstract

Accurate interpretation of the hematoxylin and eosin (H&E) slide has remained the foundation of pathological analysis and diagnostic medicine for over a century. 1 For the pathologist, the H&E slide is equivalent to a high-quality patient history or physical exam. It combines art and science to help triage and guide more focused and specialized ancillary studies. Unfortunately, the perceived value of histomorphologic analysis in the era of precision medicine is diminishing in recent years due to the emergence of more contemporary and data-rich molecular studies. 2 , 3 , 4 Ironically, this is no different than the scrutiny that the patient history and physical exam have faced, in light of widely available whole-body imaging technologies. 5 , 6 , 7 Some have even proposed that given the exponential decrease in sequencing costs, medical assessment could effectively begin with whole-genome analysis. 8 Here, we discuss the current state and the possible future of the H&E stain by highlighting some of its strengths and shortcomings. It may well be that the scrutiny that the H&E microscopic exam has faced in recent years 4 is no fault of its own, but the lack of effective approaches to routinely extract more of the rich morphologic information it contains.