Digital pathology and image analysis in tissue biomarker research

• Published in: Methods (San Diego, Calif.) Vol. 70; no. 1; pp. 59 - 73
• Main Authors: Hamilton, Peter W., Bankhead, Peter, Wang, Yinhai, Hutchinson, Ryan, Kieran, Declan, McArt, Darragh G., James, Jacqueline, Salto-Tellez, Manuel
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2014
• Subjects: Biobank; Biological Specimen Banks; Biomarker; Biomarkers - chemistry; Breast Neoplasms - metabolism; Colorectal Neoplasms - metabolism; Computational Biology - methods; Digital pathology; DNA - chemistry; Drug discovery; Female; Fluorescent Dyes - chemistry; Genotype; Humans; Image Processing, Computer-Assisted - methods; Immunohistochemistry - methods; In Situ Hybridization, Fluorescence; Male; Microscopy, Fluorescence - methods; Molecular pathology; Pattern Recognition, Automated; Personalized medicine; Phenotype; Precision Medicine - methods; Prostatic Neoplasms - metabolism; Software; Tissue Array Analysis; Tissue microarrays; Tumour analysis; Digital pathology; Biomarker; Tissue microarrays; Biobank; Tumour analysis; Molecular pathology; Drug discovery; Personalized medicine
• ISSN: 1046-2023; 1095-9130
• DOI: 10.1016/j.ymeth.2014.06.015

Digital pathology and the adoption of image analysis have grown rapidly in the last few years. This is largely due to the implementation of whole slide scanning, advances in software and computer processing capacity and the increasing importance of tissue-based research for biomarker discovery and stratified medicine. This review sets out the key application areas for digital pathology and image analysis, with a particular focus on research and biomarker discovery. A variety of image analysis applications are reviewed including nuclear morphometry and tissue architecture analysis, but with emphasis on immunohistochemistry and fluorescence analysis of tissue biomarkers. Digital pathology and image analysis have important roles across the drug/companion diagnostic development pipeline including biobanking, molecular pathology, tissue microarray analysis, molecular profiling of tissue and these important developments are reviewed. Underpinning all of these important developments is the need for high quality tissue samples and the impact of pre-analytical variables on tissue research is discussed. This requirement is combined with practical advice on setting up and running a digital pathology laboratory. Finally, we discuss the need to integrate digital image analysis data with epidemiological, clinical and genomic data in order to fully understand the relationship between genotype and phenotype and to drive discovery and the delivery of personalized medicine.