Historically, pathologists perform manual evaluation of H&E-or immunohistochemically-stained slides, which can be subjective, inconsistent, and, at best, semiquantitative. As the complexity of staining and demand for increased precision of manual evaluation increase, the pathologist's assessment will include automated analyses (i.e., ''digital pathology'') to increase the accuracy, efficiency, and speed of diagnosis and hypothesis testing and as an important biomedical research and diagnostic tool. This commentary introduces the many roles for pathologists in designing and conducting high-throughput digital image analysis. Pathology review is central to the entire course of a digital pathology study, including experimental design, sample quality verification, specimen annotation, analytical algorithm development, and report preparation. The pathologist performs these roles by reviewing work undertaken by technicians and scientists with training and expertise in image analysis instruments and software. These roles require regular, face-to-face interactions between team members and the lead pathologist. Traditional pathology training is suitable preparation for entry-level participation on image analysis teams. The future of pathology is very exciting, with the expanding utilization of digital image analysis set to expand pathology roles in research and drug development with increasing and new career opportunities for pathologists.

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The anatomic pathology discipline is slowly moving toward a digital workflow, where pathologists will evaluate whole-slide images on a computer monitor rather than glass slides through a microscope. One of the driving factors in this workflow is computer-assisted scoring, which depends on appropriate selection of regions of interest. With advances in tissue pattern recognition techniques, a more precise region of the tissue can be evaluated, no longer bound by the pathologist's patience in manually outlining target tissue areas. Pathologists use entire tissues from which to determine a score in a region of interest when making manual immunohistochemistry assessments. Tissue pattern recognition theoretically offers this same advantage; however, error rates exist in any tissue pattern recognition program, and these error rates contribute to errors in the overall score. To provide a real-world example of tissue pattern recognition, 11 HER2-stained upper gastrointestinal malignancies with high heterogeneity were evaluated. HER2 scoring of gastric cancer was chosen due to its increasing importance in gastrointestinal disease. A method is introduced for quantifying the error rates of tissue pattern recognition. The trade-off between fully sampling tumor with a given tissue pattern recognition error rate versus randomly sampling a limited number of fields of view with higher target accuracy was modeled with a Monte-Carlo simulation. Under most scenarios, stereological methods of sampling-limited fields of view outperformed whole-slide tissue pattern recognition approaches for accurate immunohistochemistry analysis. The importance of educating pathologists in the use of statistical sampling is discussed, along with the emerging role of hybrid whole-tissue imaging and stereological approaches.

There is an emerging need for more effective approaches to accurately quantitate protein expression in tissue samples. In many clinical studies and particularly in pharmaceutical clinical trials, access to adequate tissue samples is a major bottleneck, and thus techniques to measure protein expression in these valuable tissue specimens is important. This study will review current approaches in multiplexing of protein expression in tissue, and discusses new approaches using a novel image registration technique across multiple tissue sections.

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