title: Tissue pattern recognition error rates and tumor heterogeneity in gastric cancer.
abstract: 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.
authors: Flagship Biosciences, Flagstaff, AZ, USA. steve@flagshipbio.com
authors: Steven J
authors: SJ
authors: Potts
authors: Sarah E
authors: SE
authors: Huff
authors: Holger
authors: H
authors: Lange
authors: Vladislav
authors: V
authors: Zakharov
authors: David A
authors: DA
authors: Eberhard
authors: Joseph S
authors: JS
authors: Krueger
authors: David G
authors: DG
authors: Hicks
authors: George David
authors: GD
authors: Young
authors: Trevor
authors: T
authors: Johnson
authors: Christa L
authors: CL
authors: Whitney-Miller
mesh_terms: D000230:Adenocarcinoma / Q000378:metabolism / Q000473:pathology*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted; D003951:Diagnostic Errors; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D007150:Immunohistochemistry / Q000379:methods*; D008853:Microscopy; D009010:Monte Carlo Method; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000276:immunology / Q000378:metabolism*; D013274:Stomach Neoplasms / Q000378:metabolism / Q000473:pathology*; D057188:Workflow
chemical_list: D018719:Erb-b2 Receptor Tyrosine Kinases; C508053:ERBB2 protein, human