TissueCypher™: A systems biology approach to anatomic pathology

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Background:

Current histologic methods for diagnosis are limited by intra- and inter-observer variability. Immunohistochemistry (IHC) methods are frequently used to assess biomarkers to aid diagnoses, however, IHC staining is variable and nonlinear and the manual interpretation is subjective. Furthermore, the biomarkers assessed clinically are typically biomarkers of epithelial cell processes. Tumors and premalignant tissues are not composed only of epithelial cells but are interacting systems of multiple cell types, including various stromal cell types that are involved in cancer development. The complex network of the tissue system highlights the need for a systems biology approach to anatomic pathology, in which quantification of system processes is combined with informatics tools to produce actionable scores to aid clinical decision-making.

Aims:

Here, we describe a quantitative, multiplexed biomarker imaging approach termed TissueCypher™ that applies systems biology to anatomic pathology. Applications of TissueCypher™ in understanding the tissue system of Barrett's esophagus (BE) and the potential use as an adjunctive tool in the diagnosis of BE are described.

Patients and Methods:

The TissueCypher™ Image Analysis Platform was used to assess 14 epithelial and stromal biomarkers with known diagnostic significance in BE in a set of BE biopsies with nondysplastic BE with reactive atypia (RA, n = 22) and Barrett's with high-grade dysplasia (HGD, n = 17). Biomarker and morphology features were extracted and evaluated in the confirmed BE HGD cases versus the nondysplastic BE cases with RA.

Results:

Multiple image analysis features derived from epithelial and stromal biomarkers, including immune biomarkers and morphology, showed significant differences between HGD and RA.

Conclusions:

The assessment of epithelial cell abnormalities combined with an assessment of cellular changes in the lamina propria may serve as an adjunct to conventional pathology in the assessment of BE.

Related collections

Most cited references 82

Record: found
Abstract: not found
Book Chapter: not found

Contrast Limited Adaptive Histogram Equalization

Karel Zuiderveld (1994)

0 comments Cited 229 times – based on 0 reviews

Bookmark

Record: found
Abstract: found
Article: not found

Validating whole slide imaging for diagnostic purposes in pathology: guideline from the College of American Pathologists Pathology and Laboratory Quality Center.

Liron Pantanowitz, John Sinard, Walter H Henricks … (2013)

There is increasing interest in using whole slide imaging (WSI) for diagnostic purposes (primary and/or consultation). An important consideration is whether WSI can safely replace conventional light microscopy as the method by which pathologists review histologic sections, cytology slides, and/or hematology slides to render diagnoses. Validation of WSI is crucial to ensure that diagnostic performance based on digitized slides is at least equivalent to that of glass slides and light microscopy. Currently, there are no standard guidelines regarding validation of WSI for diagnostic use. To recommend validation requirements for WSI systems to be used for diagnostic purposes. The College of American Pathologists Pathology and Laboratory Quality Center convened a nonvendor panel from North America with expertise in digital pathology to develop these validation recommendations. A literature review was performed in which 767 international publications that met search term requirements were identified. Studies outside the scope of this effort and those related solely to technical elements, education, and image analysis were excluded. A total of 27 publications were graded and underwent data extraction for evidence evaluation. Recommendations were derived from the strength of evidence determined from 23 of these published studies, open comment feedback, and expert panel consensus. Twelve guideline statements were established to help pathology laboratories validate their own WSI systems intended for clinical use. Validation of the entire WSI system, involving pathologists trained to use the system, should be performed in a manner that emulates the laboratory's actual clinical environment. It is recommended that such a validation study include at least 60 routine cases per application, comparing intraobserver diagnostic concordance between digitized and glass slides viewed at least 2 weeks apart. It is important that the validation process confirm that all material present on a glass slide to be scanned is included in the digital image. Validation should demonstrate that the WSI system under review produces acceptable digital slides for diagnostic interpretation. The intention of validating WSI systems is to permit the clinical use of this technology in a manner that does not compromise patient care.

0 comments Cited 209 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution.

Adnan Mujahid Khan, Nasir M. Rajpoot, Darren Treanor … (2014)

Histopathology diagnosis is based on visual examination of the morphology of histological sections under a microscope. With the increasing popularity of digital slide scanners, decision support systems based on the analysis of digital pathology images are in high demand. However, computerized decision support systems are fraught with problems that stem from color variations in tissue appearance due to variation in tissue preparation, variation in stain reactivity from different manufacturers/batches, user or protocol variation, and the use of scanners from different manufacturers. In this paper, we present a novel approach to stain normalization in histopathology images. The method is based on nonlinear mapping of a source image to a target image using a representation derived from color deconvolution. Color deconvolution is a method to obtain stain concentration values when the stain matrix, describing how the color is affected by the stain concentration, is given. Rather than relying on standard stain matrices, which may be inappropriate for a given image, we propose the use of a color-based classifier that incorporates a novel stain color descriptor to calculate image-specific stain matrix. In order to demonstrate the efficacy of the proposed stain matrix estimation and stain normalization methods, they are applied to the problem of tumor segmentation in breast histopathology images. The experimental results suggest that the paradigm of color normalization, as a preprocessing step, can significantly help histological image analysis algorithms to demonstrate stable performance which is insensitive to imaging conditions in general and scanner variations in particular.

0 comments Cited 138 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Rebecca J. Critchley-Thorne

Journal

Journal ID (nlm-ta): J Pathol Inform

Journal ID (iso-abbrev): J Pathol Inform

Journal ID (publisher-id): JPI

Title: Journal of Pathology Informatics

Publisher: Medknow Publications & Media Pvt Ltd (India )

ISSN (Print): 2229-5089

ISSN (Electronic): 2153-3539

Publication date Collection: 2015

Publication date (Electronic): 31 August 2015

Volume: 6

Electronic Location Identifier: 48

Affiliations

[1 ]Department of Pathology and Laboratory Medicine, Geisinger Medical Center, Danville, PA 17822, USA

[2 ]Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA

[3 ]Cernostics, Inc., 235 William Pitt Way, Pittsburgh, PA 15238, USA

[4 ]Department of Computational and Systems Biology, Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15260, USA

Author notes

[* ]Corresponding Author

Article

Publisher ID: JPI-6-48

DOI: 10.4103/2153-3539.163987

PMC ID: 4584447

PubMed ID: 26430536

SO-VID: 7f9dd6f9-18cd-4e6e-a064-0824f4f41ff8

License:

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.