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Proceedings Paper

Automated wholeslide analysis of multiplex-brightfield IHC images for cancer cells and carcinoma-associated fibroblasts
Author(s): Auranuch Lorsakul; Emilia Andersson; Suzana Vega Harring; Hadassah Sade; Oliver Grimm; Joerg Bredno
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Paper Abstract

Multiplex-brightfield immunohistochemistry (IHC) staining and quantitative measurement of multiple biomarkers can support therapeutic targeting of carcinoma-associated fibroblasts (CAF). This paper presents an automated digitalpathology solution to simultaneously analyze multiple biomarker expressions within a single tissue section stained with an IHC duplex assay. Our method was verified against ground truth provided by expert pathologists. In the first stage, the automated method quantified epithelial-carcinoma cells expressing cytokeratin (CK) using robust nucleus detection and supervised cell-by-cell classification algorithms with a combination of nucleus and contextual features. Using fibroblast activation protein (FAP) as biomarker for CAFs, the algorithm was trained, based on ground truth obtained from pathologists, to automatically identify tumor-associated stroma using a supervised-generation rule. The algorithm reported distance to nearest neighbor in the populations of tumor cells and activated-stromal fibroblasts as a wholeslide measure of spatial relationships. A total of 45 slides from six indications (breast, pancreatic, colorectal, lung, ovarian, and head-and-neck cancers) were included for training and verification. CK-positive cells detected by the algorithm were verified by a pathologist with good agreement (R2=0.98) to ground-truth count. For the area occupied by FAP-positive cells, the inter-observer agreement between two sets of ground-truth measurements was R2=0.93 whereas the algorithm reproduced the pathologists’ areas with R2=0.96. The proposed methodology enables automated image analysis to measure spatial relationships of cells stained in an IHC-multiplex assay. Our proof-of-concept results show an automated algorithm can be trained to reproduce the expert assessment and provide quantitative readouts that potentially support a cutoff determination in hypothesis testing related to CAF-targeting-therapy decisions.

Paper Details

Date Published: 1 March 2017
PDF: 6 pages
Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 1014007 (1 March 2017); doi: 10.1117/12.2254459
Show Author Affiliations
Auranuch Lorsakul, Roche Tissue Diagnostics (United States)
Emilia Andersson, Roche Innovation Ctr. Munich (Germany)
Suzana Vega Harring, Roche Innovation Ctr. Munich (Germany)
Hadassah Sade, Roche Innovation Ctr. Munich (Germany)
Oliver Grimm, Roche Innovation Ctr. Munich (Germany)
Joerg Bredno, Roche Tissue Diagnostics (United States)

Published in SPIE Proceedings Vol. 10140:
Medical Imaging 2017: Digital Pathology
Metin N. Gurcan; John E. Tomaszewski, Editor(s)

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