Automated wholeslide analysis of multiplex-brightfield IHC images for cancer cells and carcinoma-associated fibroblasts

Auranuch Lorsakul; Emilia Andersson; Suzana Vega Harring; Hadassah Sade; Oliver Grimm; Joerg Bredno

doi:10.1117/12.2254459

1 March 2017 Automated wholeslide analysis of multiplex-brightfield IHC images for cancer cells and carcinoma-associated fibroblasts

Auranuch Lorsakul, Emilia Andersson, Suzana Vega Harring, Hadassah Sade, Oliver Grimm, Joerg Bredno

Author Affiliations +

Proceedings Volume 10140, Medical Imaging 2017: Digital Pathology; 1014007 (2017) https://doi.org/10.1117/12.2254459
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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.

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Citation Download Citation

Auranuch Lorsakul, Emilia Andersson, Suzana Vega Harring, Hadassah Sade, Oliver Grimm, and Joerg Bredno "Automated wholeslide analysis of multiplex-brightfield IHC images for cancer cells and carcinoma-associated fibroblasts", Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 1014007 (1 March 2017); https://doi.org/10.1117/12.2254459

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