Pixelwise H-score: A novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images
Authors: Pamela Vizcarra
Steven Pirie-Shepherd
Eric L Powell
Shibing Deng
Amy Jackson-Fisher
C L Painter
Xiaoling Xia
Pamela Whalen
Sripad Ram
Journal: PLoS ONE (2021)
Discipline: Multidisciplinary
Keywords: Immunohistochemistry; digital image analysis; biomarker expression; pixelwise H-score; whole-slide scanned images
Affiliations: Drug-Safety Research and Development, Pfizer Inc., San Diego, California, United States of America., Tumor Morphology Group, Oncology Research and Development, Pfizer Inc., San Diego, California, United States of America., Biostatistics Unit, Oncology Research and Development, Pfizer Inc., San Diego, California, United States of America.

Abstract:  Immunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images. 

Retracted: No

PubPeer comments:
 The authors describe the Pixelwise H-score, a method to analyse the prevalence and intensity of DAB stained digital pathology images. This approach removes the need for cell object detection in digital pathology images, which is particularly useful when studying markers that stain the membrane of cells (such as PDL1 which the authors demonstrate). The analysis routines are provided for three major platforms: QuPath, Visiopharm and Indica Labs Halo. **Of note for anyone running the Visiopharm implementation**, while Equation 3 in the paper lists the ratio of weighted pixel areas divided by the total pixel area, all multiplied by 100 (to replicate the traditional H-score range of [0-300]) the provided Visiopharm APPs (for the three markers) are missing the  multiply by 100 step thus providing an H-Score range from [0-3]. The APPs provided can be easily updated with this step. Hopefully this comment will prevent confusion for people trying to replicate the results in the paper. ,