QuPath: The global impact of an open source digital pathology system

Abstract

QuPath, originally created at the Centre for Cancer Research & Cell Biology at Queen’s University Belfast as part of a research programme in digital pathology (DP) funded by Invest Northern Ireland and Cancer Research UK, is arguably the most wildly used image analysis software program in the world. On the back of the explosion of DP and a need to comprehensively visualise and analyse whole slides images (WSI), QuPath was developed to address the many needs associated with tissue based image analysis; these were several fold and, predominantly, translational in nature: from the requirement to visualise images containing billions of pixels from files several GBs in size, to the demand for high-throughput reproducible analysis, which the paradigm of routine visual pathological assessment continues to struggle to deliver. Resultantly, large-scale biomarker quantification must increasingly be augmented with DP. Here we highlight the impact of the open source Quantitative Pathology & Bioimage Analysis DP system since its inception, by discussing the scope of scientific research in which QuPath has been cited, as the system of choice for researchers.

Ki67 and LSD1 Expression in Testicular Germ Cell Tumors Is Not Associated with Patient Outcome: Investigation Using a Digital Pathology Algorithm

“…QuPath is a new platform created for image analysis, with an increasing interest found in oncology research. Digital pathology is revolutionizing pathology practice and research, fulfilling the need for accurate biomarker analysis in leading reference hospitals; the need for a cost-effective tool with reproducible, consistent, and accurate results that is universally applicable; and the requirement for solutions allowing remote pathology diagnosis, for instance, in the context of pandemics [35].…”

Assessment of Ovarian Tumor Growth in Wild-Type and Lumican-Deficient Mice: Insights Using Infrared Spectral Imaging, Histopathology, and Immunohistochemistry

“…The number of cyclin D1 positive and negative nuclei were assessed in whole tumor areas, and their ratio was compared between Lum +/+ and Lum −/− mice. All quantitative analyses were performed using QuPath software [ 45 ].…”

Statistical and machine learning methods for spatially resolved transcriptomics with histology

“…Although progress has been made in integrating histology and gene expression, current methods mainly focus on the global pattern in histology images while the more granular information e.g., morphology of nucleus in each spot, is ignored. Nuclei segmentation in histopathology images is routinely done for pathology diagnosis [78] , [79] , [80] . However, such information has only been utilized to verify results after gene expression data are analyzed, but not directly used in analysis [27] .…”