Computer-assisted tumor grading, validation of PD-L1 scoring, and quantification of CD8-positive immune cell density in urothelial carcinoma, a visual guide for pathologists using QuPath

Abstract

Background Advances in digital imaging in pathology and the new capacity to scan high-quality images have change the way to practice and research in surgical pathology. QuPath is an open-source pathology software that offers a reproducible way to analyze quantified variables. We aimed to present the functionality of biomarker scoring using QuPath and provide a guide for the validation of pathologic grading using a series of cases of urothelial carcinomas. Methods Tissue microarrays of urothelial carcinomas were constructed and scanned. The images stained with HE, CD8 and PD-L1 immunohistochemistry were imported into QuPath and dearrayed. Training images were used to build a grade classifier and applied to all cases. Quantification of CD8 and PD-L1 was undertaken for each core using cytoplasmic and membrane color segmentation and output measurement and compared with pathologists semi-quantitative assessments. Results There was a good correlation between tumor grade by the pathologist and by QuPath software (Kappa agreement 0.73). For low-grade carcinomas (by the report and pathologist), the concordance was not as high. Of the 32 low-grade tumors, 22 were correctly classified as low-grade, but 11 (34%) were diagnosed as high-grade, with the high-grade to the low-grade ratio in these misclassified cases ranging from 0.41 to 0.58. The median ratio for bona fide high-grade carcinomas was 0.59. Some of the reasons the authors list as potential mimickers for high-grade cases are fulguration artifact, nuclear hyperchromasia, folded tissues, and inconsistency in staining. The correlation analysis between the software and the pathologist showed that the CD8 marker showed a moderate (r = 0.595) and statistically significant (p < 0.001) correlation. The internal consistency of this parameter showed an index of 0.470. The correlation analysis between the software and the pathologist showed that the PDL1 marker showed a robust (r = 0.834) and significant (p < 0.001) correlation. The internal consistency of this parameter showed a CCI of 0.851. Conclusions We were able to demonstrate the utility of QuPath in identifying and scoring tumor cells and IHC quantification of two biomarkers. The protocol we present uses a free open-source platform to help researchers deal with imaging and data processing in the surgical pathology field.

Correlation Between ImageJ and Conventional Manual Scoring Methods for Programmed Death-Ligand 1 Immuno-Histochemically Stained Sections

“…Using QuPath, no significant difference was found between the automated image analysis scoring algorithm and pathologist HNSCC CPS scores 10 or in urothelial carcinomas. 21 Similarly, no significant difference between the automated FDA-cleared Aperio Imagescope IHC Membrane Image Analysis software scoring algorithm and pathologist scoring in gastric cancer was found. 26 Likewise, no significant difference between the automated image analysis scoring algorithm and pathologist scores in pancreatic cancer was obtained using PyRadiomics software.…”

“…An advantage of using semi-automated image analysis scoring over conventional scoring is that it measures DAB color staining in the entire annotated tumor region as a continuous variable with a single density score in comparison to pathologists scoring which provides only a visual estimate of PD-L1 expression in the same tumor region. 21 – 23 Another study suggested that the automated image analysis of the Dako 22C3 IHC assay yielded slightly lower percentages of positive cells compared with conventional scores. 24 This agrees with our results for DLBCL using the HSB method and for HNSCC using both RGB and HSB methods.…”

Standardized Digital Image Analysis of PD-L1 Expression in Head and Neck Squamous Cell Carcinoma Reveals Intra- and Inter-Sample Heterogeneity with Therapeutic Implications

“…Scoring of PD-L1 in non-small cell lung cancer and urothelial carcinomas by QuPath has already been validated and has shown a good reliability [ 16 , 17 ]. So, we decided to evaluate membranous staining of PD-L1 in an automated, quantitative, objective, and standardized manner by using the analysis software QuPath v3.4 (Open Software for Bioimage Analysis) after scanning the slides with 200× magnification (Nanozoomer HT2.0, Hamamatsu Photonics, Hamamatsu, Japan) [ 18 ].…”

The Quest for the Application of Artificial Intelligence to Whole Slide Imaging: Unique Prospective from New Advanced Tools

“…Probably the most relevant key point of QuPath is linked with its inherent flexibility, which is demonstrated by its application to a wide range of human pathology research and clinical areas. QuPath has been successfully utilized for computer-assisted tumor grading, PD-L1 scoring, and quantification of CD8positive immune cell density in urothelial carcinoma [36] and to detect and measure cell morphology. Another study [37] used QuPath to identify MLH1-deficient inflammatory bowel disease-associated colorectal cancers from tissue microarray.…”

“…From this review, it emerges that machine learning algorithms have improved diagnostic accuracy by providing an excellent tool for the identification of anomalies in whole slide images (WSIs) that may be missed, in some cases, by human pathologists. For example, recent studies have demonstrated improved accuracy in tumor grading and biomarker scoring, such as PD-L1 and CD8+ cell density measurements in cancer tissues [36,37]. The adoption of these tools drastically reduced the time for manual review, allowing pathologists to focus on those cases that are inherently more complex [37,38].…”