A Multi-Institutional Study to Evaluate Automated Whole Slide Scoring of Immunohistochemistry for Assessment of Programmed Death-Ligand 1 (PD-L1) Expression in Non-Small Cell Lung Cancer.

Assessment of programmed death-ligand 1 (PD-L1) expression is a critical part of patient management for immunotherapy. However, studies have shown that pathologist-based analysis lacks reproducibility, especially for immune cell expression. The purpose of this study was to validate reproducibility of the automated machine-based Optra image analysis for PD-L1 immunohistochemistry for both tumor cells (TCs) and immune cells. We compared conventional pathologists' scores for both tumor and immune cell positivity separately using 22c3 antibody on the Dako Link 48 platform for PD-L1 expression in non-small cell lung carcinoma. We assessed interpretation first by pathologists and second by PD-L1 image analysis scores. Lin's concordance correlation coefficients (LCCs) for each pathologist were measured to assess variability between pathologists and between pathologists and Optra automated quantitative scores in scoring both tumor and immune cells. Lin's LCCs to evaluate the correlation between pathologists for TC was 0.75 [95% confidence interval (CI), 0.64-0.81] and 0.40 (95% CI, 0.40-0.62) for immune cell scoring. Pathologists were highly concordant for tumor scoring, but not for immune cell scoring, which is similar to previously reported studies where agreement is higher in TCs than immune cells. The LCCs between conventional pathologists' read and the machine score were 0.80 (95% CI, 0.74-0.85) for TCs and 0.70 (95% CI, 0.60-0.76) for immune cell population. This is considered excellent agreement for TCs and good concordance for immune cells. The automated scoring methods showed concordance with the pathologists' average scores that were comparable to interpathologist scores. This suggests promise for Optra automated assessment of PD-L1 in non-small cell lung cancer.

An international multicenter study to evaluate reproducibility of automated scoring for assessment of Ki67 in breast cancer.

The nuclear proliferation biomarker Ki67 has potential prognostic, predictive, and monitoring roles in breast cancer. Unacceptable between-laboratory variability has limited its clinical value. The International Ki67 in Breast Cancer Working Group investigated whether Ki67 immunohistochemistry can be analytically validated and standardized across laboratories using automated machine-based scoring. Sets of pre-stained core-cut biopsy sections of 30 breast tumors were circulated to 14 laboratories for scanning and automated assessment of the average and maximum percentage of tumor cells positive for Ki67. Seven unique scanners and 10 software platforms were involved in this study. Pre-specified analyses included evaluation of reproducibility between all laboratories (primary) as well as among those using scanners from a single vendor (secondary). The primary reproducibility metric was intraclass correlation coefficient between laboratories, with success considered to be intraclass correlation coefficient >0.80. Intraclass correlation coefficient for automated average scores across 16 operators was 0.83 (95% credible interval: 0.73-0.91) and intraclass correlation coefficient for maximum scores across 10 operators was 0.63 (95% credible interval: 0.44-0.80). For the laboratories using scanners from a single vendor (8 score sets), intraclass correlation coefficient for average automated scores was 0.89 (95% credible interval: 0.81-0.96), which was similar to the intraclass correlation coefficient of 0.87 (95% credible interval: 0.81-0.93) achieved using these same slides in a prior visual-reading reproducibility study. Automated machine assessment of average Ki67 has the potential to achieve between-laboratory reproducibility similar to that for a rigorously standardized pathologist-based visual assessment of Ki67. The observed intraclass correlation coefficient was worse for maximum compared to average scoring methods, suggesting that maximum score methods may be suboptimal for consistent measurement of proliferation. Automated average scoring methods show promise for assessment of Ki67 scoring, but requires further standardization and subsequent clinical validation.