Performance validation of an artificial intelligence-powered PD-L1 combined positive score analyzer in six cancer types.

Abstract

e13553 Background: Programmed death-ligand 1 (PD-L1) expression is a predictive marker for immune checkpoint inhibitors (ICI) treatment in various cancer types. The evaluation of PD-L1 expression level by combined positive score (CPS) correlates with immunotherapeutic response in biliary tract, colorectum, liver, pancreas, prostate, and gastric cancers. This study aimed to assess the performance of an artificial intelligence (AI)-powered PD-L1 CPS analyzer on these six cancer types, and to investigate whether the AI assistance could improve concordance among pathologists. Methods: Lunit SCOPE PD-L1 CPS was developed with 1.51 x 106 tumor cells and 8.73 x 105 immune cells from 2,372 PD-L1 stained whole-slide images (WSI) or tissue microarray cores from various cancer and normal tissues. The algorithm consisted of tissue area segmentation and cell detection AI models. The AI models calculated the CPS by detecting tumor cells over the tumor area and immune cells over the tumor and adjacent area. The model performance was validated on 135 PD-L1 stained WSIs including the six cancer types, which were interpreted by three pathologists. The concordant CPS classification (≥1 or <1) by two or more pathologists was considered as the consensus. Each pathologist revisited to evaluate WSIs with AI assistance (including visualization and scoring) if there was a discrepancy between the pathologist and the AI model. Results: Of 135 WSIs, 122 (90.4%) were classified as the same CPS subgroup by all three pathologists. The CPS ≥1 and <1 subgroup included 67 (49.6%) and 68 (50.4%) cases, respectively. The overall percent agreement (OPA) of the AI model to the consensus of pathologists was 84.4%, ranging from 78.3% (liver) to 91.3% (biliary tract). The AI-assisted re-evaluation by three pathologists was performed in 17, 19, and 22 WSIs, respectively. According to the AI-assisted revision, the unanimous agreement level was increased to 92.6% (125 cases). The OPA of the AI model to the consensus of pathologists was also increased to 91.9%, ranging from 82.6% (liver) to 100.0% (pancreas). Conclusions: This study shows that an AI-powered PD-L1 CPS analyzer can evaluate the CPS in the six cancer types analyzed here at a comparable level to pathologists. AI assistance can improve the concordance of pathologists' CPS interpretation.[Table: see text]

Digital Pathology Applications for PD-L1 Scoring in Head and Neck Squamous Cell Carcinoma: A Challenging Series

“…In terms of clinical impact, here, interpretation variability could have been relevant either for therapeutic (case n° 2, CPS score 1) or prognostic (case n° 7, CPS from 15 to 22) purposes, again stressing the potential impact of the different staining techniques around sensitive cutoffs (LDT/board vs. CDx/board k = 0.63). To address this evaluation heterogeneity, digital pathology can be a natural solution for the assessment of CPS scoring [ 18 ]. Here, we adopted the intrinsic capabilities of QuPath to perform subjective qualitative or semi-quantitative evaluation to establish eventual differences in terms of DAB distribution/intensity among the different PD-L1 preparations in order to understand technical/interpretative discrepancies observed by human eyes.…”

Clinical Validation of Artificial Intelligence–Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non–Small Cell Lung Cancer

“…Despite the complexity in analysis, AI-powered PD-L1 CPS analyzer has been developed and validated in urothelial carcinoma, 28 head and neck SqCC, and various other cancer types, posing the potential applicability in reading PD-L1 CPS in NSCLC. 29,30 The AI-powered PD-L1 IHC analyzer can also be used in combination with other biomarkers to predict the therapeutic response and patient prognosis more accurately. Our recent study deciphered that immune phenotype of the tumor characterized by distribution pattern of tumorinfiltrating lymphocytes (TILs) had strong predictive value for the ICI treatment outcome in patients with NSCLC when combining with PD-L1 TPS.…”