ArticlePathologica2024
A preliminary study on the diagnostic performance of the uPath PD-L1 (SP263) artificial intelligence (AI) algorithm in patients with NSCLC treated with PD-1/PD-L1 checkpoint blockade.
Article in Pathologica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Clinical relevance of AI-based PD-L1 scoring in non-small cell lung cancer.Frontiers in oncology · 2026Article
- Enhanced patient selection with quantitative continuous scoring of PD-L1 expression for IO treatment in metastatic NSCLC.NPJ precision oncology · 2025Article
- Comparative performance of PD-L1 scoring by pathologists and AI algorithms.Histopathology · 2025Article
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14 authors.
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Abstract
Objective: The uPath PD-L1 (SP263) is an AI-based platform designed to aid pathologists in identifying and quantifying PD-L1 positive tumor cells in non-small cell lung cancer (NSCLC) samples stained with the SP263 assay. Methods: In this preliminary study, we explored the diagnostic performance of the uPath PD-L1 algorithm in defining PD-L1 tumor proportion score (TPS) and predict clinical outcomes in a series of patients with advanced stage NSCLC treated with single agent PD-1/PD-L1 checkpoint blockade previously assessed with the SP263 assay in clinical practice. Results: 44 patients treated from August 2015 to January 2019 were included, with baseline PD-L1 TPS of ≥ 50%, 1-49% and < 1% in 38.6%, 25.0% and 36.4%, respectively. The median uPath PD-L1 score was 6 with a significant correlation with the baseline PD-L1 TPS (r: 0.83, p < 0.01). However, only 27 cases (61.4%) were scored within the same clinically relevant range of expression (≥ vs < 50%). In the study population the baseline PD-L1 TPS was not significantly associated with clinical outcomes, while the uPath PD-L1 score showed a good diagnostic ability for the risk of death at the ROC curve analysis [AUC: 0.81 (95%CI: 0.66-0.91), optimal cut-off of ≥ 3.2], resulting in 19 patients (43.2%) being u-Path low and 25 patients (56.8%) being uPath high. The objective response rate in uPath high and low was 51.6% and 25.0% (p = 0.1), respectively, although the uPath was significantly associated with overall survival (OS, HR 2.45, 95%CI: 1.19-5.05) and progression free survival (PFS, HR 3.04, 95%CI: 1.51-6.14). At the inverse probability of treatment weighting analysis used to balance baseline covariates, the uPath categories confirmed to be independently associated with OS and PFS. Conclusions: This preliminary analysis suggests that AI-based, digital pathology tools such as uPath PD-L1 (SP263) can be used to optimize already available biomarkers for immune-oncology treatment in patients with NSCLC.
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