1. 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.
- Author
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Cortellini A, Zampacorta C, De Tursi M, Grillo LR, Ricciardi S, Bria E, Martini M, Giusti R, Filetti M, Dal Mas A, Russano M, Dall'Olio FG, Buttitta F, and Marchetti A
- Subjects
- Humans, Male, Female, Middle Aged, Aged, Biomarkers, Tumor analysis, Aged, 80 and over, Adult, Programmed Cell Death 1 Receptor antagonists & inhibitors, Carcinoma, Non-Small-Cell Lung drug therapy, Carcinoma, Non-Small-Cell Lung pathology, Carcinoma, Non-Small-Cell Lung diagnosis, B7-H1 Antigen analysis, Lung Neoplasms pathology, Lung Neoplasms drug therapy, Lung Neoplasms diagnosis, Immune Checkpoint Inhibitors therapeutic use, Algorithms, Artificial Intelligence
- 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., (Copyright © 2024 Società Italiana di Anatomia Patologica e Citopatologia Diagnostica, Divisione Italiana della International Academy of Pathology.)
- Published
- 2024
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