Mod Pathol. 2024 Apr 6:100485. doi: 10.1016/j.modpat.2024.100485. Online ahead of print.
ABSTRACT
Several studies have developed various artificial intelligence (AI) models for immunohistochemical analysis of programmed death ligand 1 (PD-L1) in patients with non-small cell lung carcinoma; however, none have focused on specific ways by which AI-assisted systems could help pathologists determine the tumor proportion score (TPS). Herein, we developed an AI model to calculate the TPS of the PD-L1 22C3 assay and evaluated whether and how this AI-assisted system could help pathologists determine the TPS and analyze how AI-assisted systems could affect pathologists’ assessment accuracy. We assessed the four methods of the AI-assisted system: 1) and 2) pathologists first assessed and then referred to automated AI scoring results (1, positive tumor cell percentage; 2, positive tumor cell percentage and visualized overlay image) for a final confirmation, and 3) and 4) pathologists referred to the automated AI scoring results (3, positive tumor cell percentage; 4, positive tumor cell percentage and visualized overlay image) while determining TPS. Mixed model analysis was used to calculate the odds ratios (ORs) with 95% confidence intervals for AI-assisted TPS 1) to 4) compared with pathologists’ scoring. For all 584 samples of tissue microarray, the OR for AI-assisted TPS 1) to 4) was 0.94-1.07 and not statistically significant. Of them, we found 332 cases of discordant cases, on which the pathologists’ judgments were inconsistent; the ORs for AI-assisted TPS 1), 2), 3), and 4) were 1.28 (1.06-1.54, p = 0.012), 1.29 (1.06-1.55, p = 0.010), 1.28 (1.06-1.54, p = 0.012), and 1.29 (1.06-1.55, p = 0.010), respectively, which were statistically significant. For discordant cases, the OR for each AI-assisted TPS compared with the others was 0.99-1.01 and not statistically significant. This study emphasized the usefulness of the AI-assisted system for cases wherein pathologists had difficulty determining the PD-L1 TPS.
PMID:38588885 | DOI:10.1016/j.modpat.2024.100485
