A Deep Learning-Based Assay for Programmed Death Ligand 1 Immunohistochemistry Scoring in Non-Small Cell Lung Carcinoma: Does it Help Pathologists Score?

Mod Pathol. 2024 Jun;37(6):100485. doi: 10.1016/j.modpat.2024.100485. Epub 2024 Apr 6.

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). In this study, 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 4 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 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% CI for AI-assisted TPS methods 1 to 4 compared with pathologists' scoring. For all 584 samples of the tissue microarray, the OR for AI-assisted TPS methods 1 to 4 was 0.94 to 1.07 and not statistically significant. Of them, we found 332 discordant cases, on which the pathologists' judgments were inconsistent; the ORs for AI-assisted TPS methods 1, 2, 3, and 4 were 1.28 (1.06-1.54; P = .012), 1.29 (1.06-1.55; P = .010), 1.28 (1.06-1.54; P = .012), and 1.29 (1.06-1.55; P = .010), respectively, which were statistically significant. For discordant cases, the OR for each AI-assisted TPS method compared with the others was 0.99 to 1.01 and not statistically significant. This study emphasized the usefulness of the AI-assisted system for cases in which pathologists had difficulty determining the PD-L1 TPS.

Keywords: AI-assisted system; PD-L1; deep learning-based assay; immunohistochemistry; non-small cell lung carcinoma.

MeSH terms

  • B7-H1 Antigen* / analysis
  • Biomarkers, Tumor* / analysis
  • Carcinoma, Non-Small-Cell Lung* / metabolism
  • Carcinoma, Non-Small-Cell Lung* / pathology
  • Deep Learning*
  • Female
  • Humans
  • Immunohistochemistry* / methods
  • Lung Neoplasms* / metabolism
  • Lung Neoplasms* / pathology
  • Male
  • Pathologists*
  • Reproducibility of Results

Substances

  • B7-H1 Antigen
  • CD274 protein, human
  • Biomarkers, Tumor