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Area of Science:

  • Computational pathology
  • Genomic analysis
  • Artificial intelligence in oncology

Background:

  • Artificial intelligence (AI) models offer rapid, low-cost prediction of genomic alterations from pathology slides, potentially accelerating lung cancer treatment decisions.
  • The generalizability of AI models across diverse patient populations and tissue types remains largely unknown.

Purpose of the Study:

  • To evaluate the performance and generalizability of two open-source AI pathology models for predicting EGFR mutation status in lung adenocarcinoma (LUAD).
  • To assess model performance across independent cohorts and diverse ancestral subgroups.

Main Methods:

  • A cohort study included LUAD patients from two independent cohorts (Dana-Farber Cancer Institute and a European-based trial) with paired next-generation sequencing and whole-slide imaging data.
  • Genetic ancestry was inferred using germline genotype data in the DFCI cohort.
  • Model performance was measured by the area under the receiver operating characteristic curve (AUC), evaluated overall, by ancestry subgroup, and by sample type.

Main Results:

  • One AI model achieved a higher overall AUC (0.83) compared to the other (0.68) in the DFCI cohort.
  • Performance varied across ancestry subgroups, with the higher-performing model showing AUCs of 0.84 (European), 0.85 (African), and 0.68 (Asian).
  • Model performance declined in pleural specimens (AUC, 0.66) compared to lung specimens (AUC, 0.86). AI-guided triage suggested a potential 57% reduction in rapid EGFR testing with high sensitivity (0.84) and specificity (0.99).

Conclusions:

  • AI-based pathology tools show potential as preliminary adjuncts for EGFR prediction in lung cancer.
  • Performance differences across ancestral subgroups necessitate careful interpretation and further validation.
  • AI tools may streamline EGFR testing workflows, improving efficiency in lung cancer management.