Abstract 84: Deep-learning prediction of progression-free survival to EGFR inhibitors from H&E tissue slides in advanced EGFR -mutated non-small cell lung cancer.

Abstract Background: Third-generation tyrosine kinase inhibitors (TKIs) like osimertinib are standard for untreated advanced non-small cell lung cancer (aNSCLC) with common EGFR mutations (exon 19 deletion/L858R). Combination with chemotherapy, bispecific antibodies or anti-angiogenic agents prolong survival but raises toxicity. No biomarker identifies patients benefiting from combinations versus osimertinib alone, yet. Methods: We trained a multiple-instance model to predict progression-free survival (PFS) to first line EGFR-TKIs from hematoxylin-eosin (H n=68 erlotinib/gefitinib). We trained on Gustave Roussy (GR, France) and inferred on Campus Bio-Medico (CBM, Italy) and University Hospital of Udine (U, Italy). Patients were mostly female (65%, 62%, 72%), with no smoking habit (63%, 59%, 55%), had adenocarcinoma (92%, 94%, 100%) and exon 19 deletions (56%, 56%, 55%) across GR, CBM, U cohorts. Median (IQR) age was 65 (56-74), 71 (62-78), 68 (63-73) years in GR, CBM, U, respectively. Median true PFS and model performances are presented in the table. Conclusion: This is the first study to predict PFS to EGFR-TKIs from H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 84.