Abstract 84: Deep-learning prediction of progression-free survival to EGFR inhibitors from H&amp;E tissue slides in advanced <i>EGFR</i> -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&amp;E) tissue slides, adjusting to the TKI type. Diagnostic slides from primary tumors of aNSCLC with common EGFR mutations were digitized at 20× at three independent institutions, and retrospectively included with clinical data. Performance was evaluated with C-index for continuous PFS and AUC for 1-year PFS, using bootstrapping. Results: In total, 141 patients were included (n=73 osimertinib; 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&amp;E slides. Despite limited sample size, these findings support further investigation of digital pathology for PFS prediction in EGFR-mutated NSCLC. Results on additional cohorts, combination of the model with clinical variables, and interpretability will be presented at the congress. Citation Format: Lodovica Zullo, Elin Samuelsson, Fabrizio Citarella, Mina Farag, Francesco Cortiula, Andrea De Giglio, Frank Aboubakar, Katharina von Loga, Etienne Hatton, Lorenzo Nibid, Giovanna De Maglio, Francesca Ambrosi, Beatrice Ramella Pollone, Melanie Janson, Alessandro Russo, Andrew Whittum, Ingrid Garberis, Victor Aubert, Hortense Deslandes, David Planchard, Fabrice Andre, Cecile Badoual, Benjamin Besse, Maria Rosa Ghigna, Mihaela Aldea. Deep-learning prediction of progression-free survival to EGFR inhibitors from H&amp;E tissue slides in advanced EGFR-mutated non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 84.