Abstract 1448: Deep learning integration of molecular and histopathological data for prognostic stratification in non small cell lung cancer.

Le résumé fourni par la source

Abstract Background: Co-mutations, PD-L1 and TILs are key NSCLC biomarkers. We applied deep learning to a multimodal patient cohort to identify prognostic patterns integrating morphology, mutations, and clinical features. Methods: 367 NSCLC patients from 18 Hellenic Cooperative Oncology Group-affiliated centers were retrospectively assessed for PD-L1 status (Dako 22C3 pharmDx), TILs (H&E slides), and somatic pathogenic variants with a 38-gene next-generation sequencing (NGS) panel. Whole slide images (WSI) were digitized by an optical microscope scanner. Ten pathology foundation models were benchmarked for predicting mutation, co-mutation, PD-L1 and TILs status. Mutated genes with >5% prevalence were considered for mutation and co-mutation endpoints (TP53, KRAS, STK11, PTEN, EGFR). A vision transformer model was trained on WSI features to predict endpoints and evaluate AUROC. Kaplan-Meier analysis assessed prognostic relevance of models and top feature tiles from model attention maps provided morphological explainability. The STAMP digital pathology pipeline supported feature extraction and model training. Results: Single mutation models yielded AUROC scores of 0.6-0.85, with STK11 prediction from HOptimus1 features highest. Co-mutation models produced AUROC scores of 0.69-0.77 with EGFR-TP53 prediction from Uni2 features the best. The KRAS-TP53 co-mutation model (AUROC 0.69, Uni2) showed significant separation in overall survival curves (p=0.05) between classes. Best-performing PD-L1 and TIL models also demonstrated significant survival separation (p=0.005 and p=0.05). Conclusion: Findings demonstrate the potential of pathology foundation models to derive complex clinically-relevant prognostic models for NSCLC with multimodal explainability. Citation Format: Sanddhya Jayabalan, Konstantinos Efthymiadis, Alexia Eliades, Kyriaki Papadopoulou, Abraham Pouliakis, Elena Fountzilas, Sofia Lampaki, Mattheos Bobos, Anna Goussia, Soultana Meditskou, Konstantinos Kyritsis, Helena Linardou, George Pentheroudakis, Dimitrios Bafaloukos, Dimitrios Pectasides, Epaminondas Samantas, Zunamys I. Carrero, George Fountzilas, Jakob N. Kather. Deep learning integration of molecular and histopathological data for prognostic stratification in 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 1448.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.