Work

38eP AI-based identification of TKI-sensitive driver mutations using routine histopathology in lung adenocarcinoma

Year: 2026

Type: article

Abstract: Tyrosine kinase inhibitors (TKIs) are a cornerstone of targeted therapy in lung adenocarcinoma, with TKI-sensitive driver mutations guiding treatment selection and predicting therapeutic response. Identifying these mutations currently relies on tumor genomic testing, which can be costly and may limit access in some clinical settings. Although prior studies have shown that histopathology images can predict specific mutations such as EGFR, they have not focused on clinically actionable TKI sensitivity and have often relied on task-specific end-to-end models.

Source: ESMO Open

DOI: 10.1016/j.esmoop.2026.106142

Authors: A. Ahmed, K. Nijjer

Institutions: York University, Stanford Medicine, Stanford University

Language: English

Topic: Lung Cancer Treatments and Mutations

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,910