Peer-Reviewed Publication
J Thorac Dis2025;17(10):8700-8714.October 31, 2025Journal Article
Predicting targeted therapy resistance in non-small cell lung cancer using multimodal machine learning.
Peiying Hua1, Andrea Olofson2, Faraz Farhadi3, Liesbeth Hondelink4, Gregory Tsongalis5, Konstantin Dragnev6, Dagmar Hoegemann Savellano3, Arief Suriawinata5, Laura Tafe5, Saeed Hassanpour1,7,8
1Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
2Department of Pathology, Ochsner Health System, New Orleans, LA, USA.
3Department of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA.
4Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands.
5Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA.
6Department of Medical Oncology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA.
7Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
8Department of Computer Science, Dartmouth College, Hanover, NH, USA.
Abstract
BACKGROUND: Resistance to tyrosine kinase inhibitors remains a major clinical challenge in the treatment of non-small cell lung cancer (NSCLC) with activating epidermal growth factor receptor (EGFR) mutations. Despite the efficacy of third-generation EGFR inhibitors, no standard tool currently exists to predict resistance using routinely available clinical data. In this study, we aim to develop a…
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