Predicting targeted therapy resistance in non-small cell lung cancer using multimodal machine learning
This study presents a robust multimodal machine learning model for predicting therapy resistance in <i>EGFR</i>-mutant NSCLC, leveraging routinely collected clinical data without manual feature engineering. The model demonstrated superior performance over unimodal models and effective hazard stratification, suggesting utility for personalized treatment decisions. These findings underscore the potential of multimodal artificial intelligence (AI) tools to advance precision oncology, particularly in resource-limited settings. Further validation in larger, diverse cohorts is warranted.
Authors
- Arief A. Suriawinata
- Faraz Farhadi (ORCID: https://orcid.org/0000-0002-6958-0811)
- Gregory Tsongalis
- Laura J. Tafe (ORCID: https://orcid.org/0000-0001-9613-641X)
- Saeed Hassanpour (ORCID: https://orcid.org/0000-0001-9460-6414)
- Konstantin H. Dragnev (ORCID: https://orcid.org/0000-0001-8277-6993)
- Peiying Hua
- Liesbeth M. Hondelink (ORCID: https://orcid.org/0000-0002-7173-7443)
- Andrea M. Olofson
- Dagmar Hoegemann Savellano
Institutions
- Dartmouth College (US)
- Dartmouth–Hitchcock Medical Center (US)
- Leiden University Medical Center (NL)
Publication Details
- Journal
- Journal of Thoracic Disease
- Published
- 2025-10-01
- DOI
- https://doi.org/10.21037/jtd-2025-1012
- Citations
- 2
- Primary Topic
- Lung Cancer Treatments and Mutations
- Type
- article
- Field-Weighted Citation Impact
- 3.04