Toward Response-Adaptive Therapy in Locally Advanced NSCLC: Integrating ctDNA and Radiomics for Risk Stratification
This spotlight highlights a novel multimodal risk stratification model for patients with locally advanced non-small cell lung cancer undergoing chemoradiation, integrating mid-treatment ctDNA kinetics with radiomic features from baseline imaging. The study by Moding and colleagues provides compelling proof-of-concept data for real-time response-adaptive therapy, though further work is needed to address implementation barriers and standardize these biomarkers for clinical use. See related article by Moding et al., p. 1609.
Authors
- Charu Aggarwal (ORCID: https://orcid.org/0000-0002-3181-9460)
- Valsamo Anagnostou (ORCID: https://orcid.org/0000-0001-9480-3047)
Institutions
- Johns Hopkins University (US)
- Johns Hopkins Medicine (US)
- Sidney Kimmel Comprehensive Cancer Center (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Cancer Discovery
- Published
- 2025-08-04
- DOI
- https://doi.org/10.1158/2159-8290.cd-25-0898
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72