Prediction of Lymph Node Metastasis in Non–Small Cell Lung Carcinoma Using Primary Tumor Somatic Mutation Data
ML models leveraging primary tumor SNP data can enhance LNM prediction in NSCLC, outperforming traditional diagnostic methods. These findings underscore the potential of integrating genomics and ML to develop noninvasive biomarkers, enabling precise risk stratification and personalized treatment strategies in oncology.
Authors
- Nicholas Moore (ORCID: https://orcid.org/0000-0002-0079-7035)
- Patrick Oh (ORCID: https://orcid.org/0000-0001-9722-8754)
- Sanjay Aneja (ORCID: https://orcid.org/0000-0001-5681-7528)
- Henry Soo-Min Park (ORCID: https://orcid.org/0000-0002-9366-8514)
- Joshua Doyle
- Shari Bodofsky
- Abhijit A. Patel (ORCID: https://orcid.org/0000-0003-3943-5610)
- Robert Homer (ORCID: https://orcid.org/0000-0002-2055-5885)
- Victor Lee (ORCID: https://orcid.org/0000-0001-5075-0502)
- Sajid Hossain (ORCID: https://orcid.org/0000-0001-5319-2761)
- Daniel Hicks
Institutions
- Yale University (US)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2025-05-30
- DOI
- https://doi.org/10.1200/cci-24-00303
- Citations
- 1
- Primary Topic
- Cancer Genomics and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.94