Decoding Recurrence in Early-Stage and Locoregionally Advanced Non–Small Cell Lung Cancer: Insights From Electronic Health Records and Natural Language Processing
Our scalable NLP system enabled us to generate real-world insights into NSCLC recurrences, paving the way for predictive models for preventing, diagnosing, and treating NSCLC recurrence.
Authors
- Gustavo Stolovitzky (ORCID: https://orcid.org/0000-0002-9618-2819)
- Zongzhi Liu (ORCID: https://orcid.org/0000-0003-1578-9967)
- Iftekhar Kalsekar (ORCID: https://orcid.org/0000-0001-5232-5972)
- David Corrigan (ORCID: https://orcid.org/0000-0002-8239-129X)
- Kyeryoung Lee (ORCID: https://orcid.org/0000-0002-6937-9931)
- Tomi Jun (ORCID: https://orcid.org/0000-0002-2120-1704)
- William Oh (ORCID: https://orcid.org/0000-0001-5113-8147)
- Qing Huang (ORCID: https://orcid.org/0000-0001-6441-7591)
- Ravi Rajaram (ORCID: https://orcid.org/0000-0003-2782-7756)
- Xiaoyan Wang (ORCID: https://orcid.org/0000-0002-5487-0584)
Institutions
- The University of Texas MD Anderson Cancer Center (US)
- Sema4 (United States) (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1200/cci-24-00227
- Citations
- 1
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52