Predicting Postoperative Lung Cancer Recurrence and Survival Using Cox Proportional Hazards Regression and Machine Learning
These promising findings demonstrate the feasibility of predicting postoperative lung cancer recurrence and survival time using preoperative chest CT scans. However, further validation using larger, multisite cohort is necessary to ensure robustness and facilitate integration into clinical practice for improved cancer management.
Authors
- Rajeev Dhupar (ORCID: https://orcid.org/0000-0002-9581-3340)
- Lucy Pu
- Xin Meng (ORCID: https://orcid.org/0000-0001-7239-801X)
Institutions
- University of Pittsburgh (US)
- Wake Forest University (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Cancers
- Published
- 2024-12-26
- DOI
- https://doi.org/10.3390/cancers17010033
- Citations
- 13
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 5.45
Funders
- University of Pittsburgh
- National Institutes of Health
- Medical Center, University of Pittsburgh