Machine learning to predict completion of treatment for pancreatic cancer
Our findings underscore the potential of ML to forecast PC treatment completion, highlighting the importance of specific preoperative factors. Increasing data volumes may enhance predictive accuracy, offering valuable insights for personalized patient strategies.
Authors
- Lyudmyla Demyan (ORCID: https://orcid.org/0000-0001-5494-5956)
- Elliot Newman (ORCID: https://orcid.org/0000-0002-7932-2891)
- Abdullah Khalid (ORCID: https://orcid.org/0009-0005-0238-9157)
- Sarah J. Hartman
- Daniel A. King (ORCID: https://orcid.org/0000-0001-6451-712X)
- Todd Levy (ORCID: https://orcid.org/0000-0003-0440-7791)
- Theodoros P. Zanos (ORCID: https://orcid.org/0000-0002-9204-9551)
- Shamsher A. Pasha
- Marcovalerio Melis (ORCID: https://orcid.org/0000-0001-7697-6796)
- Matthew J. Weiss (ORCID: https://orcid.org/0000-0002-1052-0128)
Institutions
- Northwell Health (US)
- Lenox Hill Hospital (US)
Publication Details
- Journal
- Journal of Surgical Oncology
- Published
- 2024-08-19
- DOI
- https://doi.org/10.1002/jso.27812
- Citations
- 4
- Primary Topic
- Pancreatic and Hepatic Oncology Research
- Type
- article
- Field-Weighted Citation Impact
- 1.13