Machine learning predicting acute pain and opioid dose in radiation treated oropharyngeal cancer patients
ML models showed promise in predicting end-of-treatment pain intensity, opioid requirements and analgesics efficacy in OCC/OPC patients. Baseline pain score and vital signs are crucial predictors. Their implementation in clinical practice could facilitate early risk stratification and personalized pain management.
Authors
- Renjie He (ORCID: https://orcid.org/0000-0001-9166-6286)
- G. Brandon Gunn (ORCID: https://orcid.org/0000-0001-8197-0380)
- Ariana J Sahli (ORCID: https://orcid.org/0000-0003-0562-9468)
- Kareem A. Wahid (ORCID: https://orcid.org/0000-0002-0503-0175)
- Laia Humbert‐Vidan (ORCID: https://orcid.org/0000-0002-8005-6770)
- David I. Rosenthal (ORCID: https://orcid.org/0000-0001-7124-7447)
- Katherine A. Hutcheson (ORCID: https://orcid.org/0000-0003-3710-5706)
- Mohamed A. Naser (ORCID: https://orcid.org/0000-0003-1020-4966)
- Clifton D. Fuller (ORCID: https://orcid.org/0000-0002-5264-3994)
- Vivian Salama (ORCID: https://orcid.org/0000-0002-4870-4635)
- Dina El-Habashy (ORCID: https://orcid.org/0000-0001-5182-9976)
- Amy C. Moreno (ORCID: https://orcid.org/0000-0001-6762-6807)
- Brandon Godinich (ORCID: https://orcid.org/0000-0002-4525-1693)
- Abdallah Mohamed (ORCID: https://orcid.org/0000-0003-2064-7613)
Institutions
- West Virginia University (US)
- The University of Texas MD Anderson Cancer Center (US)
- University Medical Center of El Paso (US)
- West Virginia University Hospitals (US)
Publication Details
- Journal
- Frontiers in Pain Research
- Published
- 2025-04-04
- DOI
- https://doi.org/10.3389/fpain.2025.1567632
- Primary Topic
- Oral health in cancer treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00