Machine learning predicting acute pain and opioid dose in radiation treated oropharyngeal cancer patients
IntroductionAcute pain is common among oral cavity/oropharyngeal cancer (OCC/OPC) patients undergoing radiation therapy (RT). This study aimed to predict acute pain severity and opioid doses during RT using machine learning (ML), facilitating risk-stratification models for clinical trials.MethodsA r...
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-04-01
|
| Series: | Frontiers in Pain Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpain.2025.1567632/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849735728517349376 |
|---|---|
| author | Vivian Salama Vivian Salama Laia Humbert-Vidan Brandon Godinich Brandon Godinich Kareem A. Wahid Kareem A. Wahid Dina M. ElHabashy Mohamed A. Naser Renjie He Abdallah S. R. Mohamed Ariana J. Sahli Katherine A. Hutcheson Gary Brandon Gunn David I. Rosenthal Clifton D. Fuller Amy C. Moreno |
| author_facet | Vivian Salama Vivian Salama Laia Humbert-Vidan Brandon Godinich Brandon Godinich Kareem A. Wahid Kareem A. Wahid Dina M. ElHabashy Mohamed A. Naser Renjie He Abdallah S. R. Mohamed Ariana J. Sahli Katherine A. Hutcheson Gary Brandon Gunn David I. Rosenthal Clifton D. Fuller Amy C. Moreno |
| author_sort | Vivian Salama |
| collection | DOAJ |
| description | IntroductionAcute pain is common among oral cavity/oropharyngeal cancer (OCC/OPC) patients undergoing radiation therapy (RT). This study aimed to predict acute pain severity and opioid doses during RT using machine learning (ML), facilitating risk-stratification models for clinical trials.MethodsA retrospective study examined 900 OCC/OPC patients treated with RT during 2017–2023. Pain intensity was assessed using NRS (0-none, 10-worst) and total opioid doses were calculated using morphine equivalent daily dose (MEDD) conversion factors. Analgesics efficacy was assessed using combined pain intensity and total MEDD. ML predictive models were developed and validated, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machine (GBM). Model performance was evaluated using discrimination and calibration metrics, while feature importance was investigated using bootstrapping.ResultsFor predicting pain intensity, the GBM demonstrated superior discrimination performance (AUROC 0.71, recall 0.39, and F1 score 0.48). For predicting the total MEDD, LR model outperformed other models (AUROC 0.67). For predicting analgesics efficacy, the SVM achieved the highest specificity (0.97), while the RF and GBM models achieved the highest AUROC (0.68). RF model emerged as the best calibrated model with an ECE of 0.02 and 0.05 for pain intensity and MEDD prediction, respectively. Baseline pain scores and vital signs demonstrated the most contributing features.ConclusionML 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. |
| format | Article |
| id | doaj-art-2378f0cb5ff24bb98c0f252f4ceebde4 |
| institution | DOAJ |
| issn | 2673-561X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pain Research |
| spelling | doaj-art-2378f0cb5ff24bb98c0f252f4ceebde42025-08-20T03:07:28ZengFrontiers Media S.A.Frontiers in Pain Research2673-561X2025-04-01610.3389/fpain.2025.15676321567632Machine learning predicting acute pain and opioid dose in radiation treated oropharyngeal cancer patientsVivian Salama0Vivian Salama1Laia Humbert-Vidan2Brandon Godinich3Brandon Godinich4Kareem A. Wahid5Kareem A. Wahid6Dina M. ElHabashy7Mohamed A. Naser8Renjie He9Abdallah S. R. Mohamed10Ariana J. Sahli11Katherine A. Hutcheson12Gary Brandon Gunn13David I. Rosenthal14Clifton D. Fuller15Amy C. Moreno16Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United StatesDepartment of Medical Oncology and Radiation Oncology, West Virginia University Cancer Institute, Morgantown, WV, United StatesDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United StatesDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United StatesDepartment of Medical Education, Paul L. Foster School of Medicine, Texas Tech Health Sciences Center, El Paso, TX, United StatesDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United StatesDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United StatesDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United StatesDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United StatesDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United StatesDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United StatesDepartment of Head and Neck Surgery, The University of Texas, MD Anderson Cancer Center, Houston, TX, United StatesDepartment of Head and Neck Surgery, The University of Texas, MD Anderson Cancer Center, Houston, TX, United StatesDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United StatesDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United StatesDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United StatesDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United StatesIntroductionAcute pain is common among oral cavity/oropharyngeal cancer (OCC/OPC) patients undergoing radiation therapy (RT). This study aimed to predict acute pain severity and opioid doses during RT using machine learning (ML), facilitating risk-stratification models for clinical trials.MethodsA retrospective study examined 900 OCC/OPC patients treated with RT during 2017–2023. Pain intensity was assessed using NRS (0-none, 10-worst) and total opioid doses were calculated using morphine equivalent daily dose (MEDD) conversion factors. Analgesics efficacy was assessed using combined pain intensity and total MEDD. ML predictive models were developed and validated, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machine (GBM). Model performance was evaluated using discrimination and calibration metrics, while feature importance was investigated using bootstrapping.ResultsFor predicting pain intensity, the GBM demonstrated superior discrimination performance (AUROC 0.71, recall 0.39, and F1 score 0.48). For predicting the total MEDD, LR model outperformed other models (AUROC 0.67). For predicting analgesics efficacy, the SVM achieved the highest specificity (0.97), while the RF and GBM models achieved the highest AUROC (0.68). RF model emerged as the best calibrated model with an ECE of 0.02 and 0.05 for pain intensity and MEDD prediction, respectively. Baseline pain scores and vital signs demonstrated the most contributing features.ConclusionML 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.https://www.frontiersin.org/articles/10.3389/fpain.2025.1567632/fullacute painopioid doseradiation therapyhead and neck cancersoral cavity and oropharyngeal cancersmachine learning |
| spellingShingle | Vivian Salama Vivian Salama Laia Humbert-Vidan Brandon Godinich Brandon Godinich Kareem A. Wahid Kareem A. Wahid Dina M. ElHabashy Mohamed A. Naser Renjie He Abdallah S. R. Mohamed Ariana J. Sahli Katherine A. Hutcheson Gary Brandon Gunn David I. Rosenthal Clifton D. Fuller Amy C. Moreno Machine learning predicting acute pain and opioid dose in radiation treated oropharyngeal cancer patients Frontiers in Pain Research acute pain opioid dose radiation therapy head and neck cancers oral cavity and oropharyngeal cancers machine learning |
| title | Machine learning predicting acute pain and opioid dose in radiation treated oropharyngeal cancer patients |
| title_full | Machine learning predicting acute pain and opioid dose in radiation treated oropharyngeal cancer patients |
| title_fullStr | Machine learning predicting acute pain and opioid dose in radiation treated oropharyngeal cancer patients |
| title_full_unstemmed | Machine learning predicting acute pain and opioid dose in radiation treated oropharyngeal cancer patients |
| title_short | Machine learning predicting acute pain and opioid dose in radiation treated oropharyngeal cancer patients |
| title_sort | machine learning predicting acute pain and opioid dose in radiation treated oropharyngeal cancer patients |
| topic | acute pain opioid dose radiation therapy head and neck cancers oral cavity and oropharyngeal cancers machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fpain.2025.1567632/full |
| work_keys_str_mv | AT viviansalama machinelearningpredictingacutepainandopioiddoseinradiationtreatedoropharyngealcancerpatients AT viviansalama machinelearningpredictingacutepainandopioiddoseinradiationtreatedoropharyngealcancerpatients AT laiahumbertvidan machinelearningpredictingacutepainandopioiddoseinradiationtreatedoropharyngealcancerpatients AT brandongodinich machinelearningpredictingacutepainandopioiddoseinradiationtreatedoropharyngealcancerpatients AT brandongodinich machinelearningpredictingacutepainandopioiddoseinradiationtreatedoropharyngealcancerpatients AT kareemawahid machinelearningpredictingacutepainandopioiddoseinradiationtreatedoropharyngealcancerpatients AT kareemawahid machinelearningpredictingacutepainandopioiddoseinradiationtreatedoropharyngealcancerpatients AT dinamelhabashy machinelearningpredictingacutepainandopioiddoseinradiationtreatedoropharyngealcancerpatients AT mohamedanaser machinelearningpredictingacutepainandopioiddoseinradiationtreatedoropharyngealcancerpatients AT renjiehe machinelearningpredictingacutepainandopioiddoseinradiationtreatedoropharyngealcancerpatients AT abdallahsrmohamed machinelearningpredictingacutepainandopioiddoseinradiationtreatedoropharyngealcancerpatients AT arianajsahli machinelearningpredictingacutepainandopioiddoseinradiationtreatedoropharyngealcancerpatients AT katherineahutcheson machinelearningpredictingacutepainandopioiddoseinradiationtreatedoropharyngealcancerpatients AT garybrandongunn machinelearningpredictingacutepainandopioiddoseinradiationtreatedoropharyngealcancerpatients AT davidirosenthal machinelearningpredictingacutepainandopioiddoseinradiationtreatedoropharyngealcancerpatients AT cliftondfuller machinelearningpredictingacutepainandopioiddoseinradiationtreatedoropharyngealcancerpatients AT amycmoreno machinelearningpredictingacutepainandopioiddoseinradiationtreatedoropharyngealcancerpatients |