Improved Efficacy of a Predictive Model for Swallowing-Induced Breakthrough Pain Based on a Redefined Delineation Method in Locally Advanced Nasopharyngeal Carcinoma
Jian-Da Sun MD , Ze-Kai Chen MM , Shu-Peng Liu PhD , Feng Ye MD , Ting-Xi Tang MD , Zhen-Hua Zhou MD , Han-Bin Zhang MM , Long-Shan Zhang MD , Ting Xiao BS , Lin-Lin Xiao MM , Xiao-Qing Wang MD , Jian Guan MD
{"title":"Improved Efficacy of a Predictive Model for Swallowing-Induced Breakthrough Pain Based on a Redefined Delineation Method in Locally Advanced Nasopharyngeal Carcinoma","authors":"Jian-Da Sun MD , Ze-Kai Chen MM , Shu-Peng Liu PhD , Feng Ye MD , Ting-Xi Tang MD , Zhen-Hua Zhou MD , Han-Bin Zhang MM , Long-Shan Zhang MD , Ting Xiao BS , Lin-Lin Xiao MM , Xiao-Qing Wang MD , Jian Guan MD","doi":"10.1016/j.adro.2024.101690","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><div>The objective of this study was to explore the performance of a predictive model for swallowing-induced breakthrough pain established using a redefined delineation method based on the common occurrence sites of radiation-induced oral mucositis (RIOM) in locally advanced nasopharyngeal carcinoma (NPC).</div></div><div><h3>Methods and Materials</h3><div>A total of 208 patients with locally advanced NPC were included in the study cohort, and the test cohort consisted of 88 patients. The oral mucosa structure was contoured using oral cavity contour (OCC), mucosal surface contour (MSC), and oral-pharyngeal mucosa (OPM) methods, and relevant dosimetric parameters were collected. Assessment of the severity of RIOM was made with the National Cancer Institute's Common Terminology Criteria for Adverse Events, version 4.0. The random forest classification method was chosen to establish and validate the predictive models based on 3 contouring methods.</div></div><div><h3>Results</h3><div>The area under the curve of the OPM-based model was higher than that of the OCC- and MSC-based models in both the validation cohort and the test cohort (0.800, 0.739, and 0.750; 0.670, 0.605, and 0.609, respectively). Better predictive performance could also be observed under the OPM method than the OCC and MSC methods in terms of accuracy. The OPM-based model showed high specificity (greater than 90%) in both the validation cohort and the test cohort. According to the mean decrease in the Gini index, the maximum dose was the most important predictor of severe oral mucositis in the OPM-based model.</div></div><div><h3>Conclusions</h3><div>We redefined a delineation method for oral mucosa structure based on the common occurrence sites of RIOM in locally advanced NPC. The model for swallowing-induced breakthrough pain constructed based on this method demonstrated good predictive performance. New parameters were found as predictors of severe swallowing-induced breakthrough pain in locally advanced NPC.</div></div>","PeriodicalId":7390,"journal":{"name":"Advances in Radiation Oncology","volume":"10 2","pages":"Article 101690"},"PeriodicalIF":2.2000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11770509/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Radiation Oncology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2452109424002537","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose
The objective of this study was to explore the performance of a predictive model for swallowing-induced breakthrough pain established using a redefined delineation method based on the common occurrence sites of radiation-induced oral mucositis (RIOM) in locally advanced nasopharyngeal carcinoma (NPC).
Methods and Materials
A total of 208 patients with locally advanced NPC were included in the study cohort, and the test cohort consisted of 88 patients. The oral mucosa structure was contoured using oral cavity contour (OCC), mucosal surface contour (MSC), and oral-pharyngeal mucosa (OPM) methods, and relevant dosimetric parameters were collected. Assessment of the severity of RIOM was made with the National Cancer Institute's Common Terminology Criteria for Adverse Events, version 4.0. The random forest classification method was chosen to establish and validate the predictive models based on 3 contouring methods.
Results
The area under the curve of the OPM-based model was higher than that of the OCC- and MSC-based models in both the validation cohort and the test cohort (0.800, 0.739, and 0.750; 0.670, 0.605, and 0.609, respectively). Better predictive performance could also be observed under the OPM method than the OCC and MSC methods in terms of accuracy. The OPM-based model showed high specificity (greater than 90%) in both the validation cohort and the test cohort. According to the mean decrease in the Gini index, the maximum dose was the most important predictor of severe oral mucositis in the OPM-based model.
Conclusions
We redefined a delineation method for oral mucosa structure based on the common occurrence sites of RIOM in locally advanced NPC. The model for swallowing-induced breakthrough pain constructed based on this method demonstrated good predictive performance. New parameters were found as predictors of severe swallowing-induced breakthrough pain in locally advanced NPC.
期刊介绍:
The purpose of Advances is to provide information for clinicians who use radiation therapy by publishing: Clinical trial reports and reanalyses. Basic science original reports. Manuscripts examining health services research, comparative and cost effectiveness research, and systematic reviews. Case reports documenting unusual problems and solutions. High quality multi and single institutional series, as well as other novel retrospective hypothesis generating series. Timely critical reviews on important topics in radiation oncology, such as side effects. Articles reporting the natural history of disease and patterns of failure, particularly as they relate to treatment volume delineation. Articles on safety and quality in radiation therapy. Essays on clinical experience. Articles on practice transformation in radiation oncology, in particular: Aspects of health policy that may impact the future practice of radiation oncology. How information technology, such as data analytics and systems innovations, will change radiation oncology practice. Articles on imaging as they relate to radiation therapy treatment.