Radiation-induced Oropharyngeal Mucositis, Swallowing-induced Breakthrough Pain
Conditions
Keywords
Nasopharyngeal carcinoma, Radiation, Oropharyngeal mucositis, Swallowing-induced breakthrough pain, Delineation method
Brief summary
The performance of the predictive models for severe oropharyngeal mucositis established using current oral mucosal contouring method was unsatisfactory in nasopharyngeal carcinoma (NPC). Whereas the predictive model of a mucosal contouring method based on swallowing-induced breakthrough pain exhibited better overall performance. The aim of this prospective, multicenter, observational study was to further explore the predictive efficacy of this mucosal delineation method for radiation-induced oropharyngeal mucositis in NPC.
Detailed description
Nasopharyngeal carcinoma (NPC) is particularly prevalent in southern China. Radiation-induced oropharyngeal mucositis is one of the most common acute toxicities in patients with NPC receiving radiotherapy. Swallowing-induced breakthrough pain is a prominent clinical challenge for radiation-induced oropharyngeal mucositis, which has a great impact on patients' quality of life and treatment outcomes. Nonetheless, no particularly effective therapeutic methods or medication are available currently, thus making timely and accurate prediction, identifying high-risk patients, and providing appropriate interventions are critical in reducing or delaying the occurrence of severe oropharyngeal mucositis. It has been found that the performance of the predictive models for severe oropharyngeal mucositis established using either oral cavity contouring method or mucosa surface contouring method was unsatisfactory in NPC. The investigators defined a delineation method based on the mucosal areas of radiation-induced injury resulting in swallowing-induced breakthrough pain in locally advanced NPC, and our preliminary results demonstrated that the predictive model exhibited better overall performance. Therefore, the investigators aimed to conduct a prospective, multicenter, observational study to further explore the predictive efficacy of this mucosal delineation method for radiation-induced oropharyngeal mucositis in NPC.
Interventions
None listed
Sponsors
Nanfang Hospital, Southern Medical University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Provide informed written consent. 2. Age ≥ 18 years. 3. Histologically confirmed as nasopharyngeal carcinoma, and currently undergoing radical radiotherapy or chemoradiotherapy. 4. Complete and continuous records of oral / oropharyngeal mucositis grading, and self-reported swallowing-induced breakthrough pain.
Exclusion criteria
1. Existence of poor oral hygiene, untreated dental or periodontal diseases, and metal dental restorations. 2. Current untreated or unresolved conditions like unstable heart diseases requiring treatment, and poorly controlled diabetes mellitus. 3. Body mass index (BMI) \<18.5. 4. Unsuitable to participate in current study, according to researchers' assessment.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The AUC of the predictive model | Through study completion, up to 3 years | The area under the ROC (receiver operating characteristic) curve (AUC) of the predictive model |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The accuracy, sensitivity, specificity, positive predictive value, negative predictive value and F1 score of the predictive model | Through study completion, up to 3 years | To obtained these metrics, true positive (TP), false positive (FP), true negative (TN), and false negative (FN) were calculated from the confusion matrix. Sensitivity = TP/(TP + FN) Specificity = TN/(TN + FP) Accuracy = (TP+TN)/(Σ Total population) Positive Predictive value (PPV) = TP/(TP + FP) Negative Predictive value (NPV) = TN/(TN + FN) F1 score = 2TP/(2TP + FP + FN) |
| The important predictors of severe oropharyngeal mucositis in the predictive model | Through study completion, up to 3 years | The importance of variables included in the predictive model was measure, and those with a higher value indicating a greater contribution to the model's classification accuracy were viewed as the important predictors. |
Countries
China
Contacts
Primary ContactJian Guan, Ph.D.
Outcome results
None listed