The performance of the predictive models for the occurrence and severity of oropharyngeal mucositis established using either oral cavity contouring method or mucosa surface 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 in locally advanced NPC. Therefore, the investigators aimed to conduct a prospective, multicenter, real-world observational study to further assess the predictive efficacy of this mucosal delineation method for radiation-induced oropharyngeal mucositis in NPC.
Swallowing-induced breakthrough pain as a prominent clinical challenge for radiation-induced oropharyngeal mucositis, occurs in almost all patients with nasopharyngeal carcinoma (NPC) undergoing radiotherapy, and has a significant impact on patients' quality of life and treatment outcomes. Radiation-induced oropharyngeal mucositis is closely related to the irradiated dose and volume, and the performance of the predictive models for its occurrence and severity established using either oral cavity contouring method or mucosa surface contouring method was unsatisfactory. Thus, it is difficult to carry out risk assessment, precise screening and early intervention through dosimetric parameters, thereby reducing the occurrence of severe radiation-induced oropharyngeal mucositis. 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, real-world observational study to further explore the predictive efficacy of this mucosal delineation method for radiation-induced oropharyngeal mucositis in NPC.
Study Type
OBSERVATIONAL
Enrollment
718
Affiliated Cancer Hospital & Institute of Guangzhou Medical University
Guangzhou, Guangdong, China
NOT_YET_RECRUITINGNanfang hospital, Southern medical university
Guangzhou, Guangdong, China
RECRUITINGSun Yat-Sen Memorial Hospital, Sun Yat-Sen University
Guangzhou, Guangdong, China
NOT_YET_RECRUITINGSun Yat-sen University Cancer Center
Guangzhou, Guangdong, China
NOT_YET_RECRUITINGThe First Affiliated Hospital, Sun Yat-sen University
Guangzhou, Guangdong, China
NOT_YET_RECRUITINGYuebei People's Hospital
Shaoguan, Guangdong, China
NOT_YET_RECRUITINGPeking University Shenzhen Hospital
Shenzhen, Guangdong, China
NOT_YET_RECRUITINGThe Fifth Affiliated Hospital, Sun Yat-sen University
Zhuhai, Guangdong, China
NOT_YET_RECRUITINGFudan University Shanghai Cancer Center
Shanghai, Shanghai Municipality, China
NOT_YET_RECRUITINGSichuan Cancer Hospital
Chengdu, Sichuan, China
NOT_YET_RECRUITINGThe AUC of the predictive model
The area under the ROC (receiver operating characteristic) curve (AUC) of the predictive model
Time frame: Through study completion, up to 3 years
The accuracy of the predictive model
To obtained this index, true positive (TP) and true negative (TN) were calculated from the confusion matrix. Accuracy = (TP+TN)/(Σ Total population)
Time frame: Through study completion, up to 3 years
The sensitivity of the predictive model
To obtained this index, true positive (TP) and false negative (FN) were calculated from the confusion matrix. Sensitivity = TP/(TP + FN)
Time frame: Through study completion, up to 3 years
The specificity of the predictive model
To obtained this index, false positive (FP) and true negative (TN) were calculated from the confusion matrix. Specificity = TN/(TN + FP)
Time frame: Through study completion, up to 3 years
The positive predictive value of the predictive model
To obtained this index, true positive (TP) and false positive (FP) were calculated from the confusion matrix. Positive Predictive value (PPV) = TP/(TP + FP)
Time frame: Through study completion, up to 3 years
The negative predictive value of the predictive model
To obtained this index, true negative (TN) and false negative (FN) were calculated from the confusion matrix. Negative Predictive value (NPV) = TN/(TN + FN)
Time frame: Through study completion, up to 3 years
The F1 score of the predictive model
To obtained this index, true positive (TP), false positive (FP), and false negative (FN) were calculated from the confusion matrix. F1 score = 2TP/(2TP + FP + FN)
Time frame: Through study completion, up to 3 years
The important predictors of severe oropharyngeal mucositis in the predictive model
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.
Time frame: Through study completion, up to 3 years
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.