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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.
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| Measure | Description | Time Frame |
|---|---|---|
| The AUC of the predictive model | The area under the ROC (receiver operating characteristic) curve (AUC) of the predictive model | Through study completion, up to 3 years |
| Measure | Description | Time Frame |
|---|---|---|
| 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) | Through study completion, up to 3 years |
| The sensitivity of the predictive model |
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Inclusion Criteria:
Exclusion Criteria:
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Patients with nasopharyngeal carcinoma receiving radical radiotherapy or chemoradiotherapy
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Jian Guan, Ph.D. | Contact | +86-13632102247 | guanjian5461@163.com |
| Name | Affiliation | Role |
|---|---|---|
| Jian Guan, Ph.D. | Nanfang Hospital, Southern Medical University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Affiliated Cancer Hospital & Institute of Guangzhou Medical University | Not yet recruiting | Guangzhou | Guangdong | China |
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To obtained this index, true positive (TP) and false negative (FN) were calculated from the confusion matrix. Sensitivity = TP/(TP + FN) |
| 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) | 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) | 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) | 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) | 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. | Through study completion, up to 3 years |
| Nanfang hospital, Southern medical university | Recruiting | Guangzhou | Guangdong | China |
|
| Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University | Not yet recruiting | Guangzhou | Guangdong | China |
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| Sun Yat-sen University Cancer Center | Not yet recruiting | Guangzhou | Guangdong | China |
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| The First Affiliated Hospital, Sun Yat-sen University | Not yet recruiting | Guangzhou | Guangdong | China |
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| Yuebei People's Hospital | Not yet recruiting | Shaoguan | Guangdong | China |
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| Peking University Shenzhen Hospital | Not yet recruiting | Shenzhen | Guangdong | China |
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| The Fifth Affiliated Hospital, Sun Yat-sen University | Not yet recruiting | Zhuhai | Guangdong | China |
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| Fudan University Shanghai Cancer Center | Not yet recruiting | Shanghai | Shanghai Municipality | China |
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| Sichuan Cancer Hospital | Not yet recruiting | Chengdu | Sichuan | China |
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| ID | Term |
|---|---|
| D000077274 | Nasopharyngeal Carcinoma |
| ID | Term |
|---|---|
| D002277 | Carcinoma |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009370 | Neoplasms by Histologic Type |
| D009369 | Neoplasms |
| D009303 | Nasopharyngeal Neoplasms |
| D010610 | Pharyngeal Neoplasms |
| D010039 | Otorhinolaryngologic Neoplasms |
| D006258 | Head and Neck Neoplasms |
| D009371 | Neoplasms by Site |
| D009302 | Nasopharyngeal Diseases |
| D010608 | Pharyngeal Diseases |
| D009057 | Stomatognathic Diseases |
| D010038 | Otorhinolaryngologic Diseases |
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