Predicting severe oral mucositis in nasopharyngeal carcinoma using deep learning
Prospective Observational Study to Predict Severe Oral Mucositis Associated With Chemoradiotherapy in Nasopharyngeal Carcinoma Based on Deep Learning
This study is testing a new deep learning tool to see if it can better predict severe mouth sores in patients with nasopharyngeal cancer undergoing radiation and chemotherapy.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 480 (estimated) |
| Sex | All |
| Sponsor | Sun Yat-sen University Academic / other |
| Drugs / interventions | radiation, chemotherapy |
| Locations | 1 site (Guangzhou, Guangdong) |
| Trial ID | NCT06032767 on ClinicalTrials.gov |
What this trial studies
This observational study aims to utilize a convolutional neural network (CNN)-based deep learning method to analyze three-dimensional spatial information from intensity-modulated radiation therapy (IMRT) dose distributions. The goal is to predict the likelihood of severe oral mucositis resulting from chemoradiotherapy in patients with nasopharyngeal carcinoma. By comparing this deep learning model with traditional dosimetry and dose-response models, the study seeks to enhance prediction accuracy and inform clinical planning to minimize complications. Ultimately, this approach may improve patient quality of life by reducing the incidence of oral mucositis.
Who should consider this trial
Good fit: Ideal candidates for this study are patients newly diagnosed with non-keratotic nasopharyngeal carcinoma who are scheduled to receive intensity-modulated radiotherapy.
Not a fit: Patients with recurrent nasopharyngeal carcinoma or those who have previously received radiotherapy are unlikely to benefit from this study.
Why it matters
Potential benefit: If successful, this study could lead to better prediction and prevention of severe oral mucositis, enhancing the quality of life for patients undergoing treatment for nasopharyngeal carcinoma.
How similar studies have performed: While the application of deep learning in predicting treatment outcomes is a growing field, this specific approach to predicting oral mucositis in nasopharyngeal carcinoma is relatively novel and has not been extensively tested in prior studies.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Initial diagnosis, pathological histological diagnosis, the pathological type is non-keratotic carcinoma (according to the WHO pathological classification). * Initial intensity-modulated radiotherapy (Intensity modulated radiation therapy, IMRT). * No previous radiotherapy was received. Exclusion Criteria: * Patients with recurrent nasopharyngeal carcinoma. * Radiotherapy plan cannot be obtained. * Previous history of malignancy; previous radiotherapy. * The primary lesion and cervical metastatic lesions have received surgical treatment (except for diagnostic treatment).
Where this trial is running
Guangzhou, Guangdong
- Sun Yat-sen University Cancer Center — Guangzhou, Guangdong, China (Recruiting)
Study contacts
- Principal investigator: Fang-Yun Xie, M.D. — Sun Yat-sen University
- Study coordinator: Fang-Yun Xie, M.D.
- Email: xiefy@sysucc.org.cn
- Phone: +8613902205880
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.