Prospective Observational Study to Predict Severe Oral Mucositis Associated With Chemoradiotherapy in Nasopharyngeal Carcinoma Based on Deep Learning
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Fang-Yun Xie, M.D.
- Phone Number: +8613902205880
- Email: xiefy@sysucc.org.cn
Study Contact Backup
- Name: Pu-Yun OuYang, M.D.
- Phone Number: +8618565382769
- Email: ouyangpy@sysucc.org.cn
Study Locations
-
-
Guangdong
-
Guangzhou, Guangdong, China, 510060
- Recruiting
- Sun Yat-sen University Cancer Center
-
Contact:
- Fang-Yun Xie, professor
- Phone Number: +86-20-87342926
- Email: xiefy@sysucc.org.cn
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
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).
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
RTOG/EROTC Acute Radiation Reaction Scoring Standard
Time Frame: through radiation therapy, an average of 7 weeks
|
Toxicity records of oral mucosal Reaction in patients are conducted by professionally trained oncologists
|
through radiation therapy, an average of 7 weeks
|
Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Fang-Yun Xie, M.D., Sun Yat-sen University
Publications and helpful links
General Publications
- Wolden SL, Chen WC, Pfister DG, Kraus DH, Berry SL, Zelefsky MJ. Intensity-modulated radiation therapy (IMRT) for nasopharynx cancer: update of the Memorial Sloan-Kettering experience. Int J Radiat Oncol Biol Phys. 2006 Jan 1;64(1):57-62. doi: 10.1016/j.ijrobp.2005.03.057. Epub 2005 Jun 2.
- Li K, Yang L, Hu QY, Chen XZ, Chen M, Chen Y. Oral Mucosa Dose Parameters Predicting Grade >/=3 Acute Toxicity in Locally Advanced Nasopharyngeal Carcinoma Patients Treated With Concurrent Intensity-Modulated Radiation Therapy and Chemotherapy: An Independent Validation Study Comparing Oral Cavity versus Mucosal Surface Contouring Techniques. Transl Oncol. 2017 Oct;10(5):752-759. doi: 10.1016/j.tranon.2017.06.011. Epub 2017 Jul 21.
- Elad S, Yarom N, Zadik Y, Kuten-Shorrer M, Sonis ST. The broadening scope of oral mucositis and oral ulcerative mucosal toxicities of anticancer therapies. CA Cancer J Clin. 2022 Jan;72(1):57-77. doi: 10.3322/caac.21704. Epub 2021 Oct 29.
- Soutome S, Yanamoto S, Nishii M, Kojima Y, Hasegawa T, Funahara M, Akashi M, Saito T, Umeda M. Risk factors for severe radiation-induced oral mucositis in patients with oral cancer. J Dent Sci. 2021 Oct;16(4):1241-1246. doi: 10.1016/j.jds.2021.01.009. Epub 2021 Feb 9.
- Li PJ, Li KX, Jin T, Lin HM, Fang JB, Yang SY, Shen W, Chen J, Zhang J, Chen XZ, Chen M, Chen YY. Predictive Model and Precaution for Oral Mucositis During Chemo-Radiotherapy in Nasopharyngeal Carcinoma Patients. Front Oncol. 2020 Nov 5;10:596822. doi: 10.3389/fonc.2020.596822. eCollection 2020.
- Gabrys HS, Buettner F, Sterzing F, Hauswald H, Bangert M. Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia. Front Oncol. 2018 Mar 5;8:35. doi: 10.3389/fonc.2018.00035. eCollection 2018.
- Zhen X, Chen J, Zhong Z, Hrycushko B, Zhou L, Jiang S, Albuquerque K, Gu X. Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study. Phys Med Biol. 2017 Oct 12;62(21):8246-8263. doi: 10.1088/1361-6560/aa8d09.
- Ibragimov B, Toesca D, Chang D, Yuan Y, Koong A, Xing L. Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT. Med Phys. 2018 Oct;45(10):4763-4774. doi: 10.1002/mp.13122. Epub 2018 Sep 10.
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
- Digestive System Diseases
- Neoplasms by Histologic Type
- Neoplasms
- Neoplasms by Site
- Neoplasms, Glandular and Epithelial
- Gastrointestinal Diseases
- Gastroenteritis
- Pharyngeal Neoplasms
- Otorhinolaryngologic Neoplasms
- Head and Neck Neoplasms
- Nasopharyngeal Diseases
- Pharyngeal Diseases
- Stomatognathic Diseases
- Otorhinolaryngologic Diseases
- Mouth Diseases
- Nasopharyngeal Neoplasms
- Carcinoma
- Nasopharyngeal Carcinoma
- Mucositis
- Stomatitis
Other Study ID Numbers
Other Study ID Numbers
- B2022-420-Y01
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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