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The goal of this observational study is to apply the CNN-based DL method to extract the three-dimensional spatial information of IMRT dose distribution to predict the occurrence probability of serious radiotherapy and chemotherapy induced oral mucositis(SRCOM), and compare with a model based on dosimetry, NTCP or doseomics to improve the prediction accuracy of SRCOM, thus guiding the clinical planning design, reducing the occurrence probability of OM, and may have the potential value of preventing serious complications and improving the quality of life in patients with nasopharyngeal carcinoma.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| observational group | Other | patients initially diagnosed with nasopharyngeal carcinoma treated with IMRT |
| Measure | Description | Time Frame |
|---|---|---|
| RTOG/EROTC Acute Radiation Reaction Scoring Standard | Toxicity records of oral mucosal Reaction in patients are conducted by professionally trained oncologists | through radiation therapy, an average of 7 weeks |
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Inclusion Criteria:
Exclusion Criteria:
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Primary-treated NPC patients undergoing IMRT
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Fang-Yun Xie, M.D. | Contact | +8613902205880 | xiefy@sysucc.org.cn | |
| Pu-Yun OuYang, M.D. | Contact | +8618565382769 | ouyangpy@sysucc.org.cn |
| Name | Affiliation | Role |
|---|---|---|
| Fang-Yun Xie, M.D. | Sun Yat-sen University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Sun Yat-sen University Cancer Center | Recruiting | Guangzhou | Guangdong | 510060 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 15936155 | Result | 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. | |
| 28738294 | Result | 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. |
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| 34714553 | Result | 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. |
| 34484592 | Result | 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. |
| 33224892 | Result | 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. |
| 29556480 | Result | 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. |
| 28914611 | Result | 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. |
| 30098025 | Result | 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. |
| ID | Term |
|---|---|
| D000077274 | Nasopharyngeal Carcinoma |
| D013280 | Stomatitis |
| 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 |
| D009059 | Mouth Diseases |
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