Prospective Observational Study to Predict Severe Oral Mucositis Associated With Chemoradiotherapy in Nasopharyngeal Carcinoma Based on Deep Learning

NCT06032767 | Unknown

• 1 other identifier: B2022-420-Y01
• Study Type: observational
• Target: 480
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Nasopharyngeal Carcinoma; Oral Mucositis

Keywords

Nasopharyngeal Carcinoma; Oral Mucositis; deep learning

Outcome Measures

Primary Outcomes (1)

• 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

Interventions

observational group OTHER

patients initially diagnosed with nasopharyngeal carcinoma treated with IMRT

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Primary-treated NPC patients undergoing IMRT

You may qualify if:

• 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.

You may not qualify if:

• 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).

Sponsors & Collaborators

• Sun Yat-sen University: lead

Study Sites (1)

Sun Yat-sen University Cancer Center

Guangzhou, Guangdong, 510060, China

RECRUITING

Related Publications (8)

• 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.

  PMID: 15936155 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.

  PMID: 28738294 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.

  PMID: 34714553 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.

  PMID: 34484592 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.

  PMID: 33224892 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.

  PMID: 29556480 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.

  PMID: 28914611 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.

  PMID: 30098025 RESULT

MeSH Terms

Conditions

Nasopharyngeal Carcinoma; Stomatitis

Condition Hierarchy (Ancestors)

Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Nasopharyngeal Neoplasms; Pharyngeal Neoplasms; Otorhinolaryngologic Neoplasms; Head and Neck Neoplasms; Neoplasms by Site; Nasopharyngeal Diseases; Pharyngeal Diseases; Stomatognathic Diseases; Otorhinolaryngologic Diseases; Mouth Diseases

Study Officials

• Fang-Yun Xie, M.D.

  Sun Yat-sen University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Fang-Yun Xie, M.D.

CONTACT

+8613902205880; xiefy@sysucc.org.cn

Pu-Yun OuYang, M.D.

CONTACT

+8618565382769; ouyangpy@sysucc.org.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: professor

Study Record Dates

• First Submitted: September 4, 2023
• First Posted: September 13, 2023
• Study Start: August 14, 2023
• Primary Completion: September 30, 2024
• Study Completion: December 30, 2024
• Last Updated: January 5, 2024

Record last verified: 2024-01

Locations

CN (1)