Clinical trial · Observational
Prospective Observational Study to Predict Severe Oral Mucositis Associated With Chemoradiotherapy in Nasopharyngeal Carcinoma Based on Deep Learning
NCT06032767CI-TRIAL-00072780unknownClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Nasopharyngeal Carcinoma | Nasopharyngeal Carcinoma | ONTOLOGY_EXACT | 0.98 |
| Oral Mucositis | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| observational group | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- RTOG/EROTC Acute Radiation Reaction Scoring Standard
- timeFrame
- through radiation therapy, an average of 7 weeks
- description
- Toxicity records of oral mucosal Reaction in patients are conducted by professionally trained oncologists
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
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).
References
Publications (8)
- RESULTWolden 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
- RESULTLi 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
- RESULTElad 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
- RESULTSoutome 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
- RESULTLi 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
- RESULTGabrys 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.