Clinical trial NCT06032767: Prospective Observational Study to Predict Severe Oral Mucositis Associated With Chemoradiotherapy in Nasopharyngeal Carcinoma Based on Deep Learning
Official title: Prospective Observational Study to Predict Severe Oral Mucositis Associated With Chemoradiotherapy in Nasopharyngeal Carcinoma Based on Deep Learning
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Nasopharyngeal Carcinoma
Oral Mucositis
Keywords: Nasopharyngeal Carcinoma
Oral Mucositis
deep learning
Lead sponsor: Sun Yat-sen University
Sponsor class: OTHER
First posted: 2023-09-13
Enrollment: 480
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Primary-treated NPC patients undergoing IMRT

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.


Eligibility 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).

Primary outcomes:
measure: RTOG/EROTC Acute Radiation Reaction Scoring Standard
timeFrame: through radiation therapy, an average of 7 weeks
Secondary outcomes:
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Sun Yat-sen University Cancer Center
state: Guangdong
Study officials:
affiliation: Sun Yat-sen University
name: Fang-Yun Xie, M.D.
role: PRINCIPAL_INVESTIGATOR
References:
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citation: 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.
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