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NCT05607225 Clinical Trial

Deep Learning-based Classification and Prediction of Radiation Dermatitis in Head and Neck Patients

Head and Neck Cancer Clinical Trial

Official title:
Deep Learning-based Classification and Prediction of Radiation Dermatitis in Head and Neck

Clinical Trial Details — Status: Recruiting

Administrative data
NCT number NCT05607225
Other study ID # JS2022-62
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date July 1, 2022
Est. completion date June 30, 2025

Study information
Verified date October 2022
Source Cancer Institute and Hospital, Chinese Academy of Medical Sciences
Contact Li Ma, MD
Phone 86-755-66618168
Email ml_1990@126.com
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

to develop a deep learning-based model to grade the severity of radiation dermatitis (RD) and predict the severity of radiation dermatitis in patients with head and neck cancer undergoing radiotherapy, so as to provide support for doctors' diagnosis and prediction.

Description:

1. Image acquisition The images of the neck area were collected from the enrolled patients one week before and every week during radiotherapy. The photographs were taken from three angles (front, left and right oblique) of the neck area. 2. Grading evaluation Each image was individually graded by three experienced radiotherapy experts according to the RD criteria of RTOG 3. Data analysis Construct a dermatitis grading model basing on deep learning. Evaluate the performance of model using accuracy, precision, recall, F1-measure, dice value.

Recruitment information / eligibility
Status Recruiting
Enrollment 300
Est. completion date June 30, 2025
Est. primary completion date June 30, 2025
Accepts healthy volunteers No
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria: - Age = 18 years old. - Histologically or cytologically confirmed head and neck carcinoma confirmed by pathology. - Receive radical radiotherapy including neck area - Informed consent. Exclusion Criteria: - unable to cooperate with image acquisition

Study Design

Related Conditions & MeSH terms

Locations
Country Name City State
China Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College Beijing
China Shenzhen Cancer Hospital, Chinese Academy of Medical Sciences Shenzhen Guangdong

Sponsors (1)
Lead Sponsor Collaborator
Cancer Institute and Hospital, Chinese Academy of Medical Sciences

Country where clinical trial is conducted

China, 

Outcome
Type Measure Description Time frame Safety issue
Primary Accuracy Evaluate the rate of deep learning based rating model in accordance with experts' assessment. July 1, 2022 to June 30, 2025
Primary Precision The proportion of positive samples in the positive prediction result July 1, 2022 to June 30, 2025
Primary Recall The proportion of positive samples that were predicted to be positive July 1, 2022 to June 30, 2025
Primary F1-measure The harmonic average of precision and recall July 1, 2022 to June 30, 2025
Primary ROC curve July 1, 2022 to June 30, 2025
Secondary dice value Ratio of overlap and distance between artificial and automatic neck segmentation regions July 1, 2022 to June 30, 2025
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