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

Chinese Academy of Medical Sciences & Peking Union Medical College

Status: Enrolling

Conditions: Radiation Dermatitis; Head and Neck Cancer

Study type: Observational

Funder types: Other

Identifiers

NCT05607225

JS2022-62

Details and patient eligibility

About

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.

Full 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.

Enrollment: 300 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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