NCT05607225

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

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

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 300 (estimated)
• Sponsor: Cancer Institute and Hospital, Chinese Academy of Medical Sciences · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

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.

Detailed 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.

Conditions

• Radiation Dermatitis
• Head and Neck Cancer

Timeline

• Start date: 2022-07-01
• Primary completion: 2025-06-30
• Completion: 2025-06-30
• First posted: 2022-11-07
• Last updated: 2022-11-07

Locations

2 sites across 1 country: China