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

Sponsor

Cancer Institute and Hospital, Chinese Academy of Medical Sciences (Other)

Overall Status

Recruiting

CT.gov ID

NCT05607225

Collaborator

(none)

300

Enrollment

Locations

Anticipated Duration (Months)

150

Patients Per Site

4.2

Patients Per Site Per Month

Study Details

Study Description

Brief 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.

Condition or Disease	Intervention/Treatment	Phase
Radiation Dermatitis Head and Neck Cancer

Detailed Description

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.
Grading evaluation Each image was individually graded by three experienced radiotherapy experts according to the RD criteria of RTOG
Data analysis Construct a dermatitis grading model basing on deep learning. Evaluate the performance of model using accuracy, precision, recall, F1-measure, dice value.

Study Design

Study Type:

Observational

Anticipated Enrollment :

300 participants

Observational Model:

Cohort

Time Perspective:

Prospective

Official Title:

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

Actual Study Start Date :

Jul 1, 2022

Anticipated Primary Completion Date :

Jun 30, 2025

Anticipated Study Completion Date :

Jun 30, 2025

Outcome Measures

Primary Outcome Measures

Accuracy [July 1, 2022 to June 30, 2025]
Evaluate the rate of deep learning based rating model in accordance with experts' assessment.
Precision [July 1, 2022 to June 30, 2025]
The proportion of positive samples in the positive prediction result
Recall [July 1, 2022 to June 30, 2025]
The proportion of positive samples that were predicted to be positive
F1-measure [July 1, 2022 to June 30, 2025]
The harmonic average of precision and recall
ROC curve [July 1, 2022 to June 30, 2025]

Secondary Outcome Measures

dice value [July 1, 2022 to June 30, 2025]
Ratio of overlap and distance between artificial and automatic neck segmentation regions

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

All

Accepts 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

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Shenzhen Cancer Hospital, Chinese Academy of Medical Sciences	Shenzhen	Guangdong	China	518116
2	Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College	Beijing		China	100080

Sponsors and Collaborators

Cancer Institute and Hospital, Chinese Academy of Medical Sciences

Investigators

Principal Investigator: Ye Zhang, MD, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College
Principal Investigator: Li Ma, MD, Shenzhen Cancer Hospital, Chinese Academy of Medical Sciences

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

Responsible Party:

YE ZHANG, Professor, Cancer Institute and Hospital, Chinese Academy of Medical Sciences

ClinicalTrials.gov Identifier:

NCT05607225

Other Study ID Numbers:

JS2022-62

First Posted:

Nov 7, 2022

Last Update Posted:

Nov 7, 2022

Last Verified:

Oct 1, 2022

Individual Participant Data (IPD) Sharing Statement:

Plan to Share IPD:

Studies a U.S. FDA-regulated Drug Product:

Studies a U.S. FDA-regulated Device Product:

Additional relevant MeSH terms:

Dermatitis

Radiodermatitis

Study Results

No Results Posted as of Nov 7, 2022