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

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05607225

Enrollment

300

Registered

2022-11-07

Start date

2022-07-01

Completion date

2025-06-30

Last updated

2022-11-07

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Head and Neck Cancer, Radiation Dermatitis

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.

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

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

Design outcomes

Primary

Measure	Time frame	Description
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

Measure	Time frame	Description
dice value	July 1, 2022 to June 30, 2025	Ratio of overlap and distance between artificial and automatic neck segmentation regions

Countries

China

Contacts

Primary ContactLi Ma, MD

ml_1990@126.com86-755-66618168

Outcome results