Ignite Creation Date:
2025-12-24 @ 3:31 PM
Ignite Modification Date:
2025-12-30 @ 7:29 AM
Study NCT ID:
NCT04363892
Status:
TERMINATED
Last Update Posted:
2024-08-21
First Post:
2020-04-06
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Combined Optical and Infrared Imaging for Early Prediction of Erythema During Breast and Chestwall Radiotherapy
Sponsor:
Nova Scotia Health Authority
Organization:
Study Overview
Official Title:
A Prospective Cohort Clinical Trial to Assess the Skin Imaging Spectral and Morphological Changes During Breast Adjuvant Radiotherapy as an Early Predictor of Acute Skin Toxicities
Status:
TERMINATED
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
The PI resigned
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The aim of this study is to develop a computer model, based on photographs and heat images of patients' skin, to provide early prediction of painful reddening of the skin during radiotherapy treatment.
Detailed Description:
There is currently no reliable tool to quantify and detect erythema of the skin during radiotherapy. This side-effect may lead to painful moist desquamation, and eventually permanent delayed side effects like telangiectasia. If such a tool would be available, several interventions could be staged, including (1) the use of steroid cream\], (2) the re-simulation and/or re-planning of patients to decrease the skin dose by spreading out the entrance of the beams, (3) adjusting or eliminating the use of bolus on the skin surface (which boosts superficial dose) or (4) the use of other treatment techniques including prone technique.
Given that the dose delivered is not a reliable metric to predict for erythema in a given patient, a new method for monitoring, staging and ultimately predicting skin response is needed. By analyzing images of the skin using both visible and infrared spectral regions, and by carefully converting the information in the images to quantitative metrics, it may be possible to characterize the stage of a patient's response to radiation, and to understand which patients may go on to experience chronic pain, severe burns or other more serious side effects while it is still early enough to intervene.
The proposed research is to develop a software model that will take as input patient skin image data and the patient known clinical outcomes and algorithmically generalize a model to predict a biological response of skin to ionizing radiation for any future patient, after a few initial images.
In the first stage of this study, the data will be aggregated to devise the dose response curve. In later phases, the model will be refined and used for predictive purposes, i.e., once a new patient has begun radiotherapy sessions, their initial response will be quantified, and fed into the model to predict the skin response endpoint after the course of radiation therapy ends. As mentioned, this information could be used to adapt the radiation course and optimize the therapy for the individual, potentially preventing morbidity from overdose, or risk of recurrence from under dose.
Given that the dose delivered is not a reliable metric to predict for erythema in a given patient, a new method for monitoring, staging and ultimately predicting skin response is needed. By analyzing images of the skin using both visible and infrared spectral regions, and by carefully converting the information in the images to quantitative metrics, it may be possible to characterize the stage of a patient's response to radiation, and to understand which patients may go on to experience chronic pain, severe burns or other more serious side effects while it is still early enough to intervene.
The proposed research is to develop a software model that will take as input patient skin image data and the patient known clinical outcomes and algorithmically generalize a model to predict a biological response of skin to ionizing radiation for any future patient, after a few initial images.
In the first stage of this study, the data will be aggregated to devise the dose response curve. In later phases, the model will be refined and used for predictive purposes, i.e., once a new patient has begun radiotherapy sessions, their initial response will be quantified, and fed into the model to predict the skin response endpoint after the course of radiation therapy ends. As mentioned, this information could be used to adapt the radiation course and optimize the therapy for the individual, potentially preventing morbidity from overdose, or risk of recurrence from under dose.
Study Oversight
Has Oversight DMC:
True
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: