Melanoma Clinical Trial
— iToBoSOfficial title:
iToBoS: Intelligent Total Body Scanner for Early Detection of Melanoma - Clinical Data Acquisition Study
The (overarching) iToBoS Project involves 18 research partners spanning the European Union (including UK and Israel), and 1 Australian partner. The overall aim is to develop an AI assisted diagnostic platform for the early detection of melanoma. The Clinical Data Acquisition Study (this study) will recruit 600 participants across 3 international sites (Brisbane, Italy, and Spain). The primary objective is to compare the quality and resolution of conventional dermoscopic images of skin lesions with the full-body images captured by the iToBoS imaging system. Secondary objectives are to collect imaging, clinical and genetic data across the three sites, to create labelled datasets for use in training the iToBoS AI component. Also, to refine and develop a holistic melanoma risk score method to be used for the iToBoS system. Lastly, to assess safety of the iToBoS system. At study site we will aim to recruit 200 participants stratified by risk (of melanoma) categories (low/normal, high, and ultra-high). Participants will be required to attend 3 study visits (months 0, 6 and 12), for total body imaging with the iToBoS system, and dermoscopic images of individual moles. Genetic research and clinical testing are an optional part of the study.
Status | Not yet recruiting |
Enrollment | 600 |
Est. completion date | March 30, 2025 |
Est. primary completion date | March 30, 2025 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: 1. Aged 18 years or older 2. Able to provide written informed consent 3. Willing to attend 3 clinical visits over a 12-month period 4. Willing to provide a genetic sample (optional) 5. Willing to follow the clinical procedures (e.g. no physical restrictions from imaging process) 6. Have a BMI between 18.5 to 40 kg/m2 7. Have a height between 140 - 190 cm 8. Have a thorax height (participant lying face up) of 20 - 45 cm 9. Have an elbow to elbow width (breadth) of 40-50cm Exclusion Criteria: 1. Have a pacemaker 2. Are pregnant, or planning to become pregnant 3. Any condition in which the investigator's opinion may adversely affect the participant's ability to complete the study, or its measures, or which may pose significant risk to the participant. |
Country | Name | City | State |
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n/a |
Lead Sponsor | Collaborator |
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The University of Queensland | Fundacion Clinic per a la Recerca Biomédica, IBM Israel - Science and Technology Ltd, Robert Bosch Espana Fabrica, Trilateral Research Limited, Universitat de Girona, University of Trieste |
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | iToBoS image quality assessment | The primary outcome measure of this study is the assessment of image quality of total-body photography taken with the iToBoS system. A clinical and quantitative comparison of conventional dermoscopy images of individual lesions with iToBoS images will be systematically assessed by a panel of dermatologically trained clinicians and research assistants. The panel will be asked to independently assess the iToBoS image and dermoscopic images as acceptable or not acceptable. Three panel members will assess each image, with consensus being considered with agreement of 2 or more. The order of the images presented to each panel member will be randomised. | Baseline (Month 0) | |
Secondary | Creation of labelled datasets for AI training | The secondary outcome measure will include classification, by field experts, of dermoscopic images into 9 diagnostic categories (e.g., naevus, melanoma, basal cell carcinoma, squamous cell carcinoma, actinic keratosis & intraepidermal squamous cell carcinoma, benign keratosis, dermatofibroma, vascular tumour, and other). The field experts will also be asked to provide a binary classification of suspicious or benign. Sequential dermoscopy images of the same lesion taken over different visits will additionally be assessed for clinically significant phenotypic change (yes/no). This dataset of labelled lesion images will be used to train the iToBoS artificial intelligence algorithms for development of computer aided diagnosis. | Baseline (Month 0) | |
Secondary | Refinement of a holistic risk score algorithm | An additional secondary outcome measure is the holistic risk score algorithm developed as part of the wider iToBoS project, which will be tested on the complete prospective dataset from the initial visits of all 600 individuals in three centres. The ability of the model to discriminate between case and control individuals will be assessed. In addition, the net reclassification improvement index will be calculated to quantify the sensitivity and specificity of model classification. These factors will enable assessment as to the relative contribution of individual risk factors in accurate risk stratification. The goal of this task will be to refine the clinical protocol for stratification of participants into low/average, high and ultra-high-risk categories to be used in a subsequent clinical validation study (separate protocol). | Month 12 | |
Secondary | Safety Assessment | A protocol for recording any Adverse Events, and Serious Adverse Events is established.
Adverse events are catogrised by the nature of the event, the intensity (mild/moderate/severe), the duration, and the relatedness to the investigational device (probably/possibly/not related), and the outcome is recorded (recovered/resoved, recovering/resolving, not recovered/not resolved, recovered with sequelae, fatal, unknown). AEs and SAEs are not expected in the study, and therefore any reported AEs and SAEs will be reviewed by the trial investigator within 48 hours. |
At completion of study (Month 12) | |
Secondary | Detecting change in lesions imaged (from baseline to month 12) | Images of the same skin lesions taken at baseline, compared to the participant's last study visit at 12 months will be used to detect any changes in the lesion. This will be important in training the iToBoS AI component to detect changes in skin lesions. An expert panel of clinicians will classify subsequent images as 'not changed', or 'changed'.
Cohen's Kappa will be used to compare agreement between the lesion classification of change vs. no change. |
At completion of the study (Month 12) |
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