MoleGazer: A Feasibility Study for Early Detection of Melanoma
Trial ID: NCT05015816
Status: ACTIVE_NOT_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-09-13
Completion date: 2026-02-28
Sponsor: Oxford University Hospitals NHS Trust
Collaborators: University of Southampton
Conditions: Melanoma (Skin); Moles Multiple Benign
Interventions: Total body photography
Locations: Churchill Hospital
Countries: United Kingdom
Enrollment: 374
Age range: 18 Years - 80 Years
Sex: ALL

Summary:
Melanoma (skin cancer) frequently develops from existing moles on the skin. Current practice relies on expert dermatologists being able to successfully identify new/changing moles in individuals with multiple moles. Total body photography (TBP-high-quality images of the entire skin) can track and monitor moles over time to detect melanoma.

However, TBP is currently used as a visual guide when diagnosing melanoma, requiring visual inspection of each mole sequentially. This process is challenging, time-consuming and inefficient. Artificial intelligence (AI) is ideally suited to automate this process. Comparing baseline TBP images to newly acquired photographs, AI techniques can be used to accurately identify and highlight changing moles, and potentially distinguish harmless moles from cancerous changes.

Astrophysicists face a similar problem when they map the night sky to detect new events, such as exploding stars. Using AI, based on two or more images, astrophysicists detect new events and accurately predict how they will appear subsequently. This project, called MoleGazer, is a collaboration with astrophysicists aiming to apply AI methods that are currently used for astronomical sky surveys, to TBP images. The MoleGazer algorithm, developed at Oxford University Hospitals NHS Foundation Trust, will automatically identify the appearance of new moles and characterise changes in existing ones, when new TBP images are taken. To optimise this MoleGazer algorithm TBP images will be taken at multiple time-points, as there are no existing datasets of TBP images that are publicly available. The investigators invite a) high-risk patients attending skin cancer screening clinics to attend sequential three-monthly TBP imaging and clinical assessment and b) any patient who undergoes TBP as standard care to share images so that the investigators can develop the MoleGazer algorithm. The ultimate goal is for the MoleGazer algorithm to 'map moles' over a patient's lifetime to detect changes, with the eventual aim to detect melanoma as early as possible.

Eligibility criteria:
Inclusion Criteria:

* Participant is willing and able to give informed consent for participation in the study
* Male or Female, aged 18-80 years old

In addition for Group A:

1. Willing to attend for additional study visits and total body photography imaging
2. High-risk melanoma patients including:

   * Dysplastic / atypical naevus syndrome (\> 60 moles +/- personal history of melanoma)
   * Family history of melanoma
   * Past history of at least two primary melanoma or melanoma-in situ
   * At least 3 first-degree or second-degree relatives with prior melanoma
   * CDKN2A or CDK4 germline mutation
   * Individuals with multiple naevi (\>25) who are immunosuppressed from any cause (e.g. organ transplant recipients, chronic lymphocytic leukaemia, etc.)

In addition for Group B:

● Has previously had total body photography imaging OR will have total body photography as part of standard care

Exclusion Criteria:

The participant may not enter the study if ANY of the following apply:

* Patient unable to consent
* Patient with active malignancy affecting any organ and receiving any cancer-specific treatment
* Poor mobility / unable to hold recommended positions for standard TBP imaging
* Individuals who do not understand English

In addition for Group A:

● Unable to attend for three-monthly study visits

Outcomes:
Functional algorithm to map naevi sequentially; Number of TBP images in database