MoleGazer: A Feasibility Study for Early Detection of Melanoma
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 374
- 试验地点
- 2
- 主要终点
- Functional algorithm to map naevi sequentially
研究概览
简要总结
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.
详细描述
Background
Melanoma incidence is rapidly increasing with 15,906 new United Kingdom (UK) cases in 2015 resulting in 2,285 deaths. Diagnosing melanoma early is essential as early stage disease has > 95% 5-year relative survival rate compared with 8-25% for advanced melanoma. In the UK, skin cancer costs are predicted to exceed £180 million by 2020 and pose significant morbidity (and mortality) to individuals affected. Up to 60% of melanoma arise from pre-existing naevi (moles). Early melanoma detection relies on individuals recognising changes in naevi and for those individuals with multiple naevi expert assessment of these naevi by trained dermatologists using diagnostic aids such as dermoscopy (x10 magnification). Furthermore there is evidence that sequential surveillance of naevi also increases melanoma detection rates.
Total body photography (TBP) is a diagnostic aid for monitoring of multiple naevi
For patients at high-risk of developing melanoma with multiple naevi (>60), total body photography (TBP) (standardised body-part images taken using high-resolution camera), is used as an aid to track, compare and monitor naevi over time and has been demonstrated to improve melanoma diagnosis. Recommended short-term surveillance monitoring of naevi is 3-months but is largely confined to single lesions. In a resource-constrained National Health Service (NHS), frequent surveillance for multiple naevi by a dermatologist is impractical and inefficient such that early diagnosis of melanoma effectively relies on patient self-surveillance. A potential solution is automated analysis of TBP images using artificial intelligence (AI) to track and monitor naevi over time.
Artificial intelligence applied to TBP could improve efficiency of 'mole-mapping'
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Parallel
- 主要目的
- Other
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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:
- •Willing to attend for additional study visits and total body photography imaging
- •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
排除标准
- •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
研究组 & 干预措施
Group A: Time series
Individuals at high risk of developing melanoma will be invited to attend for sequential TBP imaging, full body skin examination by a Dermatologist and completion of a case report form (CRF) every three months for two years. At the end of the study participants will also be invited to complete a feasibility questionnaire
干预措施: Total body photography (Diagnostic Test)
Group B: Baseline cohort
All patients who undergo standard care and are selected for total body photography (TBP) imaging will be invited to consent to this group. Any individuals who have had previous TBP imaging will also be eligible to enter Group B of this study. A baseline CRF will be completed and a participant feasibility questionnaire. There will be no additional images taken for the purposes of the study and no additional clinic visits in relation to this part of the study. However, individuals who consent to Group B will also agree to share any future TBP images taken in the department over the next two years so that any sequential images can also be included in the analysis
结局指标
主要结局
Functional algorithm to map naevi sequentially
时间窗: 3 years
The primary objective of this study is to develop the MoleGazer algorithm
Number of TBP images in database
时间窗: 3 years
To develop an anonymised database of digital total body photography images
次要结局
- The proportion of participants who complete a dataset of three-monthly imaging (Group A)(2 years)
- The number of naevi detected by our algorithm from TBP images compared to those determined by an experienced dermatologist(1 year)
- The distribution of naevi detected by our algorithm from TBP images compared to those determined by an experienced dermatologist(1 year)
- Proportion of high quality images amenable to evaluation(3 years)
- The proportion of TBP images that can be registered and consistently deformed using existing astronomical software adapted for this purpose(1 year)
- The proportion of sequential TBP images that can be used for naevi detection.(3 years)
- The proportion of naevi that are detected and measured in all sequential TBP images(3 years)
- The proportion of naevi (as determined by a trained dermatologist) in TBP images discarded when considering an optimal feature set(3 years)
研究者
Rubeta Matin
Primary Investigator
Oxford University Hospitals NHS Trust