Ignite Creation Date:
2024-05-06 @ 4:32 PM
Last Modification Date:
2024-10-26 @ 2:11 PM
Study NCT ID:
NCT05015816
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-06-21
First Post:
2020-12-19
Brief Title:
MoleGazer Development Feasibility Study
Sponsor:
Oxford University Hospitals NHS Trust
Organization:
Oxford University Hospitals NHS Trust
Study Overview
Official Title:
MoleGazer A Feasibility Study for Early Detection of Melanoma
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Melanoma skin cancer frequently develops from existing moles on the skin Current practice relies on expert dermatologists being able to successfully identify newchanging 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 patients lifetime to detect changes with the eventual aim to detect melanoma as early as possible
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 patients lifetime to detect changes with the eventual aim to detect melanoma as early as possible
Detailed Description:
Background
Melanoma incidence is rapidly increasing with 15906 new United Kingdom UK cases in 2015 resulting in 2285 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
Previous AI evaluation of skin lesions has demonstrated equivalent accuracy to trained dermatologists in skin cancer diagnosis however this relied on single-lesion analysis at static time-points with biopsy-proven diagnoses The use of lesions scheduled for excision ie high clinical suspicion of melanoma severely limits clinical applicability and a Cochrane review concluded that utility of computer-aided detection for melanoma diagnosis in secondary care remains unknownThe more clinically-relevant question is whether automated detection of changes in naevi using sequential TBP images referred to clinically as mole mapping can indeed improve early diagnosis of melanoma
To date TBP systems in the NHS have limited automation restricted to storing and retrieving images Although one automated total body scanning system exists and in the future may incorporate AI-based diagnosis in addition to current image acquisition and lesion matching algorithms a full clinical validation and any subsequent implementation in the NHS will be costly due to the investment required in the scanning system current cost US 1 million Whether the same or better results can be achieved using more conventional image acquisition equipment and sophisticated AI techniques is unknown The investigators propose a novel application of astronomical AI methods for early melanoma detection using standard TBP-based surveillance of naevi which is currently employed in the NHS and can be used as an adjunct to clinical review of individuals
Application of astronomical AI techniques to TBP monitoring of multiple naevi
Transient science in astronomy aims to detect and track evolution of new astronomical sources such as exploding stars Exhibiting both long- and short-term evolution individual events are detected by comparing new images with archival data and classified based on a feature set including transient brightness colour proper motion and extent Cutting-edge astronomical surveys monitor the sky every night over multi-year timescales to identify subtle changes AI techniques such as random forests and recurrent neural networks RNN which use the full time-series history and contextual information are routinely used to identify and classify events probabilistically With each new observation providing additional information astronomical transient surveys can routinely detect and characterise new sources such that the evolution of new sources can be predicted with 995 accuracy based on only three time-points
This challenge faced in astronomy is analogous to mole mapping for individuals at high-risk of developing melanoma both naevi and astronomical sources can be characterised as distinct sources against a homogeneous background which are tracked across multiple images to detect change The investigators therefore hypothesise that astronomical AI techniques are ideally suited to address this clinical problem and are developing the MoleGazer project to test this
Rationale
To develop the MoleGazer algorithm the investigators require a baseline dataset to apply astronomical AI algorithms to TBP images to detect and track naevi across sequential images There are currently no publicly available databases of TBP images for the investigators to test this feasibility and therefore in this study the aim is to collect
1 a time-series cohort of TBP images taken at fixed sequential time-points over 2 years
2 a baseline cohort of TBP images with sequential images taken at any time-points By collecting TBP images it will allow the investigators to study the sensitivity of naevi detection and characterisation on skin tone lighting levels image registration and background subtraction techniques enabling the investigators to also automate detection of naevi and track their evolution in any sequential image that the study team has The development of this database will allow the investigators to demonstrate feasibility of the application of astronomical AI methods to TBP images
Melanoma incidence is rapidly increasing with 15906 new United Kingdom UK cases in 2015 resulting in 2285 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
Previous AI evaluation of skin lesions has demonstrated equivalent accuracy to trained dermatologists in skin cancer diagnosis however this relied on single-lesion analysis at static time-points with biopsy-proven diagnoses The use of lesions scheduled for excision ie high clinical suspicion of melanoma severely limits clinical applicability and a Cochrane review concluded that utility of computer-aided detection for melanoma diagnosis in secondary care remains unknownThe more clinically-relevant question is whether automated detection of changes in naevi using sequential TBP images referred to clinically as mole mapping can indeed improve early diagnosis of melanoma
To date TBP systems in the NHS have limited automation restricted to storing and retrieving images Although one automated total body scanning system exists and in the future may incorporate AI-based diagnosis in addition to current image acquisition and lesion matching algorithms a full clinical validation and any subsequent implementation in the NHS will be costly due to the investment required in the scanning system current cost US 1 million Whether the same or better results can be achieved using more conventional image acquisition equipment and sophisticated AI techniques is unknown The investigators propose a novel application of astronomical AI methods for early melanoma detection using standard TBP-based surveillance of naevi which is currently employed in the NHS and can be used as an adjunct to clinical review of individuals
Application of astronomical AI techniques to TBP monitoring of multiple naevi
Transient science in astronomy aims to detect and track evolution of new astronomical sources such as exploding stars Exhibiting both long- and short-term evolution individual events are detected by comparing new images with archival data and classified based on a feature set including transient brightness colour proper motion and extent Cutting-edge astronomical surveys monitor the sky every night over multi-year timescales to identify subtle changes AI techniques such as random forests and recurrent neural networks RNN which use the full time-series history and contextual information are routinely used to identify and classify events probabilistically With each new observation providing additional information astronomical transient surveys can routinely detect and characterise new sources such that the evolution of new sources can be predicted with 995 accuracy based on only three time-points
This challenge faced in astronomy is analogous to mole mapping for individuals at high-risk of developing melanoma both naevi and astronomical sources can be characterised as distinct sources against a homogeneous background which are tracked across multiple images to detect change The investigators therefore hypothesise that astronomical AI techniques are ideally suited to address this clinical problem and are developing the MoleGazer project to test this
Rationale
To develop the MoleGazer algorithm the investigators require a baseline dataset to apply astronomical AI algorithms to TBP images to detect and track naevi across sequential images There are currently no publicly available databases of TBP images for the investigators to test this feasibility and therefore in this study the aim is to collect
1 a time-series cohort of TBP images taken at fixed sequential time-points over 2 years
2 a baseline cohort of TBP images with sequential images taken at any time-points By collecting TBP images it will allow the investigators to study the sensitivity of naevi detection and characterisation on skin tone lighting levels image registration and background subtraction techniques enabling the investigators to also automate detection of naevi and track their evolution in any sequential image that the study team has The development of this database will allow the investigators to demonstrate feasibility of the application of astronomical AI methods to TBP images
Study Oversight
Has Oversight DMC:
None
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?:
None