Basal Cell Carcinoma, Malignant Skin Melanoma T0, Squamous Cell Carcinoma
Conditions
Brief summary
This study aims establish the effectiveness of Image Analysing Algorithm (DERM) to identify melanoma, Squamous Cell Carcinoma (SCC) and Basal Cell Carcinoma (BCC) when used to analyse dermoscopic images of skin lesions within the US and European population.
Interventions
An AI-based diagnosis support tool
Sponsors
Skin Analytics Limited
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Inclusion criteria
* Willing and able to give informed consent for participation in the study, * Male or Female, aged 18 years or above, * Have at least one suitable skin lesion that will be biopsied due to a suspicion of skin cancer, To be suitable for inclusion, a skin lesion must NOT have ANY of the following limitations: located on an anatomical site of different skin structure: palms of hands or soles of feet (acral lesion), mucosal surfaces (lips and eyes) or under nail (ungal lesion), a diameter greater than the diameter of the dermoscopic lenses, located on an anatomical site unsuitable for photographing, including on surface of genitals and hair-bearing areas, has been previously biopsied, excised, treated or otherwise traumatised, located in an area of visible scarring or tattooing. \- In the Investigator's opinion, able and willing to comply with all study requirements.
Exclusion criteria
* Any other significant disease or disorder which, in the opinion of the Investigator, may either put the participant at risk because of participation in the study, or may influence the result of the study, or the participant's ability to participate in the study.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity of DERM to detect Malignant conditions | Through study completion, on average of 1 day | Sensitivity of DERM to detect Melanoma, SCC and BCC combined |
| Specificity of DERM to detect Malignant conditions. | Through study completion, on average of 1 day | Specificity of DERM to detect Melanoma, SCC and BCC combined |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity of DERM to detect Squamous Cell Carcinoma | Through study completion, on average of 1 day | Sensitivity of DERM to correctly classify SCC |
| Specificity of DERM to detect Squamous Cell Carcinoma | Through study completion, on average of 1 day | Specificity of DERM to correctly classify SCC |
| Sensitivity of DERM to detect Melanoma | Through study completion, on average of 1 day | Sensitivity of DERM to detect Melanoma |
| Specificity of DERM to detect Basal Cell Carcinoma | Through study completion, on average of 1 day | Specificity of DERM to correctly classify BCC |
| Accuracy of mole/not mole algorithm | Through study completion, on average of 1 day | Accuracy of mole/not mole algorithm |
| Sensitivity of DERM to detect Basal Cell Carcinoma | Through study completion, on average of 1 day | Sensitivity of DERM to correctly classify BCC |
| Specificity of DERM to detect Melanoma | Through study completion, on average of 1 day | Specificity of DERM to detect Melanoma |
Other
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic accuracy measures | Through study completion, on average of 1 day | AUROC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) etc. of DERM to detect melanoma, SCC, BCC, premalignant, and benign conditions grouped and individually |
| Percentage of images taken that are rejected by the IQ check (MoleNotMole + Image Quality), where a second image is successfully taken | Through study completion, on average of 1 day | Percentage of images taken that are rejected by the IQ check (MoleNotMole + Image Quality), where a second image is successfully taken |
| Probability that the most probable lesion label DERM returns matches the lesion diagnosis | Through study completion, on average of 1 day | Probability that the alternative classification label DERM that returns, matches the lesion diagnosis |
| The impact of patient characteristics on the diagnostic accuracy of DERM | Through study completion, on average of 1 day | Such as sex, age, and Fitzpatrick skin type |
| The impact of lesions characteristic on the diagnostic accuracy of DERM | Through study completion, on average of 1 day | Such as location, size, growth, stage and sub-type |
| AUROC of DERM to identify malignant conditions for each individual camera / lens type | Through study completion, on average of 1 day | AUROC of DERM to identify malignant conditions for each individual camera / lens type |
| Concordance of DERM results by each individual camera / lens type | Through study completion, on average of 1 day | Concordance of DERM results by each individual camera / lens type |
| Strength of association between correct classification and acceptance/rejection status of images | Through study completion, on average of 1 day | Strength of association between correct classification and acceptance/rejection status of images |
| AUROC of DERM when macro images are used both to train the algorithm and as test images | Through study completion, on average of 1 day | AUROC of DERM when macro images are used both to train the algorithm and as test images |
| Correlation between clinician assessment of likelihood of skin cancer with histopathology diagnosis | Through study completion, on average of 1 day | Correlation between clinician assessment of likelihood of skin cancer with histopathology diagnosis |
Countries
Italy, United States
Outcome results
None listed