Investigation of the Performance of the Medical Device Dermalyser in Determining Melanoma Thickness Based on Dermoscopy Images

NCT06788665 | Not Yet Recruiting

• 1 other identifier: CIV-24-06-047957
• Study Type: observational
• Target: 900
• Locations: 1 country
• Sites: 1

Brief Summary

In Sweden, approximately 9,000 Swedes are affected by melanoma annually, and each year, 500 individuals die from metastatic melanoma. The prognosis for melanoma primarily depends on the thickness of the tumor at diagnosis. Melanomas that only grow in the epidermis and have not yet grown into the dermis are called melanoma in situ or pre-melanoma. These melanomas lack the potential to spread in the body. Melanomas that grow into the dermis, on the other hand, are called invasive or malignant melanomas. Invasive melanomas have the potential to spread in the body. To improve melanoma diagnostics, a dermatoscope is used. A dermatoscope is a type of magnifying glass equipped with a strong light. Using a dermatoscope makes the structures in the epidermis and dermis clearer. Although most melanomas are relatively easy to detect, it is often difficult to determine whether melanomas are invasive or in situ based on the dermatoscopic image. Despite the fact that all suspected melanomas (regardless of melanoma depth) should be operated on, it is important to form an opinion on whether the melanoma is invasive or in situ. This decision is important because it:

1. 1.Provides guidance on how quickly healthcare must prioritize a patient for surgery.
2. 2.Provides guidance on the margin the surgeon chooses for the first operation.
3. 3.Affects the information we give the patient even before the first operation.

Conditions

Melanoma (Skin Cancer)

Keywords

melanoma; machine learning; artificial intelligence; diagnostic accuracy; clinical desicion supportive tool; Neoplasms by Histologic Type; Neoplasms; Nevi and Melanomas; Skin Neoplasms; Neoplasms by Site; Skin Diseases

Outcome Measures

Primary Outcomes (1)

• Area under the curve (AUC) for discrimination between invansive and in situ melanoma for the "in distribution data set".

  Please note that preoperatively there will still be uncertainty if an included lesion will be histopathologically verified as a melanoma. After histopathological analysis all lesions will be separated into an "in distrubution data set", i.e. lesions that were confirmed as melanomas and an "out-of distribution set", i.e., lesions that proved to have an alternative diagnosis. Please note that the primary outcome measure will be limited the "in distrubution data set", i.e., lesions that turned out to be a melanoma. Image analysis with the help of AI tool Dermalyser in a prospective setting.

  From enrollment to the end of the inclusion when the images of the lesion have been obtained. This will most often be achieved on the same day as that the patient is included in the study.

Secondary Outcomes (1)

• Sensitivity and Specificity

  From enrollment to the end of the inclusion when the images of the lesion have been obtained. This will most often be achieved on the same day as that the patient is included in the study.

Other Outcomes (1)

• Algorithmic and physicians assessment of the "out of distribution set"

  From enrollment to the end of the inclusion when the images of the lesion have been obtained. This will most often be achieved on the same day as that the patient is included in the study.

Study Arms (1)

Cutaneous melanoma

Device: Medical Device Dermalyser

Interventions

Medical Device Dermalyser DEVICE

Dermalyser is an Artificial Intelligence (AI) application (app) that allows medical professionals to take pictures of cutaneous lesions with the help of a smartphone camera. A dermatoscope is connected to the smartphone camera and is used to take the digital image of cutaneous lesions with suspicion of melanoma. Based on image processing algorithms, the app does a detailed analysis of the captured cutaneous lesion. In this clinical investigation, the objective is to test the device performance in a prospective setting in patients with a suspicion of primary cutaneous melanoma, to validate the added AI component. The intended purpose of the device is not to replace the physician's assessment, but rather to assist physicians in their assessment. Consequently, the final device should be regarded as a second opinion to augment clinical decision-making. The ultimate aim is to develop a tool that may augment clinical decision-making.

Cutaneous melanoma

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The investigational population are patients ≥18 years with suspected cutaneous melanoma with Fitzpatrick skin phototypes I, II, III and IV. We aim to include 300 melanomas with histopathological verification. Importantly, we will include only suspected melanomas, meaning that some lesions will end up being either dysplastic nevi (DN) or other skin tumours. This implies that we need to include up to 600-900 lesions. Considering the non-invasive nature of the investigational product pregnant and breastfeeding women, immune-compromised and elderly subjects will be eligible for inclusion if they fulfil all inclusion criteria.

You may qualify if:

• The patient has a suspected primary melanoma where surgery is planned
• Willingness and ability to provide informed consent

You may not qualify if:

• The suspected lesion size is too small or too large to fit the white circle in the screen even after zooming in and out at its maximum. The lesion should not be \< 2 mm or \> 20 mm in diameter.
• As per judgement by the investigator, to exclude when there are factors that may affect the quality of the photo such as when:
• Lesion with any form of modifications such as tattoos, pen markings, and/or pigments covering or surrounding skin lesions.
• Lesions located on areas such as genitalia, acral skin (foot sole or palm of the hand), mucosal surfaces (mouth, eye, nose, etc.), psoriasis or similar skin conditions.
• Lesions masked by an excessive amount of hair, generally found in armpits, scalp, beards or other hairy parts of the body.
• Lesions that are scarred, non-intact, injured, previously biopsied or undergone surgery. in areas that are not suitable for dermoscopy imaging.
• Individuals with skin type V and VI according to the Fitzpatrick scale (darker brown or black coloured skin)
• Patients that do not perform surgery or die before the planned surgery
• Missing or uninterpretable diagnostic data from the Department of Pathology.

Sponsors & Collaborators

• Vastra Gotaland Region: lead

Study Sites (1)

Department of Dermatology and Venereology Sahlgrenska University Hospital, Gröna stråket 16

Gothenburg, 413 45, Sweden

Location

MeSH Terms

Conditions

Melanoma; Neoplasms by Histologic Type; Neoplasms; Nevi and Melanomas; Skin Neoplasms; Neoplasms by Site; Skin Diseases

Condition Hierarchy (Ancestors)

Neuroendocrine Tumors; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms, Nerve Tissue; Skin and Connective Tissue Diseases

Central Study Contacts

Sam Polesie, MD, PhD

CONTACT

+4670-2241915; sam.polesie@vgregion.se

Filippos Giannopoulos, MD

CONTACT

+46729102556; filippos.giannopoulos@vgregion.se

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER GOV
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 16, 2025
• First Posted: January 23, 2025
• Study Start: January 31, 2025
• Primary Completion: January 1, 2026
• Study Completion: February 1, 2026
• Last Updated: January 23, 2025

Record last verified: 2024-10

Locations

SE (1)