investigation of the performance of the medical device dermalyser in determining melanoma thickness based on dermoscopy images

Conditions: Melanoma (Skin Cancer)

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. provides guidance on how quickly healthcare must prioritize a patient for surgery. 2. provides guidance on the margin the surgeon chooses for the first operation. 3. affects the information we give the patient even before the first operation. in recent years, several applications of machine learning have shown great potential in research contexts within dermatology and venereology. however, these tools have been evaluated to a very limited extent in clinical trials, which is naturally a prerequisite before they can be safely implemented in routine healthcare.


Eligibility criteria:
inclusion criteria: 1. patient needs to be ≥18 years of age at inclusion 2. the patient has a suspected primary melanoma where surgery is planned 3. willingness and ability to provide informed consent exclusion criteria: 1. 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 \ 20 mm in diameter. 2. as per judgement by the investigator, to exclude when there are factors that may affect the quality of the photo such as when: 1. lesion with any form of modifications such as tattoos, pen markings, and/or pigments covering or surrounding skin lesions. 2. 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. 3. lesions masked by an excessive amount of hair, generally found in armpits, scalp, beards or other hairy parts of the body. 4. lesions that are scarred, non-intact, injured, previously biopsied or undergone surgery. in areas that are not suitable for dermoscopy imaging. 3. individuals with skin type v and vi according to the fitzpatrick scale (darker brown or black coloured skin) 4. patients that do not perform surgery or die before the planned surgery 5. missing or uninterpretable diagnostic data from the department of pathology.

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