Oleh Dzyubachyk

LUMC

In short, Oleh Dzyubachyk's research entails the following:

Identifying Individuals at High Risk of Melanoma Using Total-Body Photographs: A Deep-Learning Approach

Melanoma is an aggressive type of skin cancer associated with significant mortality and healthcare costs. The lifetime risk of developing melanoma in the Netherlands is 1%, with certain subgroups being at higher risk due to hereditary susceptibility, multiple atypical nevi (moles), fair skin type and excessive exposure to ultraviolet radiation. Early diagnosis is critical to prevent metastatic dissemination, and periodic skin examinations are recommended for patients at high risk. Current criteria to identify patients who are advised to undergo lifelong periodic screening are crude and need refinement. The screenings are associated with considerable healthcare costs and are not feasible for all individuals at risk (estimated 1 million persons in the Netherlands).

We have collected a unique set of total-body photographs and detailed clinical data from patients at increased risk of melanoma. By leveraging the potential of deep-learning, the algorithm will identify those patients with atypical nevi and inherited melanoma susceptibility who are at a particularly high risk of developing melanoma. This will enable clinicians to select patients at high risk of developing melanoma for periodic skin examination, instruction for skin self-examination, sequential photography, and suspicious skin lesion removal, while reducing unnecessary skin cancer screening. Capacity of deep-learning algorithms to detect patterns associated with high melanoma risk in these multi-modal data, can, in turn, facilitate development of precision prevention strategies for those at high and at moderate risk of melanoma.