Quantitative Digital Assessment of PRAME Expression in Melanoma

Melanoma cases are increasing each year and can be difficult to diagnose. Pathologists look at special markers in skin samples under the microscope to help determine whether a growth is harmless or cancerous, but current methods rely on subjective judgment and can sometimes lead to disagreement even among experts. A promising marker called PRAME has been found to be more common in melanoma than in benign growths, but it is not yet measured in a way precise enough to fully leverage its diagnostic potential. This study aims to improve how PRAME is analyzed by using advanced digital tools. We will examine skin tissue samples and create detailed images of PRAME in melanocytes (the cells involved in melanoma). By calculating a new measurement called the PRAME index, we will be able to precisely determine the percentage of melanocytes that display the PRAME marker. This will provide a more precise and objective way to assess PRAME. By refining PRAME testing, our study aims to identify patterns that could lead to better diagnosis and treatment and lay the groundwork for using PRAME in targeted cancer therapies. We will also explore the use of artificial intelligence to predict PRAME index from routine pathology images. This could make PRAME analysis faster, more accessible, and more consistent, helping pathologists make better-informed decisions. By combining cutting-edge tools in laboratory science, digital pathology, and artificial intelligence, this research represents a major step toward making early detection and treatment of melanoma more reliable and accessible to patients everywhere.