Lipid fingerprint‐based histology accurately classifies nevus, primary melanoma, and metastatic melanoma samples

Probably, the most important factor for the survival of a melanoma patient is early detection and precise diagnosis. Although in most cases these tasks are readily carried out by pathologists and dermatologists, there are still difficult cases in which no consensus among experts is achieved. To deal with such cases, new methodologies are required. Following this motivation, we explore here the use of lipid imaging mass spectrometry as a complementary tool for the aid in the diagnosis. Thus, 53 samples (15 nevus, 24 primary melanomas, and 14 metastasis) were explored with the aid of a mass spectrometer, using negative polarity. The rich lipid fingerprint obtained from the samples allowed us to set up an artificial intelligence‐based classification model that achieved 100% of specificity and precision both in training and validation data sets. A deeper analysis of the image data shows that the technique reports important information on the tumor microenvironment that may give invaluable insights in the prognosis of the lesion, with the correct interpretation. What's new? Here, the authors evaluate the potential of matrix‐assisted laser desorption/ionization lipid imaging mass spectrometry (LIMS) together with classification models built using artificial intelligence to classify samples of nevus, melanoma, and metastatic melanoma. They find that, looking at the alterations in the lipid profile of the tissues and having built a library of lipid signatures using LIMS, it is possible to automatically detect the presence of tumor cells and even determine if the sample is a primary tumor or a metastasis. The findings pave the way for the development of fast, accurate, and automatized protocols for the screening of melanoma samples.