Predicting targeted- and immunotherapeutic response outcomes in melanoma with single-cell Raman Spectroscopy and AI
This study demonstrates that a non-destructive, single-cell Raman spectroscopy approach combined with machine learning can accurately differentiate melanoma cell phenotypes and predict therapeutic resistance to targeted and immunotherapies in both cell lines and patient-derived samples, offering a rapid and scalable tool for precision medicine.