Depending on the type of lung cancer, the prognosis and successful treatments differ. While it previously took several days to precisely determine the underlying lung tumor mutation, a research team has been able to reliably perform this determination in just one step. They used a combination of quantum cascade laser-based infrared microscopy and machine learning.
Researchers do not need to mark the examined tissue for this. Within half an hour, the analysis ascertains whether the tissue sample contains tumor cells, what type of tumor it is, and whether it contains a particular mutation.
The researchers were able to verify the procedure on samples from over 200 lung cancer patients in their work. When identifying mutations, they concentrated on by far the most common lung tumor, adenocarcinoma, which accounts for around 50 percent of tumors. It is possible to determine the most common genetic mutations with a sensitivity and specificity of 95 percent compared to laborious genetic analysis. The researchers were able to identify spectral markers that allow for a spatially resolved distinction between various molecular conditions in lung tumors.