Cancer is a leading cause of death worldwide, and incidence and mortality are growing rapidly. Metastasis, the occurrence of cancer at sites different from the primary tumor, can affect multiple organs, compromising their function or altering their metabolism, and eventually leads to death. In fact, metastasis is the main cause of death in the majority of cancer patients. Considering the severe implications of cancer on patient survival and quality of life, the early identification of metastases is pivotal in the effective treatment of cancer. Since routine diagnostic practice relies on a time-consuming staining process and the use of antibodies to detect selected molecular markers, it is limited by a lack of real-time data and the availability of molecular information. Against this background, techniques based on rapid chemical analysis to identify migratory properties are highly desirable. Fourier-Transform Infrared (FTIR) spectroscopy has a long history in the label-free identification of infrared marker bands for cancer detection. However, it requires extensive post-processing of the acquired spectra, is not suitable for analysis in aqueous environments and has poor spatial resolution. To overcome these challenges, we are using a new method termed Optical Photothermal Infrared (O-PTIR) spectroscopy to detect local absorption to establish IR tumor markers and classification models. We report on experimental outcomes using machine learning and IR spectroscopy for the classification of cells and the identification of markers for cancer and migratory properties, comparing conventional FTIR microscopy to a custom O-PTIR instrument.
Holub et al. (Sun,) studied this question.