Mid infrared multispectral imaging for tumorous tissue detection

As the number of cancers is steadily increasing, doctors are in need of automatic tools with better and faster analysis methods to help them with the diagnosis. One way to tackle this challenge is to propose label-free methods capable to analyze a large number of samples. Recent development in photonics components could enable to use infrared light to detect abnormal tissues and Mid-IR imaging can provide an unequivocal information about the biochemical composition of human cells. The combination of a set of Quantum Cascade Lasers (QCLs) and lensfree imaging with uncooled bolometer matrix will allow the biochemical mapping over a wide field of view. This experimental setup coupled to machine learning algorithms (Random Forest, Neural Networks, K-means) can help to classify the biological cells in a fast and reproducible way. Images from the frozen section tissue of nude mice bearing human orthotropic oral cavity tumors from the CAL33 cell line have been acquired and analyzed. Using amide and DNA absorption bands, we achieved up to 94% of successful predictions of cancer cells with a population of 325 pixels corresponding to muscle tissues and 325 pixels corresponding to cancer tissues. This work may lead to the development of an imaging device, that could be used for cancer diagnosis at hospital.