Artificial intelligence and infrared imaging automatically classify tumours and are faster than previous methods. The immense progress in the field of therapy options over the past years has significantly improved the chances of cure for patients with colon cancer. However, these new approaches, such as immunotherapies, require precise diagnosis so that they can be specifically tailored to the individual. Researchers at the Centre for Protein Diagnostics PRODI at Ruhr University Bochum, Germany, are using artificial intelligence in combination with infrared imaging to optimally tailor colon cancer therapy to individual patients.
The label-free and automatable method can complement existing pathological analyses. The PRODI team has been developing a new digital imaging method over the last years: the so-called label-free infrared IR imaging measures the genomic and proteomic composition of the examined tissue, i.
This information is decoded with the help of artificial intelligence and displayed as false-colour images. To do this, the researchers use image analysis methods from the field of deep learning.
In cooperation with clinical partners, the PRODI team was able to show that the use of deep neural networks makes it possible to reliably determine the so-called microsatellite status, a prognostically and therapeutically relevant parameter, in colon cancer. In this process, the tissue sample goes through a standardised, user-independent, automated process and enables a spatially resolved differential classification of the tumour within one hour.
In classical diagnostics, microsatellite status is determined either by complex immunostaining of various proteins or by DNA analysis. With the ever-improving therapy options, the fast and uncomplicated determination of such biomarkers is also becoming more and more important. Unlike immunostaining, this approach does not require dyes and is significantly faster than DNA analysis.
