Research & Development

IR imaging and AI classifies colorectal cancer

A new approach in infrared microscopy can automatically detect the type of an intestinal tumour within 30 minutes. These results are then used to make targeted therapy decisions.

A research team from the Prodi Centre for Protein Diagnostics at Ruhr-Universität Bochum (RUB) has used infrared (IR) microscopes based on quantum cascade lasers to classify tissue samples of colorectal cancer from routine clinical operations in a marker-free and automated way. Artificial intelligence (AI) enabled the researchers to differentiate between tumour types with great accuracy within approximately 30 minutes. Based on the classification, doctors can predict which course the disease will take and, consequently, choose the appropriate therapy.

To date, differential diagnosis has been carried out by immunohistochemical staining of tissue samples with subsequent complex genetic analysis. The protein research team has significantly improved the method by optimising it for the detection of a molecular change in the tissue. Previously, the tissue could be only morphologically visualised.

The potential of IR imaging as a diagnostic tool for the classification of tissue, the so-called label-free digital pathology, had already been demonstrated in earlier studies by the group headed by Professor Klaus Gerwert from the RUB Department of Biophysics. The method recognizes cancer tissue without prior staining or other marking and, consequently, also works automatically with the aid of artificial intelligence.

The new differential diagnosis method requires only about half an hour. In collaboration with the Institute of Pathology at RUB and the Department of Haematology and Oncology at the RUB St. Josef Hospital, the research team conducted a feasibility study with 100 patients. It showed a sensitivity of 100 % and a specificity of 93 %. An expanded clinical trial is now starting. In future, the method is to be introduced into the clinical workflow to assess its potential for precision oncology.


Original publication:
Angela Kallenbach-Thieltges et al., Label-free, automated classification of microsatellite status in colorectal cancer by infrared imaging, Scientific Reports, 2020, DOI: 10.1038/s41598-020-67052-z]

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