Artificial intelligence and infrared imaging automatically classify tumors 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 team led by Professor Klaus Gerwert reports in the "European Journal of Cancer" in January 2023.
Deep insights into human tissue within one hour
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.e. provides molecular information based on the infrared spectra. This information is decoded with the help of artificial intelligence and displayed as false-color 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 standardized, user-independent, automated process and enables a spatially resolved differential classification of the tumor within one hour.
Indication of the effectiveness of therapies
In classical diagnostics, microsatellite status is determined either by complex immunostaining of various proteins or by DNA analysis.
15 to 20 per cent of colon cancer patients show microsatellite instability in the tumor tissue. This instability is a positive biomarker indicating that immunotherapy will be effective."
Professor Andrea Tannapfel, Head of the Institute of Pathology at Ruhr University
With the ever-improving therapy options, the fast and uncomplicated determination of such biomarkers is also becoming more and more important. Based on IR microscopic data, neuronal networks were modified, optimized, and trained at PRODI to establish label-free diagnostics. Unlike immunostaining, this approach does not require dyes and is significantly faster than DNA analysis. "We were able to show that the accuracy of IR imaging for determining microsatellite status comes close to the most common method used in the clinic, immunostaining," says PhD student Stephanie Schörner. "Through constant further development and optimization of the method, we expect a further increase in accuracy," adds Dr. Frederik Groβerüschkamp.
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This project was made possible by a long-standing, intensive cooperation between the Institute of Pathology at Ruhr University (Professor Andrea Tannapfel), the Clinic for Haematology and Oncology at the St. Josef Hospital, Clinical Centre of Ruhr University (Professor Anke Reinacher-Schick) and the Centre for Protein Diagnostics (Professor Klaus Gerwert).
The PRODI researchers were able to access the ColoPredict Plus 2.0 molecular registry, a non-interventional, multi-center registry study for patients with early-stage colorectal cancer, to develop this diagnostic approach. "The ColoPredict registry also enables a more targeted therapy for patients through the targeted analysis of biomarkers. Thus, the registry recently serves as a study platform for precision oncology approaches," says Anke Reinacher-Schick. In addition to providing tissue samples, the registry offers a sound database of prognostically and therapeutically relevant baseline characteristics. "In such a project, it is of immense importance to be able to draw on an excellent cohort and pathological expertise," emphasizes Klaus Gerwert. "Our work on the classification of microsatellite status in colon cancer patients is based on one of the largest cohorts we have published to date and clearly demonstrates the potential for use in translational cancer research," says Andrea Tannapfel.
The work of the Research Centre for Protein Diagnostics (PRODI) was funded by the State of North Rhine-Westphalia, Ministry of Culture and Science (grant number: 111.08.03.05-133974). The register study was funded by Roche Pharma AG. Parts of the project were funded by the Slide2Mol project through the Computational Life Science program of the Federal Ministry of Education and Research.
Gerwert, K., et al. (2023) Fast and label-free automated detection of microsatellite status in early colon cancer using artificial intelligence integrated infrared imaging. European Journal of Cancer. doi.org/10.1016/j.ejca.2022.12.026.
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Tags: Artificial Intelligence, Biomarker, Cancer, Cancer diagnostics, Cancer Therapy, Colon Cancer, Colorectal, Colorectal Cancer, Deep Learning, Diagnostic, Diagnostics, DNA, Education, Genomic, Haematology, Hospital, Imaging, Immunotherapy, Life science, Medicine, Oncology, Pathology, Protein, Research
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