AI Combined With Infrared Imaging Automatically Classifies Tumors

In recent years, there has been a massive advancement in the available treatments for colon cancer. To ensure these therapies, such as immunotherapies, are effective, it is important to accurately diagnose the individual patient to provide specifically tailored treatment. Now, researchers have paired artificial intelligence (AI) with infrared (IR) imaging to develop an automated and precise method for diagnosing colon cancer and tailoring treatments to the patient. This label-free and automated technique complements existing methods for analyzing tissue samples.

Over the course of the past several years, a research team at the Centre for Protein Diagnostics (PRODI) at Ruhr University Bochum (Bochum, Germany) has been working on creating a new digital imaging method known as label-free IR imaging. This method measures the genomic and proteomic composition of the examined tissue, providing molecular information based on the infrared spectra. The information is then decoded using AI and displayed as false-color images utilizing image analysis methods from the field of deep learning.

Image: AI with infrared imaging enables precise colon cancer diagnostics (Photo courtesy of Pexels)

The PRODI team successfully demonstrated that using deep neural networks, it was possible to effectively determine the microsatellite status, a prognostically and therapeutically relevant parameter, in colon cancer. In this process, the tissue sample passes through a standardized, user-independent, automated process and allows for spatially resolved differential classification of the tumor within an hour. On the other hand, classical diagnostics is used to determine the microsatellite status either through complex immunostaining of various proteins or via DNA analysis.

The ever-improving therapy options have made fast and uncomplicated determination of such biomarkers extremely important. Based on IR microscopic data, the researchers modified, optimized, and trained neuronal networks to establish label-free diagnostics. In contrast to immunostaining, the new approach does not need dyes and is much 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,” said PhD student Stephanie Schörner.

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Ruhr University Bochum