Artificial Intelligence, MRI Detect Early Signs Of Tumor Cell Death
Researchers created a non-invasive approach for measuring cancer treatment response using AI and MRI technology.
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By - Massachusetts General Hospital (MGH) researchers found that magnetic resonance imaging (MRI) and artificial intelligence (AI) can be used to detect early signs of tumor cell death in response to a novel virus-based cancer therapy.
According to researchers, a recent therapeutic virus has shown promise in selectively killing cancer cells while sparing normal tissue, creating hope for treating aggressive brain tumors. To improve the virus-based therapy, frequent non-invasive monitoring of the treatment response must be performed. The monitoring is important to understand the interactions between the virus and cancer cells.
The research team used quantitative molecular MRI images to measure multiple tissue properties. According to the researchers, this method allows therapeutic response monitoring much sooner than other techniques, visible 48 hours after viral therapy.
“We programmed an MRI scanner to create unique signal ‘fingerprints’ for different molecular compounds and cellular pH. A deep learning neural network was then used to decode the fingerprints and generate quantitative pH and molecular maps,” investigator at the Athinoula A. Martinos Center for Biomedical Imaging, Christian Farrar, PhD, said in a press release.
“The MRI molecular fingerprinting method was validated in a mouse brain tumor study where the tumors were treated with a novel virus-based therapy that selectively killed cancer cells.”
To further enhance the MRI method, researchers developed an AI approach for detecting tumor cell death caused by the virus, allowing for rapid detection of treatment response.
“This study demonstrates the strength and promise of implementing computerized AI-based technology in medicine for the non-invasive investigation of biological processes that underlie disease,” said Or Perlman, PhD, a research fellow at the Athinoula A. Martinos Center for Biomedical Imaging.
“One of the most interesting and key components for the success of this approach was the use of simulated molecular fingerprints to train the machine learning neural network. This concept could potentially be expanded and investigated for solving other medical and scientific challenges.”
Using this non-invasive model to evaluate cancer treatment could significantly improve patient outcomes and advance precision medicine efforts. A similar approach could also detect other conditions such as stroke and liver disease.
According to researchers, the study as mainly validated used a mouse brain tumor model. However, the researchers have demonstrated the ability to use the same method for creating quantitative pH and molecular maps in rat stroke models and healthy humans. The team plans to expand the model capabilities for patients with brain tumors and stroke in the future.
