Magnetic resonance imaging (MRI) and artificial intelligence (AI) can
detect early signs of tumor cell death after novel therapy

Date:
December 1, 2021
Source:
Massachusetts General Hospital
Summary:
A recent study demonstrates 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.



FULL STORY ==========================================================================
In a recent study published in Nature Biomedical Engineering, a team led
by researchers at Massachusetts General Hospital (MGH) has demonstrated
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.


========================================================================== Recently, a promising therapeutic virus that selectively kills cancer
cells while sparing normal tissue has sparked hope for treating aggressive brain tumors. To further optimize the virus-based therapy, frequent non-invasive monitoring of the treatment response must be performed. This monitoring is crucial for understanding the interactions between the
virus and cancer cells, such as the extent of virus spread within the
tumor and therapeutic response.

The researchers used quantitative molecular MRI images to measure multiple tissue properties, including tissue pH and protein concentration, that
are altered with cell-death. This method allows therapeutic response
monitoring much earlier than with previous techniques. The treatment
responses were visible just 48 hours after viral-therapy, long before
any changes in tumor volume were observed.

"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," says Christian Farrar, PhD, an investigator and
faculty at the Athinoula A. Martinos Center for Biomedical Imaging. "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 maximize the efficiency of
this treatment approach, the researchers developed a method for the
detection of tumor cell death caused by the virus.

This has allowed for the early and rapid detection of treatment-responsive tumor regions. Recently, the researchers have implemented this method
to quantify cellular pH and molecular compounds in the healthy human
brain. Future investigation of this approach in human brain tumor
patients would help to optimize these virus-based therapies "This study demonstrates the strength and promise of implementing computerized
AI-based technology in medicine for the noninvasive investigation
of biological processes that underlie disease," says 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." This study describes a new
method for detecting tumor cell death non-invasively using MRI. The
capacity to do this could be useful for non-invasive monitoring of cancer treatment, potentially improving patient care and tailoring the treatment
to an individual patient. The same approach might also be beneficial for detecting and characterizing other medical conditions where elevated
cell death occurs, such as stroke and liver disease. While the study
was mainly validated using a mouse brain tumor model, the researchers
have demonstrated the ability to use the same method for producing
quantitative pH and molecular maps in rat stroke models and healthy
humans. In the future, they plan to further explore the applicability
of this non-invasive imaging approach in patients with brain tumors
and stroke.

========================================================================== Story Source: Materials provided by Massachusetts_General_Hospital. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Or Perlman, Hirotaka Ito, Kai Herz, Naoyuki Shono, Hiroshi
Nakashima,
Moritz Zaiss, E. Antonio Chiocca, Ouri Cohen, Matthew S. Rosen,
Christian T. Farrar. Quantitative imaging of apoptosis following
oncolytic virotherapy by magnetic resonance fingerprinting aided
by deep learning.

Nature Biomedical Engineering, 2021; DOI: 10.1038/s41551-021-00809-7 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/12/211201150118.htm

--- up 2 weeks, 6 days, 2 hours, 55 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1:317/3)