AI tool decodes brain cancer's genome during surgery
Real-time tumor profiling can guide surgical, treatment decisions

Date:
July 7, 2023
Source:
Harvard Medical School
Summary:
New AI tool enables in-surgery genomic profiling of gliomas, the
most aggressive and most common brain tumors. This information
offers critical clues about how aggressive a cancer is, its
future behavior, and its likely response to treatment. The tool
can provide real-time guidance to surgeons on the optimal surgical
approach for removal of cancerous tissue.


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FULL STORY ========================================================================== Scientists have designed an AI tool that can rapidly decode a brain
tumor's DNA to determine its molecular identity during surgery -- critical information that under the current approach can take a few days and up
to a few weeks.

Knowing a tumor's molecular type enables neurosurgeons to make decisions
such as how much brain tissue to remove and whether to place tumor-killing drugs directly into the brain -- while the patient is still on the
operating table.

A report on the work, led by Harvard Medical School researchers, is
published July 7 in the journalMed.

Accurate molecular diagnosis -- which details DNA alterations in a cell -
- during surgery can help a neurosurgeon decide how much brain tissue to remove. Removing too much when the tumor is less aggressive can affect
a patient's neurologic and cognitive function. Likewise, removing too
little when the tumor is highly aggressive may leave behind malignant
tissue that can grow and spread quickly.

"Right now, even state-of-the-art clinical practice cannot profile
tumors molecularly during surgery. Our tool overcomes this challenge by extracting thus-far untapped biomedical signals from frozen pathology
slides," said study senior author Kun-Hsing Yu, assistant professor of biomedical informatics in the Blavatnik Institute at HMS.

Knowing a tumor's molecular identity during surgery is also valuable
because certain tumors benefit from on-the-spot treatment with drug-coated wafers placed directly into the brain at the time of the operation,
Yu said.

"The ability to determine intraoperative molecular diagnosis in real
time, during surgery, can propel the development of real-time precision oncology," Yu added.

The standard intraoperative diagnostic approach used now involves taking
brain tissue, freezing it, and examining it under a microscope. A major drawback is that freezing the tissue tends to alter the appearance of
cells under a microscope and can interfere with the accuracy of clinical evaluation.

Furthermore, the human eye, even when using potent microscopes, cannot
reliably detect subtle genomic variations on a slide.

The new AI approach overcomes these challenges.

The tool, called CHARM (Cryosection Histopathology Assessment and Review Machine), is freely available to other researchers. It still has to be clinically validated through testing in real-world settings and cleared
by the FDA before deployment in hospitals, the research team said.

Cracking cancer's molecular code Recent advances in genomics have
allowed pathologists to differentiate the molecular signatures -- and
the behaviors that such signatures portend - - across various types
of brain cancer as well as within specific types of brain cancer. For
example, glioma -- the most aggressive brain tumor and the most common
form of brain cancer -- has three main subvariants that carry different molecular markers and have different propensities for growth and spread.

The new tool's ability to expedite molecular diagnosis could be
particularly valuable in areas with limited access to technology to
perform rapid cancer genetic sequencing.

Beyond the decisions made during surgery, knowledge of a tumor's
molecular type provides clues about its aggressiveness, behavior,
and likely response to various treatments. Such knowledge can inform post-operative decisions.

Furthermore, the new tool enables during-surgery diagnoses aligned with
the World Health Organization's recently updated classification system
for diagnosing and grading the severity of gliomas, which calls for such diagnoses to be made based on a tumor's genomic profile.

Training CHARM CHARM was developed using 2,334 brain tumor samples from
1,524 people with glioma from three different patient populations. When
tested on a never-before- seen set of brain samples, the tool
distinguished tumors with specific molecular mutations at 93 percent
accuracy and successfully classified three major types of gliomas with
distinct molecular features that carry different prognoses and respond differently to treatments.

Going a step further, the tool successfully captured visual
characteristics of the tissue surrounding the malignant cells. It was
capable of spotting telltale areas with greater cellular density and
more cell death within samples, both of which signal more aggressive
glioma types.

The tool was also able to pinpoint clinically important molecular
alterations in a subset of low-grade gliomas, a subtype of glioma that
is less aggressive and therefore less likely to invade surrounding
tissue. Each of these changes also signals different propensity for
growth, spread, and treatment response.

The tool further connected the appearance of the cells -- the shape of
their nuclei, the presence of edema around the cells -- with the molecular profile of the tumor. This means that the algorithm can pinpoint how a
cell's appearance relates to the molecular type of a tumor.

This ability to assess the broader context around the image renders the
model more accurate and closer to how a human pathologist would visually
assess a tumor sample, Yu said.

The researchers say that while the model was trained and tested on
glioma samples, it could be successfully retrained to identify other
brain cancer subtypes.

Scientists have already designed AI models to profile other types of
cancer - - colon, lung, breast -- but gliomas have remained particularly challenging due to their molecular complexity and huge variation in
tumor cells' shape and appearance.

The CHARM tool would have to be retrained periodically to reflect new
disease classifications as they emerge from new knowledge, Yu said.

"Just like human clinicians who must engage in ongoing education and
training, AI tools must keep up with the latest knowledge to remain at
peak performance." Authorship, funding, disclosures Coinvestigators
included MacLean P. Nasrallah, Junhan Zhao, Cheng Che Tsai, David
Meredith, Eliana Marostica, Keith L. Ligon, and Jeffrey A. Golden.

This work was supported in part by the National Institute of General
Medical Sciences grant R35GM142879, the Google Research Scholar Award,
the Blavatnik Center for Computational Biomedicine Award, the Partners Innovation Discovery Grant, and the Schlager Family Award for Early-Stage Digital Health Innovations.

* RELATED_TOPICS
o Health_&_Medicine
# Brain_Tumor # Cancer # Lung_Cancer
o Mind_&_Brain
# Brain-Computer_Interfaces # Intelligence # Brain_Injury
o Computers_&_Math
# Neural_Interfaces # Computer_Modeling # Communications
* RELATED_TERMS
o Histology o Cancer o Robotic_surgery o Aggression o Surgery
o Colorectal_cancer o Breast_cancer o Bullying

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========================================================================== Journal Reference:
1. MacLean P. Nasrallah, Junhan Zhao, Cheng Che Tsai, David Meredith,
Eliana
Marostica, Keith L. Ligon, Jeffrey A. Golden, Kun-Hsing Yu. Machine
learning for cryosection pathology predicts the 2021 WHO
classification of glioma. Med, 2023; DOI: 10.1016/j.medj.2023.06.002 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/07/230707111646.htm

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