The innovative approach analyses the spatial arrangement of cells in tissue samples
Researchers have
developed an artificial intelligence model that accurately predicts outcomes
for cancer patients from tissue samples, marking a significant advancement in
using AI for likely course of the disease and personalised treatment
strategies.
The
innovative approach, described in the journal Nature Communications, analyses
the spatial arrangement of cells in tissue samples.
“The
study showcases the remarkable ability of AI to grasp these intricate spatial
relationships among cells within tissues, extracting subtle information previously
beyond human comprehension while predicting patient outcomes,” said study
leader Guanghua Xiao, a professor at the University of Texas Southwestern
Medical Center in the US.
Tissue samples are routinely collected from patients and placed on slides for
interpretation by pathologists, who analyse them to make diagnoses.
However,
this process is time-consuming, and interpretations can vary among
pathologists, the researchers said.
In
addition, the human brain can miss subtle features present in pathology images
that might provide important clues to a patient’s condition, they said.
Various AI models built in the past several years can perform some aspects of a
pathologist’s job, for example, identifying cell types or using cell proximity
as a proxy for interactions between cells.
However,
these models don’t successfully recapitulate more complex aspects of how
pathologists interpret tissue images, such as discerning patterns in cell
spatial organisation and excluding extraneous “noise” in images that can muddle
interpretations.
The
new AI model, named Ceograph, mimics how pathologists read tissue slides,
starting with detecting cells in images and their positions.
From there, it identifies cell types as well as their morphology and spatial
distribution, creating a map in which the arrangement, distribution, and
interactions of cells can be analysed.
The
researchers successfully applied this tool to three clinical scenarios using
pathology slides.
In
one, they used Ceograph to distinguish between two subtypes of lung cancer,
adenocarcinoma or squamous cell carcinoma.
In another, they predicted the likelihood of potentially malignant oral
disorders—precancerous lesions of the mouth—progressing to cancer.
In
the third, the team identified which lung cancer patients were most likely to
respond to a class of medications called epidermal growth factor receptor
inhibitors.
In
each scenario, the Ceograph model significantly outperformed traditional
methods in predicting patient outcomes.
Importantly,
the cell spatial organisation features identified by Ceograph are interpretable
and lead to biological insights into how individual cell-cell spatial
interaction change could produce diverse functional consequences, Xiao said.
These
findings highlight a growing role for AI in medical care, he added, offering a
way to improve the efficiency and accuracy of pathology analyses.
“This method has the potential to streamline targeted preventive measures for high-risk populations and optimise treatment selection for individual patients,” Xiao added.
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