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E-Newsletter • January 2025 |
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Editor's E-Note
Happy New Year! AI has begun to reshape the way medical imaging is used and interpreted, and that will no doubt continue for the foreseeable future. This month’s exclusive looks at how AI may be used to detect brain cancer earlier than what’s currently possible.
For more of the latest imaging news, visit us on X, formerly known as Twitter, and/or Facebook.
Enjoy the newsletter.
— Dave Yeager, editor |
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| In This E-Newsletter
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AI Model Detects Brain Cancer Spread
Researchers have developed an AI model to detect the spread of metastatic brain cancer using MRI scans, offering insights into patients’ cancer without aggressive surgery.
The proof-of-concept study, co-led by McGill University researchers Matthew Dankner and Reza Forghani, alongside an international team of clinicians and scientists, demonstrated the AI model can detect the presence of cancer cells in surrounding brain tissue with 85% accuracy.
Researchers tested the model using MRI scans from over 130 patients who had surgery to remove brain metastases at The Neuro (Montreal Neurological Institute-Hospital). They validated the AI’s accuracy by comparing its results to what doctors observed in the tumor tissue under a microscope.
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Brain metastases, the most common type of brain cancer, occur when cancer cells from other parts of the body spread to the brain. These tumors can be particularly aggressive when invasive cancer cells grow into surrounding healthy brain tissue, making them harder to treat.
“Our previous research found that invasive brain metastases are linked to shorter survival and a higher risk of tumor regrowth. These findings demonstrate the enormous potential of machine learning to soon improve our understanding of cancer and its treatment,” says Dankner, an internal medicine resident at McGill and postdoctoral researcher at the Rosalind & Morris Goodman Cancer Institute.
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Analyzing Radiological Reports With AI
Researchers at the University Hospital Bonn and the University of Bonn demonstrated the privacy capabilities of using large language models to compile radiology report findings. Their research is published in Radiology.
COPD Identified by Deep Learning Model
A study published in Radiology: Cardiothoracic Imaging reveals that with one inhalation lung CT scan, a deep learning model can diagnose COPD, the third leading cause of death worldwide, according to the World Health Organization.
MRI and Biopsy Speed Up Bladder Cancer Treatment
The BladderPath trial, conducted by researchers at the University of Birmingham’s Bladder Cancer Research Centre and Cancer Research UK Clinical Trials Unit, found that using MRI and biopsy leads to “correct treatment” faster than other methods. Their research is published in Journal of Clinical Oncology. |
“While PAH [pulmonary arterial hypertension] has traditionally been evaluated through hemodynamic measurements and echocardiography, my colleagues and I sought to determine if imaging the fibroblast activation protein could predict PAH disease progression. … The ability to detect fibroblast activation in PAH patients is significant as it could provide an early marker of disease progression, potentially before irreversible structural changes occur.”
— Cheng Hong, MD, PhD, a pulmonary vascular medicine specialist at the First Affiliated Hospital of Guangzhou Medical University, and Xinlu Wang, MD, PhD, a nuclear medicine specialist at First Affiliated Hospital of Guangzhou Medical University, on a novel molecular imaging technique that can detect pulmonary arterial hypertension early |
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COVER STORY
Time for a Chat About LLMs?
A recent study demonstrates ChatGPT’s potential usefulness in aiding radiologists’ interpretation of medical images.
FEATURE
Mapping a Complex Landscape
Breakthroughs with functional MRI aid physicians in better understanding and treating all forms of depression.
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