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[1]
New AI model offers hope for detecting brain cancer spread without surgery Researchers Have Developed A...
McGill UniversityJan 15 2025 Researchers have developed an artificial intelligence (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 Dr. Matthew Dankner and Dr. 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-per-cent 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. 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." Dr. Matthew Dankner, an Internal Medicine Resident at McGill and post-doctoral researcher at the Rosalind & Morris Goodman Cancer Institute AI detects subtle cancer clues The AI model detects subtle changes in the surrounding brain tissue that indicate cancer has spread, spotting patterns often too faint for traditional imaging methods that rely on human interpretation. It was developed by Forghani's lab during his time at the Research Institute of the McGill University Health Centre and the University of Florida College of Medicine. Earlier this year, the researchers identified drugs that could potentially treat some brain metastases. However, to determine which patients may ultimately benefit from this approach, doctors need to know whether the cancer has spread into the surrounding tissue. Surgery is the most common solution, but it isn't always an option for patients, especially if their tumours are hard to reach or their health makes surgery too risky. "With further development, our AI model could become a part of clinical practice, which can help us catch cancer spread within the brain earlier and more accurately," said Dr. Benjamin Rehany, a Radiology Resident at the University of Toronto and one of the primary authors of the publication. While their work is still in the early stages, the researchers plan to expand the study with larger datasets and refine the AI model for clinical use. The research was supported by the Canadian Cancer Society, the Canadian Institutes of Health Research, the Brain Canada Foundation, Health Canada, Fonds de recherche du Québec - Santé, and the Fondation de l'Association des radiologistes du Québec. McGill University Journal reference: Najafian, K., et al. (2024). Machine learning prediction of brain metastasis invasion pattern on brain MRI scans. Neuro-Oncology Advances. doi.org/10.1093/noajnl/vdae200.
[2]
AI innovation unlocks non-surgical way to detect brain cancer spread
Researchers have developed an artificial intelligence (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 Dr. Matthew Dankner and Dr. 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-per-cent 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 tumour tissue under a microscope. Brain metastases, the most common type of brain cancer, occur when cancer cells from other parts of the body spread to the brain. These tumours 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 tumour regrowth. These findings demonstrate the enormous potential of machine learning to soon improve our understanding of cancer and its treatment," said Dankner, an Internal Medicine Resident at McGill and post-doctoral researcher at the Rosalind & Morris Goodman Cancer Institute. AI detects subtle cancer clues The AI model detects subtle changes in the surrounding brain tissue that indicate cancer has spread, spotting patterns often too faint for traditional imaging methods that rely on human interpretation. It was developed by Forghani's lab during his time at the Research Institute of the McGill University Health Centre and the University of Florida College of Medicine. Earlier this year, the researchers identified drugs that could potentially treat some brain metastases. However, to determine which patients may ultimately benefit from this approach, doctors need to know whether the cancer has spread into the surrounding tissue. Surgery is the most common solution, but it isn't always an option for patients, especially if their tumours are hard to reach or their health makes surgery too risky. "With further development, our AI model could become a part of clinical practice, which can help us catch cancer spread within the brain earlier and more accurately," said Dr. Benjamin Rehany, a Radiology Resident at the University of Toronto and one of the primary authors of the publication. While their work is still in the early stages, the researchers plan to expand the study with larger datasets and refine the AI model for clinical use. The research was supported by the Canadian Cancer Society, the Canadian Institutes of Health Research, the Brain Canada Foundation, Health Canada, Fonds de recherche du Québec -- Santé, and the Fondation de l'Association des radiologistes du Québec.
[3]
AI innovation unlocks non-surgical way to detect brain cancer spread
Researchers have developed an artificial intelligence (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 Dr. Matthew Dankner and Dr. 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. The research is published in Neuro-Oncology Advances. Researchers tested the model using MRI scans from more than 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. 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," said Dankner, an Internal Medicine Resident at McGill and post-doctoral researcher at the Rosalind & Morris Goodman Cancer Institute. AI detects subtle cancer clues The AI model detects subtle changes in the surrounding brain tissue that indicate cancer has spread, spotting patterns often too faint for traditional imaging methods that rely on human interpretation. It was developed by Forghani's lab during his time at the Research Institute of the McGill University Health Center and the University of Florida College of Medicine. In a study earlier this year, the researchers identified drugs that could potentially treat some brain metastases. However, to determine which patients may ultimately benefit from this approach, doctors need to know whether the cancer has spread into the surrounding tissue. Surgery is the most common solution, but it isn't always an option for patients, especially if their tumors are hard to reach or their health makes surgery too risky. "With further development, our AI model could become a part of clinical practice, which can help us catch cancer spread within the brain earlier and more accurately," said Dr. Benjamin Rehany, a Radiology Resident at the University of Toronto and one of the primary authors of the new publication. While their work is still in the early stages, the researchers plan to expand the study with larger datasets and refine the AI model for clinical use.
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Researchers have developed an AI model that can detect the spread of metastatic brain cancer using MRI scans with 85% accuracy, potentially eliminating the need for invasive surgery in some cases.
Researchers from McGill University have developed a groundbreaking artificial intelligence (AI) model that can detect the spread of metastatic brain cancer using MRI scans, potentially eliminating the need for invasive surgery in many cases. The proof-of-concept study, led by Dr. Matthew Dankner and Dr. Reza Forghani, demonstrated an impressive 85% accuracy in detecting cancer cells in surrounding brain tissue 123.
The AI model is designed to detect subtle changes in the brain tissue surrounding tumors, identifying patterns that are often too faint for traditional imaging methods relying on human interpretation. This innovative approach could revolutionize the way brain metastases are diagnosed and treated 123.
The research team tested the AI model using MRI scans from over 130 patients who had undergone surgery to remove brain metastases at The Neuro (Montreal Neurological Institute-Hospital). To validate the AI's accuracy, they compared its results with microscopic observations of tumor tissue made by doctors 123.
Brain metastases, which occur when cancer cells from other parts of the body spread to the brain, are the most common type of brain cancer. These tumors can be particularly aggressive when they invade surrounding healthy brain tissue, making them challenging to treat 123.
Dr. Dankner emphasized the significance of their findings: "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" 123.
While the research is still in its early stages, the team plans to expand the study with larger datasets and refine the AI model for clinical use. Dr. Benjamin Rehany, a Radiology Resident at the University of Toronto and one of the primary authors of the publication, stated, "With further development, our AI model could become a part of clinical practice, which can help us catch cancer spread within the brain earlier and more accurately" 123.
Earlier this year, the researchers identified drugs that could potentially treat some brain metastases. However, determining which patients may benefit from this approach requires knowledge of whether the cancer has spread into the surrounding tissue. The AI model could provide this crucial information without resorting to surgery, which is not always a viable option for patients with hard-to-reach tumors or health conditions that make surgery risky 123.
The study, published in Neuro-Oncology Advances, was supported by various organizations, including the Canadian Cancer Society, the Canadian Institutes of Health Research, the Brain Canada Foundation, Health Canada, Fonds de recherche du Québec - Santé, and the Fondation de l'Association des radiologistes du Québec 13.
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