AI Detects Cancer Invasion Patterns, Revolutionizing Brain Metastasis Treatment

Researchers unveil an innovative AI model that uses MRI scans to predict cancer's invasion patterns, potentially transforming brain metastasis diagnosis and treatment.

​​​​​​​Research: Machine learning prediction of brain metastasis invasion pattern on brain magnetic resonance imaging scans. ​​​​​​​Image Credit: Teeradej / Shutterstock

Researchers have developed an artificial intelligence (AI) model to predict the brain metastasis invasion pattern (BMIP) using MRI scans, offering insights into patients' cancer without aggressive surgery.

The proof-of-concept study, published in the journal Neuro-Oncology Advances, was co-led by McGill University researchers Dr. Matthew Dankner and Dr. Reza Forghani. Alongside an international team of clinicians and scientists, the study demonstrated that the AI model can detect the presence of cancer cells in surrounding brain tissue with 85 percent 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 doctors' observations in the tumour tissue under a microscope. The AI combined traditional machine learning and convolution-based deep learning approaches, with the best-performing ensemble achieving an 85% accuracy and a 90% F1 score.

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. Invasive BMIP is a biomarker associated with shorter survival, tumor recurrence, and poorer responses to emerging therapies, as demonstrated in preclinical models.

"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. Forghani's lab was developed during his time at the Research Institute of the McGill University Health Centre and the University of Florida College of Medicine. The model focused on features within peritumoral edema in MRI scans, which is hypothesized to capture critical predictive information about cancer invasion patterns.

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 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 current study's small sample size and lack of external validation were acknowledged as limitations. Future studies with more extensive and more diverse datasets are essential for validating its clinical utility.

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.