Can AI Detect Cancerous Brain Tumor in 10 Seconds? Study Sheds Light - Video

Researchers have developed an AI powered model that -- in 10 seconds -- can determine during surgery if any part of a cancerous brain tumor that could be removed remains, a study published in Nature suggests. The technology, called FastGlioma, outperformed conventional methods for identifying what remains of a tumor by a wide margin, according to the research team led by University of Michigan and University of California San Francisco.

When a neurosurgeon removes a life threatening tumor from a patient's brain, they are rarely able to remove the entire mass. Neurosurgical teams employ different methods to locate that residual tumor during a procedure. They may get MRI imaging, which requires intraoperative machinery that is not available everywhere. The surgeon might also use a fluorescent imaging agent to identify tumor tissue, which is not applicable for all tumor types.

In this international study of the AI driven technology, neurosurgical teams analyzed fresh, unprocessed specimens sampled from 220 patients who had operations for low- or high-grade diffuse glioma. FastGlioma detected and calculated how much tumor remained with an average accuracy of approximately 92%.

In a comparison of surgeries guided by FastGlioma predictions or image- and fluorescent-guided methods, the AI technology missed high-risk, residual tumor just 3.8% of the time -- compared to a nearly 25% miss rate for conventional methods. To assess what remains of a brain tumor, FastGlioma combines microscopic optical imaging with a type of artificial intelligence called foundation models.

These are AI models, such as GPT-4 and DALL·E 3, trained on massive, diverse datasets that can be adapted to a wide range of tasks. After large scale training, foundation models can classify images, act as chatbots, reply to emails, and generate images from text descriptions. To build FastGlioma, investigators pre-trained the visual foundation model using over 11,000 surgical specimens and 4 million unique microscopic fields of view.

Reference: Hollon, T., Kondepudi, A., Pekmezci, M., Hou, X., Scotford, K., Jiang, C., ... & Hervey-Jumper, S. (2024). Visual foundation models for fast, label-free detection of diffuse glioma infiltration.