Researchers have developed an AI-powered model that — in 10 seconds — can determine during surgery if any part of cancerous brain tumors that could be removed remains, a study published in Nature suggests. Researchers say the technology could one day be applied to other cancers.
The technology, called FastGlioma, outperformed conventional methods for identifying what remains of brain tumors by a wide margin, according to the research team led by the University of Michigan and the University of California San Francisco.
“FastGlioma is an artificial intelligence-based diagnostic system that has the potential to change the field of neurosurgery by immediately improving comprehensive management of patients with diffuse gliomas,” said senior author Todd Hollon, M.D., a neurosurgeon at the University of Michigan Health and assistant professor of neurosurgery at U-M Medical School.
“The technology works faster and more accurately than the current standard of care methods for tumor detection and could be generalized to other pediatric and adult brain tumor diagnoses. It could serve as a foundational model for guiding brain tumor surgery.”
When a neurosurgeon removes a life-threatening tumor from a patient’s brain, they are rarely able to remove the entire mass.
What remains is known as residual brain tumors.
Commonly, the brain tumors are missed during the operation because surgeons are not able to differentiate between a healthy brain and a residual tumor in the cavity where the mass was removed.
Residual brain tumors may resemble a healthy brain, which remains a major challenge in surgery.
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 to all tumor types.
These limitations prevent their widespread use.
Detecting missed brain tumors
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 of the brain tumors 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 tumors just 3.8% of the time — compared to a nearly 25% miss rate for conventional methods.
“This model is an innovative departure from existing surgical techniques by rapidly identifying tumor infiltration at microscopic resolution using AI, greatly reducing the risk of missing residual tumor in the area where a glioma is resected,” said co-senior author Shawn Hervey-Jumper, M.D., professor of neurosurgery at the University of California San Francisco and a former neurosurgery resident at U-M Health.
“The development of FastGlioma can minimize the reliance on radiographic imaging, contrast enhancement, or fluorescent labels to achieve maximal tumor removal.”
How it works
To assess what remains of brain tumors, 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.
The tumor specimens are imaged through stimulated Raman histology, a method of rapid, high-resolution optical imaging developed at U-M.
The same technology was used to train DeepGlioma, an AI-based diagnostic screening system that detects a brain tumor’s genetic mutations in under 90 seconds.
“FastGlioma can detect residual tumor tissue without relying on time-consuming histology procedures and large, labeled datasets in medical AI, which are scarce,” said Honglak Lee, Ph.D., co-author and professor of computer science and engineering at U-M.
Full-resolution images take around 100 seconds to acquire using stimulated Raman histology; a “fast mode” lower-resolution image takes just 10 seconds.
Researchers found that the full-resolution model achieved accuracy up to 92%, with the fast mode slightly lower at approximately 90%.
“This means that we can detect tumor infiltration in seconds with extremely high accuracy, which could inform surgeons if more resection is needed during an operation,” Hollon said.
AI’s future in cancer
Over the last 20 years, the rates of residual tumors after neurosurgery have not improved.
Not only do residual brain tumors result in worse quality of life and earlier death for patients, but they increase the burden on a health system that anticipates 45 million annual surgical procedures needed worldwide by 2030.
Global cancer initiatives have recommended incorporating new technologies, including advanced methods of imaging and AI, into cancer surgery.
In 2015, The Lancet Oncology Commission on global cancer surgery noted that “the need for cost-effective… approaches to address surgical margins in cancer surgery provides a potent drive for novel technologies.”
Not only is FastGlioma an accessible and affordable tool for neurosurgical teams operating on gliomas, but researchers say, it can also accurately detect residual brain tumors for several non-glioma tumor diagnoses, including pediatric brain tumors, such as medulloblastoma and ependymoma, and meningiomas.
“These results demonstrate the advantage of visual foundation models such as FastGlioma for medical AI applications and the potential to generalize to other human cancers without requiring extensive model retraining or fine-tuning,” said co-author Aditya S. Pandey, M.D., chair of the Department of Neurosurgery at U-M Health.
“In future studies, we will focus on applying the FastGlioma workflow to other cancers, including lung, prostate, breast, and head and neck cancers.”
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This article was written by Noah Fromson at Michigan Medicine-University of Michigan
