Researchers have developed an AI powered model that — in 10 seconds — can determine during surgery if any part of a cancerous brain tumour that could be removed remains, a study published in Nature suggests.

 The technology, called FastGlioma, outperformed conventional methods for identifying what remains of a tumour by a wide margin, according to the research team led by University of Michigan and 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 University of Michigan Health and assistant professor of neurosurgery at U-M Medical School.

 “The technology works faster and more accurately than current standard of care methods for tumour detection and could be generalised to other paediatric and adult brain tumour diagnoses. It could serve as a foundational model for guiding brain tumour surgery.”  

When a neurosurgeon removes a life threatening tumour from a patient’s brain, they are rarely able to remove the entire mass.

What remains is known as residual tumour.

  Commonly, the tumour is missed during the operation because surgeons are not able to differentiate between healthy brain and residual tumour in the cavity where the mass was removed.

Residual tumour’s ability to resemble healthy brain tissue remains a major challenge in surgery.

 Neurosurgical teams employ different methods to locate that residual tumour 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 tumour tissue, which is not applicable for all tumour types.

These limitations prevent their widespread use.

In this international study of the AI-driven technology, neurosurgical teams analysed fresh, unprocessed specimens sampled from 220 patients who had operations for low- or high-grade diffuse glioma.

FastGlioma detected and calculated how much tumour 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 tumour 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 tumour infiltration at microscopic resolution using AI, greatly reducing the risk of missing residual tumour in the area where a glioma is resected,” said co-senior author Shawn Hervey-Jumper, M.D., professor of neurosurgery at University of California San Francisco and a former neurosurgery resident at U-M Health.  

“The development of FastGlioma can minimise the reliance on radiographic imaging, contrast enhancement or fluorescent labels to achieve maximal tumour removal.”  

How it works 

To assess what remains of a brain tumour, 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 tumour 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 tumour’s genetic mutations in under 90 seconds.  

“FastGlioma can detect residual tumour tissue without relying on time-consuming histology procedures and large, labelled 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 tumour 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 tumour after neurosurgery have not improved.

 Not only does residual tumour result in worse quality of life and earlier death for patients, but it increases 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 tumour for several non-glioma tumour diagnoses, including paediatric brain tumours, 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 generalise to other human cancers without requiring extensive model retraining or fine-tuning,” said co-author said 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.” 

Source: Michigan Medicine - University of Michigan