A neural network helped brain surgeons decide how much healthy tissue to cut out when removing tumors — while the patients were on the operating table.
What’s new: Researchers from Amsterdam University Medical Centers and Princess Máxima Center for Pediatric Oncology in the Netherlands built a system to assess how aggressively surgeons should treat tumors. It worked accurately and quickly enough to enable doctors to adjust their approach in the operating room.
Key insight: Brain surgeons don’t know the type of tumor they will remove until an operation is underway. When they have a sample — about the size of a kernel of corn — they can classify it by looking at it under a microscope. Alternatively, they can send it out for DNA sequencing, which can take weeks, requiring a second surgery. However, faster, less precise DNA sequencing can be performed on-site, and a neural network can classify such preliminary DNA sequences quickly and accurately. This way, a doctor can proceed with the operation with confidence in the tumor’s classification.
How it works: The authors trained a system of four vanilla neural networks to classify brain tumors.
- The authors made a labeled dataset of nearly 17 million artificial DNA sequences of around 90 tumor types, each constructed by assembling random parts from one of 2,800 sequences of tumor and non-tumor DNA. This approach simulated the messy nature of the fast DNA sequencing process.
- For each neural network, they randomly selected half the sequences for training and used the other half for testing and validation. They trained the networks to classify the tumor types.
- At inference, all four models classified each DNA sample. The system selected the classification from the model that had the highest confidence above a certain threshold. Samples that didn’t clear the confidence threshold received no classification.
Results: The authors’ system performed well on tumor DNA samples in an existing collection as well as those gathered in an operating room. Tested on samples from 415 tumors, it classified 60.7 percent of them accurately, misclassified 1.9 percent, and was unable to classify 37.3 percent. Tested on samples collected during 25 real surgeries, it correctly classified 18 tumors and was unable to classify 7. In all cases, it returned results within 90 minutes (45 minutes to collect the DNA and 45 minutes to analyze it).
Why it matters: 90 minutes is fast enough to inform brain surgeons what kind of tumor they’re dealing with in the early phase of an operation. If this technique can be rolled out widely, it may help save many lives.
We’re thinking: Inferencing presumably takes seconds. The authors say the quick sequencing method processes DNA in 20 to 40 minutes. Speeding up that step offers great potential to accelerate the process.