Surgical tool smokes out cancer in seconds

Sniffing for telltale molecules, method analyzes tissue with every cut

A new tool could tell surgeons within seconds whether they are slicing through cancerous or healthy tissue. The tool, which analyzes smoke produced by electric currents used to cut or destroy tissue, was about 95 percent accurate in identifying cancers and other human tissues during surgery.

Testing the smoke could help surgeons identify the outer margins of a tumor and remove as much of it as possible, leaving healthy tissue intact. Currently, if doctors need information about the extent of a cancer during a surgical procedure, they must wait 20 to 30 minutes for a tissue sample to be examined under a microscope. The new tool, nicknamed the iKnife, delivers a diagnosis in 2.5 seconds or less, researchers report July 17 in Science Translational Medicine.

The iKnife consists of an electric blade hooked up to an instrument that performs chemical analysis. “They are basically blowing up tissue, making smoke out of it and then sampling that smoke with a mass spectrometer,” says Nicholas Winograd, a chemist at Pennsylvania State University. “I don’t think it’s at all obvious that this kind of thing would work and I give them a lot of credit for developing it.”

Mass spectrometry is an analytical method that converts molecules in complex biological mixtures into electrically charged particles and then identifies them.

Because tools that use electric currents to cut tissue generate a haze of charged particles from human tissues, the researchers realized they could directly analyze the smoke with a mass spectrometer. In surgery, these electrical cutting tools “are as common as scalpels,” says study leader Zoltán Takáts of Imperial College London.

One type of charged particle in the surgical smoke is fat. Takáts’ team found that smoke from each type of tissue and cancer had characteristic proportions of different fat molecules. The researchers discovered this when they created a database of mass spectrometry results from nearly 3,000 tissue samples from 302 patients. When analyzing a new sample, the iKnife can compare its spectrum to the ones in the database and predict its tissue type.

Then surgeons removing many kinds of cancers used the iKnife to test its predictions. For approximately 95 percent of the samples from 91 surgeries, the iKnife gave a diagnosis that matched the results from standard postoperation tests. In 11 cases, the iKnife revealed that the preoperation diagnosis had been incorrect.

To get regulatory approval, Takáts and his team need to complete formal clinical trials. These trials will test whether the new approach improves outcomes for patients going under the knife.

Editor’s Note: This story was updated July 24, 2013, to provide the correct number of cases in which iKnife revealed an incorrect preoperation diagnosis.

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