Cancer tissue analysis brought up to date
Cancer tissue analysis brought up to date © amandabhslater

Chemical imaging brings cancer tissue analysis into digital age

A new method for analysing biological samples based on their chemical makeup is set to transform the way medical scientists examine diseased tissue.

When tests are carried out on a patient’s tissue, for example when looking for cancer, the test has to be interpreted by a histology specialist and can take weeks to obtain a full result.

Mass spectrometry imaging (MSI) uses technologies that reveal how hundreds or thousands of chemical components are distributed in a tissue sample. Scientists have proposed using MSI to identify tissue types for many years, but until now, no method has been devised to apply such technology to any type of tissue.

Researchers at Imperial College London have outlined a recipe for processing MSI data and building a database of tissue types. In MSI, a beam moves across the surface of a sample, producing a pixelated image. Each pixel contains data on thousands of chemicals present in that part of the sample. By analysing many samples and comparing them to the results of traditional histological analysis, a computer can learn to identify different types of tissue.

A single test taking a few hours can provide much more detailed information than standard histological tests. It can show not only if a tissue is cancerous, but what the type and sub-type of cancer is, which can be important for choosing the best treatment. The technology can also be applied in research to offer new insights into cancer biology.

Dr Kirill Veselkov, corresponding author of the study from the Department of Surgery and Cancer at Imperial College London, said: “MSI is an extremely promising technology, but the analysis required to provide information that doctors or scientists can interpret easily is very complex. This work overcomes some of the obstacles to translating MSI’s potential into the clinic. It’s the first step towards creating the next generation of fully automated histological analysis.”

The research has been published in Proceedings of the National Academy of Sciences.