‘Digistain’ technology offers revolution in cancer diagnoses

The following has not yet been verified. Please improve it by logging in and editing it. If you believe that is not sufficient to solve the problem, please discuss it with the community on the Talk Page. If you think that this article should be removed, please contact [email protected]

The way cancer is diagnosed could soon be more accurate and reliable thanks to a team of British scientists.

Dr Hemmel Amrania and his team from Imperial College London, developed a new imaging technology to grade tumour biopsies.

Publishing their results today in the journal Convergent Science Physical Oncology, they describe how their new method promises to significantly reduce the subjectivity and variability in grading the severity of cancers.

Nearly all cancers are still diagnosed by doctors taking a sample of the tumour, a so-called biopsy, then slicing it thinly and staining it with two vegetable dyes used for more than 100 years. They look at this ‘H+E stained’ sample under a microscope and then judge the severity of the disease by eye alone.

Life-changing treatment decisions must be based on this ‘grading’ process, yet it is well known that if you give the same slice to different practitioners they will only agree on its grade about 70% of the time. This results in an overtreatment problem that constitutes a massive unmet need worldwide.

The team’s new ‘Digistain ’technology addresses this problem by using invisible mid-infrared light to photograph the tissue slices in a way that maps out the chemical changes that signal the onset of cancer. In particular, they measure the “nuclear-to-cytoplasmic-ratio” (NCR): a recognized biological marker for a wide range of cancers.

Dr Amrania said: “Our machine gives a quantitative ‘Digistain index’ (DI) score, corresponding to the NCR, and this study shows that it is an extremely reliable indicator of the degree of progression of the disease. Because it is based on a physical measurement, rather than a human judgement, it promises to remove the element of chance in cancer diagnosis. “

In the experiment reported today, the team carried out a double-blind clinical pilot trial using two adjacent slices taken from 75 breast cancer biopsies. The first slice was graded by clinicians as usual, using the standard H+E protocol. It was also used to identify the so-called “’region of interest’ (RoI), i.e. the part of the slice containing the tumour.

The team then used the Digistain imager to get a DI value averaged over the corresponding RoI on the other, unstained slice, and ran a statistical analysis on the results.

Dr Amrania said: “Even with this modest number of samples, the correlation we saw between the DI score and the H+E grade would only happen by chance 1 time in 1400 trials. The strength of this correlation makes us extremely optimistic that Digistain will be able to eliminate subjectivity and variability in biopsy grading.

Looking to the future, the NCR factor that Digistain measures is known to be common to a wide range of cancers; it happens when the reproductive cell cycle gets disrupted in the tumour cell nuclei get distorted with rogue DNA. Because of this, it is likely that in the long run, Digistain will help with the diagnosis of all the different types of cancer that there are.

At a practical level, the Digistain imaging technology can easily and cheaply be incorporated into existing hospital labs, and be used by their staff. It’s easy to prove its worth by checking it with the thousands of existing biopsy specimens that are already held in hospital archives. Together these facts will smooth the path into the clinic, and it could be saving lives in only a couple of years.

  • Share
  • Sources

    Paper: The final version of ‘Mid-infrared imaging in breast cancer tissue: an objective measure of grading breast cancer biopsies’ Amrania et al (2018 Converg. Sci. Phys. Oncol. 4 025001) will be available at 0000 GMT on Tuesday March 13 2018 at http://iopscience.iop.org/article/10.1088/2057-1739/aaabc3
    DOI: 10.1088/2057-1739/aaabc3

Subscribe to our newsletter

Be the first to collaborate on our developing articles

WikiTribune Open menu Close Search Like Back Next Open menu Close menu Play video RSS Feed Share on Facebook Share on Twitter Share on Reddit Follow us on Instagram Follow us on Youtube Connect with us on Linkedin Connect with us on Discord Email us