Mon Nov 01 2021
A massive game-changer for cancer diagnosis and decision making is said to be as accurate lab-testing – with results found in minutes.
Diagnosis of routine cancer samples has been sped up due to a ground-breaking AI-based test that predicts the most effective form of treatment from images of routine cancer samples, which also cuts costs and saves time on lab-testing – and it’s now approved for use in the UK and EU.
It’s called the PANProfiler, developed by Cambridge-based company Panakeia, and it works by analysing digital images of routine breast tumour samples that normally require being observed under a microscope by a trained pathologist to judge the best course of action.
After the pathologist has checked the sample, a further sample is sent off to discover the next steps that need to be taken, with the wait time being days or weeks for results, and costing hundreds or even thousands of pounds.
But the PANProfiler Breast Test removes all that wait time and costing. It can scan and analyse the digital image of the sample and predict whether it contains ER or PR receptors, which then categorises patients as needing hormone therapy, or HER2 which is treated by the drug Herceptin.
The test far exceeds existing tests in terms of time and cost efficiency and the accuracy is comparable to lab testing, and it’s able to do all of this in mere minutes just from a digital image. Time, in these instances, is the most precious resource for both patients and doctors. Having the patient journey significantly reduced means the time from diagnosis to treatment is cut tremendously, but also the burden on busy laboratory services is reduced – COVID-19 has undeniably created a backlog on cancer diagnosis during the pandemic and the new AI-based testing method will go a long way to freeing up those services.
As of 13th October, the test now has UKCA and CE approval for clinical use by health services in the UK and EU. It seamlessly integrates into current digital procedures being employed in cancer pathology, and is being trialled in hospitals around the UK, with expansion plans for Europe, North America and Asia.
So how did Panakeia’s innovative technology come to be?
Co-founders Pahini Pandya, a former cancer scientist at the University of Cambridge, and AI researcher Pandu Raharja-Liu found in their research that there were almost imperceptibly small differences in the appearance of cancer cells – so small in fact, that they require a computer to see – and these differences reveal the best treatment options due to the information gleaned from their molecular state.
The Panakeia team is now developing similar tests for other tumour types, in the wake of the PANProfiler Breast test’s release.
The company’s mission is to speed-up the decision-making in cancer diagnosis and treatment, spawned by Pandya’s lived experienced of waiting and waiting for the results of tests for blood cancer – a disease she sadly lost her childhood best friend to – but fortunately the results of Pandya’s blood tests came back negative.
I know first-hand the anxiety of waiting for your test results. Due to the pressure on labs, even in the best healthcare systems, diagnosis and treatment decisions can take weeks – an unacceptable and stressful delay when dealing with a fast-growing cancer. We’re excited to be rolling out PANProfiler to hospitals here in the UK and around the world to speed up access to treatment and help save lives.
Raharja-Liu, who has unfortunately lost family members to the disease, adds:
This is a golden opportunity to transform cancer diagnosis. We can now do something that nobody has achieved before – to see more from every tumour sample, gathering rich information about what these cells are like and how best to treat them.
Professor David Harrison, director of iCAIRD, which is one of five centres of excellence in the UK focused on AI applications in pathology and radiology and funded by Innovate UK as part of the government’s Industrial Strategy Challenge Fund said:
This exciting technology has the potential to save laboratory resources and also to improve turnaround time for biomarker results for patients with invasive breast cancer.
Mon Nov 01 2021