'Intelligent Knife' Tells Surgeon which Tissue is Cancerous

Scientists have developed an "intelligent knife" that can tell surgeons immediately whether the tissue they are cutting is cancerous or not. In the first study to test the invention in the operating theatre, the "iKnife" diagnosed tissue samples from 91 patients with 100 per cent accuracy, instantly providing information that normally takes up to half an hour to reveal using laboratory tests.

The findings, by researchers at Imperial College London, are published today in the journal Science Translational Medicine. The study was funded by the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre, the European Research Council and the Hungarian National Office for Research and Technology.

In cancers involving solid tumours, removal of the cancer in surgery is generally the best hope for treatment. The surgeon normally takes out the tumour with a margin of healthy tissue. However, it is often impossible to tell by sight which tissue is cancerous. One in five breast cancer patients who have surgery require a second operation to fully remove the cancer. In cases of uncertainty, the removed tissue is sent to a lab for examination while the patient remains under general anaesthetic.

The iKnife is based on electrosurgery, a technology invented in the 1920s that is commonly used today. Electrosurgical knives use an electrical current to rapidly heat tissue, cutting through it while minimising blood loss. In doing so, they vaporise the tissue, creating smoke that is normally sucked away by extraction systems.

The inventor of the iKnife, Dr Zoltan Takats of Imperial College London, realised that this smoke would be a rich source of biological information. To create the iKnife, he connected an electrosurgical knife to a mass spectrometer, an analytical instrument used to identify what chemicals are present in a sample. Different types of cell produce thousands of metabolites in different concentrations, so the profile of chemicals in a biological sample can reveal information about the state of that tissue.

In the new study, the researchers first used the iKnife to analyse tissue samples collected from 302 surgery patients, recording the characteristics of thousands of cancerous and non-cancerous tissues, including brain, lung, breast, stomach, colon and liver tumours to create a reference library. The iKnife works by matching its readings during surgery to the reference library to determine what type of tissue is being cut, giving a result in less than three seconds.

The technology was then transferred to the operating theatre to perform real-time analysis during surgery. In all 91 tests, the tissue type identified by the iKnife matched the post-operative diagnosis based on traditional methods.

While the iKnife was being tested, surgeons were unable to see the results of its readings. The researchers hope to carry out a clinical trial to see whether giving surgeons access to the iKnife's analysis can improve patients' outcomes.

"These results provide compelling evidence that the iKnife can be applied in a wide range of cancer surgery procedures," Dr Takats said. "It provides a result almost instantly, allowing surgeons to carry out procedures with a level of accuracy that hasn't been possible before. We believe it has the potential to reduce tumour recurrence rates and enable more patients to survive."

Although the current study focussed on cancer diagnosis, Dr Takats says the iKnife can identify many other features, such as tissue with an inadequate blood supply, or types of bacteria present in the tissue. He has also carried out experiments using it to distinguish horsemeat from beef.

Professor Jeremy Nicholson, Head of the Department of Surgery and Cancer at Imperial College London, who co-authored the study, said: "The iKnife is one manifestation of several advanced chemical profiling technologies developed in labs our that are contributing to surgical decision-making and real-time diagnostics. These methods are part of a new framework of patient journey optimisation that we are building at Imperial to help doctors diagnose disease, select the best treatments, and monitor individual patients' progress as part our personalised healthcare plan."

Lord Darzi, Professor of Surgery at Imperial College London, who also co-authored the study, said: "In cancer surgery, you want to take out as little healthy tissue as possible, but you have to ensure that you remove all of the cancer. There is a real need for technology that can help the surgeon determine which tissue to cut out and which to leave in. This study shows that the iKnife has the potential to do this, and the impact on cancer surgery could be enormous."

Lord Howe, Health Minister, said: "We want to be among the best countries in the world at treating cancer and know that new technologies have the potential to save lives. The iKnife could reduce the need for people needing secondary operations for cancer and improve accuracy, and I'm delighted we could support the work of researchers at Imperial College London. This project shows once again how Government funding is putting the UK at the forefront of world-leading health research."

J. Balog et al. 'Intraoperative tissue identification using rapid evaporative ionization mass spectrometry.' Sci. Transl. Med. 5, 194ra93 (2013).

Most Popular Now

Stepping Hill Hospital Announced as SPAR…

Stepping Hill Hospital, part of Stockport NHS Foundation Trust, has replaced its bedside units with state-of-the art devices running a full range of information, engagement, communications and productivity apps, to...

DMEA 2025: Digital Health Worldwide in B…

8 - 10 April 2025, Berlin, Germany. From the AI Act, to the potential of the European Health Data Space, to the power of patient data in Scandinavia - DMEA 2025...

Is AI in Medicine Playing Fair?

As artificial intelligence (AI) rapidly integrates into health care, a new study by researchers at the Icahn School of Medicine at Mount Sinai reveals that all generative AI models may...

New System for the Early Detection of Au…

A team from the Human-Tech Institute-Universitat Politècnica de València has developed a new system for the early detection of Autism Spectrum Disorder (ASD) using virtual reality and artificial intelligence. The...

Generative AI's Diagnostic Capabili…

The use of generative AI for diagnostics has attracted attention in the medical field and many research papers have been published on this topic. However, because the evaluation criteria were...

Diagnoses and Treatment Recommendations …

A new study led by Prof. Dan Zeltzer, a digital health expert from the Berglas School of Economics at Tel Aviv University, compared the quality of diagnostic and treatment recommendations...

AI Tool can Track Effectiveness of Multi…

A new artificial intelligence (AI) tool that can help interpret and assess how well treatments are working for patients with multiple sclerosis (MS) has been developed by UCL researchers. AI uses...

Surrey and Sussex Healthcare NHS Trust g…

Surrey and Sussex Healthcare NHS Trust has marked an important milestone in connecting busy radiologists across large parts of South East England, following the successful go live of Sectra's enterprise...

Dr Jason Broch Joins the Highland Market…

The Highland Marketing advisory board has welcomed a new member - Dr Jason Broch, a GP and director with a strong track record in the NHS and IT-enabled transformation. Dr Broch...

DMEA 2025 Ends with Record Attendance an…

8 - 10 April 2025, Berlin, Germany. DMEA 2025 came to a successful close with record attendance and an impressive program. 20,500 participants attended Europe's leading digital health event over the...

Multi-Resistance in Bacteria Predicted b…

An AI model trained on large amounts of genetic data can predict whether bacteria will become antibiotic-resistant. The new study shows that antibiotic resistance is more easily transmitted between genetically...

AI-Driven Smart Devices to Transform Hea…

AI-powered, internet-connected medical devices have the potential to revolutionise healthcare by enabling early disease detection, real-time patient monitoring, and personalised treatments, a new study suggests. They are already saving lives...