An artificial nose helps neurosurgeons to identify cancerous tissue during surgery


An artificial nose developed at Tampere University, Finland, helps neurosurgeons to identify cancerous tissue during surgery and enables the more precise excision of tumours.

Electrosurgical resection using devices such as an electric knife or diathermy blade is currently a widely used technique in neurosurgery.

When tissue is burned, tissue molecules are dispersed in the form of surgical smoke.

In the method developed by researchers at Tampere University, the surgical smoke is fed into a new type of measuring system that can identify malignant tissue and distinguish it from healthy tissue.

An article on using surgical smoke to identify brain tumours was recently published in the Journal of Neurosurgery.

“In current clinical practice, frozen section analysis is the gold standard for intraoperative tumour identification.

In that method, a small sample of the tumour is given to a pathologist during surgery,” says researcher Ilkka Haapala from Tampere University.

The pathologist undertakes a microscopic analysis of the sample and phones the operating theatre to report the results.

“Our new method offers both a promising way to identify malignant tissue in real time and the ability to study several samples from different points of the tumour,” Haapala explains.

“The specific advantage of the equipment is that it can be connected to the instrumentation already present in neurosurgical operating theatres,” Haapala points out.

The technology is based on differential mobility spectrometry (DMS), wherein flue gas ions are fed into an electric field.

The distribution of ions in the electric field is tissue-specific, and the tissue can be identified on the basis of the resulting “odour fingerprint”.

This shows a surgery

Flue gas created by an electric knife is fed directly into the measurement system. The image is credited to Antti Roine.

The study analysed 694 tissue samples collected from 28 brain tumours and control specimens.

The equipment used was developed specifically for the study.

It consists of a machine learning system, which analyses the flue gas with DMS technology, and an electric knife, which is used to produce the flue gas from the tissues.

The system’s classification accuracy was 83% when all the samples were analysed.

The accuracy improved in more restricted settings.

When comparing low malignancy tumours (gliomas) to control samples, the classification accuracy of the system was 94%, reaching to 97% sensitivity and 90% specificity.

Professor Han Jin from Ningbo University focuses on developing novel diagnostic technologies based on profiling volatile organic compounds (VOCs) in breath samples.

VOC patterns can be used to identify different diseases and distinguish between different types of cancer.

To achieve his goal of developing a novel VOC breath sensor for early stage disease diagnosis, Professor Jin must create a highly sensitive and accurate sensor that can be used in clinical environments.

The team and cooperators must also develop a detailed database that associates specific VOC biomarkers with certain diseases.

Cancer is the second leading cause of death globally. According to the World Health Organization, one in six deaths is due to cancer. Survival rates can vary drastically depending on the type of cancer, its severity and how early it is detected.

Testicular cancer, prostate cancer and malignant melanoma all have survival rates in excess of 80%.

However, other cancer types, such as lung, stomach, brain and pancreatic, are much more difficult to diagnose and treat and survival is often less than 20%.

There are many processes involved in breath testing. This image gives an overview of them.

Early diagnosis significantly enhances treatment success and survival rate.

Indeed, disease prevention is a much more cost-effective approach compared to treatment of the cancer. However, some available diagnostic methods lack enough sensitivity and specificity to be truly effective at detecting cancer in its earliest stages.

This inspired Professor Han Jin and his team to develop novel, inexpensive and non-invasive technologies to improve cancer detection, increasing the chance of survival.

These pioneering diagnostic tools are based on the analysis of volatile organic compounds (VOCs) which originate from diseased cells.

Volatile organic compounds
VOCs are compounds that become vapours at room temperature.

They can occur both naturally and artificially in consumer products such as cigarettes, air fresheners and paints.

In terms of biochemistry, VOCs offer an insight into the physiological and pathophysiological processes occurring in both healthy and diseased humans.

VOCs derived from cancer patients exhibit specific patterns that are significantly different from healthy individuals.

Breath analysis can identify the specific VOCs released from cells and their surrounding micro-environment.

Interestingly, it has been shown that VOCs derived from cancer patients exhibit specific patterns that are significantly different from healthy individuals.

This exciting finding suggests that breath testing has the potential to diagnose some early-stage cancers.

To further advance this as a suitable screening tool, Professor Jin and his team need to overcome two key challenges:

i) the development of an informative database that contains all the VOC profiles that correspond to specific cancers and

ii) the development of highly sensitive VOC sensors.

In collaboration with Qi Diagnostics Ltd (a pioneering biotechnology company based in Hong Kong that designs innovative tools for detecting early-stage diseases), Professor Jin and his team are co-developing a tailor-made artificial intelligence sensor array based on volatolomics (the study of VOCs).

With the help of Qi Diagnostics Ltd, the team are also collating a highly detailed VOC database and creating an artificial intelligence algorithm for data analysis.

Schema of the light-regulated electrochemical reaction in sensing of volatile compounds.

Developing a VOC database
A thoroughly detailed database which can match VOC biomarkers to a specific disease is essential for the success of the screening.

For instance, five kinds of VOCs have been verified as lung cancer diagnostic biomarkers including ethylbenzene, styrene and hexanal.

However, despite the potential of VOC profiling as a diagnostic tool, only a few VOC databases have been developed by the scientific community.

Of those that exist, the majority are focused on the structural information of scent or microbial emitted VOCs, rather than specifically focussing on VOCs as biomarkers for disease.

Therefore, more profiling needs to be done to be able to effectively identify a wide range of diseases, including different types of cancer.

The team (including Qi Diagnostics Ltd) are currently working on establishing this database which will include both detailed qualitative and quantitative information on the VOC profile.

To do this, Professor Jin and his colleagues will use gas chromatography-mass spectrometry (GC/MS) which is an effective tool to identify VOC biomarkers in exhaled breath.

However, it is important to note that confounding factors such as the patient’s age, gender and clinical history may affect the VOC profile.

The accuracy of the test must therefore be interrogated.

The team will begin by establishing a lung-cancer specific database with breath samples collected from Hong Kong hospitals and will then create a database for lung cancer, colorectal cancer and heart failure.

VOC Sensors
Currently, there are a wide range of gas sensors and sensor arrays used for the detection of VOCs.

For example, colorectal sensors, quartz microbalance sensors, surface acoustic wave sensors and chemiresistors or chemicapacitors.

However, these methods used for VOC detection are limited by humidity which can interfere with their performance.

Furthermore, these sensors are not effective at classifying complex VOC mixtures.

Therefore, Professor Jin and his team are focused on developing an inexpensive, portable and highly sensitive VOC detector.

(a) a light-regulated YSZ-based sensor array composed of three kinds of sensing electrodes (SE) and Mn-based reference electrode (RE);

(b) response patterns of the sensor array (operated at light off and/or on) towards six kinds of VOCs plotted in the form of a heat map;

(c) principle component analysis (PCA) scheme obtained with the sensor array operated at light off and/or on.

Electrochemical sensors using yttria-stabilised zirconia (YSZ) electrolytes have the potential to be a candidate for VOC detection.

Currently these gas sensors are used to monitor exhaust gases due to their high selectivity and reliable performance under harsh conditions e.g. high humidity and temperature.

However, these electrochemical sensors have a detection limit of dozens of parts per million (ppm), whereas the concentration of VOC markers from human breath is typically tens of parts per billion (ppb): therefore, a higher sensitivity is required.

Breath testing of VOCs has the potential to give clinicians information that is currently only available through procedures such as CT scans.

Recently, Professor Jin and his colleagues investigated ways in which to improve the sensitivity of electrochemical sensors.

One way in which this was achieved was by using UV illumination to enhance the electrochemical reaction.

Interestingly, the performance of the illuminated sensor was not affected by low operating temperatures and humidity changes.

Furthermore, illumination regulation of the electrochemical reaction resulted in a two-fold increase in the sensing magnitude and sensitivity and these sensors were able to identify more gases (e.g. six VOCs by using three sensors) compared to non-illuminated sensors. These exciting findings suggest that the light-regulated electrochemical reaction used in these studies could be used for designing compact and highly effective VOC sensing devices for clinical applications.

Future challenges
Invested in by Qi Diagnostics Ltd, Professor Jin and his team have designed revolutionary diagnostic tools based on VOCs that have the potential to identify cancers early on, increasing the chance of survival.

However, there are many challenges that must be overcome before these VOC sensors can be clinically used.

Currently, many scientists focus on materials science and investigating novel algorithms.

However, it is of vital importance that more researchers focus their efforts on developing novel sensing devices and improve their knowledge of the complex relationship between VOC biomarkers and the disease in question.

University of Tampere
Media Contacts: 
Ilkka Haapala – University of Tampere
Image Source:
The image is credited to Antti Roine.

Original Research: Closed access
“Identifying brain tumors by differential mobility spectrometry analysis of diathermy smoke”. Ilkka Haapala et al.
Journal of Neurosurgery. doi:10.3171/2019.3.JNS19274


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