Innovations in surgical oncology – exploring tumour-sensing technology

Procedures and guidance for surgical oncology

The performance of surgical oncology can be assessed by two key metrics:  

1. How invasive the procedure is (and the risks it poses to the patient) 
2. How effective it is at removing the cancer 

In terms of invasiveness, traditional surgery often requires large incisions associated with long recovery times, a high risk of infection and extensive postoperative pain, with the potential for significant damage to surrounding healthy tissues.  

Surgeons can face significant challenges with limited access to certain tumours and difficulties in accurately identifying all cancerous tissue, leading to the issue of incomplete tumour removal. 

To overcome these challenges, advances in surgical oncology are increasingly being dominated by minimally invasive surgical procedures, advanced image-guided technologies and other surgery staging techniques.  

Minimally invasive surgical procedures

Minimally invasive surgical procedures, such as laparoscopy and robotic-assisted surgery, leverage a few small incisions instead of one large incision and a camera to perform operations inside the body. Because of the much smaller incision, this method allows for quicker recovery, less pain and smaller scars compared to traditional open surgery.  

Robotic systems like the Da Vinci Surgical System offer unparalleled precision and control through minimally invasive means. However, they come with high costs and complex training requirements. Despite these factors, the use of surgical robotics is becoming increasingly common. In the UK, for example, it is reported that over 90% of prostatectomies are now robot-assisted.  

Image-guidance techniques for surgical oncology

Surgical interventions (both traditional and minimally invasive) are enhanced by image-guidance methods applied both preoperatively and intraoperatively. 

Preoperative imaging modalities, such as ultrasound (US), X-ray computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET), aim to identify the tumour’s location, size and relationship to surrounding tissues, facilitating effective surgical planning.  

Intraoperative imaging provides real-time visualisation through techniques like intraoperative ultrasound, fluoroscopy, CT fluoroscopy and intraoperative MRI, enabling dynamic monitoring during the procedure. However, X-ray, MRI, PET and US imaging primarily detects changes in tissue density, which can be similar between tumours and healthy tissues, complicating differentiation.  

Although contrast agents suitable for all the above technologies have been designed to enhance image quality, they often lack specificity to the cellular or molecular level for tumour cells. Consequently, while imaging guidance is essential for planning and positioning, differentiating healthy tissue from tumour tissue remains a significant challenge for surgeons during cancer surgery. 

Tumour-sensing technology – differentiating tumours from healthy tissue

The current standard for tumour margin assessment is a process known as frozen section analysis, where a small sample is taken from the edge of the removed tissue, known as the resection margin, before being quickly frozen and examined. Unfortunately, this method is not always feasible intraoperatively.  

Another approach is direct visual inspection and palpation, which involves inspecting and feeling the tissue. This method raises concerns due to its inherent limitations in precision.  

Given the complexity and variability of tumours, relying solely on these techniques can lead to incomplete removal and an increased risk of recurrence. These difficulties make advanced imaging and technological methods the preferred approach for ensuring thorough treatment. 

Advances in technologies originally developed for cancer diagnostics are now being applied in surgery. Below are innovations that have enhanced surgical precision by providing intraoperative guidance to distinguish between tumour cells and healthy tissue.

Fluorescence-Guided Surgery (FGS) 

Fluorescence-guided surgery uses systemically delivered fluorescence biomarkers (probes) that selectively accumulate in tumour cells. When exposed to specific light wavelengths, these fluorophores emit fluorescence signals, detected by a camera, enabling surgeons to distinguish between cancerous and healthy tissues.  

A major development in this space has been the creation of highly tumour-specific fluorescent probes, which have helped to advance ‘precision surgical therapy’. An example is the 1788 Platform by Stryker, which uses CYTALUX, an FDA-approved fluorescence probe for lung cancer, to identify additional cancerous lesions during surgery that might otherwise go undetected. 

Hyperspectral Imaging (HSI) 

This technology integrates a digital camera with a spectrometer to capture detailed spectral data from biological tissues. Each pixel of the resulting image, called a “hypercube”, contains a spectrum of light that can differentiate tissue types based on their unique spectral signatures.  

This method provides vast amounts of data, but interpreting the information can be challenging. To address this, deep learning algorithms are increasingly being used to detect and classify different tissues automatically. For instance, Gustavo M. Callico et al. demonstrated HSI’s ability to identify brain tumours, including both high- and low-grade gliomas. Similarly, Su Woong Yoo et al. utilised an endoscopic HSI system to detect tumours in a pancreatic tumour model.  

While most hyperspectral imaging systems are still primarily in research or developmental phases, there is clear momentum towards clinical use. Hypervision Surgical is a notable example, as its AI-driven HSI system has been accepted into the FDA’s Safer Technologies Program as of April 2024. 

Raman Spectroscopy (RS) 

Another powerful photonic-based diagnostic technique that is being used in surgical guidance is Raman spectroscopy. Like hyperspectral imaging, RS uses spectroscopy to characterise tissue. Raman spectroscopy operates by measuring molecular vibrations using near-infrared (NIR) light, providing highly specific chemical composition information. This technique typically utilises point-based measurements, lacking the broad spatial field of HSI.  

Reveal Surgical is a first of its kind company aspiring to identify cancerous tissue intraoperatively using RS and machine learning (ML) with its Sentry System. A recent study demonstrated that the Sentry System discriminated tumour–containing tissue from non-tumoral brain tissue in real time and prior to resection with diagnostic accuracies over 90%. This indicates great potential for Raman spectroscopy in cancer treatment. 

Considerations in tumour-sensing technology development 

When developing sensing technologies and surgical devices to differentiate between tumour and healthy tissue, several key scientific and technological considerations come into play, including:  

1. Compatibility with minimally invasive approaches  

While bulky instrumentation is feasible in open surgery, minimally invasive procedures like laparoscopic or robotic surgeries demand miniaturised systems.  

Developing optical systems (like FGS, HIS and RS), that perform effectively in a miniaturised format can be challenging. Simulations can be a useful tool to evaluate trade-offs between cost, form factor and performance, helping to identify design adaptations and solutions for improving compatibility with minimally invasive procedures.  

To address movement and positioning inaccuracies, consider integrating motion compensation algorithms and image registration methods. Furthermore, pressure feedback mechanisms for contact measurements can help maintain consistent sensor-tissue interaction. 

2. Specificity challenges of the differentiation metric  

A key challenge in tumour-sensing technology development is ensuring it accurately distinguishes between cancerous and non-cancerous cells or biomarkers – otherwise known as the differentiation metric. Often, non–specific signals, like fluorescence binding to normal cells, can lead to healthy tissue being identified as cancerous and vice-versa. 

To reduce non-specific signal errors, a multi-modal approach can be applied to combine different sensing methods which cross-validate tissue characterisation. Understanding the origin and characteristics of non-specific signals can also inform the development of post-processing algorithms to filter out irrelevant signals. 

Optimising detection thresholds is also critical. Adjusting these thresholds allows clinicians to balance the risks of false positives (incorrectly identifying non-cancerous conditions as cancer) and false negatives (failing to detect actual cancer cases) in each clinical use case. 

3. Sensitivity and signal differentiation  

High sensitivity allows tumour-sensing technologies to detect subtle differences between cancerous and non-cancerous tissues, even in the presence of interfering signals. Beyond noise reduction and calibration processes, consider multidimensional metrics, such as time-dependent metrics, to improve sensitivity.  

Time-dependent metrics have the potential to be more sensitive to tissue microstructure due to dynamic effects. These metrics offer more robust results by averaging “noise” over time. Time-dependent diffusion is an emerging technique that demonstrates greater sensitivity than traditional diffusion-weighted MRI, claiming the ability to offer sensitivity at the single–cell scale. 

4. Managing variability  

While establishing the correlation between sensing data and tumour identification is the primary focus of tumour-sensing technologies, sources of variability are often overlooked. Early-stage methodologies can help address this issue, such as in vitro or ex vivo experimentation with tissue surrogates of varying properties and low-cost sensor prototypes.  

Characterising variability in early-stage experimentation can identify key risks and prioritise areas for development. Implementing Design of Experiments (DOE) for robustness testing further supports this by identifying critical factors that influence the performance and reliability of sensing systems under varying conditions.  

These findings can be leveraged to inform system design before investing in more complex prototypes.

Addressing surgical oncology challenges through surgical device innovation

Innovations in minimally invasive surgery and tumour-sensing technology have valuable applications for optimising surgical interventions for cancer treatment. Improving surgical precision and reducing risk through state-of-the-art robotics systems and optical systems, suggests a bright future for enhancing patient outcomes.  

However, challenges remain in accurately distinguishing between cancerous cells and healthy tissue and ensuring complete tumour removal. By addressing key factors such as the specificity of differentiation metrics, signal variability and compatibility with minimally invasive techniques in tumour-sensing technology, developers can drive the creation of more effective surgical devices. 

This blog is part of a series on “The future of oncology – medical technologies that are transforming cancer treatment”. Look out for upcoming blogs on innovations in radiotherapy and surgical ablation and targeted intratumoural drug delivery.