Active Digestion, Kidneys & Other Organs Cancer

U-SAS: Ultra-thin Scattering, Absorption and Shape Endoscope for Early Pancreatic Cancer Detection

In plain English

AI plain-English summary

A 0.7mm endoscope will project patterned laser light onto pancreatic cysts to spot early cancers that current biopsies routinely miss. Every year, 10,000 people in the UK develop pancreatic cancer, which has a 5% ten-year survival rate. Early detection is critical, but existing methods fail: cell biopsies miss over 28% of cancers and carry a 10% risk of repeat procedures that can trigger fatal pancreatitis. Many hospitals avoid cell collection entirely. The U-SAS endoscope solves this by capturing three tissue features—absorption, scattering, and 3D texture—in a single device thin enough to fit through a 19-gauge needle. It produces malignancy heat maps over a 1cm field-of-view, guiding biopsies to the most suspicious areas. If successful, the device could prevent delayed treatment in over 600 patients annually in the UK and save £13–16 million in healthcare costs by confidently ruling out cancer in healthy patients, removing them from unnecessary surveillance. Globally, it could enable 100,000 earlier detections and save £1–2 billion. Beyond the pancreas, the endoscope’s tiny size opens diagnostics for ovaries, bile ducts, tear ducts, and deep brain tissue.

View original technical description
We will develop a miniature imaging device to improve early cancer diagnosis in pancreatic cysts—fluid sacs that may become cancerous—leading to better patient outcomes and reducing surveillance and repeat biopsies. Every year 500,000 people worldwide including 10,000 in the UK develop pancreatic cancer, an aggressive disease with a mere 5% ten-year survival [CRUK,WCRF]. Early detection is crucial but hindered by the lack of effective screening programmes. However, some pancreatic cancers begin as cysts and so examining these holds great promise for early detection [Fischer2018]. Currently, doctors insert a long needle via the small intestine to extract cyst fluid for analysis. However, this method often misses early-stage cancers since the hallmark chemical is also produced by benign cysts. Therefore, for a definitive diagnosis, doctors use a ‘cell biopsy’ that collects cells for analysis using special tweezers [Cizginer2011]. However, this carries >10% chance of insufficient cell collection, necessitating a repeat attempt with 10% risk of potentially fatal pancreatitis [Balaban2021]. Further, cell biopsy may miss over 28% of cancers (sensitivity ranges 40-72%, depending on cyst type) partly due to inadequate sampling during biopsy [Genevay2011;Balaban2021]. Consequently, many hospitals avoid cell collection entirely. Direct visualization of cyst wall cell structures could enhance biopsy accuracy by guiding sampling to promising areas, reducing repeats and the risk of missing cancers [Genevay2011;Pitman2008]. Our commissioned health economic analysis indicates that confidently ruling out cancer in healthy patients, removing them from surveillance, could save £13-16 million in UK healthcare costs and prevent delayed treatment in over 600 patients annually. Globally, this could lead to 100,000 earlier pancreatic cancer detections and save £1-2 billion in healthcare costs. We have developed a 3mm diameter prototype imaging device that enhances cancer detection by capturing three additional imaging features. Unlike existing methods, our prototype projects an engineered light pattern onto tissue to capture absorption (lightness/darkness), scattering (surface roughness), and 3D texture using lasers [Crowley2024]. We call our device the Ultra-thin Scattering, Absorption, and Shape (U-SAS) endoscope. While each of these features individually can identify cancer in various tissues (pancreas, colon, oesophagus, skin) [Sweer2019;Angelo2017;Picot2017;Awe2020;Rex2019], no existing imaging instrument combines them in a package small enough for endoscopic use while offering the wide field-of-view required to guide biopsies [Angelo2017]. Our proposed device addresses this unmet clinical need. In this project we will develop a second-generation U-SAS endoscope suitable for clinical deployment. Specific objectives are: Improve hardware design for compactness (<0.7mm diameter to fit into 19-gauge needles used for pancreatic cyst biopsy), robustness, and manufacturability. Enhance software to produce high-quality malignancy “heat maps” over a 1cm field-of-view for guided biopsies. Conduct pre-clinical validation studies on ex vivo human pancreatic tissue. In future our device could complement emerging pancreatic cancer screening methods like blood and breath tests that may boost referrals for cyst investigation [Rhim2014;Markar2018]. In addition to improving biopsies, it could classify cysts by risk in hospitals where pancreatic cell biopsies are not routine. Further when paired with emerging cyst ablation therapies—which can avoid invasive, life-altering surgeries [Cho2024]—it could catalyse a dramatic shift in the pancreatic cancer treatment pathway, greatly improving patient survival and quality of life. Finally, the device’s small size opens possibilities for diagnosing diseases in other organs, such as the ovaries, bile duct, urethra, tear ducts, salivary glands, and even deep within the brain.

View the original record at the funder ↗

Researchers

Abhik Mukherjee (Co-Investigator)David Gomez (Co-Investigator)George Gordon (Principal Investigator)Guru Aithal (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

Laparoscopic contrast enhanced ultrasound for cancer detection
A Computer-assisted 3D Navigation System for Endoscopic-Ultrasound-Guided Diagnosis and Minimally-invasive Treatment of Pancreatic Lesions
Self-propelled soft robotic endoscopes for next-generation gastrointestinal surgery and beyond
Imaging the stars from within: Super-resolution contrast ultrasound imaging
Multi-Modal analysis of composition and spatial architecture in human premalignant pancreatic lesions to enhance early detection.

Original classification

Research and Innovation

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.