Researchers at the Beckman Laser Institute have developed a non-invasive fiber optic probe capable of imaging and detecting cancerous tissue within the head and neck regions. The probe also helps to guide surgeons in effectively performing tumor removal.
·Non-invasive cancer screening and diagnostic for head and neck regions
·Guide system for doctors during tumor removal surgeries
·Non-invasive fiber optic probe
·Rapid, real-time imaging
·Wide sampling area
·Display shows 3D comprehensive view and spatial colormap of cancer cell regions and boundaries within tissue
·Cloud-based machine learning algorithm for interpreting OCT data and for diagnosing variety of pathologies
·Co-registration of OCT data with CT scans and histology
Head and neck cancers spread throughout sensitive and vital organ systems in the body, requiring special care to be taken during certain types of treatments. Doctors have to be especially careful during surgical treatments to ensure that all cancerous tissue is removed while keeping all other tissue and organs intact. Surgeons rely on subjective measures such as surgical intuition and palpation and quantitative measures such as CT images and frozen section biopsies to determine the region and extent of cancerous tissue in the area. However, CT scans suffer from poor spatial resolution and sampling error could result in biopsies containing misleading information.
To combat these issues, researchers at the Beckman Laser Institute have designed a non-invasive cancer screening system that uses optical coherence tomography (OCT). This system contains a novel probe that transmits specific wavelengths of light into the tissue. The light can be scattered or absorbed by the tissue and these interactions reveal key information about the spatial depth and provide unique patterns that represent the presence or absence of cancer cells in the area. These signals can be processed to produce a 3D visual representation of the boundaries of the tumor and a spatial color map of the tissue region. Additionally, a cloud-based machine learning algorithm is employed to determine relationships between the data and presence or absence of cancer cells, and subsequently help to diagnose a variety of pathologies. Finally, this system has the ability to co-register OCT signals with results of current methods (CT scans, biopsies) to improve the screening and detection of head and neck cancer and guide doctors during tumor removal surgeries.
Prototype in development