Digital Pathology is an ever-expanding ecosystem of bioinformatics and whole-slide scanning solutions that are helping to revolutionize workflows within medical diagnostics. These systems offer cost-effective, high-throughput solutions for the collection, storage and interpretation of digital images of stained (labeled) tissue specimens.
The number of new cancer cases is expected to rise to more than 20 million by 2030, so digital pathology is increasingly essential as it can increase throughput with slide scanners, machine learning and artificial intelligence. The FDA continues to approve new processes and instruments such as the Phillips IntelliSite Pathology Solutions (PIPS).As these established and powerful techniques continue to act as the cornerstones of the pathology workflow, there is a real need for considerable ongoing investment in high-quality sample preparation to ensure accuracy and efficiency.
Until very recently, labeling or chemical staining via IHC stains or fluorescent tags was the only real option for a pathologist or researcher to visualize the diagnostic markers that were present within tissue sections. Additionally, the use of labels can limit the potential discovery of new biomarkers within the sample.
Label-Free Digital Pathology
The Digital Pathology community is continually working to offer a new range of powerful, label-free digital imaging solutions that could considerably reduce healthcare costs whilst at the same time accelerating discovery and simplifying the histology workflow.
Mid-infrared (MIR) and second harmonic generation (SHG) microscopy are two established optical techniques that have shown viability as label-free digital pathology solutions due to both their ease-of-use and advancements in technology.
Thanks to recent developments, MIR digital imaging has experienced a more than 100 fold increase in throughput, meaning that single large-tissue specimens can now be analyzed in minutes rather than days. This was made possible due to the development of new MIR quantum cascade laser (QCL) sources being employed in a novel wide-field illumination mode.
Spanning approximately 3,000 to 12,000 nm, the MIR spectral region is full of organic molecular information that can be analyzed in order to perform quantitative digital tissue classification. This allows disease progression to be identified and tracked, as well as being able to study the tumor microenvironment.
A team of researchers at the PURE institute are utilizing Spero-QT® MIR microscopes to undertake quick, automated and label-free tissue classification of cancer tissues.
They are coupling quantitative MIR data with advanced machine learning and AI image analysis, and this has allowed the research group at PURE to automate tissue image analytics as well as the rendering of differential diagnoses in multiple organ systems.
The team has analyzed 110 tissue samples from stage-2 colorectal cancer patients to date, with results indicating 100% selectivity and 96% sensitivity when compared to those of classical histopathology.
Additionally, the team has compared result from two different Spero-QT® machines, finding that results were independent of the machine or user. This is likely to pave the way for much broader implementation of Spero-based digital pathology, with larger colon cancer studies and various other primary organ studies already underway.
The Spero-QT® microscope is the only commercially-available, QCL-based, wide-field MIR digital imaging platform currently on the market.
This information has been sourced, reviewed and adapted from materials provided by Daylight Solutions Inc.
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