Leverage Radiomics to Revolutionize Cancer Care
Radiomics is the rapidly growing field in radiology that extracts high-dimensional data from standard images to define a novel set of quantifiable imaging patterns or imaging biomarkers. These markers have proven to be both predictive and prognostic regarding clinical outcomes and treatment pathways.
HealthMyne believes radiomics is the next wave in healthcare, unleashing the hidden power of imaging data to speed drug and treatment development and the achievement of personalized medicine.
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Radiomic Value Across the Cancer Journey
"Across all of every cancer journey, there is an image that's a hallmark of a particular stage in that journey. We're able to ascertain from each of those points along this timeline different elements of information that help us understand whether or not something is actually working as expected or, if it's not, allows for the treatment to be modified and adjusted."
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Radiomics Leverages AI & Deep Learning for Informed Decision-Making
Radiomics is poised to be the next great advance in the world’s ongoing battle against cancer. Radiomics enables healthcare and life science organizations to analyze traditional images, such as MRIs and PET scans, then use artificial intelligence (AI) to extract more than 2,300 data points about the biology of a tumor or lesion. By comparing this newly available data to past images, as well as the biology of healthy organs, clinicians can gain a much deeper understanding of how a tumor or lesion is responding to a specific therapy, informing care and treatment decisions along every step. Following is a look at how AI and deep learning informs the radiomic decision-making process, in addition to the outcome models produced by radiomics.
Dr. Robert Gillies of Moffitt Cancer Center is often called the ‘Father of Radiomics’ and in his paper, “Radiomics: Images Are More than Pictures, They Are Data” he predicts that Radiomics are the next frontier in healthcare.
The Science Behind Radiomics
HealthMyne relies on the expert skill of clinicians to identify lesions, but when it comes to quickly distinguishing between lesion and healthy tissue, our proprietary algorithms can help. Our AI and Deep Learning algorithms go far beyond just looking at Hounsfield Units (HU) or density gradients. Our algorithms are built with a combination of the following disciplines to product accurate 3D lesion contours that take every voxel into account:
Some of the same technologies that assist in the fields of computer-aided engineering (CAE) and 3D reconstruction allow us to build structures that make the most physical and anatomical sense.
Robust Statistics & Bayesian Probability
Modern statistical sampling methods offer an intelligence vital to finding the edges of tissues in a way that simple thresholding cannot.
The same mathematical modeling principles that are used by computer graphics to synthesize texture and allow computers to identify real-life objects in a photo allow us to distinguish contiguous tissue from unassociated anatomy.
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HealthMyne’s radiomic and quantitative imaging solution receives scientific validation from a study published in Cancer Control
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