Much of the recent advanced visualization news comes from the 2012 annual meeting of the Society for Imaging Informatics in Medicine (SIIM).
The biggest development by far is the new learning track debuted by SIIM: Quantitative Imaging.
According to the society, “In recent years, the focus has changed such that for some disease categories (e.g. oncology) we now perceive medical imaging as a phenotypic expression of the genetic makeup of that disease. To that end, imaging now serves as a biomarker of genetic disease subtypes with features that may offer clues to understanding the natural behavior of the disease and specific changes that may occur as part of a therapeutic response. It is now well recognized that there is a substantial amount of objective information contained within diagnostic imaging studies that can be exploited beyond the level of simple measurements.”
Quantitative imaging represents imaging’s next great frontier, according to its proponents. Skeptics, however, question whether these techniques are ready for prime time. Speaking during the SIIM annual conference, Luciano M.S. Prevedello, MD, of Brigham & Women’s Hospital in Boston, and Adam E. Flanders, MD, of Thomas Jefferson University Hospital in Philadelphia, shared the optimists’ and pessimists’ views.
Quantitative imaging could fill the gap by extracting quantifiable features from medical images for the assessment of normal tissue versus disease with ability to assess the degree of change over time.
Different parameters can affect quantitative imaging values. Flanders offered measurement of stenosis in blood vessels as an example. Carotid lesions can be complicated, making it difficult to produce reliable measurements. Changing inputs such as slice thickness, vendor platform and algorithms can yield different results.
Flanders cited a second example, explaining that widely used physiologic MRI tests depend heavily on pre-processing, filtering, pre-defined thresholding and non-standardized algorithms.
Given the tremendous uncertainty and variability surrounding quantitative measures, Flanders concluded, “We [radiologists] need to be responsible. We are in a precarious position. We are giving these tools to clinicians and they are generating their own quantitative results without understanding the data behind it and then making a clinical decision support.”
However, quantitative imaging continues to develop at near breakneck speed. “We can improve the clinical reliability of what we measure,” said Flanders. Advances such as reference datasets, validated algorithms, reproducibility and recommendations with automated measures all promise to improve the utility and value of quantitative imaging.
Several other sessions touched on the importance of quantitative imaging and how it is impacting healthcare. The general consensus is that we are only beginning to see the promise and potential of quantitative imaging—widely perceived as the next great leap in advanced visualization. Quantitative imaging, via advanced visualization tools, is helping clinicians better understand disease and treatment response, as well as make further inroads in personalized care.
Is quantitative imaging transforming clinical practice at your organization? Let us know.
Beth Walsh, editor