Uses Across Other Therapeutic Areas
The beauty of quantitative imaging is that it is not limited to use with a single therapeutic area. VirtualScopics is at the forefront of advancing the use of quantitative imaging across the study of numerous disease areas and all phases of study. We have assisted sponsors in the study of diseases in areas such as central nervous system, cardiovascular, COPD, rheumatoid and osteo-arthritis, muscle wasting, liver fat, as well as medical device technology. Below are examples of areas where quantitative imaging analysis is improving the method for studying a disease. You can learn more about our full breadth of trial work on our Therapeutic Areas page.
Guided by the information present in the images, as well as embedded anatomical knowledge, our algorithms enable segmentation of different tissue types such as bone, muscle, fat and fluid. From an MRI knee scan, for instance, it is possible to produce a three-dimensional reconstruction that graphically distinguishes cartilage from underlying bone, as well as from ligaments, fluid, degenerated menisci or inflamed synovium. This capability provides a valuable assessment tool for clinical research in osteoarthritis — a disease with multiple endpoints — because it allows the very sensitive and specific measurement of all the components of the knee joint and the detection of small changes in any of those components over time.
In the study of cartilage repair devices, the primary shortcoming has been the use of standard endpoints, such as pain or functionality scoring to determine success. These endpoints are largely subjective and difficult to reproduce. Quantitative imaging allows the replacement of a subjective evaluation — knee pain ranked on a scale of 1 to 10 — with an objective quantification — cartilage volume in cubic millimeters. Not only are we able to determine whether new cartilage is growing in a joint, we can measure the volume and quality of that cartilage as well as the percent of the cartilage defect that has been filled by new cartilage.
Automation in the image analysis process — using a computer algorithm to measure lesion size rather than a clinician with a ruler — provides a degree of accuracy and reproducibility that cannot be duplicated by manual techniques. A good example of this phenomenon is provided by the measurement, using MRI, of lesion burden in multiple sclerosis (MS) patients. MS lesions generally are irregularly shaped, and tend to have fuzzy, indistinct boundaries. As a result, several studies have estimated the inter-operator coefficient of variability (CV) in white matter lesion burden measurement at 20% or more and the intra-operator CV at ~7%. Introducing automation into this process can reduce this variation to 2% or less, allowing statistically significant efficacy findings to be obtained far earlier in the development process.