BioClinica Collaborates with CDDA on Fatty Infiltration Tool (FIT)
The presence of fatty infiltration in muscles of the lower extremities (thigh and calf) serves as a proxy for impaired muscle function and metabolic status, often seen with musculoskeletal diseases like osteoarthritis (OA), Sarcopenia, Duchenne Muscular Dystrophy, Pompe disease and Polymyositis/Dermatomyositis. Most clinical research for musculoskeletal diseases relies on non-invasive imaging methodologies for assessing changes in inter-muscular adipose tissue (inter-MAT), intra-muscular adipose tissue (intra-MAT) and muscle in the lower extremities.
MRI is the most widely used imaging modality for the evaluation of muscle fatty infiltration. The ability to detect changes in muscle morphology via simple acquisition such as T1-weighted MRI is essential for clinical trials and for measuring the efficacy of new drugs designed to treat musculoskeletal disorders. However, traditional manual analysis, especially of 3D imaging datasets, is time-consuming and operator-dependent and therefore not well suited for use in clinical routine or in clinical trials.
To help overcome this, scientists and computer programmers are working together to develop automated methodologies capable of deriving quantitative data from muscle MR images. In an ongoing collaboration with the Center for Dynamic Data Analytics (CDDA) at Rutger’s University, BioClinica is developing an automated method called the Fatty Infiltration Tool (FIT), for the analysis of muscle, subcutaneous adipose tissue, and inter- and intra-muscular adipose tissue. A key feature of FIT is the ability to define and reliably monitor muscle parameters including muscle quantity (maximum cross sectional area, muscle volume, fatty infiltration), muscle quality (quantifying ratios against fat), and the magnitude and distribution of muscle fat infiltration. The goal is to establish a working algorithm that can provide more reliable, more sensitive, and faster processing if MR images for clinical trials.
Recent work on the clinical application of this tool was presented at the WCO-IOF-ESCEO World Congress and will be presented at the International Conference on Frailty and Sarcopenia Research (ICFSR) later this month. The work features study results on 1) age-related changes in muscle fatty infiltration, 2) gender differences as they relate to aging in healthy population, and 3) age-related changes as a risk factor for predicting Osteoarthritis (OA). Automatic quantification techniques including intensity inhomogeneity correction, tissue labeling, and inter- and intra-MAT classification were applied to MR images of patients selected from the OAI database with and without radiographic OA. In both groups, muscle volume normalized to BMI decreased with age while adipose tissue volume increased. Differences in age related changes between subjects with and without ROA were inconsistent, although numerically correlations with age were slightly higher in the ROA group.
Each application of the automation tool to clinical trial images brings improvements and development efforts that further refine the automation tool and expand on its applications. Ongoing development efforts include enhancement of clustering and contouring accuracy and increased precision for measuring fatty infiltration. Future work using FIT will focus on capabilities for assessing and comparing individual muscle groups.