Please Wait...

Tissue Quantification Thigh MRI in Osteoarthritis

*Yu HJ, **Tan C, **Yan Z, *Belaroussi B, *Zhang Y, **Metaxas D, ***Carrino JA, *Miller CG.

* BioClinica, Inc., Newtown USA and Lyon France
** Rutgers University, Piscataway USA
***Department of Radiology and Imaging, Hospital for Special Surgery, New York USA

INTRODUCTION: Inter-muscular and intra-muscular adipose tissues are defined as the adipose tissue visible between muscle groups and muscle fibers, respectively.  The quantities of inter-muscular adipose tissue (inter-MAT), intra-muscular adipose tissue (intra-MAT) and muscle in the thigh reflect adverse metabolic effects and muscle function.

OBJECTIVE:
1. Propose a robust and semi-automatic algorithm for the quantitative assessment of volume of thigh muscle, inter- and intra-muscular fat; Overcome the time-consuming and operator-dependent problems in traditional manual analysis, especially towards 3D datasets.
2. Apply this technique to the Osteoarthritis Initiative (OAI) MRI data to explore differences of such metrics between those with radiographic osteoarthritis (ROA) and those without ROA (non-ROA).

METHODS: A semi-automatic quantification framework was developed, which includes 5 major steps: 1) intensity inhomogeneity correction; 2) subcutaneous adipose tissue (SAT) removal; 3) tissue labeling for bone, marrow, fat and muscle; 4) inter- and intra-MAT classification; 5) tissue assessment.

The OAI database was queried for subjects with the KLG scores and mid-thigh axial T1-weighted MRIs (15 contiguous slices, 5 mm slice thickness) at baseline. 103 subjects out of 4,796 participants were drawn, and 88 subjects (51 male, 37 female; age: 45-79) were processed after image QC. The left leg of each subject was processed and were visually inspected and corrected for segmentation errors. The legs with KLG score 0 or 1 were labeled as non-ROA; those with KLG score 2, 3 or 4 were labeled as ROA.

Total volume (15 slices) was calculated and quantification differences of tissue between groups (1. ROA vs non-ROA, 2. Male vs Female) were explored using t-tests.  Correlations were examined between subject demographic, KLG status and tissue volumes.

RESULTS: There was no significant difference between ROA and non-ROA legs for inter-MAT volume presented as mean ± SD (103.09±39.24 cm3 vs 92.73±41.92 cm3, p = 0.27), intra-MAT volume (64.49±20.40 cm3 vs 65.49±23.52 cm3, p = 0.84), and total muscle volume (713.32±173.99 cm3 vs 743.35±194.42 cm3, p = 0.47).

Gender differences were found for inter-MAT volume (109.88±40.69 cm3 vs 86.17±35.45 cm3, p < 0.01), intra-MAT volume (74.45±17.25 cm3 vs 51.49±19.14 cm3, p < 0.01), and total muscle volume (822.31±141.99 cm3 vs 585.01±129.17 cm3, p < 0.01), where male has higher volumes than female, with no significant gender differences for KLG status and total thigh volume. Figure 5 shows the KLG scores to the volume of thigh inter-MAT, intra-MAT and muscle. The boxplots are divided into two groups by genders. Each column in the boxplots represents different KLG score to the corresponding tissue assessment.

These tissue assessments were also significantly correlated to BMI (r-values ranging from 0.34 to 0.75, p < 0.01), with no age dependency, showing as figure 6.

CONCLUSION: Preliminary results showed gender differences in adipose tissue and muscle content in thigh tissue quantification but according to KLG status.  The proposed framework provides a semi-automated approach for quantitative thigh tissue assessment, which has a potential for clinical and clinical trial applications.

Further validation is required and there are ongoing development efforts which include comparison to manual segmentation, enhancement of clustering and contouring accuracy and precision for fatty infiltration, and individual muscle group segmentation.

SPONSOR: BioClinica Inc.
DICLOSURE STATEMENT: None
ACKNOWLEDGMENT: None
CORRESPONDENCE ADDRESS: colin.miller@bioclinica.com

LEARN MORE OR SPEAK WITH OUR EXPERTS

CONTACT US
Leader in Clinical Trial
Management Solutions

Successful clinical trials require the ability to see key details and uncover hidden insights. Bioclinica utilizes science and technology to bring clarity to clinical trials, helping companies to develop new life-improving therapies more efficiently and safely.

Still time to catch up on summer reading on recent developments in #Alzheimer's Disease Fluid Biomarkers here int t… https://t.co/kyQmz7FxlN
bioclinica (3 days ago)
Our clients have substantial success w #eSource, some spanning well over a decade. Still other organizations remain… https://t.co/UwVs6ajPp4
bioclinica (4 days ago)
Our clients have substantial success w #eSource, some spanning well over a decade. Still some in the industry remai… https://t.co/ASuUvN6xb9
bioclinica (6 days ago)
RT @RfwrightLSL: When Do You Think #BYOD (Bring Your Own Device) Will Become Widely Used To Capture PRO Data In #Clinical #Research? https:…
bioclinica (2 weeks ago)
ICYMI: Our V.P./Head of Neuroscience & Cardiovascular Svcs., Dr. Joyce Suhy, talks about challenges in determining… https://t.co/Lns6B3VPPN
bioclinica (2 weeks ago)
RT @FCR_News_Today: FDA Issues New Guides on Use of Electronic Health Information in #ClinicalTrials. #ClinicalResearch https://t.co/j8LHV2…
bioclinica (2 weeks ago)

Latest Blogs:

Latin America: Benefit from the Right Partner
Removing Risk from Clinical Trial Management System (CTMS) Implementations
Collaboration Between Clinical Operations and the Logistics and Supply Chain Teams is Key to Trial Success
The Value of Protocol Review
CTMS and RBM: Hot Topics at OCT Nordics in Copenhagen