Muscle Imaging Project Using ANatomopathology and Full Field Optical Coherence Tomography

Condition(s) studied

Study summary

Myositis is an inflammatory disease of the skeletal muscles that can be caused by infections, autoimmune diseases, or medications. Early diagnosis of these conditions is essential for optimal treatment. Anatomic pathology is the conventional method used to analyze muscle lesions, but it requires several weeks to obtain results. Full-field optical coherence tomography (OCT), a non-invasive imaging technique, could offer advantages in terms of speed and resolution for visualizing muscle inflammation.

This research project compares histological pathology and full-field OCT for the analysis of muscle biopsies from healthy individuals and patients suspected of having myositis. The aim of this study is to establish an OCT reference standard by analyzing control muscle biopsies and then comparing muscle inflammation in confirmed cases of myositis. The advantages and limitations of both methods will also be evaluated.

Eligibility

Primary outcome measure(s)

• Evaluation of the spatial resolution of the two methods — 2 years
  Image acquisitions will be performed on the same samples (fresh tissue for OCT and frozen tissue for histology) with an identical section thickness. To obtain structural tissue data, OCT acquisitions will be performed in full field mode (FF-OCT), and Hematoxylin-Eosin, Gomori's trichrome and NADH staining will be carried out on histological sections. For each method, the criteria evaluated will be the ability to detect fiber structure, the presence of fibrosis and blood vessels. Image comparison will be performed by specialized pathologists and then by an autoML type artificial intelligence, with assessment of accuracy and recall.
• Evaluation of inflammation detection of the two methods — 2 years
  Image acquisition will be performed on the same samples (fresh tissue for OCT and frozen tissue for histology) with an identical section thickness. OCT acquisitions will be carried out in dynamic colorimetric mode (DCI) to observe the intensity and variability of metabolic activity across the entire fresh tissue sample using a color panel ranging from blue to red. Inflammation detection on histological sections will be observed using the markers HLA1, HLA-DR, C5B9, P62, CD31, CD56. The potential correlation between metabolic activity and inflammation will be visually assessed by specialized pathologists and by an autoML type artificial intelligence with assessment of accuracy and recall.

Trial sites (1)

Other trials for the same condition