Objectives: Anti-differentiation-associated gene 5 (MDA5) antibody-positive dermatomyositis, which has been described as clinically amyopathic dermatomyositis, complicates rapidly progressive interstitial lung disease (ILD). Owing to the absence of significant muscle symptoms, musculoskeletal MRI is often not performed. In this study, we aimed to devise a simple evaluation method using musculoskeletal MRI findings to elucidate the relationship between MRI findings and ILD prognosis and development.
Methods: The medical records and MRI scans of the proximal muscles at the time of diagnosis were retrospectively reviewed for 28 patients with anti-MDA5 antibody-positive dermatomyositis who were admitted to The Jikei University Hospital and The Jikei University Kashiwa Hospital between January 2008 and March 2022. Three observers evaluated nine proximal muscles for high signals on either short-tau inversion recovery images and/or fat-saturated gadolinium-enhanced T1-weighted images in the fascia and/or in the margins of the muscles in contact with the fascia (fascial pattern), and/or high signals in the muscles away from the fascia (intramuscular pattern), and a consensus was reached.
Results: Of the 28 patients, 15 presented with 'radiological myositis', where an intramuscular pattern was observed at any site. Patients with radiological myositis had significantly higher survival rates than those without radiological myositis, despite the lower rate of triple therapy with prednisolone, calcineurin inhibitors and cyclophosphamide. The spread of ILD on chest CT negatively and significantly correlated with the proportion of intramuscular lesions.
Conclusion: The detection of intramuscular lesions on musculoskeletal MRI using our novel evaluation method could be clinically useful as a favourable prognostic marker.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10462980 | PMC |
http://dx.doi.org/10.1136/rmdopen-2023-003271 | DOI Listing |
Physiol Rep
February 2025
Motion and Exercise Science, University of Stuttgart, Stuttgart, Germany.
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training.
View Article and Find Full Text PDFJ Int Med Res
January 2025
Department of Orthopaedics, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine; Key Laboratory of Musculoskeletal System Degeneration and Regeneration Translational Research of Zhejiang Province, Hangzhou, China.
An 18-year-old female patient presented with a 1-month history of low back pain, which had worsened and was accompanied by radiating pain in the right lower limb for half a month. She was admitted to our hospital with computed tomography and magnetic resonance imaging findings suggesting calcification of the L3/4 disc and a large intraspinal mass at the L2-4 level. The patient's symptoms did not improve with conservative treatment, and her muscle strength rapidly declined.
View Article and Find Full Text PDFJ Comput Assist Tomogr
January 2025
Department of Radiology, Division of Musculoskeletal Imaging and Intervention, Massachusetts General Hospital, Boston, MA.
Objective: To determine the utility of a triangular margin as an imaging diagnostic feature for fibrous dysplasia.
Materials And Methods: We retrospectively reviewed all surgically biopsied or managed benign and malignant bone tumors by a single orthopedic oncologist over 19 years (2003 to 2022). A musculoskeletal radiologist and an orthopedic oncologist, both with >10 years of experience, retrospectively evaluated all imaging in consensus.
Radiology
January 2025
From the Department of Radiology, Division of Musculoskeletal Radiology, NYU Grossman School of Medicine, 660 1st Ave, 3rd Fl, Rm 313, New York, NY 10016 (S.S.W., J.V., R.K., E.H.P., J.F.); Department for Diagnostic and Interventional Radiology, Eberhard Karls University Tübingen, University Hospital Tübingen, Tübingen, Germany (S.S.W.); Department of Radiology, University Hospital Basel, Basel, Switzerland (J.V.); Department of Radiology, Hospital do Coraçao, São Paulo, Brazil (T.C.R.); Academic Surgical Unit, South West London Elective Orthopaedic Centre (SWLEOC), London, United Kingdom (D.D.); Department of Radiology, Balgrist University Hospital, Zurich, Switzerland (B.F.); Department of Radiology, Jeonbuk National University Hospital, Jeonju, Republic of Korea (E.H.P.); Research Institute of Clinical Medicine of Jeonbuk National University, Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea (E.H.P.); Medscanlagos Radiology, Cabo Frio, Brazil (A.S.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Siemens Healthineers AG, Erlangen, Germany (I.B.); and Siemens Medical Solutions USA, Malvern, Pa (G.K.).
Background Deep learning (DL) methods can improve accelerated MRI but require validation against an independent reference standard to ensure robustness and accuracy. Purpose To validate the diagnostic performance of twofold-simultaneous-multislice (SMSx2) twofold-parallel-imaging (PIx2)-accelerated DL superresolution MRI in the knee against conventional SMSx2-PIx2-accelerated MRI using arthroscopy as the reference standard. Materials and Methods Adults with painful knee conditions were prospectively enrolled from December 2021 to October 2022.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
Introduction: Benign and malignant myxoid soft tissue tumors have shared clinical, imaging, and histologic features that can make diagnosis challenging. The purpose of this study is comparison of the diagnostic performance of a radiomic based machine learning (ML) model to musculoskeletal radiologists.
Methods: Manual segmentation of 90 myxoid soft tissue tumors (45 myxomas and 45 myxofibrosarcomas) was performed on axial T1, and T2FS or STIR magnetic resonance imaging sequences.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!