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Recent Updates in Imaging of Rheumatologic and Neoplastic Musculoskeletal Diseases
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Recent Updates in Imaging of Rheumatologic and Neoplastic Musculoskeletal Diseases

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Nuclear Medicine & Radiology".

Deadline for manuscript submissions: 25 September 2025 | Viewed by 472

Special Issue Editor

Dr. Olympia G. Papakonstantinou

E-Mail Website
Guest Editor
Department of Radiology, National and Kapodistrian University of Athens, 157-72 Athens, Greece
Interests: musculoskeletal imaging; radiology

Special Issue Information

Dear Colleagues,

Musculoskeletal disorders, including injuries and rheumatologic disorders, remain one of the leading causes of disability. On the other hand, musculoskeletal tumors, though rare, may have devastating consequences if not recognized early and confidently. Integrated conventional imaging techniques, including radiographies, CT, MRI, and, in certain occasions, ultrasound, remain the main stays for the diagnostic assessment and surveillance of musculoskeletal diseases and constitute the basis for effective treatment decisions. Advaced techniques, such as functional imaging, enhance the diagnostic capabilities of imaging, whereas minimally invasive techniques using imaging guidance offer theurapeutic benefits at lower cost and significantly less discomfort.

This Special Issue, “Recent Updates in Imaging of Rheumatologic and Neoplastic Musculoskeletal Diseases”, aims to attract high-quality research from imaging scientists, radiologists, orthopedic surgeons, and experts in related fields, and to promote the application of state-of-the-art imaging technology in science and clinical settings, including the diagnosis, treatment, and management of a wide spectrum of musculoskeletal diseases. We seek relevant cutting-edge research in the form of original articles or reviews, and we look forward to your contribution to provide the latest insights for scholars in this field.

Dr. Olympia G. Papakonstantinou
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • musculoskeletal imaging
  • radiology
  • functional imaging
  • artificial intelligence
  • advanced MRI techniques
  • musculoskeletal systems
  • orthopedics
  • sport medicine
  • traumatology
  • rehabilitation
  • fracture
  • muscle
  • connective tissues
  • clinical relevance
  • soft tissues
  • joints
  • multidisciplinary management
  • diagnosis
  • treatment
  • management
  • challenges

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Published Papers (1 paper)

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Research

12 pages, 394 KiB  
Article
Improving the Reliability of Muscle Tissue Characterization Post-Stroke: A Secondary Statistical Analysis of Echotexture Features
by Borhan Asadi, Juan Nicolás Cuenca-Zaldívar, Alberto Carcasona-Otal, Pablo Herrero and Diego Lapuente-Hernández
J. Clin. Med. 2025, 14(9), 2902; https://doi.org/10.3390/jcm14092902 - 23 Apr 2025
Viewed by 217
Abstract
Background/Objectives: Ultrasound (US) imaging and echotexture analysis are emerging techniques for assessing muscle tissue quality in the post-stroke population. Clinical studies suggest that echovariation (EV) and echointensity (EI) serve as objective indicators of muscle impairment, although methodological limitations hinder their clinical translation. This [...] Read more.
Background/Objectives: Ultrasound (US) imaging and echotexture analysis are emerging techniques for assessing muscle tissue quality in the post-stroke population. Clinical studies suggest that echovariation (EV) and echointensity (EI) serve as objective indicators of muscle impairment, although methodological limitations hinder their clinical translation. This secondary analysis aimed to refine the assessment of echotexture by using robust statistical techniques. Methods: A total of 130 regions of interest (ROIs) extracted from the gastrocnemius medialis of 22 post-stroke individuals were analyzed. First, inter-examiner reliability between two physiotherapists was assessed by using Cohen’s kappa for muscle impairment classification (low/high) for each echotexture feature. For each examiner, the correlation between the classification of the degree of impairment and the modified Heckmatt scale for each feature was analyzed. The dataset was then reduced to 44 ROIs (one image per leg per patient) and assessed by three physiotherapists to analyze inter-examiner reliability by using Light´s kappa and correlation between both assessment methods globally. Statistical differences in 21 echotexture features were evaluated according to the degree of muscle impairment. A binary logistic regression model was developed by using features with a Cohen’s kappa value greater than 0.9 as predictors. Results: A strong and significant degree of agreement was observed among the three examiners regarding the degree of muscle impairment (Kappalight = 0.85, p < 0.001), with nine of the 21 features showing excellent inter-examiner reliability. The correlation between muscle impairment classification with the modified Heckmatt scale was very high and significant both globally and for each echotexture feature. Significant differences (<0.05) were found for EV, EI, dissimilarity, energy, contrast, maximum likelihood, skewness, and the modified Heckmatt scale. Logistic regression highlighted dissimilarity, entropy, EV, Gray-Level Uniformity (GLU), and EI as the main predictors of muscle tissue impairment. The EV and EI models showed high explanatory power (Nagelkerke’s pseudo-R2 = 0.74 and 0.76) and robust classification performance (AUC = 94.20% and 95.45%). Conclusions: This secondary analysis confirms echotexture analysis as a reliable tool for post-stroke muscle assessment, validating EV and EI as key indicators while identifying dissimilarity, entropy, and GLU as additional relevant features. Full article
(This article belongs to the Special Issue Recent Updates in Imaging of Rheumatologic and Neoplastic Musculoskeletal Diseases)
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