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Diagnostics
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Innovative Diagnostic Imaging Technology in Musculoskeletal Tumors
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Innovative Diagnostic Imaging Technology in Musculoskeletal Tumors

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 906

Special Issue Editor

Dr. Min Wook Joo

E-Mail Website
Guest Editor
Department of Orthopaedic Surgery, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
Interests: osteosarcoma; metastasis; clinical oncology; cancer metastasis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on innovative diagnostic imaging technology in the field of musculoskeletal tumors. It presents cutting-edge research and advancements in imaging modalities, techniques, and protocols that have significantly improved the detection, diagnosis, and management of these tumors. The contributions cover a wide range of topics, including the application of advanced imaging techniques such as deep learning and artificial intelligence in tumor classification and staging, as well as the challenges and pitfalls encountered in imaging musculoskeletal tumors. The Special Issue aims to provide a comprehensive overview of the latest developments in diagnostic imaging technology for musculoskeletal tumors and to foster further research and collaboration in this important area.

Dr. Min Wook Joo
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 250 words) can be sent to the Editorial Office for assessment.

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. Diagnostics 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 tumors
  • diagnostic imaging
  • innovative technology
  • advanced imaging modalities
  • tumor classification
  • tumor staging
  • imaging protocols
  • image analysis techniques

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Published Papers (1 paper)

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Research

14 pages, 1881 KB  
Article
MRI Radiomics for Predicting the Diffuse Type of Tenosynovial Giant Cell Tumor: An Exploratory Study
by Seul Ki Lee, Min Wook Joo, Jee-Young Kim and Mingeon Kim
Diagnostics 2025, 15(18), 2399; https://doi.org/10.3390/diagnostics15182399 - 20 Sep 2025
Viewed by 646
Abstract
Objective: To develop and validate a radiomics-based MRI model for prediction of diffuse-type tenosynovial giant cell tumor (D-TGCT), which has higher postoperative recurrence and more aggressive behavior than localized-type (L-TGCT). The study was conducted under the hypothesis that MRI-based radiomics models can predict [...] Read more.
Objective: To develop and validate a radiomics-based MRI model for prediction of diffuse-type tenosynovial giant cell tumor (D-TGCT), which has higher postoperative recurrence and more aggressive behavior than localized-type (L-TGCT). The study was conducted under the hypothesis that MRI-based radiomics models can predict D-TGCT with diagnostic performance significantly greater than chance level, as measured by the area under the receiver operating characteristic (ROC) curve (AUC) (null hypothesis: AUC ≤ 0.5; alternative hypothesis: AUC > 0.5). Materials and Methods: This retrospective study included 84 patients with histologically confirmed TGCT (54 L-TGCT, 30 D-TGCT) who underwent preoperative MRI between January 2005 and December 2024. Tumor segmentation was manually performed on T2-weighted (T2WI) and contrast-enhanced T1-weighted images. After standardized preprocessing, 1691 radiomic features were extracted, and feature selection was performed using minimum redundancy maximum relevance. Multivariate logistic regression (MLR) and random forest (RF) classifiers were developed using a training cohort (n = 52) and tested in an independent test cohort (n = 32). Model performance was assessed AUC, sensitivity, specificity, and accuracy. Results: In the training set, D-TGCT prevalence was 32.6%; in the test set, it was 40.6%. The MLR model used three T2WI features: wavelet-LHL_glszm_GrayLevelNonUniformity, wavelet-HLL_gldm_LowGrayLevelEmphasis, and square_firstorder_Median. Training performance was high (AUC 0.94; sensitivity 75.0%; specificity 90.9%; accuracy 85.7%) but dropped in testing (AUC 0.60; sensitivity 62.5%; specificity 60.6%; accuracy 61.2%). The RF classifier demonstrated more stable performance [(training) AUC 0.85; sensitivity 43.8%; specificity 87.9%; accuracy 73.5% and (test) AUC 0.73; sensitivity 56.2%; specificity 72.7%; accuracy 67.3%]. Conclusions: Radiomics-based MRI models may help predict D-TGCT. While the MLR model overfitted, the RF classifier demonstrated relatively greater robustness and generalizability, suggesting that it may support clinical decision-making for D-TGCT in the future. Full article
(This article belongs to the Special Issue Innovative Diagnostic Imaging Technology in Musculoskeletal Tumors)
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