ArticleInsights into imaging2024
CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies.
Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Automated detection of bone lesions using CT and MRI: a systematic review.La Radiologia medica · 2024Pooled it
- The use of artificial intelligence in musculoskeletal ultrasound: a systematic review of the literature.La Radiologia medica · 2024Pooled it
- Minimal residual disease and relapse surveillance in osteosarcoma: an action-linked framework integrating liquid biopsy and imaging biomarkers.Journal of bone oncology · 2026Review
- ESR Bridges: new developments in imaging and treatment of soft tissue sarcoma-a multidisciplinary view.European radiology · 2026Article
- Radiomics-based MRI model for predicting the severity of rotator cuff tears.Orthopadie (Heidelberg, Germany) · 2026Article
- Diagnosis and Management of Soft-Tissue Sarcomas: Current Insights.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026Review
- Signals Alongside Scans: A Genomics-Guided Framework for Liquid Biopsy in Bone and Soft-Tissue Sarcomas.Cells · 2026Review
- AI in Musculoskeletal Imaging: An End-to-End Perspective.Journal of clinical medicine · 2026Review
- Medical image pretraining-based transfer learning for generalizable and robust diagnosis of bone tumors on radiographs: a multi-center study.Insights into imaging · 2026Article
- Radiomics features as imaging biomarkers in alveolar and craniofacial bone healing: a scoping review.Odontology · 2026Review
- RADS ALPHABET: news and tips for young and general radiologists.Insights into imaging · 2026Article
- Physeal breach as a potential predictor of pulmonary metastasis in paediatric osteosarcoma.Frontiers in oncology · 2026Article
- Review
- Radiomics in Action: Multimodal Synergies for Imaging Biomarkers.Bioengineering (Basel, Switzerland) · 2025Review
- New Approaches to Investigate Novel Agents in Ewing Sarcoma: A Report From the Children's Oncology Group Bone Tumor Committee.Pediatric blood & cancer · 2025Review
- Deep learning for automated segmentation of central cartilage tumors on MRI.European radiology experimental · 2025Article
- Article
- Deep learning in rib fracture imaging: study quality assessment using the Must AI Criteria-10 (MAIC-10) checklist for artificial intelligence in medical imaging.Insights into imaging · 2025Article
- Deep learning image enhancement algorithms in PET/CT imaging: a phantom and sarcoma patient radiomic evaluation.European journal of nuclear medicine and molecular imaging · 2025Article
- Quality appraisal of radiomics-based studies on chondrosarcoma using METhodological RadiomICs Score (METRICS) and Radiomics Quality Score (RQS).Insights into imaging · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020.
methodsA literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed.
resultsOut of 201 identified papers, 55 were included. They dealt with radiomics of bone (n = 23) or soft-tissue (n = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies.
conclusionsCompared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage. CRITICAL RELEVANCE STATEMENT: An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage. KEY POINTS: • 2021-2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed. • Feature reproducibility was assessed in more than half (59%) of the studies. • Model clinical validation was performed in 69% of the studies. • Internal (44%) and/or external (29%) test datasets were employed for clinical validation.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.