ArticleInsights into imaging2021
CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies.
Article in Insights into imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 5 of them syntheses that pooled it.
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Who cites it
48 citing papers in PubMed, 5 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
- Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review.BMC oral health · 2024Pooled it
- A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers.Journal of orthopaedic surgery and research · 2023Pooled it
- Radiomics-based approaches outperform visual analysis for differentiating lipoma from atypical lipomatous tumors: a review.Skeletal radiology · 2023Pooled it
- MRI-Based radiomic signature for predicting pathologic treatment response to neoadjuvant chemoradiotherapy and radioimmunotherapy in soft tissue sarcoma.Skeletal radiology · 2026Article
- Association between preoperative MRI radiomic features and methylation-defined phenotypes in non-functioning pituitary adenomas.Pituitary · 2026Article
- Measuring the optimistic bias of cross-validation in radiomics.Scientific reports · 2026Article
- Fusion of Deep Transfer Learning and Radiomics in MRI-Based Prediction of Post-Surgical Recurrence in Soft Tissue Sarcoma.Journal of imaging informatics in medicine · 2026Article
- AI in Musculoskeletal Imaging: An End-to-End Perspective.Journal of clinical medicine · 2026Review
- Retractions of publications in radiomics: An underestimated problem?European radiology · 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
- MRI Diffusion Imaging as an Additional Biomarker for Monitoring Chemotherapy Efficacy in Tumors.Medicina (Kaunas, Lithuania) · 2026Article
- Evaluation of Diagnostic Accuracy of Preoperative CT-Based Radiomics in Primary Retroperitoneal Sarcoma.Annals of surgical oncology · 2025Article
- Deep learning for automated segmentation of central cartilage tumors on MRI.European radiology experimental · 2025Article
- 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
- Multi-modal radiomics model based on four imaging modalities for predicting pathological complete response to neoadjuvant treatment in breast cancer.BMC cancer · 2025Article
- Grading chondroid tumors through MRI radiomics: enchondroma, low-grade chondrosarcoma and higher-grade chondrosarcoma.BMC cancer · 2025Article
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Authors and funding
8 authors.
Funding
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Abstract
backgroundFeature reproducibility and model validation are two main challenges of radiomics. This study aims to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The ultimate goal is to promote achieving a consensus on these aspects in radiomic workflows and facilitate clinical transferability.
resultsOut of 278 identified papers, forty-nine papers published between 2008 and 2020 were included. They dealt with radiomics of bone (n = 12) or soft-tissue (n = 37) tumors. Eighteen (37%) studies included a feature reproducibility analysis. Inter-/intra-reader segmentation variability was the theme of reproducibility analysis in 16 (33%) investigations, outnumbering the analyses focused on image acquisition or post-processing (n = 2, 4%). The intraclass correlation coefficient was the most commonly used statistical method to assess reproducibility, which ranged from 0.6 and 0.9. At least one machine learning validation technique was used for model development in 25 (51%) papers, and K-fold cross-validation was the most commonly employed. A clinical validation of the model was reported in 19 (39%) papers. It was performed using a separate dataset from the primary institution (i.e., internal validation) in 14 (29%) studies and an independent dataset related to different scanners or from another institution (i.e., independent validation) in 5 (10%) studies.
conclusionsThe issues of radiomic feature reproducibility and model validation varied largely among the studies dealing with musculoskeletal sarcomas and should be addressed in future investigations to bring the field of radiomics from a preclinical research area to the clinical stage.
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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.