ArticleFrontiers in oncology2021
Radiomics Analysis of Fat-Saturated T2-Weighted MRI Sequences for the Prediction of Prognosis in Soft Tissue Sarcoma of the Extremities and Trunk Treated With Neoadjuvant Radiotherapy.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Integrating radiomics, artificial intelligence, and molecular signatures in bone and soft tissue tumors: advances in diagnosis and prognostication.Frontiers in oncology · 2025Pooled it
- MRI-Based radiomic signature for predicting pathologic treatment response to neoadjuvant chemoradiotherapy and radioimmunotherapy in soft tissue sarcoma.Skeletal radiology · 2026Article
- Radiomics of soft tissue sarcoma metastases to assess prognostic factors related to intrapatient intertumor heterogeneity.Translational cancer research · 2026Article
- Development and temporal validation of a machine learning-based model to predict postoperative recurrence and guide adjuvant radiotherapy in patients with soft tissue sarcoma: a retrospective cohort study.American journal of cancer research · 2026Article
- Review
- Toward New Assessment in Sarcoma Identification and Grading Using Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2025Article
- Radiomics model for predicting distant metastasis in soft tissue sarcoma of the extremities and trunk treated with surgery.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Local relapse patterns after preoperative radiotherapy of limb and trunk wall soft tissue sarcomas: Prognostic role of imaging and pathologic response factors.Clinical and translational radiation oncology · 2024Article
- The impact of radiomics in the management of soft tissue sarcoma.Discover oncology · 2024Review
- CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies.Insights into imaging · 2024Article
- Prediction of histopathologic grades of myxofibrosarcoma with radiomics based on magnetic resonance imaging.Journal of cancer research and clinical oncology · 2023Article
- The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy.Cancer communications (London, England) · 2022Review
- Prediction tools for the personalized management of soft-tissue sarcomas of the extremity.The bone & joint journal · 2022Review
Corrections and comments
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Authors and funding
13 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo create a prognostic prediction radiomics model for soft tissue sarcoma (STS) of the extremities and trunk treated with neoadjuvant radiotherapy.
methodsThis study included 62 patients with STS of the extremities and trunk who underwent magnetic resonance imaging (MRI) before neoadjuvant radiotherapy. After tumour segmentation and preprocessing, 851 radiomics features were extracted. The radiomics score was constructed according to the least absolute shrinkage and selection operator (LASSO) method. Survival analysis (disease-free survival; DFS) was performed using the log-rank test and Cox's proportional hazards regression model. The nomogram model was established based on the log-rank test and Cox regression model. Harrell's concordance index (C-index), calibration curve and receiver operating characteristic (ROC) curve analysis were used to evaluate the prognostic factors. The clinical utility of the model was assessed by decision curve analysis (DCA).
resultsThe univariate survival analysis showed that tumour location (p = 0.032), clinical stage (p = 0.022), tumour size (p = 0.005) and the radiomics score were correlated with DFS (p < 0.05). The multivariate analysis showed that tumour location, tumour size, and the radiomics score were independent prognostic factors for DFS (p < 0.05). The combined clinical-radiomics model based on the multivariate analysis showed the best predictive ability for DFS (C-index: 0.781; Area Under Curve: 0.791). DCA revealed that the use of the radiomics score-based nomogram was associated with better benefit gains relative to the prediction of 2-year DFS events than other models in the threshold probability range between 0.12 and 0.38.
conclusionThe radiomics score from pretreatment MRI is an independent prognostic factor for DFS in patients with STS of the extremities and trunk. The radiomics score-based nomogram could improve prognostic stratification ability and thus contribute to individualized therapy for STS patients.
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