ArticleEBioMedicine2019
Tumor grading of soft tissue sarcomas using MRI-based radiomics.
Article in EBioMedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03798795 (Development of an MRI-based Radiomic Model for Non-invasive Tumor Grading of Soft Tissue Sarcomas.), which is not on this map. Cited by 69 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Development of an MRI-based Radiomic Model for Non-invasive Tumor Grading of Soft Tissue Sarcomas.
Who cites it
69 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Bayesian approach for diagnostic accuracy of malignant peripheral nerve sheath tumors: a systematic review and meta-analysis.Neuro-oncology · 2021Pooled it
- MRI-Based radiomic signature for predicting pathologic treatment response to neoadjuvant chemoradiotherapy and radioimmunotherapy in soft tissue sarcoma.Skeletal radiology · 2026Article
- Redefining Soft Tissue Sarcoma Imaging: The Expanding Role of Positron Emission Tomography/Computed Tomography: A Review.World journal of nuclear medicine · 2026Review
- Diagnosis and Management of Soft-Tissue Sarcomas: Current Insights.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026Review
- Toward clinical value, not just accuracy: deep learning segmentation of soft tissue sarcoma.Skeletal radiology · 2026Article
- MRI features outperform histologic grade for outcome prediction in translocation-driven enriched soft-tissue sarcoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Interpretable Deep Learning Radiomics Model for Preoperative Prediction of High-Grade Soft Tissue Sarcomas: A Multicenter MRI Study.Journal of imaging informatics in medicine · 2026Article
- A Predictive MRI Radiomics Model for Histologic Differentiation in Soft Tissue Sarcomas.Cancers · 2026Article
- Radiomics identifies distinct cortical bone texture alterations in patients with CKD using HR-pQCT.Bone research · 2026Article
- [European journal of nuclear medicine and molecular imaging · 2026Article
- Radiomics of soft tissue sarcoma metastases to assess prognostic factors related to intrapatient intertumor heterogeneity.Translational cancer research · 2026Article
- Bio-interpretable ensemble learning model for invasive pulmonary adenocarcinoma grade using CT and histopathology images.NPJ precision oncology · 2025Article
- Prediction of Histopathological Grade of Hepatocellular Carcinoma by Gadoxetic Acid-Enhanced Magnetic Resonance Imaging Radiomics Features.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2025Article
- Biological Interpretable Machine Learning Model for Predicting Pathological Grading in Clear Cell Renal Cell Carcinoma Based on CT Urography Peritumoral Radiomics Features.Bioengineering (Basel, Switzerland) · 2025Article
- B-mode ultrasound and contrast-enhanced ultrasound-based radiomics interpretable analysis for the prediction of macrotrabecular-massive subtype of hepatocellular carcinoma.The ultrasound journal · 2025Article
- Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care.Diagnostics (Basel, Switzerland) · 2025Review
- Heterogeneity in response to neoadjuvant radiotherapy between soft tissue sarcoma histotypes: associations between radiology and pathology findings.European radiology · 2025Article
- Review
- Diagnostic utility of MRI-based convolutional neural networks in soft tissue sarcomas: a mini-review.Frontiers in oncology · 2025Review
- Predicting the Malignancy Grade of Soft Tissue Sarcomas on MRI Using Conventional Image Reading and Radiomics.Diagnostics (Basel, Switzerland) · 2024Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTreatment decisions for multimodal therapy in soft tissue sarcoma (STS) patients greatly depend on the differentiation between low-grade and high-grade tumors. We developed MRI-based radiomics grading models for the differentiation between low-grade (G1) and high-grade (G2/G3) STS.
methodsThe study was registered at ClinicalTrials.gov (number NCT03798795). Contrast-enhanced T1-weighted fat saturated (T1FSGd), fat-saturated T2-weighted (T2FS) MRI sequences, and tumor grading following the French Federation of Cancer Centers Sarcoma Group obtained from pre-therapeutic biopsies were gathered from two independent retrospective patient cohorts. Volumes of interest were manually segmented. After preprocessing, 1394 radiomics features were extracted from each sequence. Features unstable in 21 independent multiple-segmentations were excluded. Least absolute shrinkage and selection operator models were developed using nested cross-validation on a training patient cohort (122 patients). The influence of ComBatHarmonization was assessed for correction of batch effects.
findingsThree radiomic models based on T2FS, T1FSGd and a combined model achieved predictive performances with an area under the receiver operator characteristic curve (AUC) of 0.78, 0.69, and 0.76 on the independent validation set (103 patients), respectively. The T2FS-based model showed the best reproducibility. The radiomics model involving T1FSGd-based features achieved significant patient stratification. Combining the T2FS radiomic model into a nomogram with clinical staging improved prognostic performance and the clinical net benefit above clinical staging alone.
interpretationMRI-based radiomics tumor grading models effectively classify low-grade and high-grade soft tissue sarcomas. FUND: The authors received support by the medical faculty of the Technical University of Munich and the German Cancer Consortium.
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