Clinical trial · Observational
Radiomics for Tumor Grading of Soft Tissue Sarcomas.
Development of an MRI-based Radiomic Model for Non-invasive Tumor Grading of Soft Tissue Sarcomas.
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
Radiomics is defined as a quantitative high-throughput analysis of imaging data combined with model development aiming to predict biological correlates or clinical endpoints. The investigators of this study hypothesize that radiomic features may correlate with pathology-defined tumor grading in soft tissue sarcoma patients. The aim of this study is to develop a predictive radiomics model for tumor grading determination.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Sarcoma, Soft Tissue | Soft Tissue Sarcoma | ONTOLOGY_EXACT | 0.90 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Pathological tumor grading
- timeFrame
- Baseline
- description
- Defined by the French Fédération Nationale des Centres de Lutte Contre le Cancer (FNCLCC)
Secondary outcomes (1)
- measure
- Overall Survival
- timeFrame
- From initial pathologic diagnosis to the time point of death or the time point of censoring up to 100 months.
- description
- Overall Survival
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: * Histologically proven soft tissue sarcoma * Available pre-therapeutic MRI with a contrast-enhanced T1 weight fat saturated sequence +/- fat saturated T2 sequences (e.g. STIR) Exclusion Criteria: * Indeterminate tumor grading * Osteosarcoma * Ewing Sarcoma * Endoprothesis-dependent MRI artifacts * Previous radiotherapy or chemotherapy * Lack of a contrast-enhanced T1 weight fat saturated MRI sequence
References
Publications (3)
- BACKGROUNDLiang W, Yang P, Huang R, Xu L, Wang J, Liu W, Zhang L, Wan D, Huang Q, Lu Y, Kuang Y, Niu T. A Combined Nomogram Model to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors. Clin Cancer Res. 2019 Jan 15;25(2):584-594. doi: 10.1158/1078-0432.CCR-18-1305. Epub 2018 Nov 5. PMID 30397175
- BACKGROUNDAerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014 Jun 3;5:4006. doi: 10.1038/ncomms5006. PMID 24892406
- DERIVEDPeeken JC, Spraker MB, Knebel C, Dapper H, Pfeiffer D, Devecka M, Thamer A, Shouman MA, Ott A, von Eisenhart-Rothe R, Nusslin F, Mayr NA, Nyflot MJ, Combs SE. Tumor grading of soft tissue sarcomas using MRI-based radiomics. EBioMedicine. 2019 Oct;48:332-340. doi: 10.1016/j.ebiom.2019.08.059. Epub 2019 Sep 12. PMID 31522983