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Assessing Treatment Response in Soft Tissue Sarcoma Using Dynamic Contrast-Enhanced MRI: A Systematic Review
Peyman Mirghaderi1, Nasim Eshraghi1, Hyeyun Lee2
1Department of Radiology, Division of Musculoskeletal Imaging and Intervention, University of Washington, Seattle, WA, USA.
Dynamic contrast-enhanced MRI (DCE-MRI) shows promise for assessing neoadjuvant therapy response in soft tissue sarcomas (STS). Quantitative parameters like Ktrans correlate with treatment outcomes, aiding in predicting patient response.
Area of Science:
- Radiology and Oncology
- Medical Imaging
- Soft Tissue Sarcoma Research
Background:
- Conventional size-based criteria inadequately assess neoadjuvant therapy (NAT) response in soft tissue sarcomas (STS).
- Accurate response assessment is crucial for tailoring NAT and improving patient outcomes in STS.
Purpose of the Study:
- To systematically review the association between dynamic contrast-enhanced MRI (DCE-MRI) parameters and histopathology-based NAT response in STS.
- To identify DCE-MRI parameters with consistent associations with treatment response across diverse clinical contexts.
Main Methods:
- Systematic review following PRISMA guidelines.
- Comprehensive literature search for studies evaluating NAT response in STS using DCE-MRI.
- Inclusion of 13 studies with 234 patients.
Main Results:
- Quantitative DCE-MRI parameters, especially Ktrans, demonstrated strong predictive performance and discriminatory ability for treatment response.
- Related parameters (ΔKtrans, Kep, Ve) correlated significantly with histopathologic necrosis.
- Semi-quantitative (iAUC60, wash-in rate) and qualitative (type II curves) markers showed consistent associations with tumor perfusion, hypoxia, and favorable outcomes.
Conclusions:
- DCE-MRI, particularly quantitative pharmacokinetic parameters like Ktrans, shows significant promise for assessing NAT response in STS.
- Parameter associations with histopathology-based response are generally concordant.
- Need for standardized prospective validation due to heterogeneity and small cohort sizes.
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