ArticleLa Radiologia medica2024
Dimensional assessment on baseline MRI of soft-tissue sarcomas: longest diameter, sum and product of diameters, and volume-which is the best measurement method to predict patients' outcomes?
Article in La Radiologia medica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Imaging of ewing sarcoma: an updated analysis including presenting features, prognostic imaging biomarkers, and treatment response assessment.Skeletal radiology · 2026Review
- Imaging Analysis for Metastatic Risk Assessment in Adamantinoma: The Aid of Radiology in the Absence of a Histological Grading-An MRI-Based Risk Model Proposal.Diagnostics (Basel, Switzerland) · 2025Article
- Clear Cell Sarcoma of Soft Tissues: Radiological Analysis of 14 Patients-MRI Findings Related to Metastatic Disease.Diagnostics (Basel, Switzerland) · 2025Article
- Review
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11 authors.
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Abstract
purposeThe longest diameter (LD) is a strong prognostic factor for patients with soft-tissue sarcoma (STS). Other dimensional assessments, such as the sum of diameters (SoD), product of diameters (PoD), and volume (3D-COG - proposed by the Children Oncology Group), can be rapidly performed; however, their prognostic values have never been compared to LD. Our goal was to investigate their performance in improving patients' prognostication for STS of the lower limbs.
methodsAll consecutive adults managed with curative intent at our sarcoma reference center for a newly diagnosed STS of the lower limbs between 2000 and 2017, with pre-treatment MRI, were included in this retrospective study. Multivariable Cox regression models were trained to predict metastasis-free survival (MFS) in a Training cohort of 66.7% patients based on LD, PoD, SoD, or 3D-COG (and systematically including age, histologic grade, histotype, radiotherapy, chemotherapy, and surgical margins as covariables). The models were then compared on a validation cohort of 33.3% patients using concordance indices (c-index). The same approach was applied for overall survival (OS) and local relapse-free survival (LFS). Measurement reproducibility among three readers was evaluated with an intraclass correlation coefficient (ICC).
results382 patients were included in the survival modeling (72/253 [28.5%] metastatic relapses in Training and 36/129 [27.9%] metastatic relapses in Validation). Higher dimensions were associated with lower MFS (multivariable hazard ratio [HR] = 2.44 and P = 0.0018 for LD; HR = 1.88 and P = 0.0009 for PoD, HR = 1.52 and P = 0.0041 for SoD; and HR = 1.08 and P = 0.0195 for 3D-COG). Higher c-indices were obtained with PoD model in Training (c-index = 0.772) and Validation (c-index = 0.688), but they were not significantly higher than those obtained with LD model. None of the measurements was associated with LFS or OS. All measurements demonstrated excellent ICC (> 0.95).
conclusionRegarding its simplicity and good performance, LD appeared as the best metric to incorporate in prognostic models and nomograms for MFS.
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