ArticleCancer imaging : the official publication of the International Cancer Imaging Society2025
Building a pre-surgical multiparametric-MRI-based morphologic, qualitative, semiquantitative, first and high-order radiomic predictive treatment response model for undifferentiated pleomorphic sarcoma to replace RECIST.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2025. 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.
- Diagnostic performance of diffusion-weighted imaging with quantitative apparent diffusion coefficient for detecting residual or recurrent hepatocellular carcinoma after TACE: a meta-analysis.Abdominal radiology (New York) · 2026Article
- Evaluation of magnetic resonance imaging parameters and compliance with guidelines in soft tissue sarcomas.BMC cancer · 2026Article
- Soft Tissue Pseudomyogenic Hemangioendothelioma in the Buttock: A Case Report.Current medical imaging · 2026Article
- Interpretable radiomics-based machine learning model for differentiating glioblastoma from primary central nervous system lymphoma using contrast-enhanced T1-weighted imaging.Scientific reports · 2025Article
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Abstract
backgroundUndifferentiated pleomorphic sarcoma (UPS) is the largest subgroup of soft-tissue sarcomas. It demonstrates post-therapeutic hemosiderin deposition, granulation tissue formation, fibrosis, and calcification. Our research aims to establish the multiparametric MRI (mp-MRI) value for predicting UPS treatment response.
methodsAn IRB-approved retrospective study included 33 extremity UPS patients with pre-operative mp-MRI, including diffusion-weighted imaging (DWI), contrast-enhanced susceptibility-weighted imaging (CE-SWI), and perfusion-weighted imaging with dynamic contrast-enhancement (PWI/DCE), and surgical resection between February 2021 and May 2023. Lesions were visually classified on CE-SWI into one of 6 morphology patterns. On PWI/DCE, lesions were classified into one of 6 patterns, and time-intensity curves (TICs) were classified as types I-V. Patients were categorized into three groups based on the percentage of pathology-assessed treatment effect (PATE) in the surgical specimen: Responders (> = 90% PATE, n = 16), partial-responders (31-89% PATE, n = 10), and non-responders (< = 30% PATE, n = 7).
resultsAt post-radiation therapy (PRT), a CE-SWI Complete-Ring pattern was observed in 71% of responders (p = 7.71 × 10
conclusionMp-MRI-derived features are valuable in assessing UPS treatment response. A pre-operative model that combines PWI/DCE TIC-type II, PWI/DCE Capsular pattern, and CE-SWI Complete Ring pattern can reliably predict successfully treated UPS with > = 90% PATE, outperforming RECIST, which was proven unreliable in separating responders from partial/non-responders. Institutions that have not yet implemented CE-SWI can rely on a single-sequence approach based on PWI/DCE, combining the presence of TIC II and Capsular enhancement as criteria for response prediction.
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