ArticleAnnals of surgical oncology2025
Evaluation of Diagnostic Accuracy of Preoperative CT-Based Radiomics in Primary Retroperitoneal Sarcoma.
Article in Annals of surgical oncology, 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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Who cites it
4 citing papers in PubMed.
- A Predictive MRI Radiomics Model for Histologic Differentiation in Soft Tissue Sarcomas.Cancers · 2026Article
- Long-term survival with eribulin in an elderly patient with inoperable retroperitoneal well-differentiated liposarcoma: a case report and literature review.Frontiers in oncology · 2026Article
- Review
- CT-based radiomics nomogram for differentiating dedifferentiated liposarcoma from well-differentiated liposarcoma.Frontiers in oncology · 2025Article
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Authors and funding
12 authors.
Funding
Abstract
backgroundObtaining an accurate preoperative diagnosis in retroperitoneal sarcoma (RPS) is challenging, and radiomics-based models may offer a valid option to achieve this outcome. This study evaluated the accuracy of radiomics-based preoperative CT models at predicting tumour histology and grade in patients with primary RPS.
methodsData on consecutive patients who underwent surgery for primary retroperitoneal liposarcoma (RLPS) and leiomyosarcoma (RLMS) were analysed. Four different CT radiomics-based models were devised: 1) to distinguish between RLPS and RLMS; 2a) to predict overall tumour grade in both RLPS and RLMS; 2b and 2c) to predict tumour grade in RLPS and RMLS, respectively. The models were evaluated in a 100× random-split cross-validation.
resultsData were available for 100 patients (64 RLPS, 36 RLMS), with 34 RLPS and 22 RLMS patients having a high-grade tumour on final histology. No significant differences in terms of age (p = 0.46), sex (p = 0.13), tumour location (p = 0.52), tumour diameter (p = 0.16), or tumour volume (p = 0.45) were observed between high- and low-grade tumours. The resulting area under the curve (AUC) at distinguishing between RLPS and RLMS was 0.94. The AUC at differentiating between high- and low-grade tumours for both RLPS and RMLS was 0.74. When tumour grade was analysed separately the corresponding AUC for RLPS and RLMS was 0.87 and 0.61, respectively.
conclusionsRadiomics-based preoperative CT-models were demonstrated to be accurate at differentiating between RLMS and RLPS, and at predicting preoperative tumour grade in RLPS, whereas they performed poorly in RLMS.
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