ArticleEuropean radiology2022
Primary renal sarcomas: imaging features and discrimination from non-sarcoma renal tumors.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- Multimodal artificial intelligence model based on CT for differentiating primary renal sarcomas from renal cell carcinomas: a dual-center retrospective study.Translational andrology and urology · 2025Article
- Primary renal synovial sarcoma with synchronous pulmonary metastases: A rare case report and review of diagnostic and management strategies.Urology case reports · 2025Article
- Human immunodeficiency virus-related renal cell carcinoma: a 13.5-year experience.AIDS research and therapy · 2025Article
- Primary osteosarcoma of the kidney: A case report.Oncology letters · 2024Article
- Review
- Epidemiology, treatment and outcomes of primary renal sarcomas in adult patients.Scientific reports · 2024Article
- PI3K/AKT/mTOR Dysregulation and Reprogramming Metabolic Pathways in Renal Cancer: Crosstalk with the VHL/HIF Axis.International journal of molecular sciences · 2023Review
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14 authors at 12 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo assess imaging features of primary renal sarcomas in order to better discriminate them from non-sarcoma renal tumors.
methodsAdult patients diagnosed with renal sarcomas from 1995 to 2018 were included from 11 European tertiary referral centers (Germany, Belgium, Turkey). Renal sarcomas were 1:4 compared to patients with non-sarcoma renal tumors. CT/MRI findings were assessed using 21 predefined imaging features. A random forest model was trained to predict "renal sarcoma vs. non-sarcoma renal tumors" based on demographics and imaging features.
resultsn = 34 renal sarcomas were included and compared to n = 136 non-sarcoma renal tumors. Renal sarcomas manifested in younger patients (median 55 vs. 67 years, p < 0.01) and were more complex (high RENAL score complexity 79.4% vs. 25.7%, p < 0.01). Renal sarcomas were larger (median diameter 108 vs. 43 mm, p < 0.01) with irregular shape and ill-defined margins, and more frequently demonstrated invasion of the renal vein or inferior vena cava, tumor necrosis, direct invasion of adjacent organs, and contact to renal artery or vein, compared to non-sarcoma renal tumors (p < 0.05, each). The random forest algorithm yielded a median AUC = 93.8% to predict renal sarcoma histology, with sensitivity, specificity, and positive predictive value of 90.4%, 76.5%, and 93.9%, respectively. Tumor diameter and RENAL score were the most relevant imaging features for renal sarcoma identification.
conclusionRenal sarcomas are rare tumors commonly manifesting as large masses in young patients. A random forest model using demographics and imaging features shows good diagnostic accuracy for discrimination of renal sarcomas from non-sarcoma renal tumors, which might aid in clinical decision-making. KEY POINTS: • Renal sarcomas commonly manifest in younger patients as large, complex renal masses. • Compared to non-sarcoma renal tumors, renal sarcomas more frequently demonstrated invasion of the renal vein or inferior vena cava, tumor necrosis, direct invasion of adjacent organs, and contact to renal artery or vein. • Using demographics and standardized imaging features, a random forest showed excellent diagnostic performance for discrimination of sarcoma vs. non-sarcoma renal tumors (AUC = 93.8%, sensitivity = 90.4%, specificity = 76.5%, and PPV = 93.9%).
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