Primary renal sarcomas: imaging features and discrimination from non-sarcoma renal tumors

Abstract

Objectives To assess imaging features of primary renal sarcomas in order to better discriminate them from non-sarcoma renal tumors. Methods Adult 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. Results n = 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. Conclusion Renal 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%).

Epidemiology, treatment and outcomes of primary renal sarcomas in adult patients

“…While renal tumors with classical radiological findings often undergo upfront surgical resection, biopsies might be benefitial in patients presenting with atypical renal tumors. For example, patients with very large, heterogeneous renal tumors and infiltration of adjacent organs have a higher probability of renal sarcoma, as reported in an earlier study, and might thus benefit from a tumor biopsy 25 .…”

Primary renal synovial sarcoma with negative immunohistochemistry: a case report and literature review

“…At MRI shows mixed areas of low, intermediate, and high T2 signal intensity; hypointense on T1-weighted images with areas of hemorrhage, fluid levels, and septa; small renal sarcomas can resemble skeletal muscle on T1-wigthed images [24,29,[35][36][37][38]. The main characteristics which help distinguish a renal sarcoma vs non-sarcoma mass are in truth not so specifics and are substantially the usuals traits of malignant neoplasms: right sided, larger than non-sarcoma renal tumors and has a more irregular tumor shape and margins, showing also local aggressive growth and vessels infiltration [40,41]. Uhlig et al have suggested a diagnostic radiological imaging algorithm for the prediction of the histotype of genitourinary tract sarcomas with good results, but the same authors conclude that the only specific imaging regard more fat proportion in liposarcomas versus Ewing's sarcoma and leiomyosarcoma [40].…”

Radiomics-based machine learning approach for the prediction of grade and stage in upper urinary tract urothelial carcinoma: a step towards virtual biopsy

“…Advanced imaging techniques, such as CT and MRI, provide a wealth of information otherwise impossible by traditional 2D histopathology. It is prudent to note here that the complex datasets delivered by such advanced imaging modalities also need thorough analysis, and that is where a radiomics-based machine learning approach holds its major advantages 26 .…”