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

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%).

Авторы
Uhlig J.1, 2 , Bachanek S.1 , Uhlig A. 3, 4 , Onur M.R.5 , Kinner S.6 , Geisel D.7 , Köhler M.8 , Preibsch H.9 , Puesken M.10 , Schramm D.11 , Surov A.11, 16, 17 , May M.12 , De Visschere P.13 , Weber M.A.14, 15
Журнал
Издательство
Springer-Verlag GmbH
Язык
Английский
Статус
Опубликовано
Год
2021
Организации
  • 1 Department of Diagnostic and Interventional Radiology|University Medical Center Goettingen
  • 2 Section of Interventional Radiology|Yale School of Medicine
  • 3 Department of Urology
  • 4 Institute of Urologic Oncology|University of California at Los Angeles
  • 5 Department of Radiology|University of Hacettepe School of Medicine
  • 6 Institute for Diagnostic and Interventional Radiology|University of Essen
  • 7 Department of Radiology|Charité
  • 8 Department of Radiology|University of Muenster
  • 9 Department of Radiology|University of Tuebingen
  • 10 Department of Diagnostic and Interventional Radiology|University Hospital Cologne
  • 11 Department of Radiology|Martin-Luther-University Halle-Wittenberg
  • 12 Department of Radiology and Nuclear Medicine|Otto-von-Guericke University
  • 13 Department of Radiology|University of Leipzig
  • 14 Department of Radiology|University Hospital Erlangen
  • 15 Department of Radiology and Nuclear Medicine|Division of Genitourinary Radiology and Mammography|Ghent University Hospital
  • 16 Institute of Diagnostic and Interventional Radiology|Pediatric Radiology and Neuroradiology University Medical Center of Rostock
  • 17 Department of Diagnostic and Interventional Radiology|University Hospital of Heidelberg
Ключевые слова
machine learning; Radiological imaging; renal cancer; Renal sarcoma
Дата создания
16.12.2021
Дата изменения
16.12.2021
Постоянная ссылка
https://repository.rudn.ru/ru/records/article/record/79452/
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Другие записи

Kochetkov D., Birukou A., Ermolayeva A.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Том 12602 LNCS. 2021. С. 369-378