Erschienen in:
14.06.2023 | Oncology
The feasibility of contrast-enhanced CT to identify the adhesive renal venous tumor thrombus of renal cell carcinoma
verfasst von:
Xiaoxiao Zhang, Jincai Zhang, Gumuyang Zhang, Lili Xu, Xin Bai, Jiahui Zhang, Li Chen, Qianyu Peng, Zhengyu Jin, Hao Sun
Erschienen in:
European Radiology
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Ausgabe 11/2023
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Abstract
Objective
To identify adhesive renal venous tumor thrombus (RVTT) of renal cell carcinoma (RCC) by contrast-enhancement CT (CECT).
Materials and methods
Our retrospective study included 53 patients who underwent preoperative CECT and pathologically confirmed RCC combined with RVTT. They were divided into two groups based on the intra-operative findings of RVTT adhesion to the venous wall, with 26 cases in the adhesive RVTT group (ARVTT) and 27 cases in the non-adhesive group (NRVTT). The location, maximum diameter (MD) and CT values of tumors, the maximum length (ML) and width (MW) of RVTT, and length of inferior vena cava tumor thrombus were compared between the two groups. The presence of renal venous wall involvement, renal venous wall inflammation, and enlarged retroperitoneal lymph node was compared between the two groups. A receiver operating characteristic curve was used to analyze the diagnostic performance.
Results
The MD of RCC and the ML and MW of the RVTT were all larger in the ARVTT group than in the NRVTT group (p = 0.042, p < 0.001, and p = 0.002). The proportion of renal vein wall involvement and renal vein wall inflammation were higher in the ARVTT group than in NRVTT groups (both p < 0.001). The multivariable model including ML and vascular wall inflammation to predict ARVTT could achieve the best diagnostic performance with the area under the curve, sensitivity, specificity, and accuracy of 0.91, 88.5%, 96.3%, and 92.5%, respectively.
Conclusion
The multivariable model acquired by CECT images could be used to predict RVTT adhesion.
Clinical relevance statement
For RCC patients with tumor thrombus, contrast-enhanced CT could noninvasively predict the adhesion of tumor thrombus, thus predicting the difficulty of surgery and contributing to the selection of an appropriate treatment plan.
Key Points
• The length and width of the tumor thrombus could be used to predict its adhesion to the vessel wall.
• Adhesion of the tumor thrombus can be reflected by inflammation of the renal vein wall.
• The multivariable model from CECT can well predict whether the tumor thrombus adhered to the vein wall.