A Predictive Model for Tumor Invasion of the Inferior Vena Cava Wall Using Multimodal Imaging in Patients with Renal Cell Carcinoma and Inferior Vena Cava Tumor Thrombus.

ABSTRACT: Purpose:Developed a preoperative prediction model based on multimodality imaging to evaluate the probability of inferior vena cava (IVC) vascular wall invasion due to tumor infiltration. Materials and Methods:We retrospectively analyzed the clinical data of 110 patients with renal cell carcinoma (RCC) with level I-IV tumor thrombus who underwent radical nephrectomy and IVC thrombectomy between January 2014 and April 2019. The patients were categorized into two groups: 86 patients were used to establish the imaging model, and the data validation was conducted in 24 patients. We measured the imaging parameters and used logistic regression to evaluate the uni- and multivariable associations of the clinical and radiographic features of IVC resection and established an image prediction model to assess the probability of IVC vascular wall invasion. Results:In all of the patients, 46.5% (40/86) had IVC vascular wall invasion. The residual IVC blood flow (OR 0.170 [0.047-0.611]; P = 0.007), maximum coronal IVC diameter in mm (OR 1.203 [1.065-1.360]; P = 0.003), and presence of bland thrombus (OR 3.216 [0.870-11.887]; P = 0.080) were independent risk factors of IVC vascular wall invasion. We predicted vascular wall invasion if the probability was >42% as calculated by: {Ln?[Pre/(1 - pre)] = 0.185 × maximum?cornal?IVC?diameter + 1.168 × bland?thrombus-1.770 × residual?IVC?blood?flow-5.857}. To predict IVC vascular wall invasion, a rate of 76/86 (88.4%) was consistent with the actual treatment, and in the validation patients, 21/26 (80.8%) was consistent with the actual treatment. Conclusions:Our model of multimodal imaging associated with IVC vascular wall invasion may be used for preoperative evaluation and prediction of the probability of partial or segmental IVC resection.

SUBMITTER: Liu Z 

PROVIDER: S-EPMC7563051 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature