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Updated: Mar 15, 2026

A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies
Published on: April 12, 2017
The Staging of High-risk and Non-metastatic Renal Cell Carcinoma Preoperatively Model Predicts Pathologic T3 Stage
Paz Lotan1, Andrew L Wentland2, Daniel D Shapiro3
1Department of Urology, School of Medicine and Public Health, University of Wisconsin, Madison, WI.
Objective:
To develop a diagnostic model using preoperative abdominal CT scans that identifies pathologic T3 (pT3) stage renal cell carcinoma among patients with tumors >7 cm. Neoadjuvant clinical trials often use tumor size >7 cm for enrollment to enrich the study population with higher-risk patients. However, T stage is also an important prognostic factor, and stage pT3 tumors are associated with a significantly higher risk of metastatic progression.
Methods:
Data were analyzed for consecutive patients with non-metastatic renal tumors >7 cm treated with radical nephrectomy from 2000 to 2024. Univariable and multivariable analyses were used to evaluate radiographic features for associations with pT3. A predictive model was developed, and its accuracy and clinical utility were assessed.
Results:
A total of 326 patients with preoperative contrasted CT scans were identified. The median radiographic tumor diameter was 9.1 cm, and 59% had a pT3 stage. Four radiographic tumor characteristics were independently predictive and included in the model: 1) irregular tumor-fat interface, 2) attenuation difference of ≥15HU between the peritumor fat and contralateral side, 3) peritumor neovascularity, and 4) venous tumor thrombus. ROC curves demonstrated predictive accuracy with an AUC of 0.84. Decision curve analysis indicated clinical utility across a wide range of threshold probabilities.
Conclusion:
The Staging of High-risk And non-metastatic Renal cell carcinoma Preoperatively model improves the ability to identify stage pT3 in renal tumors >7 cm using preoperative imaging compared to size alone. This model may be used to improve neoadjuvant clinical trial design.
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