Development of a Nomogram Model to Identify Appropriate Candidates from Cytoreductive Nephrectomy in Metastatic Renal Cell Carcinoma Based on the Surveillance, Epidemiology and End Results Database

Although a survival benefit was observed in patients with metastatic renal cell carcinoma (mRCC) who underwent cytoreductive nephrectomy (CN), there is a lack of effective tools for predicting which individuals are likely to benefit from surgical intervention. Herein, we developed a predictive model using data from the Surveillance, Epidemiology, and End Results (SEER) database. Patients diagnosed with mRCC were screened from the SEER database (2010-2020), supplemented by patients from East Asia. Patients were categorized into surgical and non-surgical groups, with propensity score matching conducted to balance baseline characteristics. Logistic regression analysis was performed to identify independent factors associated with benefits and a nomogram was constructed based on these factors. This study included 11,044 cases from the SEER database and 50 cases from an external validation cohort. CN was identified as an independent protective factor for OS. A nomogram was established, and it performed well in the training and validation sets. The calibration curves and DCA confirmed that the nomogram model could precisely predict the probability of surgical benefit. We used the nomogram to classify surgical patients into benefit and non-benefit groups. Then, we found that OS was significantly higher in the benefit group than in the non-benefit group. The external validation cohort observed the same result (P=0.035). While CN offers potential benefits for patients with mRCC, its applicability varies across the patient population. Our study constructed a nomogram that quantitatively assesses the likelihood of surgical benefit in mRCC patients, facilitating more tailored therapeutic decision-making. Our study showed that cytoreductive nephrectomy (CN) improved prognosis in metastatic renal cell carcinoma, that only specific patients would benefit from CN. There are still no prediction tools to identify which patients will benefit. Consequently, we aimed to develop a nomogram using data from the Surveillance, Epidemiology, and End Results database to identify appropriate candidates with mRCC who could benefit from CN.