Impact of margin status on survival after radical nephrectomy for renal cell carcinoma

Partial and Radical Nephrectomy Provides Equivalent Oncologic Outcomes in pT3a Renal Cell Carcinoma: A Population-Based Study

Purpose

To compare the cause-specific survival (CSS) and overall survival (OS) of patients with localized T3a renal cell carcinoma (RCC) after partial nephrectomy (PN) or radical nephrectomy (RN).

Methods

We obtained the demographic and clinicopathological data of 7,127 patients with localized T3a RCC and who underwent PN or RN from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were divided into fat invasion cohort and venous invasion cohort for subsequent analysis. Kaplan–Meier analysis (KMA) and univariate and multivariate Cox proportional hazards regression analyses were used to evaluate the effects of PN or RN on OS and CSS. Meanwhile, 65 cases with clinical T1 (cT1) RCC upstaged to pathological T3a (pT3a) who were treated in Tongji Hospital (TJH) from 2011 to 2020 and underwent PN or RN were identified.

Results

In the study cohort, 2,085 (29.3%) patients died during the 1–172 months’ follow-up, of whom 1,155 (16.2%) died of RCC. In the two cohorts of fat invasion and venous invasion, KMA indicated that the PN group had favorable survival (p < 0.001). However, after propensity score matching (PSM), univariate and multivariate Cox regression analyses showed that the PN and RN groups had comparable CSS in the fat invasion cohort (p = 0.075) and the venous invasion cohort (p = 0.190). During 1–104 months of follow-up, 9 cases in the Tongji cohort had disease recurrence. There was no significant difference in recurrence-free survival between the RN group and the PN group (p = 0.170).

Conclusions

Our analysis showed that after balancing these factors, patients with localized pT3a RCC receiving PN or RN can achieve comparable oncologic outcomes. PN is safe for selected T3a patients.

Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

Background

Bone is a common target of metastasis in kidney cancer, and accurately predicting the risk of bone metastases (BMs) facilitates risk stratification and precision medicine in kidney cancer.

Methods

Patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database to comprise the training group from 2010 to 2017, and the validation group was drawn from our academic medical center. Univariate and multivariate logistic regression analyses explored the statistical relationships between the included variables and BM. Statistically significant risk factors were applied to develop a nomogram. Calibration plots, receiver operating characteristic (ROC) curves, probability density functions (PDF), and clinical utility curves (CUC) were used to verify the predictive performance. Kaplan-Meier (KM) curves demonstrated survival differences between two subgroups of kidney cancer with and without BMs. A convenient web calculator was provided for users via “shiny” package.

Results

A total of 43,503 patients were recruited in this study, of which 42,650 were training group cases and 853 validation group cases. The variables included in the nomogram were sex, pathological grade, T-stage, N-stage, sequence number, brain metastases, liver metastasis, pulmonary metastasis, histological type, primary site, and laterality. The calibration plots confirmed good agreement between the prediction model and the actual results. The area under the curve (AUC) values in the training and validation groups were 0.952 (95% CI, 0.950–0.954) and 0.836 (95% CI, 0.809–0.860), respectively. Based on CUC, we recommend a threshold probability of 5% to guide the diagnosis of BMs.

Conclusions

The comprehensive predictive tool consisting of nomogram and web calculator contributes to risk stratification which helped clinicians identify high-risk cases and provide personalized treatment options.