Abstract
Abstract only
610
Background: Personalized treatment for clinical T1 renal cortical masses (RCMs) should account for competing risks related to tumor and patient characteristics. Using a contemporary multi-institutional cohort, we developed treatment-specific prediction models for cancer-specific mortality (CSM), other-cause mortality (OCM), and 90-day complication rates for patients managed with surgery, thermal ablation (TA), and active surveillance (AS). Methods: Preoperative clinical and radiological features were collected for eligible patients aged 18-91 years treated at four academic centers from 2000-2016. Prediction models used competing risks regressions for CSM and OCM and logistic regressions for 90-day Clavien >3 complications, adjusting for tumor size as well as patient age, sex, ECOG performance status (PS), and Charlson comorbidity index (CCI). Predictions accounted for missing data using multiple imputation. Results: After excluding 25 patients with no follow-up, the cohort included 4995 patients treated with radical nephrectomy (RN, n=1270), partial nephrectomy (PN, n=2842), thermal ablation (n=479), or active surveillance (n=404). Median follow-up was 5.1 years (IQR 2.5-8.5). Predictions from the fitted model are shown in an online calculator ( https://rgulati.shinyapps.io/rcc-risk-calculator ). To illustrate the use of this calculator for a specific patient, a 70-year-old female with a 5.5 cm RCM, PS of 2, and CCI of 3 has a predicted 5-year CSM of 4-7% across treatments, 5-year OCM of 34-49%, and 90-day risk of Clavien ≥3 complications of 4%, 10%, and 6% for RN, PN, and TA respectively. Conclusions: Personalized treatment selection for cT1 RCM is challenging. We present a competing risk calculator that incorporates pretreatment features to quantify competing causes of mortality and treatment-associated complications. Pending validation, this tool may be used in clinical practice to provide patients with estimated individualized treatment-specific probabilities of competing causes of death and complication risks to facilitate shared decision-making.