MP73-07 PREDICTION OF LYMPH NODE INVASION IN PATIENTS WITH RENAL CELL CARCINOMA: RESULTS FROM A LARGE INTERNATIONAL CONSORTIUM

Abstract

You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging I1 Apr 2016MP73-07 PREDICTION OF LYMPH NODE INVASION IN PATIENTS WITH RENAL CELL CARCINOMA: RESULTS FROM A LARGE INTERNATIONAL CONSORTIUM Paolo Dell'Oglio, Grant Stewart, Tobias Klatte, Alessandro Volpe, Bulent Akdogan, Marco Roscigno, Hans Langenhuijsen, Martin Marszalek, Oscar Rodriguez Faba, Maciej Salagierski, Andrea Minervini, Sabine Brookman-May, and Umberto Capitanio Paolo Dell'OglioPaolo Dell'Oglio More articles by this author , Grant StewartGrant Stewart More articles by this author , Tobias KlatteTobias Klatte More articles by this author , Alessandro VolpeAlessandro Volpe More articles by this author , Bulent AkdoganBulent Akdogan More articles by this author , Marco RoscignoMarco Roscigno More articles by this author , Hans LangenhuijsenHans Langenhuijsen More articles by this author , Martin MarszalekMartin Marszalek More articles by this author , Oscar Rodriguez FabaOscar Rodriguez Faba More articles by this author , Maciej SalagierskiMaciej Salagierski More articles by this author , Andrea MinerviniAndrea Minervini More articles by this author , Sabine Brookman-MaySabine Brookman-May More articles by this author , and Umberto CapitanioUmberto Capitanio More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2016.02.1662AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Few models predicting the presence of lymph node invasion (LNI) in patients with renal cell carcinoma (RCC) are available. In this study, we tested the ability of LNI risk estimation relying on clinically attainable variables. METHODS Between 1987 and 2014, 4,948 RCC patients treated with either partial or radical nephrectomy within a multi-institutional cohort were identified. Multivariable logistic regression analyses were used to test the accuracy of all the available clinical characteristics in predicting LNI. A nomogram predicting the probability of LNI was constructed using the logistic regression-derived coefficients. Log transformation was applied for clinical tumor size after non-linearity analysis. Calibration plot and leave-one-out cross validation (LOOCV) were used for internal validation. RESULTS Overall, 204 patients (4.1%) had LNI. In multivariable analyses, symptoms at diagnosis (OR: 1.64; p<0.006), clinical tumor size (OR: 1.11; p<0.001), non-organ confined (OR: 2.65; p<0.001), clinical LNI (OR: 15.6; p<0.001) and presence of clinical metastases (OR: 2.4; p<0.001) were each significantly associated with the risk of LNI. The curve depicting the relationship between predicted and observed LNI closely approximates the ideal predictions, which indicates excellent calibration. In LOOCV, the C-index of our model was 92.1%. Using a 5% nomogram cut-off, 4.346 of 4.948 patients (87.8%) would be spared lymph-node dissection (LND) and LNI would be missed in 39 patients (0.9%, 19.1% of all LNI). The sensitivity, specificity, and negative predictive value associated with the 5% cut-off were 80.9%, 90.8%, and 99.1%, respectively. To minimize the number of LNI patients missed, a 1% nomogram cut-off may be considered, allowing to spare 57% of LND and missing only 7 LNI patients (0.1%, 3% of all LNI cases). CONCLUSIONS We developed and internally validated a tool capable of highly accurately predicting LNI in RCC patients. This accurate tool could be useful for patient counseling and risk stratification at medical decision-making. Based on our model, patients with a LNI risk < 1% may be safely spared LND. Given the number of LNI cases missed when higher cutoffs were considered, further studies aimed at identifying accurate biomarkers of hidden LNI are urgently needed, especially in low-stage disease. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e962-e963 Advertisement Copyright & Permissions© 2016MetricsAuthor Information Paolo Dell'Oglio More articles by this author Grant Stewart More articles by this author Tobias Klatte More articles by this author Alessandro Volpe More articles by this author Bulent Akdogan More articles by this author Marco Roscigno More articles by this author Hans Langenhuijsen More articles by this author Martin Marszalek More articles by this author Oscar Rodriguez Faba More articles by this author Maciej Salagierski More articles by this author Andrea Minervini More articles by this author Sabine Brookman-May More articles by this author Umberto Capitanio More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...