PD51-07 PREDICTING THE HISTOPATHOLOGY RESULT OF POST-CHEMO RETROPERITONEAL LYMPH NODE DISSECTION (PC-RPLND)

Abstract

You have accessJournal of UrologyCME1 May 2022PD51-07 PREDICTING THE HISTOPATHOLOGY RESULT OF POST-CHEMO RETROPERITONEAL LYMPH NODE DISSECTION (PC-RPLND) Axel Gerdtsson, Anna Thor, Anna Grenabo Bergdahl, Helene Negaard, Bjarte Almås, Ingrid Glimelius, Åsa Karlsdottir, Dag Halvorsen, Kristine Engen Andreassen, Signe Melsen Larsen, Rolf Wahlqvist, Hege Saagstuen Haugnes, Torgrim Tandstad, Göran Holmberg, Gabriella Cohn Cedermark, Olof Ståhl, Gustav Thorison, and Anders Kjellman Axel GerdtssonAxel Gerdtsson More articles by this author , Anna ThorAnna Thor More articles by this author , Anna Grenabo BergdahlAnna Grenabo Bergdahl More articles by this author , Helene NegaardHelene Negaard More articles by this author , Bjarte AlmåsBjarte Almås More articles by this author , Ingrid GlimeliusIngrid Glimelius More articles by this author , Åsa KarlsdottirÅsa Karlsdottir More articles by this author , Dag HalvorsenDag Halvorsen More articles by this author , Kristine Engen AndreassenKristine Engen Andreassen More articles by this author , Signe Melsen LarsenSigne Melsen Larsen More articles by this author , Rolf WahlqvistRolf Wahlqvist More articles by this author , Hege Saagstuen HaugnesHege Saagstuen Haugnes More articles by this author , Torgrim TandstadTorgrim Tandstad More articles by this author , Göran HolmbergGöran Holmberg More articles by this author , Gabriella Cohn CedermarkGabriella Cohn Cedermark More articles by this author , Olof StåhlOlof Ståhl More articles by this author , Gustav ThorisonGustav Thorison More articles by this author , and Anders KjellmanAnders Kjellman More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002622.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: PC-RPLND for metastatic non-seminoma is a challenging procedure. In up to 50% of patients undergoing PC-RPLND, the histopathology results reveal necrosis/fibrosis, and the surgery was hence not necessary. Several different prediction models have been introduced with the aim of selecting the right patients for surgery. The aim of this study was to try to improve the most used model (by Vergouwe) by adding new variables and to test it on our Swedish Norwegian Testicular Cancer Group (SWENOTECA) RETROP data. RETROP is a population-based dataset of patients with non-seminoma that underwent PC-RPLND between 2007 and 2014. METHODS: Patients with Non-Seminoma Germ Cell Tumour (NSGCT) in Sweden and Norway that underwent PC-RPLND between 1st September 2007 and 1st September 2014 were included. Information for the study regarding teratoma in orchiectomy specimen, lymph node size pre and post chemotherapy, AFP, HCG and LDH levels before chemotherapy, chemotherapy given, Royal Marsden clinical stage, prognostic group according to the IGCCCG and histopathology results from PC-RPLND were obtained at time of surgery, from SWENOTECA register and chart review.Statistical analysisDiscrimination and calibration analyses were used to validate Vergouwes results. Calibration plots were created and Hosmer–Lemeshow test was calculated. Clinical utility expressed as Net benefit were analyzed using Decision curve analysis.The original algoritm was trained with Random Forest, a machine learning program. Additional data such as IGCCCG prognostic group, Royal Marsden clinical stage, number of chemotherapy courses, AFP, HCG and lymph node shrinkage as continuous variables with non-linear restricted cubic spines were added. RESULTS: In total 284 patients met the criteria to be included in this study.Discrimination analysis showed good reproducibility with AUC of 0.819 (95% CI 0.765 – 0.863) compared to Vergouwes original study in 2007 with AUC between 0.77 and 0.84. The calibration plot, as well as Hosmer-Lemeshow test (p = 0.44) showed good calibration.For patients with post-chemo lymph nodes between 10-19 mm, 14 % would be classified as false negatives using the model with a 70% prediction level.Machine learning did not improve the model. CONCLUSIONS: The model was validated in this material with good reproducibility. For clinical use the model needs further development with new variables. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e845 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Axel Gerdtsson More articles by this author Anna Thor More articles by this author Anna Grenabo Bergdahl More articles by this author Helene Negaard More articles by this author Bjarte Almås More articles by this author Ingrid Glimelius More articles by this author Åsa Karlsdottir More articles by this author Dag Halvorsen More articles by this author Kristine Engen Andreassen More articles by this author Signe Melsen Larsen More articles by this author Rolf Wahlqvist More articles by this author Hege Saagstuen Haugnes More articles by this author Torgrim Tandstad More articles by this author Göran Holmberg More articles by this author Gabriella Cohn Cedermark More articles by this author Olof Ståhl More articles by this author Gustav Thorison More articles by this author Anders Kjellman More articles by this author Expand All Advertisement PDF DownloadLoading ...