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• W4360607634 abstract "You have accessJournal of UrologyCME1 Apr 2023PD10-09 A NOVEL MODEL INTEGRATING CLINICAL, MP-MRI, AND EPIGENOMIC FEATURES TO PREDICT LYMPH NODE INVASION IN PROSTATE CANCER PATIENTS UNDERGOING RADICAL PROSTATECTOMY AND PELVIC LYMPH NODE DISSECTION Marco Bandini, Roberta Lucianò, Francesca Giannese, Giulia Maria Scotti, Caterina Oneto, Francesco Barletta, Giuseppe Ottone Cirulli, Vito Cucchiara, Armando Stabile, Elio Mazzone, Gabriele Sorce, Nazario Tenace, Federico Scarfò, Giorgio Brembilla, Antonio Esposito, Marco Morelli, Dejan Lazarevic, Francesco De Cobelli, Claudio Doglioni, Giovanni Tonon, Giorgio Gandaglia, Francesco Montorsi, and Alberto Briganti Marco BandiniMarco Bandini More articles by this author , Roberta LucianòRoberta Lucianò More articles by this author , Francesca GianneseFrancesca Giannese More articles by this author , Giulia Maria ScottiGiulia Maria Scotti More articles by this author , Caterina OnetoCaterina Oneto More articles by this author , Francesco BarlettaFrancesco Barletta More articles by this author , Giuseppe Ottone CirulliGiuseppe Ottone Cirulli More articles by this author , Vito CucchiaraVito Cucchiara More articles by this author , Armando StabileArmando Stabile More articles by this author , Elio MazzoneElio Mazzone More articles by this author , Gabriele SorceGabriele Sorce More articles by this author , Nazario TenaceNazario Tenace More articles by this author , Federico ScarfòFederico Scarfò More articles by this author , Giorgio BrembillaGiorgio Brembilla More articles by this author , Antonio EspositoAntonio Esposito More articles by this author , Marco MorelliMarco Morelli More articles by this author , Dejan LazarevicDejan Lazarevic More articles by this author , Francesco De CobelliFrancesco De Cobelli More articles by this author , Claudio DoglioniClaudio Doglioni More articles by this author , Giovanni TononGiovanni Tonon More articles by this author , Giorgio GandagliaGiorgio Gandaglia More articles by this author , Francesco MontorsiFrancesco Montorsi More articles by this author , and Alberto BrigantiAlberto Briganti More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003250.09AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Multivariable models should be used to identify prostate cancer (PCa) patients candidates for extended pelvic lymph node dissection (ePLND) during radical prostatectomy (RP) to spare unnecessary ePLNDs without missing lymph node invasion (LNI). Improving LNI detection in PCa is key in reducing ePLND-related morbidity. We hypothesized that LNI can be better predicted by integrating clinical, radiologic and epigenomic information. METHODS: We recruited 172 PCa patients with a risk of LNI >5% diagnosed by target+systematic biopsy undergoing RP+ePLND between 2014-2021. Epigenetic profiles of tumor DNA biopsy cores were sequenced via reduced representation bisulfite conversion. MethylKit R package assessed the percentage methylation differences among CpG sites of patients with and without LNI. A 50% cut-off methylation difference (50-MD) identified significant (False Discovery Rate <0.001) CpGs. Enrichment analysis tested for gene pathways that were expressed among patients with and without LNI by using differentially methylated (50-MD) CpGs. Analyses were performed for target and systematic biopsy samples independently. Two signatures were created from hypermethylated CpGs from target + systematic samples and integrated with PSA, mpMRI stage, and grade group at target biopsy to develop two models predicting LNI which underwent 500 internal train-test validations and were compared with existing tools. RESULTS: Overall, 37 patients (21.5%) had LNI. We identified 508 and 511 CpGs sites within target and systematic samples that were differentially methylated among patients with and without LNI. Gene pathways involved in the transcription of potassium channels were associated with LNI in target samples. The epigenetic signatures including only hypermethylated CpGs were correlated with LNI on univariable regression (target samples LogOdds 0.12, p<0.001; systematic samples LogOdds 0.08, p<0.001). Clinical and mpMRI variables (PSA, mpMRI stage, and ISUP grade group at target biopsy) were associated with LNI (all p≤0.01). An AUC of 86% and 83% was achieved for the target model and systematic model. Both models outperformed the previous versions of the Briganti nomogram at any LNI threshold risk. CONCLUSIONS: We developed two LNI prediction models that integrated clinical, mpMRI and epigenetic features which outperformed available tools. Epigenetic features from target tumor samples appeared to better predict LNI compared to their systematic counterparts. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e329 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Marco Bandini More articles by this author Roberta Lucianò More articles by this author Francesca Giannese More articles by this author Giulia Maria Scotti More articles by this author Caterina Oneto More articles by this author Francesco Barletta More articles by this author Giuseppe Ottone Cirulli More articles by this author Vito Cucchiara More articles by this author Armando Stabile More articles by this author Elio Mazzone More articles by this author Gabriele Sorce More articles by this author Nazario Tenace More articles by this author Federico Scarfò More articles by this author Giorgio Brembilla More articles by this author Antonio Esposito More articles by this author Marco Morelli More articles by this author Dejan Lazarevic More articles by this author Francesco De Cobelli More articles by this author Claudio Doglioni More articles by this author Giovanni Tonon More articles by this author Giorgio Gandaglia More articles by this author Francesco Montorsi More articles by this author Alberto Briganti More articles by this author Expand All Advertisement PDF downloadLoading ..." @default.
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• W4360607634 title "PD10-09 A NOVEL MODEL INTEGRATING CLINICAL, MP-MRI, AND EPIGENOMIC FEATURES TO PREDICT LYMPH NODE INVASION IN PROSTATE CANCER PATIENTS UNDERGOING RADICAL PROSTATECTOMY AND PELVIC LYMPH NODE DISSECTION" @default.
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