MP56-12 THE ROLE OF PERINEURAL INVASION AS A PROGNOSTIC TOOL IN PROSTATE CANCER.

Abstract

You have accessJournal of UrologyProstate Cancer: Localized III1 Apr 2015MP56-12 THE ROLE OF PERINEURAL INVASION AS A PROGNOSTIC TOOL IN PROSTATE CANCER. Udit Singhal, Louis Lu, Ted Skolarus, Ganesh Palapattu, Jeffrey Montgomery, Alon Weizer, Brent Hollenbeck, David Miller, Jason Chan, Rohit Mehra, Scott Tomlins, Daniel Hamstra, Felix Feng, and Todd Morgan Udit SinghalUdit Singhal More articles by this author , Louis LuLouis Lu More articles by this author , Ted SkolarusTed Skolarus More articles by this author , Ganesh PalapattuGanesh Palapattu More articles by this author , Jeffrey MontgomeryJeffrey Montgomery More articles by this author , Alon WeizerAlon Weizer More articles by this author , Brent HollenbeckBrent Hollenbeck More articles by this author , David MillerDavid Miller More articles by this author , Jason ChanJason Chan More articles by this author , Rohit MehraRohit Mehra More articles by this author , Scott TomlinsScott Tomlins More articles by this author , Daniel HamstraDaniel Hamstra More articles by this author , Felix FengFelix Feng More articles by this author , and Todd MorganTodd Morgan More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.2076AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Perineural invasion (PNI) in prostate cancer is defined as cancer progression along nerve fibers of the prostate. While PNI has been previously associated with poorer clinical outcomes, its relevance as a predictor of objective long-term endpoints in newly diagnosed prostate cancer patients is not well defined. Therefore, we evaluated the role of PNI as a prognostic marker in patients with localized prostate cancer who underwent eventual treatment with surgery or radiation. METHODS We analyzed a prospectively collected cohort of 5,034 consecutive patients with localized prostate cancer treated with either surgery (n = 4207) or radiation (n = 827) at the University of Michigan between August 1994 and December 2013. The primary outcome measure was metastasis-free survival, and secondary outcomes were PSA-recurrence free survival and overall survival (OS). Covariates included age, treatment year, race, comorbidity index, pre-treatment PSA, Gleason score, and T-stage. Multivariable analysis was performed using a Cox proportional hazards model and mortality rates were estimated using the Kaplan-Meir method. RESULTS 22.6% of surgery patients and 37.5% of radiation patients had PNI on diagnostic biopsy. A total of 169 patients developed metastatic disease at a median of 44 months (IQR 21-83 months) after primary therapy. In the combined surgery and radiotherapy cohort, PNI was an independent predictor of distant metastasis and PSA recurrence, but not OS (see table). When separating out those patients who underwent surgery, PNI was independently associated with metastasis, PSA recurrence, and OS. In those patients receiving radiation as primary treatment, PNI was a predictor of metastasis and PSA recurrence, but not OS. CONCLUSIONS PNI is an independent predictor of long term, objective outcomes in newly diagnosed prostate cancer patients regardless of subsequent therapy. These data support the importance of PNI as a key factor denoting potentially aggressive prostate cancer and importing a significant increase in the likelihood of eventual metastatic progression. All Patients Surgery Radiation Outcome HR 95% CI p-value HR 95% CI p-value HR 95% CI p-value Metastasis* 1.67 1.17 - 2.38 0.005 1.57 1.01 - 2.44 0.044 2.09 1.12 - 3.92 0.021 PSA-recurrence* 1.62 1.37 - 1.91 <0.001 1.62 1.34 - 1.97 <0.001 1.73 1.26 - 2.41 0.001 Overall Survival** 1.16 0.91 - 1.49 0.23 1.56 1.04 - 2.34 0.030 1.04 0.76 - 1.42 0.82 ∗ Model was adjusted for age, treatment year, race, pre-treatment PSA, Gleason score, T-stage. Whole cohort model also adjusts for treatment type. ∗∗ Model was adjusted for age, treatment year, race, comorbidity index, pre-treatment PSA, Gleason score, T-stage. Whole cohort model also adjusts for treatment type. © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e685 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information Udit Singhal More articles by this author Louis Lu More articles by this author Ted Skolarus More articles by this author Ganesh Palapattu More articles by this author Jeffrey Montgomery More articles by this author Alon Weizer More articles by this author Brent Hollenbeck More articles by this author David Miller More articles by this author Jason Chan More articles by this author Rohit Mehra More articles by this author Scott Tomlins More articles by this author Daniel Hamstra More articles by this author Felix Feng More articles by this author Todd Morgan More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...