NY Prostate Cancer Conference - A. Stephenson - Session 4: Predicting clinical and biochemical endpoints after surgery

Gesundheit & Medizin Business

Predicting Clinical and Biochemical Endpoints after Radical Prostatectomy Andrew J. Stephenson, MD FRCSC FACS Director, Urologic Oncology Glickman Urological and Kidney Institute Cleveland Clinic

Postoperative Predictions: Why? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Postoperative vs. Pretreatment Models ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Postoperative Nomogram Kattan et al. J Clin Oncol 1999; Graefen et al. J Clin Oncol 2002; Bianco et al. J Urol 2003 Single surgeon, 996 pts, open RP, 1983-1997, BCR Endpoint: PSA 0.4 ng/mL and rising Bootstrapped c-index: 0.88 Model predictions remarkably robust when applied to diverse external validation populations C-index 0.80-0.83

Postoperative Nomogram v. 2.0 ,[object Object],[object Object],[object Object],[object Object],[object Object],Stephenson et al. J Clin Oncol 2005

Deciding Upon Secondary Therapy ,[object Object],Stephenson et al. In preparation N = 326 pts, median FU 70 mos, externally-validated on SWOG 8794, c-index 0.67

Building a Better Nomogram? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Svatek et al. Prostate 2009; Stephenson et al. J Urol 2009; Vickers et al. Cancer 2009

Building a Better Nomogram: Clinical Endpoints ,[object Object],[object Object],[object Object],[object Object],[object Object]

Postoperative Nomogram for PCSM ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Eggener et al. J Urol 2011

Prostate Cancer-Specific Survival: Gleason Score Analysis Eggener et al. J Urol 2011 Gleason 6 Glsn 3+4 Glsn 4+3 Glsn 8-10 Age < 60 Age 60-69 Age > 69

Prostate Cancer-Specific Survival: Pathological Stage Eggener et al. J Urol 2011 Organ- Confined EPE SVI LNI Age < 60 Age 60-69 Age > 69

Nomogram Predicting 15-year PCSM ,[object Object],[object Object],[object Object],[object Object],[object Object],Eggener et al. J Urol 2011

Building a Better Postoperative PCSM Nomogram ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Donovan et al. J Clin Oncol 2008; Eggener et al. Cancer 2009

Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Presented by the Johns Hopkins University School of Medicine and produced in collaboration with the Institute for Medical Education & Research (IMER). Review a downloadable slide deck by, covering the most clinically relevant new data reported from Expert Video Viewpoints on Castration-Resistant Prostate Cancer: Care Across the Continuum by: Emmanuel Antonarakis, MBBCh Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins Hospital Leonard G. Gomella, MD, FACS Thomas Jefferson University Jefferson Kimmel Cancer Center A. Oliver Sartor, MD Tulane University School of Medicine Target Audience Medical oncologists, urologists, radiation oncologists, and other healthcare professionals involved in the treatment of patients with castration-resistant prostate cancer (CRPC). There are no prerequisites. Activity Overview In this video, a panel of expert thought leaders will discuss the optimal management and emerging agents across the CRPC treatment continuum. Topics will include identification and initial treatment of CRPC, metastatic CRPC progression, future novel treatment for CRPC patients, and expert perspectives on case examples to decipher optimal treatment of CRPC. Slide Deck Disclaimer This slide deck in its original and unaltered format is for educational purposes and is current as of December 2011. All materials contained herein reflect the views of the faculty, and not those of IMER, the CME provider, or the commercial supporter. These materials may discuss therapeutic products that have not been approved by the US Food and Drug Administration and off-label uses of approved products. Readers should not rely on this information as a substitute for professional medical advice, diagnosis, or treatment. The use of any information provided is solely at your own risk, and readers should verify the prescribing information and all data before treating patients or employing any therapeutic products described in this educational activity. Usage Rights This slide deck is provided for educational purposes and individual slides may be used for personal, non-commercial presentations only if the content and references remain unchanged. No part of this slide deck may be published in print or electronically as a promotional or certified educational activity without prior written permission from IMER. Additional terms may apply. See Terms of Service on IMERonline.com for details.

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Journal of the Formosan Medical Association (2011) 110, 695e700 Available online at www.sciencedirect.com journal homepage: www.jfma-online.com ORIGINAL ARTICLE A multivariable logistic regression equation to evaluate prostate cancer Jhih-Cheng Wang a, Steven K. Huan a, Jinn-Rung Kuo b, Chin-Li Lu c, Hung Lin a, Kun-Hung Shen a,* a Division of Urology, Departments of Surgery, Chi-Mei Medical Center, Tainan, Taiwan b Division of Neurosurgery, Department of Surgery, Chi-Mei Medical Center, Tainan, Taiwan c Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan Received 29 January 2010; received in revised form 14 May 2010; accepted 9 August 2010 KEYWORDS Logistic regression; men’s health; probability; prostate cancer; risk factor; score * Corresponding author. Division of U Taiwan 710. E-mail address: [email protected] 0929-6646/$ - see front matter Copyr doi:10.1016/j.jfma.2011.09.005 Background/Purpose: A possible means of decreasing prostate cancer mortality is through improved early detection. We attempted to create an equation to predict the likelihood of having prostate cancer. Methods: Between January 2005 and May 2008, patients who received prostate biopsies were retrospective evaluated. The relationship between the possibility of prostate cancer and the following variables were evaluated: age; serum prostate specific antigen (PSA) level, prostate volume, numbers of prostatic biopsies, digital rectal examination (DRE) findings, and the pres- ence of hypoechoic nodule under transrectal ultrasonography. Results: A multivariate regression model was created to predict the possibility of having pros- tate cancer, and a receiver-operating characteristic (ROC) curve was drawn based on the predictive scoring equation. Using a predictive equation, P Z 1/(1 � e�x), where X Z �4.88, þ 1.11 (if DRE positive), þ 0.75 (if hypoechoic nodule of prostate present), þ 1.27 (when 7 < PSA � 10), þ 2.02 (when 10 < PSA � 24), þ 2.28 (when 24 < PSA � 50), þ 3.93 (when 50 < PSA), þ 1.23 (when 65 < age � 75), þ 1.66 (when 75 < age), followed by ROC curve analysis, we showed that the sensitivity was 88.5% and specificity was 79.1% in predicting the possibility of prostate cancer. Conclusion: Clinicians can tailor each patient’s follow-up according to the nomogram based on this equation to increase the efficacy of evaluating for prostate cancer. Copyright ª 2011, Elsevier Taiwan LLC & Formosan Medical Association. All rights reserved. rology, Department of Surgery, Chi-Mei Medical Center, 901 Chung Hwa Road, Yung Kang City, Tainan, il.com (K.-H. Shen). ight ª 2011, Elsevier Taiwan LLC & Formosan Medical Association. All rights reserved. mailto:[email protected] http://dx.doi.org/10.1016/j.jfma.2011.09.005 www.sciencedirect.com/science/journal/09296646 http://www.jfma-online.com http://dx.doi.org/10.1016/j.jfma.2011.09.005 http://dx.doi.org/10.1016/j.jfma.2011.09.005 696 J.-C. Wang et al. Prostate cancer is the most common solid malignancy ...

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