Literature DB >> 28666689

Optimum Tools for Predicting Clinical Outcomes in Prostate Cancer Patients Undergoing Radical Prostatectomy: A Systematic Review of Prognostic Accuracy and Validity.

Jared M Campbell1, Elspeth Raymond2, Michael E O'Callaghan3, Andrew D Vincent4, Kerri R Beckmann5, David Roder6, Sue Evans7, John McNeil7, Jeremy Millar8, John Zalcberg7, Martin Borg9, Kim L Moretti10.   

Abstract

Prostate cancer is a heterogeneous disease whose therapies frequently have adverse effects. Informed patient counseling regarding likely clinical outcomes is therefore important. In this systematic review we aimed to identify all external validations of tools that are used to predict clinical outcomes in patients undergoing radical prostatectomy and evaluate which are optimum for clinical implementation. PubMed and EMBASE were searched from 2007 to 2016. Search terms related to the inclusion criteria were: prostate cancer, clinical outcomes, radical prostatectomy, and prognosis. Titles and abstracts were screened and relevant studies were advanced to full-text review. Reference lists were reviewed for further studies. The Centre for Evidence Based Medicine prognostic tool was used for critical appraisal. Seventy-three studies externally validated 13 pre- and 41 postoperative tools for the prediction of biochemical recurrence (BCR), aggressive BCR, metastasis, and prostate cancer-specific mortality (PCSM). Recommendations for clinical implementation were made on the basis of accuracy, cohort sizes, and consistency. The accuracy of recommended tools ranged from 68% to 79% and 72% to 92% among the largest validation cohorts for pre- and postoperative tools. For preoperative prognosis we recommended the Cancer of the Prostate Risk Assessment (CAPRA) and Stephenson nomograms for BCR, the CAPRA nomogram for aggressive BCR as well as metastasis, and the D'Amico criteria for PCSM. For postoperative prognosis we recommended the CAPRA-Surgery (CAPRA-S), Stephenson, Kattan, Duke prostate cancer (DPC), and the Suardi nomograms for the prediction of BCR, the DPC nomogram for aggressive BCR, the CAPRA-S and Eggener nomograms for metastasis, and the Eggener nomogram for PCSM. Use of these tools should help clinicians deliver accurate, evidence-based counseling to patients undergoing prostatectomy.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Neoplasm; Nomogram; Prognosis; Progression; Validation

Mesh:

Year:  2017        PMID: 28666689     DOI: 10.1016/j.clgc.2017.06.001

Source DB:  PubMed          Journal:  Clin Genitourin Cancer        ISSN: 1558-7673            Impact factor:   2.872


  4 in total

1.  Can We Improve the Preoperative Prediction of Prostate Cancer Recurrence With Multiparametric MRI?

Authors:  Paolo Capogrosso; Emily A Vertosick; Nicole E Benfante; Daniel D Sjoberg; Andrew J Vickers; James A Eastham
Journal:  Clin Genitourin Cancer       Date:  2019-05-16       Impact factor: 2.872

2.  Effect of Clinical Parameters on Risk of Death from Cancer after Radical Prostatectomy in Men with Localized and Locally Advanced Prostate Cancer.

Authors:  Daimantas Milonas; Tomas Ruzgas; Zilvinas Venclovas; Daniele Jonusaite; Aivaras Jonas Matijosaitis; Darius Trumbeckas; Edmundas Varpiotas; Stasys Auskalnis; Darijus Skaudickas; Ramunas Mickevicius; Kestutis Vaiciunas; Jonas Mickevicius; Mindaugas Jievaltas
Journal:  Cancers (Basel)       Date:  2022-04-18       Impact factor: 6.575

3.  Aberrant DOCK2, GRASP, HIF3A and PKFP Hypermethylation has Potential as a Prognostic Biomarker for Prostate Cancer.

Authors:  Marianne T Bjerre; Siri H Strand; Maibritt Nørgaard; Helle Kristensen; Anne Ki Rasmussen; Martin Mørck Mortensen; Jacob Fredsøe; Peter Mouritzen; Benedicte Ulhøi; Torben Ørntoft; Michael Borre; Karina D Sørensen
Journal:  Int J Mol Sci       Date:  2019-03-07       Impact factor: 5.923

4.  Exome sequencing identified six copy number variations as a prediction model for recurrence of primary prostate cancers with distinctive prognosis.

Authors:  Jie Liu; Jiajun Yan; Ruifang Mao; Guoping Ren; Xiaoyan Liu; Yanling Zhang; Jili Wang; Yan Wang; Meiling Li; Qingchong Qiu; Lin Wang; Guanfeng Liu; Shanshan Jin; Liang Ma; Yingying Ma; Na Zhao; Hongwei Zhang; Biaoyang Lin
Journal:  Transl Cancer Res       Date:  2020-04       Impact factor: 1.241

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.