Literature DB >> 30565298

Defining clinically significant prostate cancer on the basis of pathological findings.

Andres Matoso1, Jonathan I Epstein1.   

Abstract

The definition of clinically significant prostate cancer is a dynamic process that was initiated many decades ago, when there was already evidence that a great proportion of patients with prostate cancer diagnosed at autopsy never had any clinical symptoms. Autopsy studies led to examinations of radical prostatectomy (RP) specimens and the establishment of the definition of significant cancer at RP: tumour volume of 0.5 cm3 , Gleason grade 6 [Grade Group (GrG) 1], and organ-confined disease. RP studies were then used to develop prediction models for significant cancer by the use of needle biopsies. The first such model was used to delineate the first active surveillance (AS) criteria, known as the 'Epstein' criteria, in which patients with a cancer Gleason score of 3 + 3 = 6 (GrG1) involving fewer than two cores, and <50% of any given core, and a prostate-specific antigen density of <0.15 ng/ml per cm3 had a minimal risk of significant cancer at RP. These were adopted as components of the 'very-low-risk category' of the National Comprehensive Cancer Network guidelines, in which AS is supported as a management option. With the increase in the popularity of AS, much research has been carried out to better define significant/insignificant cancer, in order to be able to safely offer AS to a larger proportion of patients without the risk of undertreatment. Research has focused on allowing higher volume tumours, focal extraprostatic extension, and a limited amount of Gleason pattern 4, and the significance of different morphological patterns of Gleason 4. Other areas of research that will probably impact on the field but that are not covered in this review include the molecular classification of tumours and imaging techniques.
© 2018 John Wiley & Sons Ltd.

Entities:  

Keywords:  active surveillance; prostate cancer; significant cancer

Mesh:

Year:  2019        PMID: 30565298     DOI: 10.1111/his.13712

Source DB:  PubMed          Journal:  Histopathology        ISSN: 0309-0167            Impact factor:   5.087


  27 in total

1.  Comparison of two commonly used methods in measurement of cancer volume in prostate biopsy.

Authors:  Viharkumar Patel; Samuel Hubbard; Wei Huang
Journal:  Int J Clin Exp Pathol       Date:  2020-04-01

2.  Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework.

Authors:  Indrani Bhattacharya; Arun Seetharaman; Christian Kunder; Wei Shao; Leo C Chen; Simon J C Soerensen; Jeffrey B Wang; Nikola C Teslovich; Richard E Fan; Pejman Ghanouni; James D Brooks; Geoffrey A Sonn; Mirabela Rusu
Journal:  Med Image Anal       Date:  2021-11-06       Impact factor: 8.545

3.  HSP90-Specific nIR Probe Identifies Aggressive Prostate Cancers: Translation from Preclinical Models to a Human Phase I Study.

Authors:  Takuya Osada; Erika J Crosby; Kensuke Kaneko; Joshua C Snyder; Joshua D Ginzel; Chaitanya R Acharya; Xiao-Yi Yang; Thomas J Polascik; Ivan Spasojevic; Rendon C Nelson; Amy Hobeika; Zachary C Hartman; Leonard M Neckers; Andre Rogatko; Philip F Hughes; Jiaoti Huang; Michael A Morse; Timothy Haystead; H Kim Lyerly
Journal:  Mol Cancer Ther       Date:  2021-10-21       Impact factor: 6.261

4.  The predictive value of the prostate health index vs. multiparametric magnetic resonance imaging for prostate cancer diagnosis in prostate biopsy.

Authors:  Jiří Stejskal; Vanda Adamcová; Miroslav Záleský; Vojtěch Novák; Otakar Čapoun; Vojtěch Fiala; Olga Dolejšová; Hana Sedláčková; Štěpán Veselý; Roman Zachoval
Journal:  World J Urol       Date:  2020-08-06       Impact factor: 4.226

Review 5.  Advances in the selection of patients with prostate cancer for active surveillance.

Authors:  James L Liu; Hiten D Patel; Nora M Haney; Jonathan I Epstein; Alan W Partin
Journal:  Nat Rev Urol       Date:  2021-02-23       Impact factor: 14.432

6.  The Primacy of High B-Value 3T-DWI Radiomics in the Prediction of Clinically Significant Prostate Cancer.

Authors:  Alessandro Bevilacqua; Margherita Mottola; Fabio Ferroni; Alice Rossi; Giampaolo Gavelli; Domenico Barone
Journal:  Diagnostics (Basel)       Date:  2021-04-21

Review 7.  Rethinking prostate cancer screening: could MRI be an alternative screening test?

Authors:  David Eldred-Evans; Henry Tam; Heminder Sokhi; Anwar R Padhani; Mathias Winkler; Hashim U Ahmed
Journal:  Nat Rev Urol       Date:  2020-07-21       Impact factor: 14.432

8.  Integrative Machine Learning Prediction of Prostate Biopsy Results From Negative Multiparametric MRI.

Authors:  Haoxin Zheng; Qi Miao; Yongkai Liu; Steven S Raman; Fabien Scalzo; Kyunghyun Sung
Journal:  J Magn Reson Imaging       Date:  2021-06-23       Impact factor: 4.813

9.  An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy.

Authors:  Sudhir Perincheri; Angelique Wolf Levi; Romulo Celli; Peter Gershkovich; David Rimm; Jon Stanley Morrow; Brandon Rothrock; Patricia Raciti; David Klimstra; John Sinard
Journal:  Mod Pathol       Date:  2021-03-29       Impact factor: 7.842

10.  Optimal PSA Threshold for Obtaining MRI-Fusion Biopsy in Biopsy-Naïve Patients.

Authors:  Luke L Wang; Brandon L Henslee; Peter B Sam; Chad A LaGrange; Shawna L Boyle
Journal:  Prostate Cancer       Date:  2021-07-01
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