Active Surveillance for Prostate Cancer: Past, Present and Future

Overview

Journal Curr Opin Oncol

Specialty Oncology

Date 2012 Mar 28

PMID 22450149

Citations 18

Authors

Eric A Singer

Aradhana Kaushal

Baris Turkbey

Anna Couvillon

Peter A Pinto

Howard L Parnes

Affiliations

Soon will be listed here.

Abstract

Purpose Of Review: This article reviews recent developments in the use of active surveillance for localized prostate cancer.

Recent Findings: The treatment of localized prostate cancer continues to be a major challenge for urologic oncologists. Screening with prostate-specific antigen has resulted in increased numbers of low-risk prostate cancers being detected. Aggressive whole-gland therapy with surgery, or radiation therapy is associated with potentially life-altering treatment-related side effects such as urinary incontinence, bowel toxicity and erectile dysfunction. The goal of active surveillance is to avoid or delay the adverse events associated with prostate cancer therapy while still allowing for curative intervention in the future, if needed.

Summary: Active surveillance is a reasonable treatment option for many men with low-risk, and some men with intermediate-risk, prostate cancer. Additional research is needed to determine the optimal active surveillance inclusion criteria, monitoring schedule, and treatment triggers. It is hoped that advances in prostate imaging, biomarkers, and focal therapy will foster greater use of active surveillance in appropriately selected men to optimize quality-of-life without compromising cancer outcomes.

Citing Articles

Genomics in active surveillance and post-prostatectomy patients: A review of when and how to use effectively.

Adetunji A, Venishetty N, Gombakomba N, Jeune K, Smith M, Winer A Curr Urol Rep. 2024; 25(10):253-260.

PMID: 38869692 DOI: 10.1007/s11934-024-01219-3.

Spatial Profiling of the Prostate Cancer Tumor Microenvironment Reveals Multiple Differences in Gene Expression and Correlation with Recurrence Risk.

Kumar V, Randhawa P, Bilodeau R, Mercola D, McClelland M, Agrawal A Cancers (Basel). 2022; 14(19).

PMID: 36230846 PMC: 9562240. DOI: 10.3390/cancers14194923.

Stochastic Sequential Modeling: Toward Improved Prostate Cancer Diagnosis Through Temporal-Ultrasound.

Nahlawi L, Imani F, Gaed M, Gomez J, Moussa M, Gibson E Ann Biomed Eng. 2020; 49(2):573-584.

PMID: 32779056 PMC: 7851024. DOI: 10.1007/s10439-020-02585-y.

Recurrence analysis on prostate cancer patients with Gleason score 7 using integrated histopathology whole-slide images and genomic data through deep neural networks.

Ren J, Karagoz K, Gatza M, Singer E, Sadimin E, Foran D J Med Imaging (Bellingham). 2019; 5(4):047501.

PMID: 30840742 PMC: 6237203. DOI: 10.1117/1.JMI.5.4.047501.

Stochastic Modeling of Temporal Enhanced Ultrasound: Impact of Temporal Properties on Prostate Cancer Characterization.

Nahlawi L, Goncalves C, Imani F, Gaed M, Gomez J, Moussa M IEEE Trans Biomed Eng. 2018; 65(8):1798-1809.

PMID: 29989922 PMC: 6112119. DOI: 10.1109/TBME.2017.2778007.

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