Nowadays, the management of prostate cancer has become more and more challenging due to the increasing number of available treatment options, therapeutic agents, and our understanding of its carcinogenesis and disease progression. Moreover, currently available risk stratification systems used to facilitate clinical decision-making have limitations, particularly in providing a personalized and patient-centered management strategy. Although prognosis and prostate cancer-specific survival have improved in recent years, the heterogenous behavior of the disease among patients included in the same risk prognostic group negatively impacts not only our clinical decision-making but also oncological outcomes, irrespective of the treatment strategy. Several biomarkers, along with available tests, have been developed to help clinicians in difficult decision-making scenarios and guide management strategies. In this review article, we focus on the scientific evidence that supports the clinical use of several biomarkers considered by professional urological societies (and included in uro-oncological guidelines) in the diagnosis process and specific difficult management strategies for clinically localized or advanced prostate cancer.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10813985 | PMC |
| http://dx.doi.org/10.3390/cancers16020316 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Optimizing Virtual Follow-Up Care: Realist Evaluation of Experiences and Perspectives of Patients With Breast and Prostate Cancer.
J Med Internet Res
January 2025
Cancer Rehabilitation and Survivorship, Department of Supportive Care, Princess Margaret Cancer Centre, Toronto, ON, Canada.
Background: Virtual follow-up (VFU) has the potential to enhance cancer survivorship care. However, a greater understanding is needed of how VFU can be optimized.
Objective: This study aims to examine how, for whom, and in what contexts VFU works for cancer survivorship care.
Clonal phylogenies inferred from bulk, single cell, and spatial transcriptomic analysis of epithelial cancers.
PLoS One
January 2025
Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom.
Epithelial cancers are typically heterogeneous with primary prostate cancer being a typical example of histological and genomic variation. Prior studies of primary prostate cancer tumour genetics revealed extensive inter and intra-patient genomic tumour heterogeneity. Recent advances in machine learning have enabled the inference of ground-truth genomic single-nucleotide and copy number variant status from transcript data.
View Article and Find Full Text PDFSystems-level liquid biopsy in advanced prostate cancer.
Endocr Relat Cancer
January 2025
S Dehm, Masonic Cancer Center, University of Minnesota, Minneapolis, United States.
Treatment for castration-resistant prostate cancer (CRPC) primarily involves the suppression of androgen receptor (AR) activity using androgen receptor signaling inhibitors (ARSIs). While ARSIs have extended patient survival, resistance inevitably develops. Mechanisms of resistance include genomic aberrations at the AR locus that reactivate AR signaling, or lineage plasticity that drives emergence of AR-independent phenotypes.
View Article and Find Full Text PDFPSMA-PET/CT Findings in Patients With High-Risk Biochemically Recurrent Prostate Cancer With No Metastatic Disease by Conventional Imaging.
JAMA Netw Open
January 2025
Ahmanson Translational Theranostics Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles.
Importance: The phase 3 randomized EMBARK trial evaluated enzalutamide with or without leuprolide in high-risk nonmetastatic hormone-sensitive prostate cancer. Eligibility relied on conventional imaging, which underdetects metastatic disease compared with prostate-specific membrane antigen-positron emission tomography (PSMA-PET).
Objective: To describe the staging information obtained by PSMA-PET/computed tomography (PSMA-PET/CT) in a patient cohort eligible for the EMBARK trial.
Predicting intermediate-risk prostate cancer using machine learning.
Int Urol Nephrol
January 2025
Faculty of Medical Sciences, Pharmacology and Toxicology Department, University of Kragujevac, Kragujevac, Serbia.
Purposes: Intermediate-risk prostate cancer (IR PCa) is the most common risk group for localized prostate cancer. This study aimed to develop a machine learning (ML) model that utilizes biopsy predictors to estimate the probability of IR PCa and assess its performance compared to the traditional clinical model.
Methods: Between January 2017 and December 2022, patients with prostate-specific antigen (PSA) values of ≤ 20 ng/mL underwent transrectal ultrasonography-guided prostate biopsies.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!