Background: Gonadotrophin releasing hormone (GnRH) agonists and antagonists reduce testosterone levels for the treatment of advanced and metastatic prostate cancer. Androgen deprivation therapy (ADT) is associated with increased risk of cardiovascular (CV) events and CV disease (CVD), especially in patients with preexisting CVD treated with GnRH agonists. Here, we investigated the potential relationship between serum levels of the cardiac biomarkers N-terminal pro-B-type natriuretic peptide (NTproBNP), D-dimer, C-reactive protein (CRP), and high-sensitivity troponin (hsTn) and the risk of new CV events in prostate cancer patients with a history of CVD receiving a GnRH agonist or antagonist.
Methods: Post-hoc analyses were performed of a phase II randomized study that prospectively assessed CV events in patients with prostate cancer and preexisting CVD, receiving GnRH agonist or antagonist. Cox proportional hazards models were used to determine whether the selected biomarkers had any predictive effect on CV events at baseline and across a 12-month treatment period.
Results: Baseline and disease characteristics of the 80 patients who took part in the study were well balanced between treatment arms. Ischemic heart disease (66%) and myocardial infarction (37%) were the most common prior CVD and the majority (92%) of patients received CV medication. We found that high levels of NTproBNP (p = 0.008), and hsTn (p = 0.004) at baseline were associated with the development of new CV events in the GnRH agonist group but not in the antagonist. In addition, a nonsignificant trend was observed between higher levels of NTproBNP over time and the development of new CV events in the GnRH agonist group.
Conclusions: The use of cardiac biomarkers may be worthy of further study as tools in the prediction of CV risk in prostate cancer patients receiving ADT. Analysis was limited by the small sample size; larger studies are required to validate biomarker use to predict CV events among patients receiving ADT.
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http://dx.doi.org/10.1038/s41391-020-0264-9 | DOI Listing |
PLoS One
January 2025
Department of Public Health, Policy and Systems, University of Liverpool, Liverpool, United Kingdom.
Introduction: Undiagnosed chronic disease has serious health consequences, and variation in rates of underdiagnosis between populations can contribute to health inequalities. We aimed to estimate the level of undiagnosed disease of 11 common conditions and its variation across sociodemographic characteristics and regions in England.
Methods: We used linked primary care, hospital and mortality data on approximately 1.
Ann Nucl Med
January 2025
Department of Biomedical Sciences, Humanitas University, Milan, Italy.
The purpose of this systematic review was to evaluate the role of PSMA PET/CT in intermediate-risk prostate cancer (PCa) patients, to determine whether it could help improve treatment strategy and prognostic stratification. A systematic literature search up to May 2024 was conducted in the PubMed, Embase and Scopus databases. Articles with mixed risk patient populations, review articles, editorials, letters, comments, or case reports were excluded.
View Article and Find Full Text PDFJ Neurooncol
January 2025
Department of Neurosurgery, Allegheny Health Network, Neuroscience Institute, Pittsburgh, PA, United States.
Langenbecks Arch Surg
January 2025
Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Purpose: Assessing surgical skills is vital for training surgeons, but creating objective, automated evaluation systems is challenging, especially in robotic surgery. Surgical procedures generally involve dissection and exposure (D/E), and their duration and proportion can be used for skill assessment. This study aimed to develop an AI model to acquire D/E parameters in robot-assisted radical prostatectomy (RARP) and verify if these parameters could distinguish between novice and expert surgeons.
View Article and Find Full Text PDFInsights Imaging
January 2025
Department of Radiology, the Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China.
Objective: To evaluate the feasibility of utilizing artificial intelligence (AI)-predicted biparametric MRI (bpMRI) image features for predicting the aggressiveness of prostate cancer (PCa).
Materials And Methods: A total of 878 PCa patients from 4 hospitals were retrospectively collected, all of whom had pathological results after radical prostatectomy (RP). A pre-trained AI algorithm was used to select suspected PCa lesions and extract lesion features for model development.
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