Prostate Cancer

Defining oligometastatic state in uro-oncological cancers.

Oligometastatic tumors illustrate a distinct state between localized and systematic disease and might harbor unique biologic features. Moreover, these tumors represent a different clinical entity, with a potential of long-term disease control or even cure, therefore they receive growing attention in the field of urologic oncology.

Prostate Cancer Incidence and Mortality in Men Exposed to α1-Adrenoceptor Antagonists.

α1-antagonists are commonly used to treat benign prostatic hyperplasia. Preclinical studies suggest they induce cell death and inhibit tumor growth. This study evaluates the risk of prostate cancer death in men using α1-antagonists.

A Model for Predicting Clinically Significant Prostate Cancer using Prostate MRI and Risk Factors.

To develop and validate a predictive model for clinically significant prostate cancer (csPCa) using prostate MRI and patient risk factors.

We identified 960 men who underwent MRI from 2015-2019 and biopsy either 6 months prior or 6 months following the MRI.

The association between neighborhood obesogenic factors and prostate cancer risk and mortality: the Southern Community Cohort Study.

Prostate cancer is one of the leading causes of cancer-related mortality among men in the United States. We examined the role of neighborhood obesogenic attributes on prostate cancer risk and mortality in the Southern Community Cohort Study (SCCS).

Prostate biopsy sepsis prevention: external validation of an alcohol needle washing protocol.

Transrectal ultrasound-guided prostate biopsy (TRUS-Bx) is associated with a 1-8% risk of post-biopsy sepsis (PBS). A recent study described an isopropyl alcohol needle washing protocol that significantly decreased PBS rates.

A Comparison of Globally Applied Prognostic Risk Groups and the Prevalence of Metastatic Disease on Prostate-specific Membrane Antigen Positron Emission Tomography in Patients with Newly Diagnosed Prostate Cancer.

Various risk classification systems (RCSs) are used globally to stratify newly diagnosed patients with prostate cancer (PCa) into prognostic groups.

To compare the predictive value of different prognostic subgroups (low-, intermediate-, and high-risk disease) within the RCSs for detecting metastatic disease on prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) for primary staging, and to assess whether further subdivision of subgroups would be beneficial.

Diffuse Pneumonitis after Lutetium-177-PSMA-617 Treatment in a Patient with Metastatic Castration-Resistant Prostate Cancer - Beyond the Abstract

We present the case of a patient with heavily pretreated metastatic castration-resistant prostate cancer (mCRPC) who underwent treatment with lutetium Lu-177 vipivotide tetraxetan (also known as 177Lu-PSMA-617) due to progressive disease despite multiple lines of therapy, including chemotherapy, hormonal therapy, and radiation, encompassing palliative mediastinal and central nervous system radiation.

Salivary excretion of systemically injected [18F]DCFPyL in prostate cancer patients undergoing PSMA scans.

Prostate-specific membrane antigen (PSMA) is present in high amounts in salivary glands, but it is unclear whether labeled binders of PSMA are excreted in the saliva.

Ten patients with prostate cancer underwent whole-body [18F]DCFPyL PET/CT (NCT03181867), and saliva samples were collected between 0-120 minutes post-injection.

Baseline serum testosterone and differential efficacy of bipolar androgen therapy and enzalutamide in the randomized TRANSFORMER trial.

Bipolar androgen therapy (BAT) is effective in a subset of metastatic castration-resistant prostate cancer (mCRPC) patients. Treatment selection biomarkers are needed due to other therapies that can be equally efficacious.

Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN.

Sequential PET/CT studies oncology patients can undergo during their treatment follow-up course is limited by radiation dosage. We propose an artificial intelligence (AI) tool to produce attenuation-corrected PET (AC-PET) images from non-attenuation-corrected PET (NAC-PET) images to reduce need for low-dose CT scans.