Prostate Cancer

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Developers are enrolling those with metastatic castration-resistant prostate cancer on a phase 1/2 trial to assess the safety and tolerability of HLD-0915.
HLD-0915 Earns FDA Fast Track Designation in Metastatic CRPC

August 14th 2025

Developers are enrolling those with metastatic castration-resistant prostate cancer on a phase 1/2 trial to assess the safety and tolerability of HLD-0915.

The FDA has accepted a new drug application for the prostate-specific membrane antigen PET imaging agent.
FDA Accepts NDA for New Formulation of PSMA PET Injection in Prostate Cancer

August 7th 2025

Developers plan to initiate a phase 2b trial in patients with less severe prostate cancer variants to better assess INKmune’s antitumor effects.
INKmune Exhibits Favorable Safety in Metastatic CRPC

August 4th 2025

Findings from the phase 3 TALAPRO-2 trial showed that the safety profile of talazoparib was consistent with its known profile in metastatic CRPC.
Talazoparib Combo Significantly Improves Overall Survival in Metastatic CRPC

August 1st 2025

No dose-limiting toxicities or unexpected adverse effects occurred with carotuximab/apalutamide in metastatic castration-resistant prostate cancer.
Carotuximab/Apalutamide Shows Tolerability in Metastatic CRPC

July 16th 2025

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Salvage Brachytherapy After External-Beam Irradiation for Prostate Cancer

February 1st 2004

The options available for patients with recurrent prostate cancerare limited. Men who have failed external-beam irradiation as the primarytreatment are rarely considered for potentially curative salvagetherapy. Traditionally, only palliative treatments have been offered withhormonal intervention or simple observation. A significant percentageof these patients have only locally recurrent cancer and are thus candidatesfor curative salvage therapy. Permanent brachytherapy withiodine-125 or palladium-103 has been used in an attempt to eradicatethe remaining prostate cancer and prevent the need for additional intervention.It is critical in this population to identify patients most likelyto have distant metastases or who are unlikely to suffer death or morbidityfrom their recurrence, in order to avoid potential treatmentmorbidity in those unlikely to benefit from any intervention. Followingsalvage brachytherapy, up to 98% of these cancers may be locally controlled,and 5-year freedom from second relapse is approximately 50%.With careful case selection, relapse-free rates up to 83% may beachieved. A schema is presented, suggesting that it may be possible toidentify the patients most likely to benefit from salvage treatment basedon prostate-specific antigen (PSA) kinetics and other features. Suchfeatures include histologically confirmed local recurrence, clinical andradiologic evidence of no distant disease, adequate urinary function,age, and overall health indicative of at least a 5- to 10-year life expectancy,prolonged disease-free interval (> 2 years), slow PSA doublingtime, Gleason sum ≤ 6, and PSA < 10 ng/mL.


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Combining Artificial Neural Networks and Transrectal Ultrasound in the Diagnosis of Prostate Cancer

October 1st 2003

Arguably the most important step in the prognosis of prostate canceris early diagnosis. More than 1 million transrectal ultrasound (TRUS)-guided prostate needle biopsies are performed annually in the UnitedStates, resulting in the detection of 200,000 new cases per year. Unfortunately,the urologist's ability to diagnose prostate cancer has not keptpace with therapeutic advances; currently, many men are facing theneed for prostate biopsy with the likelihood that the result will beinconclusive. This paper will focus on the tools available to assist theclinician in predicting the outcome of the prostate needle biopsy. We willexamine the use of "machine learning" models (artificial intelligence),in the form of artificial neural networks (ANNs), to predict prostatebiopsy outcomes using prebiopsy variables. Currently, six validatedpredictive models are available. Of these, five are machine learningmodels, and one is based on logistic regression. The role of ANNs inproviding valuable predictive models to be used in conjunction withTRUS appears promising. In the few studies that have comparedmachine learning to traditional statistical methods, ANN and logisticregression appear to function equivalently when predicting biopsyoutcome. With the introduction of more complex prebiopsy variables,ANNs are in a commanding position for use in predictive models. Easyand immediate physician access to these models will be imperative iftheir full potential is to be realized.