Prostate Cancer

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Landmark Radioligand Therapy Approval Bridges Treatment Gap in mCRPC
Landmark Radioligand Therapy Approval Bridges Treatment Gap in mCRPC

April 18th 2025

Administering 177Lu for mCRPC is a “team sport”, according to Steven Finkelstein, MD, DABR, FACRO.

Rad Onc First to Dose 177Lu in US, Notes Enormity of Recent mCRPC Approval
Rad Onc First to Dose 177Lu in US, Notes Enormity of Recent mCRPC Approval

April 17th 2025

Pembrolizumab/Docetaxel Does Not Improve OS/rPFS Outcomes in mCRPC
Pembrolizumab/Docetaxel Does Not Improve OS/rPFS Outcomes in mCRPC

April 15th 2025

The phase 3 MIRAGE trial findings show that PROSTOX ultra was validated as a biomarker to predict genitourinary toxicity following SBRT.
PROSTOX ultra Reliably Predicts Long-Term Radiation AEs in Prostate Cancer

April 9th 2025

Results from PSMAfore show that lutetium Lu 177 vipivotide tetraxetan elicited a median rPFS of 9.3 months vs 5.6 months with ARPI in prostate cancer.
FDA Approves Radioligand Therapy in PSMA–Positive, Castration-Resistant PC

March 28th 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.