Genitourinary Cancers

Latest News

A machine learning-based approach found that evaluating multiple biomarker features may identify outcomes and treatment resistance in renal cell carcinoma.
Machine Learning Approach May Predict Outcomes in RCC

June 4th 2025

A machine learning-based approach found that evaluating multiple biomarker features may identify outcomes and treatment resistance in renal cell carcinoma.

The addition of CAN-2409 to a prodrug and radiation therapy in intermediate-to-high-risk prostate cancer significantly improved cancer-specific outcomes.
CAN-2409/EBRT Improves Disease-Free Survival in Localized Prostate Cancer

June 3rd 2025

Efficacy and safety outcomes in the phase 3 CONTACT-03 study were consistent regardless of prior immunotherapy or tyrosine kinase inhibitor use.
Second-Line Cabozantinib Regimens Exhibit Efficacy in Advanced RCC

June 2nd 2025

Eight votes were cast against the favorability of talazoparib and enzalutamide in the first-line setting for patients with metastatic castration-resistant prostate cancer.
ODAC Votes 8-to-0 Against First-Line Talazoparib/Enzalutamide in mCRPC

May 21st 2025

Data from the LITESPARK-015 trial supported the FDA’s decision to approve belzutifan monotherapy in patients with advanced, unresectable, or metastatic PPGL.
FDA Approves Belzutifan in Adult/Pediatric Pheochromocytoma and Paraganglioma

May 14th 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.