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Updated: Apr 1, 2026

A Bioluminescent and Fluorescent Orthotopic Syngeneic Murine Model of Androgen-dependent and Castration-resistant Prostate Cancer
Published on: March 6, 2018
Testosterone-Sparing Treatment Strategies for Biochemically Recurrent Prostate Cancer After Radical Prostatectomy
Faris Najdawi1, Rashid K Sayyid2, Thomas Ahlering3
1Division of Urology, Cook County Health and Hospitals System, Chicago, IL, USA.
Purpose Of Review:
Biochemical recurrence (BCR) after radical prostatectomy occurs in up to one-third of patients and increases the risk of metastasis and prostate cancer-specific mortality. While androgen deprivation therapy (ADT) remains a foundational approach, its routine use in low- and intermediate-risk BCR raises concerns regarding overtreatment and long-term quality-of-life (QOL) impairment. This review summarizes contemporary testosterone-sparing treatment (TST) strategies for BCR, including patient selection frameworks, emerging therapeutics, and evolving imaging modalities.
Recent Findings:
Advances in multifactorial risk stratification-including genomic classifiers and artificial intelligence (AI)-driven models-are reshaping treatment decision-making and may help identify patients unlikely to benefit from ADT. PSMA-PET has redefined disease staging and is increasingly used to guide salvage radiation therapy and metastasis-directed therapy. Several emerging TST strategies-including enzalutamide monotherapy, salvage radiation without ADT, metastasis-directed therapy, salvage lymph node dissection, radioligand therapy, and structured lifestyle interventions-demonstrate potential to delay or avoid hormonal therapy. Early evidence suggests select patients can maintain cancer control while avoiding testosterone suppression and its associated toxicities. BCR represents a heterogeneous clinical state, requiring individualized management strategies that balance oncologic efficacy with long-term survivorship concerns. Testosterone-sparing approaches may provide safe alternatives to ADT in appropriately selected patients. Ongoing work to validate predictive biomarkers and integrate AI-derived decision tools is expected to refine patient selection as emerging clinical trials mature.
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