Convergence methodologies for PSMA-targeted radiotheranostics: A transformative blueprint for precision prostate cancer management

Background PSMA-targeted radiotheranostics has become an important component of precision prostate cancer management, supported by recent clinical trials of ^177Lu-PSMA-617 and ^225Ac-PSMA ligands. Concurrently, the therapeutic landscape of metastatic prostate cancer has expanded to include androgen receptor pathway inhibitors, chemotherapy, and PARP inhibitors. Despite these advances, treatment response remains heterogeneous, and current radiotheranostic workflows remain fragmented across imaging, radiopharmaceutical design, dosimetry, and clinical implementation. Objectives This study proposes a convergence-based methodological framework that integrates molecular imaging, biomarker stratification, nanotechnology-enabled delivery, and artificial intelligence (AI)-driven dosimetry into a unified and adaptive radiotheranostic strategy for prostate cancer. Methods A focused synthesis of recent clinical and translational studies (2015–2025) was performed covering PSMA PET phenotyping, radiopharmaceutical engineering, nanoplatform-based delivery systems, biomarker-guided isotope selection, and AI-enabled dosimetry. These components were integrated into a multi-domain convergence model linking ^68Ga-PSMA PET imaging, molecular biomarkers, nanotechnology platforms, and predictive computational analytics into a continuous decision-support pipeline. Results The proposed framework demonstrates how quantitative PSMA PET metrics and molecular biomarkers can guide rational selection between β- and α-emitting radionuclides and enable adaptive dose optimization. Integration of nanoplatforms improves tumor targeting and therapeutic flexibility, while AI-driven predictive models support estimation of tumor response and organ toxicity prior to radionuclide administration. Evidence from recent preclinical and clinical studies supports the feasibility of image-guided personalization and adaptive treatment strategies within this convergence architecture. Conclusions The convergence-based framework advances PSMA radiotheranostics from isolated technological components toward a biologically informed, data-driven, and clinically scalable therapeutic paradigm. By unifying molecular imaging, biomarkers, nanotechnology, and AI-driven dosimetry, this approach addresses limitations of population-based dosing and heterogeneous treatment response, providing a foundation for next-generation precision radiotheranostic medicine.