Dosimetry Data Infrastructure for Individualized Radiopharmaceutical Therapy: International Experience and a Proposed Framework for China

Background/Objectives: There is increasing interest in moving selected radiopharmaceutical therapy (RPT) applications toward treatment informed by patient-specific quantitative data. Patient-specific dosimetry supports this transition, but routine implementation remains limited by methodological heterogeneity and incomplete linkage of absorbed dose with response, toxicity, and subsequent treatment decisions. This review examines evidence relevant to longitudinal dosimetry infrastructure and proposes a framework for China. Methods: This narrative review mapped evidence from established therapies, including 177Lu-DOTATATE, 177Lu-PSMA-617, 131I-NaI, and 131I-meta-iodobenzylguanidine (131I-MIBG), with 90Y microsphere selective internal radiation therapy used as a methodologically distinct comparator. International registries, quantitative imaging standards, and dosimetry resources were reviewed in parallel. Results: Available studies show substantial interpatient variation in absorbed dose and growing evidence of dose–response relationships. However, prescription, dosimetry methods, outcome definitions, and follow-up remain heterogeneous. Existing platforms address separate components of RPT care, but few link administration, pretherapy imaging, post-therapy quantitative imaging, dosimetry, clinical outcomes, toxicity, and later decisions across a complete treatment course. We propose seven core data domains and a three-tier architecture comprising hospital-based treatment decision platforms, regional dosimetry centers, and a national data center. Conclusions: A standardized longitudinal dosimetry infrastructure could improve multicenter comparability and provide a testable data foundation for individualized treatment, radiopharmaceutical development, and real-world evaluation. The proposed architecture is conceptual and requires staged consensus, pilot implementation, and prospective validation.