From uncertainty to clarity: The evolution of Nuclear Medicine towards precision theranostics and AI-driven digital twins.

Authors

Francesco Giammarile, Diana Paez, Anita Brink, Enrique E Lobato, Miriam Mikhail, Peter Knoll

Published in

Hellenic journal of nuclear medicine. Volume 29 Suppl. Pages 16-28.

Abstract

Nuclear medicine has progressed from an exploratory discipline, historically constrained by limited spatial resolution and qualitative interpretation, into a rigorously quantitative clinical specialty. This evolution represents a systematic conversion of uncertainty into actionable information. Uncertainty that was once implicit and observer-dependent is increasingly measured, modelled, and managed across the full imaging-therapy continuum. Early tracer work established the foundational diagnostic logic of assessing function before structure. Subsequent instrumentation advances, culminating in the contemporary deployment of long axial field-of-view total-body positron emission tomography (PET) and digital cadmium-zinc-telluride detectors, have compressed spatial ambiguity and enabled true dynamic, multi-organ kinetic modelling. Hybrid imaging further reduced uncertainty by coupling functional signals to anatomical context, while artificial intelligence has emerged as a transformative force, enabling synthetic attenuation correction, automated total tumour volume segmentation, and the deployment of 3D vision-language foundation models for multimodal analysis. Parallel progress in radiochemistry and target biology has shifted tracer development toward highly specific receptor and antigen ligands. This momentum catalysed the theranostics revolution, where diagnostic imaging serves as an explicit, quantitative gatekeeper for radiopharmaceutical therapy. As the field transitions into the "Alpha-Era"-characterized by the clinical maturation of targeted alpha therapies-dosimetry and radiobiology have become paramount. To harness this complexity, the discipline is adopting Theranostics Digital Twins, advanced computational frameworks that integrate physiologically-based radiopharmacokinetic models, radiobiological optimizers, and patient-specific multi-omics to predict dose-response and mitigate toxicity. Though modern nuclear medicine has not eliminated biological uncertainty, it has formalized its quantification, establishing a highly personalized and augmented therapeutic paradigm capable of overriding sub-clonal tumour resistance and redefining systemic oncology.

PMID:
42472318
Bibliographic data and abstract were imported from PubMed on 19 Jul 2026.

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