The Convergence of Big Data and Nuclear Medicine: Ushering in a New Era of Personalized Healthcare

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The Convergence of Big Data and Nuclear Medicine: Ushering in a New Era of Personalized Healthcare


Introduction

Theranostics—a groundbreaking approach that integrates both diagnosis and therapy through a single molecular ligand—has emerged as a transformative innovation in nuclear medicine. Recognized as a future cornerstone of oncology, theranostics is more than a clinical breakthrough; it demands a robust informatics infrastructure to support its complex, data-intensive workflows.

In this article, we explore how medical imaging informatics is accelerating the development of theranostics and enabling a new paradigm of personalized, data-driven medicine.

1. What Is Theranostics?

The term theranostics merges therapy and diagnostics, referring to a strategy that uses a single radiolabeled ligand to both identify and treat disease. This dual capability is most commonly deployed through PET (Positron Emission Tomography) or SPECT (Single Photon Emission Computed Tomography) imaging, which allows clinicians to visualize disease location and biological activity, followed by targeted radiotherapeutic destruction of tumors using the same molecular platform.

The U.S. Food and Drug Administration (FDA) has endorsed several theranostic agents, demonstrating the field’s viability:

  • 2013: Radium-223 dichloride (Xofigo)

  • 2018: Lutetium-177 dotatate (Lutathera)

  • 2022: Lutetium-177 PSMA-617 (Pluvicto)

Today, over 300 active clinical trials are exploring theranostics across various solid tumors, with projections indicating that up to 60% of nuclear medicine procedures may soon involve theranostic strategies.

2. Why Informatics Is Essential

Theranostics generates vast volumes of heterogeneous data—including imaging biomarkers, radiation dosimetry, genomic profiles, pathology reports, and therapeutic outcomes. Parameters such as SUV (Standardized Uptake Values) from PET scans, tumor volumes, dosimetry reconstructions, and blood test results must be unified within each patient record.

Traditional PACS (Picture Archiving and Communication Systems) and EHR (Electronic Health Records) are insufficient to handle this level of data integration and complexity. Enter medical informatics—the key enabler of theranostics scalability.

Through standardization, harmonization, and semantic analysis, informatics platforms elevate the precision, predictability, and efficiency of diagnosis and therapy.

3. Standardizing Language: The NucLex Ontology


While radiology has long benefited from RadLex, a standardized imaging lexicon developed by the American College of Radiology, nuclear medicine remained underserved. Complex nomenclature related to radiopharmaceuticals, dosage metrics, and anatomical specificity created major barriers to data integration.

To address this, the NucLex ontology was developed under the guidance of the SNMMI (Society of Nuclear Medicine and Molecular Imaging) AI Task Force. NucLex is an ontology extension of RadLex, purpose-built for nuclear medicine and theranostics.

By enabling semantic interoperability, NucLex facilitates cross-institutional sharing of theranostic datasets—fueling research, AI training, and harmonized clinical workflows.

4. Metadata and DICOM Extensions for Theranostics

While DICOM (Digital Imaging and Communications in Medicine) serves as the standard for imaging data exchange, theranostics demands expanded metadata schemas to accommodate unique requirements:

Key data fields requiring structured representation include:

  • Absolute calibration values of PET/SPECT systems

  • Patient-specific radiopharmaceutical dosage and administration time

  • Quantified biodistribution over multiple timepoints

  • Tumor-specific dose-response correlations

Current initiatives aim to store such information using DICOM Structured Reporting (DICOM-SR) formats, optimized for AI compatibility and multicenter standardization.

5. The Future of Informatics-Driven Theranostics

The integration of informatics with theranostics unlocks powerful new capabilities:

  • AI-driven personalized treatment planning

  • Multimodal data fusion (imaging, genomics, dosimetry) to optimize therapy schedules

  • Global theranostic registries, enabling cross-border data sharing and harmonized trials

  • Cloud-based standardized patient records, supporting longitudinal and international research

  • Digital Twin models, simulating individualized therapeutic responses

These advances are not merely technological; they are critical to patient safety, outcome predictability, and healthcare equity.

Conclusion

Theranostics represents a next-generation approach in nuclear medicine, combining molecular imaging and targeted radiotherapy to offer precise, personalized care.

Yet, realizing the full potential of theranostics requires more than advanced ligands or imaging systems. It necessitates a robust informatics infrastructure, built on interoperable ontologies like NucLex, enhanced DICOM metadata, and AI-ready datasets.

Informatics is the invisible infrastructure behind the theranostic revolution, poised to transform the medical paradigm over the next decade.

References

  • SIIM Annual Meeting. “The Informatics of Theranostics” session, May 22, 2025.

  • Bradshaw T., University of Wisconsin-Madison. Panel on theranostics and data architecture.

  • Siegel E., University of Maryland. “Tracking therapeutic response with informatics systems in theranostics.”

  • SNMMI AI Task Force. Development of NucLex: A Nuclear Medicine Ontology Extension to RadLex, 2024.

  • American College of Radiology. RadLex: A Lexicon for Uniform Medical Imaging Terminology.

  • IHE Radiology. Extending DICOM for Theranostic PET/SPECT Dosimetry Workflows.

  • Novartis. Clinical Pipeline of Lu-177 Based Radioligand Therapies, accessed 2025.

  • Journal of Nuclear Medicine, 2023–2025 issues: multi-timepoint dosimetry and theranostics-AI integration.

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