Recommendations for Standardizing Nuclear Medicine Terminology and Data in the Era of Theranostics and Artificial Intelligence

Abstract

There is a pressing need for improved standardization of terminology and data in nuclear medicine. The field is experiencing unprecedented growth, driven by advances in radiopharmaceutical therapy (RPT) and the emergence of artificial intelligence (AI). However, there are challenges that threaten to frustrate this continued progress. For instance, despite the successes of RPT, high-quality evidence on how to best personalize RPT and take full advantage of its theranostics properties is still lacking. To obtain this evidence, large, structured datasets are needed to associate different RPT strategies with patient outcomes. Large datasets are also needed for the development of AI algorithms, especially as new foundation models demand increasingly large training datasets. Both of these obstacles could be overcome by multiinstitutional data sharing. However, inconsistencies in terminology and data collection make effective data pooling difficult. This article, produced by the Society of Nuclear Medicine and Molecular Imaging AI-Dosimetry Working Group, discusses the need for standardization in nuclear medicine terminology and data. We advocate for the adoption of standardized data and metadata frameworks based on controlled biomedical ontologies to better harmonize the collection of nuclear medicine data. We provide recommendations for the field that, if followed, would facilitate multiinstitutional data sharing and allow for the collection of large datasets. We describe a use case demonstrating how standardized vocabularies and data collection can enhance efforts to associate theranostics target expression data with patient outcomes.

Keywords: artificial intelligence; data standardization; image processing; terminology standardization; theranostics.

FIGURE 1.

Example structure of hypothetical biomedical ontology for nuclear medicine concepts. Ontologies organize knowledge hierarchically using classes (general concepts, top row) and subclasses (more specific concepts, middle and bottom rows). Lines indicate an “is a” relationship, where a subclass is a type of its parent class. For instance, (the bolded path) “Preadministration Activity Measurement” is a type of “Radiopharmaceutical Administration Property,” which is a type of “Imaging Procedure Property.”

FIGURE 2.

Role of standardized terminology in integrating diverse concepts within nuclear medicine. Nuclear medicine draws on numerous fields, each with its own unique concepts and vocabularies. Shared, standardized terminology is essential for ensuring that concepts from contributing disciplines are accurately understood, communicated, and integrated within the practice and study of nuclear medicine.

FIGURE 3.

Conceptual workflow for aggregating multiinstitutional theranostics data. Diverse data (orange) and metadata (blue, italics) spanning Imaging, Clinical, Dosimetry, and Patient domains are collected at each center (dashed boxes). Applying standardized terminology (e.g., SNOMED-CT, LOINC, RadLex, NucLex) to data labels ensures consistent meaning between datasets. Standardized data exchange formats (e.g., DICOM, HL7, FHIR) facilitate interoperability. This structured data can then be queried from clinical databases, converted into a common format using a data model, and input into a central database, enabling pooling of information from multiple centers. CDA = clinical document architecture; ICD-10 = International Classification of Diseases, 10th Revision; NCI = National Cancer Institute; PACS = picture archiving and communication system; Params = parameters; Quant. = quantitative.