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.