Recommendations for Standardizing Nuclear Medicine Terminology and Data in the Era of Theranostics and Artificial Intelligence
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.229 | 0.457 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.018 | 0.032 |
| Open science | 0.013 | 0.012 |
| Research integrity | 0.016 | 0.032 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".