Automated PET tracer classification in multi-site, multi-cohort studies using deep learning

Radiotracer labels in positron emission tomography (PET) DICOM metadata are manually entered and often unreliable or missing, requiring expert visual verification for downstream analyses. We developed and validated deep learning models to classify PET tracers directly from image data across multiple sites and cohorts. Classification was near-perfect when grouping tracers into three biological targets, and the 10-class model generalized well to the four tracers available in the independent external cohort. A few tracers with similar binding characteristics showed some overlap in latent space, with minimal impact on overall classification performance. Our results demonstrate that deep learning models can identify PET tracers from image appearance alone and have the potential to support semi-automated tracer identification and data curation in large multi-site PET studies.