Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative
metrics—the so-called radiomic features—within medical images. Radiomic features capture …

Artificial intelligence (AI) will change the face of nuclear medicine and molecular imaging as
it will in everyday life. In this review, we focus on the potential applications of AI in the field …

Multi‐omics usually refers to the crossover application of multiple high‐throughput screening
technologies represented by genomics, transcriptomics, single‐cell transcriptomics …

Purpose A major challenge for accurate quantitative SPECT imaging of some radionuclides
is the inadequacy of simple energy window-based scatter estimation methods, widely …

Background Human epidermal growth factor receptor 2 (HER2) expression status
determination significantly contributes to HER2-targeted therapy in breast cancer (BC). The …

The emergence of artificial intelligence (AI) in nuclear medicine and radiology has been
accompanied by AI commentators and experts predicting that AI would make radiologists, in …

Purpose To examine the usefulness of machine learning to predict prognosis in cervical
cancer using clinical and radiomic features of 2-deoxy-2-[18 F] fluoro-D-glucose (18 F-FDG) …

Artificial intelligence (AI) in nuclear medicine and radiology represents a significant
disruptive technology. Although there has been much debate about the impact of AI on the …

Machine learning (ML) algorithms have found increasing utility in the medical imaging field
and numerous applications in the analysis of digital biomarkers within positron emission …

Purpose Deep learning is an emerging reconstruction method for positron emission
tomography (PET), which can tackle complex PET corrections in an integrated procedure …