Artificial Intelligence for Tumor [<sup>18</sup>F]FDG PET Imaging: Advancements and Future Trends - Part II.
Authors
Affiliations (6)
Affiliations (6)
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Rajaie Cardiovascular Medical and Research Center, Rajaie Cardiovascular Institute, Iran University of Medical Sciences, Tehran, Iran.
- Joint Department of Medical Imaging, University Medical Imaging Toronto, University of Toronto, Toronto, Ontario, Canada.
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran.
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
- Department of Diagnostic & Interventional Radiology, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada.
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria. Electronic address: [email protected].
Abstract
The integration of artificial intelligence (AI) into [<sup>18</sup>F]FDG PET/CT imaging continues to expand, offering new opportunities for more precise, consistent, and personalized oncologic evaluations. Building on the foundation established in Part I, this second part explores AI-driven innovations across a broader range of malignancies, including hematological, genitourinary, melanoma, and central nervous system tumors as well applications of AI in pediatric oncology. Radiomics and machine learning algorithms are being explored for their ability to enhance diagnostic accuracy, reduce interobserver variability, and inform complex clinical decision-making, such as identifying patients with refractory lymphoma, assessing pseudoprogression in melanoma, or predicting brain metastases in extracranial malignancies. Additionally, AI-assisted lesion segmentation, quantitative feature extraction, and heterogeneity analysis are contributing to improved prediction of treatment response and long-term survival outcomes. Despite encouraging results, variability in imaging protocols, segmentation methods, and validation strategies across studies continues to challenge reproducibility and remains a barrier to clinical translation. This review evaluates recent advancements of AI, its current clinical applications, and emphasizes the need for robust standardization and prospective validation to ensure the reproducibility and generalizability of AI tools in PET imaging and clinical practice.