Artificial Intelligence for Tumor [F]FDG PET Imaging: Advancements and Future Trends - Part II.

Journal: Seminars in nuclear medicine
Published Date: Jul 18, 2025

Abstract

The integration of artificial intelligence (AI) into [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.

Authors

  • Alireza Safarian
    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.
  • Seyed Ali Mirshahvalad
    Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Canada. Electronic address: mirshahvalad.sa@gmail.com.
  • Abolfazl Farbod
    Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran.
  • Theresa Jung
    Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
  • Hadi Nasrollahi
    Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
  • Gregor Schweighofer-Zwink
    Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
  • Gundula Rendl
    Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
  • Christian Pirich
    Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
  • Reza Vali
    Department of Diagnostic Imaging, Division of Nuclear Medicine, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada. Electronic address: reza.vali@sickkids.ca.
  • Mohsen Beheshti
    Department of Nuclear Medicine, University Hospital, RWTH University, Aachen, Germany; Department of Nuclear Medicine & Endocrinology, Paracelsus Medical University, Salzburg, Austria.

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