Toward Integrated Clinical-Computational Nuclear Medicine - 19/11/25

Doi : 10.1016/j.cpet.2025.09.003

Faraz Farhadi, MD ^a,^b, Shadi A. Esfahani, MD, MPH ^a, Fereshteh Yousefirizi, PhD ^c, Monica Luo, BSc ^c, Pedro Esquinas Fernandez, PhD ^d, Arkadiusz Sitek, PhD ^a, Hamid Sabet, PhD ^a, Babak Saboury, MD, MPH ^b,^c,^e, Arman Rahmim, PhD ^c,^f,^g, Pedram Heidari, MD ^a,^⁎
^a Department of Radiology, Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, 55 Fruit Street, White 427, Boston, MA 02114, USA
^b Department of Nuclear Oncology, Institute of Nuclear Medicine, Bethesda, MD 20815, USA
^c Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada
^d Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada
^e Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), 9000 Rockville Pike, Bethesda, MD 20892, USA
^f Department of Physics & Astronomy, University of British Columbia, 325 - 6224 Agricultural Road, Vancouver, BC V6T 1Z1, Canada
^g Department of Radiology, University of British Columbia, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada

^∗Corresponding author.

Résumé

The field of clinical-computational nuclear medicine is rapidly advancing, fueled by artificial intelligence, tracer kinetic modeling, radiomics, and integrated informatics. These technologies improve imaging quality, automate lesion detection, and enable personalized radiopharmaceutical therapy through physiologically based pharmacokinetic modeling and voxel-level dosimetry. Workflow automation and Natural Language Processing further enhance operational efficiency. However, successful implementation and adoption of these tools require clinical oversight to ensure accuracy, interpretability, and patient safety. This article highlights key computational innovations and emphasizes the critical role of clinician-guided evaluation in shaping the future of precision imaging and therapy.

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Keywords : Computational nuclear medicine, Artificial intelligence (AI), Radiomics, Predictive modeling, Personalized dosimetry, Workflow automation, Natural language processing (NLP), Radiopharmaceutical therapy (RPT)

Plan

Key points

Introduction

Computational tools in theranostics

Image Reconstruction and Denoising

Lesion Detection, Segmentation, and Conventional Quantitative Biomarkers

Next-generation Quantification: Radiomics for Outcome Prediction

Tracer Kinetic Modeling and Parametric Imaging

Patient-specific Dosimetry and Treatment Planning Tools

Protocoling and Imaging Appropriateness

Computational tools in workflow optimization and system efficiency

Workflow Automation and Integration (Health Level Seven International/Fast Healthcare Interoperability Resources and Beyond)

Natural Language Processing and Automated Reporting

Resource Optimization and Safety Monitoring

Physician-in-the-loop: clinical oversight and integration

Summary

Clinics care points

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Vol 21 - N° 1

P. 17-31 - janvier 2026 Retour au numéro

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What Is Implementation Science : And Why It Matters for Bridging the Artificial Intelligence Innovation-to-Application Gap in Medical Imaging
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Large Language Models Are Reshaping Patient Data Management and Clinical Practice in Nuclear Medicine
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Toward Integrated Clinical-Computational Nuclear Medicine - 19/11/25

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