Computational and AI Methods in Theranostics and Nuclear Oncology, An Issue of PET Clinics, 1st Edition
In this issue of PET Clinics, guest editors Drs. Arman Rahmim, Kuangyu Shi, and Babak Saboury bring their considerable expertise to the topic of Computational and AI Methods toward Successful Applications of Theranostics. The field of nuclear medicine is currenting undergoing a renaissance, directly related to theranostics applications, where we can “see what we treat and treat what we see” using radiopharmaceuticals that target important disease biomarkers with high sensitivity. This special issue focuses on computational and AI methods tools and techniques available and under development to enable optimal use of theranostic applications.
ISBNコード :
9780443432019
出版日 :
27-11-2025
In this issue of PET Clinics, guest editors Drs. Arman Rahmim, Kuangyu Shi, and Babak Saboury bring their considerable expertise to the topic of Computational and AI Methods toward Successful Applications of Theranostics. The field of nuclear medicine is currenting undergoing a renaissance, directly related to theranostics applications, where we can “see what we treat and treat what we see” using radiopharmaceuticals that target important disease biomarkers with high sensitivity. This special issue focuses on computational and AI methods tools and techniques available and under development to enable optimal use of theranostic applications.
特長
- Contains 14 relevant, practice-oriented topics including PBPK models, digital twins, and verification, validation and uncertainty quantification (VVUQ) in theranostics; mathematical and computational nuclear oncology: tools and techniques towards optimized radiopharmaceutical therapies and theranostic digital twins; computational radiobiology; mathematical oncology, cancer evolution, and radiopharmaceutical therapy; and more
- Provides in-depth clinical reviews on computational and AI methods toward successful applications of theranostics, offering actionable insights for clinical practice
- Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize and distill the latest research and practice guidelines to create clinically significant, topic-based reviews
著者情報
Edited by Arman Rahmim, PhD, PET-MRI Imaging Centre, Faculty of Medicine; Kuangyu Shi, PhD, Associate Professor, Department of Nuclear Medicine, University of Bern, Switzerland and Babak Saboury, MD, MPH, Senior Clinical Advisor to National Institutes of Health (NIH), Co-Founder and Chief Innovation Officer United Theranostics, Adjunct Professor of Computer ScienceAdjunct Professor of Computer Science, University of Maryland, USA
| ISBN Number | 9780443432019 |
|---|---|
| Description Author List | Edited by Arman Rahmim, PhD, PET-MRI Imaging Centre, Faculty of Medicine; Kuangyu Shi, PhD, Associate Professor, Department of Nuclear Medicine, University of Bern, Switzerland and Babak Saboury, MD, MPH, Senior Clinical Advisor to National Institutes of Health (NIH), Co-Founder and Chief Innovation Officer United Theranostics, Adjunct Professor of Computer ScienceAdjunct Professor of Computer Science, University of Maryland, USA |
| Copyright Year | 2026 |
| Edition Number | 1 |
| Format | E-Book |
| Imprint | Elsevier |
| Page Count | 240 |
| Publication Date | 27 Nov 2025 |
| Stock Status | IN STOCK |
What Is Implementation Science: And Why It Matters for Bridging the Artificial Intelligence Innovation-to-Application Gap in Medical Imaging
Toward Integrated Clinical-Computational Nuclear Medicine
Large Language Models Are Reshaping Patient Data Management and Clinical Practice in Nuclear Medicine
Seeing More, Treating Smarter: Role of Long-axial Field-of-view PET/CT in the Evolution of Theranostics
Advances in SPECT and PET Reconstruction for Theranostics: From Diagnosis to Therapy
Artificial Intelligence for Simplified Patient-centered Dosimetry in Radiopharmaceutical Therapies
An Overview of Physiologically Based Pharmacokinetic (PBPK) and Population Pharmacokinetic (PopPK) Models: Applications to Radiopharmaceutical Therapies for Analysis and Personalization
Verification, Validation, and Uncertainty Quantification (VVUQ) of Physiologically Based Pharmacokinetic Models for Theranostic Digital Twins: Toward Reliable Model-Informed Treatment Planning for Radiopharmaceutical Therapies
Mathematical and Computational Nuclear Oncology: Toward Optimized Radiopharmaceutical Therapy via Digital Twins
Quantitative and Computational Radiobiology for Precision Radiopharmaceutical Therapies
A Brief History of Digital Twin Technology
Toward Digital Twins for Optimal Radioembolization
Radiopharmaceutical Therapy and Immunotherapy Combinations Utilizing Cancer Evolution and Computational Modeling in Metastatic Prostate Cancer
Toward Integrated Clinical-Computational Nuclear Medicine
Large Language Models Are Reshaping Patient Data Management and Clinical Practice in Nuclear Medicine
Seeing More, Treating Smarter: Role of Long-axial Field-of-view PET/CT in the Evolution of Theranostics
Advances in SPECT and PET Reconstruction for Theranostics: From Diagnosis to Therapy
Artificial Intelligence for Simplified Patient-centered Dosimetry in Radiopharmaceutical Therapies
An Overview of Physiologically Based Pharmacokinetic (PBPK) and Population Pharmacokinetic (PopPK) Models: Applications to Radiopharmaceutical Therapies for Analysis and Personalization
Verification, Validation, and Uncertainty Quantification (VVUQ) of Physiologically Based Pharmacokinetic Models for Theranostic Digital Twins: Toward Reliable Model-Informed Treatment Planning for Radiopharmaceutical Therapies
Mathematical and Computational Nuclear Oncology: Toward Optimized Radiopharmaceutical Therapy via Digital Twins
Quantitative and Computational Radiobiology for Precision Radiopharmaceutical Therapies
A Brief History of Digital Twin Technology
Toward Digital Twins for Optimal Radioembolization
Radiopharmaceutical Therapy and Immunotherapy Combinations Utilizing Cancer Evolution and Computational Modeling in Metastatic Prostate Cancer
あなたのレビューを投稿する
