Computational and AI Methods in Theranostics and Nuclear Oncology, An Issue of PET Clinics

Computational and AI Methods in Theranostics and Nuclear Oncology, An Issue of PET Clinics

  • Arman Rahmim
  • Kuangyu Shi
  • Babak Saboury
Publisher:Elsevier Health SciencesISBN 13: 9780443432019ISBN 10: 0443432015

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Computational and AI Methods in Theranostics and Nuclear Oncology, An Issue of PET Clinics is written by Arman Rahmim and published by Elsevier Health Sciences. It's available with International Standard Book Number or ISBN identification 0443432015 (ISBN 10) and 9780443432019 (ISBN 13).

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. - 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