Application of Artificial Intelligence in Oncologic Molecular PET-Imaging: A Narrative Review on Beyond [18F]F-FDG Tracers - Part I. PSMA, Choline, and DOTA Radiotracers
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Title
Application of Artificial Intelligence in Oncologic Molecular PET-Imaging: A Narrative Review on Beyond [18F]F-FDG Tracers - Part I. PSMA, Choline, and DOTA Radiotracers
Authors
Keywords
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Journal
SEMINARS IN NUCLEAR MEDICINE
Volume -, Issue -, Pages -
Publisher
Elsevier BV
Online
2023-09-24
DOI
10.1053/j.semnuclmed.2023.08.004
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- Freely Available, Fully Automated AI-Based Analysis of Primary Tumour and Metastases of Prostate Cancer in Whole-Body [18F]-PSMA-1007 PET-CT
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- Utility of radiomic zones for risk classification and clinical outcome predictions using supervised machine learning during simultaneous 11 C‐choline PET/MRI acquisition in prostate cancer patients
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- A Bibliometric Analysis of ArtificialIntelligence in Healthcare (Preprint)
- (2020) Yuqi Guo et al. JOURNAL OF MEDICAL INTERNET RESEARCH
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