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Role of Artificial Intelligence in Theranostics: Toward Routine Personalized Radiopharmaceutical Therapies

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Date

2021-10

Authors

Brosch-Lenz, Julia
Yousefirizi, Fereshteh
Zukotynski, Katherine
Beauregard, Jean-Mathieu
Gaudet, Vincent C.
Saboury, Babak
Rahmim, Arman
Uribe, Carlos F.

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

Abstract

KEY POINTS AI has shown promising applications in quantitative imaging required for dosimetry. Segmentation of organs and tumors, the most time consuming task in the dosimetry workflow, can be automated using AI. Using the theranostic approach, AI models that predict absorbed dose and therapy outcomes might play a key role in personalizing RPTs. AI has significant potential to improve accuracy and reduce times for routine implementation of patient-specific dosimetry in RPTs.

Description

The final publication is available at Elsevier via https://doi.org/10.1016/j.cpet.2021.06.002. © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

theranostics, radiopharmaceutical therapies, dosimetry, artificial intelligence, outcome prediction, segmentation, registration, quantitative imaging

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Citation

URI

https://doi.org/10.1016/j.cpet.2021.06.002
 http://hdl.handle.net/10012/20109

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Waterloo Research