| dc.contributor.author | Brosch-Lenz, Julia | |
| dc.contributor.author | Yousefirizi, Fereshteh | |
| dc.contributor.author | Zukotynski, Katherine | |
| dc.contributor.author | Beauregard, Jean-Mathieu | |
| dc.contributor.author | Gaudet, Vincent C. | |
| dc.contributor.author | Saboury, Babak | |
| dc.contributor.author | Rahmim, Arman | |
| dc.contributor.author | Uribe, Carlos F. | |
| dc.date.accessioned | 2023-11-21 16:06:11 (GMT) | |
| dc.date.available | 2023-11-21 16:06:11 (GMT) | |
| dc.date.issued | 2021-10 | |
| dc.identifier.uri | https://doi.org/10.1016/j.cpet.2021.06.002 | |
| dc.identifier.uri | http://hdl.handle.net/10012/20109 | |
| dc.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/ | en |
| dc.description.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. | en |
| dc.description.sponsorship | Natural Sciences and Engineering Research Council of Canada (NSERC), Discovery G4rant RGPIN-2019-06467 || NSERC, Discovery Grant RGPIN-2021-02965. | en |
| dc.language.iso | en | en |
| dc.publisher | Elsevier | en |
| dc.relation.ispartofseries | PET Clinics;16(4) | |
| dc.subject | theranostics | en |
| dc.subject | radiopharmaceutical therapies | en |
| dc.subject | dosimetry | en |
| dc.subject | artificial intelligence | en |
| dc.subject | outcome prediction | en |
| dc.subject | segmentation | en |
| dc.subject | registration | en |
| dc.subject | quantitative imaging | en |
| dc.title | Role of Artificial Intelligence in Theranostics: Toward Routine Personalized Radiopharmaceutical Therapies | en |
| dc.type | Article | en |
| dcterms.bibliographicCitation | Brosch-Lenz, J., Yousefirizi, F., Zukotynski, K., Beauregard, J.-M., Gaudet, V., Saboury, B., Rahmim, A., & Uribe, C. (2021). Role of artificial intelligence in Theranostics. PET Clinics, 16(4), 627–641. https://doi.org/10.1016/j.cpet.2021.06.002 | en |
| uws.contributor.affiliation1 | Faculty of Engineering | en |
| uws.contributor.affiliation2 | Electrical and Computer Engineering | en |
| uws.typeOfResource | Text | en |
| uws.peerReviewStatus | Reviewed | en |
| uws.scholarLevel | Faculty | en |
