Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy

Hossein Azadinejad; Mohammad Farhadi Rad; Ahmad Shariftabrizi; Arman Rahmim; Hamid Abdollahi

doi:10.3390/diagnostics15030397

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Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy

Journal article

Open access

Peer reviewed

Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy

Hossein Azadinejad, Mohammad Farhadi Rad, Ahmad Shariftabrizi, Arman Rahmim and Hamid Abdollahi

Diagnostics (Basel), Vol.15(3), 397

02/06/2025

DOI: 10.3390/diagnostics15030397

PMCID: PMC11816985

PMID: 39941326

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https://doi.org/10.3390/diagnostics15030397View

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Abstract

Radioimmunotherapy (RIT) is a novel cancer treatment that combines radiotherapy and immunotherapy to precisely target tumor antigens using monoclonal antibodies conjugated with radioactive isotopes. This approach offers personalized, systemic, and durable treatment, making it effective in cancers resistant to conventional therapies. Advances in artificial intelligence (AI) present opportunities to enhance RIT by improving precision, efficiency, and personalization. AI plays a critical role in patient selection, treatment planning, dosimetry, and response assessment, while also contributing to drug design and tumor classification. This review explores the integration of AI into RIT, emphasizing its potential to optimize the entire treatment process and advance personalized cancer care.

Monoclonal Antibodies

Personalized Medicine

cancer treatment

radioimmunotherapy

Details

Title: Subtitle: Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy
Creators: Hossein Azadinejad - Kermanshah University of Medical Sciences
Mohammad Farhadi Rad - Kermanshah University of Medical Sciences
Ahmad Shariftabrizi - University of Iowa
Arman Rahmim - University of British Columbia
Hamid Abdollahi - University of British Columbia
Resource Type: Journal article
Publication Details: Diagnostics (Basel), Vol.15(3), 397
DOI: 10.3390/diagnostics15030397
PMID: 39941326
PMCID: PMC11816985
NLM abbreviation: Diagnostics (Basel)
ISSN: 2075-4418
eISSN: 2075-4418
Publisher: MDPI
Grant note: Natural Sciences and Engineering Research Council of Canada (NSERC)Networking Health Ltd.Mitacs Elevate grant: IT37616
This project was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN-2019-06467, Networking Health Ltd., and Mitacs Elevate grant IT37616.
Language: English
Date published: 02/06/2025
Academic Unit: Radiology
Record Identifier: 9984790972002771

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