Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients’ needs

Bruno Fionda; Elisa Placidi; Mischa de Ridder; Lidia Strigari; Stefano Patarnello; Kari Tanderup; Jean-Michel Hannoun-Levi; Frank-André Siebert; Luca Boldrini; Maria Antonietta Gambacorta; Marco De Spirito; Evis Sala; Luca Tagliaferri

doi:10.1016/j.ctro.2024.100865

Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients’ needs

Bruno Fionda
, Elisa Placidi^*
, Mischa de Ridder
, Lidia Strigari
, Stefano Patarnello
, Kari Tanderup
, Jean-Michel Hannoun-Levi
, Frank-André Siebert
, Luca Boldrini
, Maria Antonietta Gambacorta
, Marco De Spirito
, Evis Sala
, Luca Tagliaferri

^*Corresponding author

Utrecht University
Alma Mater Studiorum University of Bologna
Aarhus University

Research output: Contribution to journal › Article

Abstract

This review explores the integration of artificial intelligence (AI) in interventional radiotherapy (IRT), emphasizing its potential to streamline workflows and enhance patient care. Through a systematic analysis of 78 relevant papers spanning from 2002 to 2024, we identified significant advancements in contouring, treatment planning, outcome prediction, and quality assurance. AI-driven approaches offer promise in reducing procedural times, personalizing treatments, and improving treatment outcomes for oncological patients. However, challenges such as clinical validation and quality assurance protocols persist. Nonetheless, AI presents a transformative opportunity to optimize IRT and meet evolving patient needs.

Original language	English
Pages (from-to)	N/A-N/A
Journal	Clinical and Translational Radiation Oncology
Volume	49
Issue number	N/A
DOIs	https://doi.org/10.1016/j.ctro.2024.100865
Publication status	Published - 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Oncology
Radiology Nuclear Medicine and imaging

Keywords

Artificial intelligence
Brachytherapy
Deep learning
Interventional radiotherapy
Machine learning

Access to Document

10.1016/j.ctro.2024.100865

Cite this

Fionda, B., Placidi, E., de Ridder, M., Strigari, L., Patarnello, S., Tanderup, K., Hannoun-Levi, J.-M., Siebert, F.-A., Boldrini, L., Gambacorta, M. A., De Spirito, M., Sala, E., & Tagliaferri, L. (2024). Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients’ needs. Clinical and Translational Radiation Oncology, 49(N/A), N/A-N/A. https://doi.org/10.1016/j.ctro.2024.100865

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abstract = "This review explores the integration of artificial intelligence (AI) in interventional radiotherapy (IRT), emphasizing its potential to streamline workflows and enhance patient care. Through a systematic analysis of 78 relevant papers spanning from 2002 to 2024, we identified significant advancements in contouring, treatment planning, outcome prediction, and quality assurance. AI-driven approaches offer promise in reducing procedural times, personalizing treatments, and improving treatment outcomes for oncological patients. However, challenges such as clinical validation and quality assurance protocols persist. Nonetheless, AI presents a transformative opportunity to optimize IRT and meet evolving patient needs.",

keywords = "Artificial intelligence, Brachytherapy, Deep learning, Interventional radiotherapy, Machine learning, Artificial intelligence, Brachytherapy, Deep learning, Interventional radiotherapy, Machine learning",

author = "Bruno Fionda and Elisa Placidi and \{de Ridder\}, Mischa and Lidia Strigari and Stefano Patarnello and Kari Tanderup and Jean-Michel Hannoun-Levi and Frank-Andr{\'e} Siebert and Luca Boldrini and Gambacorta, \{Maria Antonietta\} and \{De Spirito\}, Marco and Evis Sala and Luca Tagliaferri",

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doi = "10.1016/j.ctro.2024.100865",

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Fionda, B, Placidi, E, de Ridder, M, Strigari, L, Patarnello, S, Tanderup, K, Hannoun-Levi, J-M, Siebert, F-A, Boldrini, L , Gambacorta, MA , De Spirito, M , Sala, E & Tagliaferri, L 2024, 'Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients’ needs', Clinical and Translational Radiation Oncology, vol. 49, no. N/A, pp. N/A-N/A. https://doi.org/10.1016/j.ctro.2024.100865

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T1 - Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients’ needs

AU - Fionda, Bruno

AU - Placidi, Elisa

AU - de Ridder, Mischa

AU - Strigari, Lidia

AU - Patarnello, Stefano

AU - Tanderup, Kari

AU - Hannoun-Levi, Jean-Michel

AU - Siebert, Frank-André

AU - Boldrini, Luca

AU - Gambacorta, Maria Antonietta

AU - De Spirito, Marco

AU - Sala, Evis

AU - Tagliaferri, Luca

PY - 2024

Y1 - 2024

N2 - This review explores the integration of artificial intelligence (AI) in interventional radiotherapy (IRT), emphasizing its potential to streamline workflows and enhance patient care. Through a systematic analysis of 78 relevant papers spanning from 2002 to 2024, we identified significant advancements in contouring, treatment planning, outcome prediction, and quality assurance. AI-driven approaches offer promise in reducing procedural times, personalizing treatments, and improving treatment outcomes for oncological patients. However, challenges such as clinical validation and quality assurance protocols persist. Nonetheless, AI presents a transformative opportunity to optimize IRT and meet evolving patient needs.

AB - This review explores the integration of artificial intelligence (AI) in interventional radiotherapy (IRT), emphasizing its potential to streamline workflows and enhance patient care. Through a systematic analysis of 78 relevant papers spanning from 2002 to 2024, we identified significant advancements in contouring, treatment planning, outcome prediction, and quality assurance. AI-driven approaches offer promise in reducing procedural times, personalizing treatments, and improving treatment outcomes for oncological patients. However, challenges such as clinical validation and quality assurance protocols persist. Nonetheless, AI presents a transformative opportunity to optimize IRT and meet evolving patient needs.

KW - Artificial intelligence

KW - Brachytherapy

KW - Deep learning

KW - Interventional radiotherapy

KW - Machine learning

KW - Artificial intelligence

KW - Brachytherapy

KW - Deep learning

KW - Interventional radiotherapy

KW - Machine learning

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SN - 2405-6308

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JO - Clinical and Translational Radiation Oncology

JF - Clinical and Translational Radiation Oncology

IS - N/A

ER -

Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients’ needs

Abstract

UN SDGs

All Science Journal Classification (ASJC) codes

Keywords

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