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 for this work

Research output: Contribution to journal › Review article › peer-review

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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.

Original language	English
Article number	100865
Number of pages	7
Journal	Clinical and translational radiation oncology
Volume	49
DOIs	https://doi.org/10.1016/j.ctro.2024.100865
Publication status	Published - Nov 2024

Keywords

Artificial intelligence
Brachytherapy
Deep learning
Interventional radiotherapy
Machine learning

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10.1016/j.ctro.2024.100865Licence: CC BY-NC-ND

1-s2.0-S2405630824001423-mainFinal published version, 1.07 MBLicence: CC BY-NC-ND

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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., Antonietta Gambacorta, M., 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, Article 100865. https://doi.org/10.1016/j.ctro.2024.100865

@article{0c60b57ddfb74af4961400f2678af985,

title = "Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients{\textquoteright} needs",

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",

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

note = "Publisher Copyright: {\textcopyright} 2024",

year = "2024",

month = nov,

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

language = "English",

volume = "49",

journal = "Clinical and translational radiation oncology",

issn = "2405-6308",

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Fionda, B, Placidi, E, de Ridder, M, Strigari, L, Patarnello, S, Tanderup, K, Hannoun-Levi, JM, Siebert, FA, Boldrini, L, Antonietta Gambacorta, M, 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, 100865. https://doi.org/10.1016/j.ctro.2024.100865

TY - JOUR

T1 - Artificial intelligence in interventional radiotherapy (brachytherapy)

T2 - 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 - Antonietta Gambacorta, Maria

AU - De Spirito, Marco

AU - Sala, Evis

AU - Tagliaferri, Luca

PY - 2024/11

Y1 - 2024/11

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

UR - https://www.scopus.com/pages/publications/85204620628

U2 - 10.1016/j.ctro.2024.100865

DO - 10.1016/j.ctro.2024.100865

M3 - Review article

AN - SCOPUS:85204620628

SN - 2405-6308

VL - 49

JO - Clinical and translational radiation oncology

JF - Clinical and translational radiation oncology

M1 - 100865

ER -

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

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