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

被引：0
作者：
Fionda, Bruno [1 ]
Placidi, Elisa [1 ]
de Ridder, Mischa [2 ]
Strigari, Lidia [3 ]
Patarnello, Stefano [4 ]
Tanderup, Kari [5 ,6 ]
Hannoun-Levi, Jean-Michel [7 ]
Siebert, Frank-Andre [8 ]
Boldrini, Luca [1 ]
Gambacorta, Maria Antonietta [1 ,9 ]
De Spirito, Marco [10 ,11 ]
Sala, Evis [1 ,9 ]
Tagliaferri, Luca [1 ,9 ]
机构：
[1] Fdn Policlin Univ A Gemelli IRCCS, Dipartimento Diagnost Immagini & Radioterapia Onco, Rome, Italy
[2] Univ Med Ctr Utrecht, Dept Radiat Oncol, Utrecht, Netherlands
[3] IRCCS Azienda Ospedaliero Univ Bologna, Dept Med Phys, Bologna, Italy
[4] Fdn Policlin Univ A Gemelli IRCCS, Real World Data Facil, Gemelli Generator, Rome, Italy
[5] Aarhus Univ Hosp, Dept Oncol, Aarhus, Denmark
[6] Aarhus Univ, Dept Clin Med, Aarhus, Denmark
[7] Univ Cote Azur, Antoine Lacassagne Canc Ctr, Dept Radiat Oncol, Nice, France
[8] Univ Hosp Schleswig Holstein, Clin Radiotherapy Radiooncol, Campus Kiel, Kiel, Germany
[9] Univ Cattolica Sacro Cuore, Ist Radiol, Rome, Italy
[10] Fdn Policlin Univ A Gemelli IRCCS, Rome, Italy
[11] Univ Cattolica Sacro Cuore, Dipartimento Neurosci, Sez Fis, Rome, Italy
来源：
CLINICAL AND TRANSLATIONAL RADIATION ONCOLOGY | 2024年 / 49卷
关键词：
Artificial intelligence; Interventional radiotherapy; Brachytherapy; Machine learning; Deep learning; DOSE-RATE BRACHYTHERAPY; AUTOMATIC SEGMENTATION; LEARNING-CURVE; PROSTATE; PREDICTION; RECONSTRUCTION; IMAGES; ART; AI;
D O I：
10.1016/j.ctro.2024.100865
中图分类号：
R73 [肿瘤学];
学科分类号：
100214 ;
摘要：
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.
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页数：7
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