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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. Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients' needs. Clin Transl Radiat Oncol 2024; 49:100865. [PMID: 39381628 PMCID: PMC11459626 DOI: 10.1016/j.ctro.2024.100865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 09/11/2024] [Accepted: 09/20/2024] [Indexed: 10/10/2024] Open
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.
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Affiliation(s)
- Bruno Fionda
- Dipartimento di Diagnostica per Immagini e Radioterapia Oncologica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Elisa Placidi
- Dipartimento di Diagnostica per Immagini e Radioterapia Oncologica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Mischa de Ridder
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lidia Strigari
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Stefano Patarnello
- Real World Data Facility, Gemelli Generator, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Kari Tanderup
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Jean-Michel Hannoun-Levi
- Department of Radiation Oncology, Antoine Lacassagne Cancer Centre, University of Côte d’Azur, Nice, France
| | - Frank-André Siebert
- Clinic of Radiotherapy (Radiooncology), University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany
| | - Luca Boldrini
- Dipartimento di Diagnostica per Immagini e Radioterapia Oncologica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
| | - Maria Antonietta Gambacorta
- Dipartimento di Diagnostica per Immagini e Radioterapia Oncologica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Marco De Spirito
- Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
- Dipartimento di Neuroscienze, Sezione di Fisica, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Evis Sala
- Dipartimento di Diagnostica per Immagini e Radioterapia Oncologica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Luca Tagliaferri
- Dipartimento di Diagnostica per Immagini e Radioterapia Oncologica, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
- Istituto di Radiologia, Università Cattolica del Sacro Cuore, Rome, Italy
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Stenhouse K, Roumeliotis M, Ciunkiewicz P, Martell K, Quirk S, Banerjee R, Doll C, Phan T, Yanushkevich S, McGeachy P. Prospective validation of a machine learning model for applicator and hybrid interstitial needle selection in high-dose-rate (HDR) cervical brachytherapy. Brachytherapy 2024; 23:368-376. [PMID: 38538415 DOI: 10.1016/j.brachy.2024.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/23/2024] [Accepted: 02/26/2024] [Indexed: 05/18/2024]
Abstract
PURPOSE To Demonstrate the clinical validation of a machine learning (ML) model for applicator and interstitial needle prediction in gynecologic brachytherapy through a prospective clinical study in a single institution. METHODS The study included cervical cancer patients receiving high-dose-rate brachytherapy using intracavitary (IC) or hybrid interstitial (IC/IS) applicators. For each patient, the primary radiation oncologist contoured the high-risk clinical target volume on a pre-brachytherapy MRI, indicated the approximate applicator location, and made a clinical determination of the first fraction applicator. A pre-trained ML model predicted the applicator and IC/IS needle arrangement using tumor geometry. Following the first fraction, ML and radiation oncologist predictions were compared and a replanning study determined the applicator providing optimal organ-at-risk (OAR) dosimetry. The ML-predicted applicator and needle arrangement and the clinical determination were compared to this dosimetric ground truth. RESULTS Ten patients were accrued from December 2020 to October 2022. Compared to the dosimetrically optimal applicator, both the radiation oncologist and ML had an accuracy of 70%. ML demonstrated better identification of patients requiring IC/IS applicators and provided balanced IC and IC/IS predictions. The needle selection model achieved an average accuracy of 82.5%. ML-predicted needle arrangements matched or improved plan quality when compared to clinically selected arrangements. Overall, ML predictions led to an average total improvement of 2.0 Gy to OAR doses over three treatment fractions when compared to clinical predictions. CONCLUSION In the context of a single institution study, the presented ML model demonstrates valuable decision-support for the applicator and needle selection process with the potential to provide improved dosimetry. Future work will include a multi-center study to assess generalizability.
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Affiliation(s)
- Kailyn Stenhouse
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada; Department of Medical Physics, Tom Baker Cancer Centre, Calgary, Alberta, Canada.
| | - Michael Roumeliotis
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD.
| | - Philip Ciunkiewicz
- Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Kevin Martell
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
| | - Sarah Quirk
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada; Department of Radiation Oncology, Brigham and Women's Hospital, Boston, MA
| | - Robyn Banerjee
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
| | - Corinne Doll
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
| | - Tien Phan
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
| | - Svetlana Yanushkevich
- Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Philip McGeachy
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada; Department of Medical Physics, Tom Baker Cancer Centre, Calgary, Alberta, Canada; Department of Oncology, University of Calgary, Calgary, Alberta, Canada
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Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073223] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.
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