1
|
Delpon G, Barateau A, Beneux A, Bessières I, Latorzeff I, Welmant J, Tallet A. [What do we need to deliver "online" adapted radiotherapy treatment plans?]. Cancer Radiother 2022; 26:794-802. [PMID: 36028418 DOI: 10.1016/j.canrad.2022.06.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 06/27/2022] [Accepted: 06/29/2022] [Indexed: 11/17/2022]
Abstract
During the joint SFRO/SFPM session of the 2019 congress, a state of the art of adaptive radiotherapy announced a strong impact in our clinical practice, in particular with the availability of treatment devices coupled to an MRI system. Three years later, it seems relevant to take stock of adaptive radiotherapy in practice, and especially the "online" strategy because it is indeed more and more accessible with recent hardware and software developments, such as coupled accelerators to a three-dimensional imaging device and algorithms based on artificial intelligence. However, the deployment of this promising strategy is complex because it contracts the usual time scale and upsets the usual organizations. So what do we need to deliver adapted treatment plans with an "online" strategy?
Collapse
Affiliation(s)
- G Delpon
- Institut de cancérologie de l'Ouest, Saint-Herblain et IMT Atlantique, Nantes université, CNRS/IN2P3, Subatech, Nantes, France.
| | - A Barateau
- Université Rennes, CLCC Eugène-Marquis, Inserm, LTSI-UMR 1099, Rennes, France
| | - A Beneux
- Hospices Civils de Lyon, Lyon, France
| | - I Bessières
- Centre Georges-François Leclerc, Dijon, France
| | | | - J Welmant
- Institut du cancer de Montpellier, Montpellier, France
| | - A Tallet
- Institut Paoli-Calmettes, Marseille, France
| |
Collapse
|
2
|
Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073223] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [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.
Collapse
|
4
|
Min H, Dowling J, Jameson MG, Cloak K, Faustino J, Sidhom M, Martin J, Ebert MA, Haworth A, Chlap P, de Leon J, Berry M, Pryor D, Greer P, Vinod SK, Holloway L. Automatic radiotherapy delineation quality assurance on prostate MRI with deep learning in a multicentre clinical trial. Phys Med Biol 2021; 66. [PMID: 34507305 DOI: 10.1088/1361-6560/ac25d5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 09/10/2021] [Indexed: 11/11/2022]
Abstract
Volume delineation quality assurance (QA) is particularly important in clinical trial settings where consistent protocol implementation is required, as outcomes will affect future as well current patients. Currently, where feasible, this is conducted manually, which is time consuming and resource intensive. Although previous studies mostly focused on automating delineation QA on CT, magnetic resonance imaging (MRI) is being increasingly used in radiotherapy treatment. In this work, we propose to perform automatic delineation QA on prostate MRI for both the clinical target volume (CTV) and organs-at-risk (OARs) by using delineations generated by 3D Unet variants as benchmarks for QA. These networks were trained on a small gold standard atlas set and applied on a multicentre radiotherapy clinical trial dataset to generate benchmark delineations. Then, a QA stage was designed to recommend 'pass', 'minor correction' and 'major correction' for each manual delineation in the trial set by thresholding its Dice similarity coefficient to the network generated delineation. Among all 3D Unet variants explored, the Unet with anatomical gates in an AtlasNet architecture performed the best in delineation QA, achieving an area under the receiver operating characteristics curve of 0.97, 0.92, 0.89 and 0.97 for identifying unacceptable (major correction) delineations with a sensitivity of 0.93, 0.73, 0.74 and 0.90 at a specificity of 0.93, 0.86, 0.86 and 0.95 for bladder, prostate CTV, rectum and gel spacer respectively. To the best of our knowledge, this is the first study to propose automated delineation QA for a multicentre radiotherapy clinical trial with treatment planning MRI. The methods proposed in this work can potentially improve the accuracy and consistency of CTV and OAR delineation in radiotherapy treatment planning.
Collapse
Affiliation(s)
- Hang Min
- CSIRO Australian e-Health Research Centre, Herston, Queensland, Australia.,Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia.,South Western Clinical School, University of New South Wales, Australia
| | - Jason Dowling
- CSIRO Australian e-Health Research Centre, Herston, Queensland, Australia.,South Western Clinical School, University of New South Wales, Australia.,Centre for Medical Radiation Physics, University of Wollongong, New South Wales, Australia.,Institute of Medical Physics, The University of Sydney, New South Wales, Australia.,School of Mathematical and Physical Sciences, University of Newcastle, New South Wales, Australia
| | - Michael G Jameson
- St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Australia.,GenesisCare, Sydney, New South Wales, Australia
| | - Kirrily Cloak
- Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia.,South Western Clinical School, University of New South Wales, Australia
| | - Joselle Faustino
- Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - Mark Sidhom
- South Western Clinical School, University of New South Wales, Australia.,Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - Jarad Martin
- Department of Radiation Oncology, Calvary Mater Newcastle, Newcastle, New South Wales, Australia
| | - Martin A Ebert
- Centre for Medical Radiation Physics, University of Wollongong, New South Wales, Australia.,Department of Radiation Oncology, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia.,School of Physics Mathematics and Computing, University of Western Australia, Perth, Western Australia, Australia
| | - Annette Haworth
- Institute of Medical Physics, The University of Sydney, New South Wales, Australia
| | - Phillip Chlap
- Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia.,South Western Clinical School, University of New South Wales, Australia.,Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - Jeremiah de Leon
- GenesisCare, Sydney, New South Wales, Australia.,Illawarra Cancer Care Centre, Wollongong, Australia
| | - Megan Berry
- South Western Clinical School, University of New South Wales, Australia.,Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - David Pryor
- Princess Alexandra Hospital, Brisbane, Queensland, Australia
| | - Peter Greer
- School of Mathematical and Physical Sciences, University of Newcastle, New South Wales, Australia.,Department of Radiation Oncology, Calvary Mater Newcastle, Newcastle, New South Wales, Australia
| | - Shalini K Vinod
- Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia.,South Western Clinical School, University of New South Wales, Australia.,Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| | - Lois Holloway
- Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia.,South Western Clinical School, University of New South Wales, Australia.,Centre for Medical Radiation Physics, University of Wollongong, New South Wales, Australia.,Institute of Medical Physics, The University of Sydney, New South Wales, Australia.,Liverpool and Macarthur Cancer therapy Centres, Liverpool Hospital, New South Wales, Australia
| |
Collapse
|