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Villegas F, Dal Bello R, Alvarez-Andres E, Dhont J, Janssen T, Milan L, Robert C, Salagean GAM, Tejedor N, Trnková P, Fusella M, Placidi L, Cusumano D. Challenges and opportunities in the development and clinical implementation of artificial intelligence based synthetic computed tomography for magnetic resonance only radiotherapy. Radiother Oncol 2024; 198:110387. [PMID: 38885905 DOI: 10.1016/j.radonc.2024.110387] [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: 10/29/2023] [Revised: 06/13/2024] [Accepted: 06/13/2024] [Indexed: 06/20/2024]
Abstract
Synthetic computed tomography (sCT) generated from magnetic resonance imaging (MRI) can serve as a substitute for planning CT in radiation therapy (RT), thereby removing registration uncertainties associated with multi-modality imaging pairing, reducing costs and patient radiation exposure. CE/FDA-approved sCT solutions are nowadays available for pelvis, brain, and head and neck, while more complex deep learning (DL) algorithms are under investigation for other anatomic sites. The main challenge in achieving a widespread clinical implementation of sCT lies in the absence of consensus on sCT commissioning and quality assurance (QA), resulting in variation of sCT approaches across different hospitals. To address this issue, a group of experts gathered at the ESTRO Physics Workshop 2022 to discuss the integration of sCT solutions into clinics and report the process and its outcomes. This position paper focuses on aspects of sCT development and commissioning, outlining key elements crucial for the safe implementation of an MRI-only RT workflow.
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Affiliation(s)
- Fernanda Villegas
- Department of Oncology-Pathology, Karolinska Institute, Solna, Sweden; Radiotherapy Physics and Engineering, Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Solna, Sweden
| | - Riccardo Dal Bello
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Emilie Alvarez-Andres
- OncoRay - National Center for Radiation Research in Oncology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany; Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Jennifer Dhont
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B), Institut Jules Bordet, Department of Medical Physics, Brussels, Belgium; Université Libre De Bruxelles (ULB), Radiophysics and MRI Physics Laboratory, Brussels, Belgium
| | - Tomas Janssen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Lisa Milan
- Medical Physics Unit, Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale, Bellinzona, Switzerland
| | - Charlotte Robert
- UMR 1030 Molecular Radiotherapy and Therapeutic Innovations, ImmunoRadAI, Paris-Saclay University, Institut Gustave Roussy, Inserm, Villejuif, France; Department of Radiation Oncology, Gustave Roussy, Villejuif, France
| | - Ghizela-Ana-Maria Salagean
- Faculty of Physics, Babes-Bolyai University, Cluj-Napoca, Romania; Department of Radiation Oncology, TopMed Medical Centre, Targu Mures, Romania
| | - Natalia Tejedor
- Department of Medical Physics and Radiation Protection, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Petra Trnková
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Marco Fusella
- Department of Radiation Oncology, Abano Terme Hospital, Italy
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Rome, Italy.
| | - Davide Cusumano
- Mater Olbia Hospital, Strada Statale Orientale Sarda 125, Olbia, Sassari, Italy
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Liu M, Tang B, Orlandini LC, Li J, Wang X, Peng Q, Thwaites D. Potential dosimetric error in the adaptive workflow of a 1.5 T MR-Linac from patient movement relative to immobilisation systems. Phys Eng Sci Med 2024; 47:351-359. [PMID: 38227140 DOI: 10.1007/s13246-023-01369-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 12/10/2023] [Indexed: 01/17/2024]
Abstract
In magnetic resonance- (MR-) based adaptive workflows for an MR-linac, the treatment plan is optimized and recalculated online using the daily MR images. The Unity MR-linac is supplied with a patient positioning device (ppd) using pelvic and abdomen thermoplastic masks attached to a board with high-density components. This study highlights the dosimetric effect of using this in such workflows when there are relative patient-ppd displacements, as these are not visualized on MR imaging and the treatment planning system assumes the patient is fixed relative to the ppd. The online adapted plans of two example rectum cancer patients treated at a Unity MR-linac were perturbed by introducing relative patient-ppd displacements, and the effect was evaluated on plan dosimetry. Forty-eight perturbed clinical adapted plans were recalculated, based on online MR-based synthetic computed tomography, and compared with the original plans, using dose-volume histogram parameters and gamma analysis. The target volume covered by the prescribed dose ( D pre ) and by at least 107% of D pre varied up to - 1.87% and + 3.67%, respectively for 0.5 cm displacements, and to - 3.18% and + 4.96% for 2 cm displacements; whilst 2%-2 mm gamma analysis showed a median value of 92.9%. The use of a patient positioning system with high-density components in a Unity MR-based online adaptive treatment workflow can introduce unrecognized errors in plan dosimetry and it is recommended not to use such a device for such treatments, without modifying the device and the workflow, followed by careful clinical evaluation, or alternatively to use other immobilization methods.
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Affiliation(s)
- Min Liu
- Radiation Oncology Department, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Chengdu, China
- Institute of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, China
| | - Bin Tang
- Radiation Oncology Department, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Chengdu, China
| | - Lucia Clara Orlandini
- Radiation Oncology Department, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Chengdu, China
| | - Jie Li
- Radiation Oncology Department, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Chengdu, China.
- Radiotherapy Research Group, Leeds Institute of Medical Research, St James's Hospital and University of Leeds, Leeds, UK.
| | - Xianliang Wang
- Radiation Oncology Department, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Chengdu, China.
- Radiotherapy Research Group, Leeds Institute of Medical Research, St James's Hospital and University of Leeds, Leeds, UK.
| | - Qian Peng
- Radiation Oncology Department, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Chengdu, China
| | - David Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia
- Sydney West Radiation Oncology Network, Crown Princess Mary Cancer Centre, Sydney, NSW, Australia
- Radiotherapy Research Group, Leeds Institute of Medical Research, St James's Hospital and University of Leeds, Leeds, UK
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Boldrini L, D'Aviero A, De Felice F, Desideri I, Grassi R, Greco C, Iorio GC, Nardone V, Piras A, Salvestrini V. Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO). LA RADIOLOGIA MEDICA 2024; 129:133-151. [PMID: 37740838 DOI: 10.1007/s11547-023-01708-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 08/16/2023] [Indexed: 09/25/2023]
Abstract
INTRODUCTION The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT. METHODS A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was "Radiomics" AND "Cone Beam Computed Tomography"; "Radiomics" AND "Magnetic Resonance guided Radiotherapy"; "Radiomics" AND "on board Magnetic Resonance Radiotherapy"; "Artificial Intelligence" AND "Cone Beam Computed Tomography"; "Artificial Intelligence" AND "Magnetic Resonance guided Radiotherapy"; "Artificial Intelligence" AND "on board Magnetic Resonance Radiotherapy" and only original articles up to 01.11.2022 were considered. RESULTS A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques. DISCUSSION AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.
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Affiliation(s)
- Luca Boldrini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario IRCCS "A. Gemelli", Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Andrea D'Aviero
- Radiation Oncology, Mater Olbia Hospital, Olbia, Sassari, Italy
| | - Francesca De Felice
- Radiation Oncology, Department of Radiological, Policlinico Umberto I, Rome, Italy
- Oncological and Pathological Sciences, "Sapienza" University of Rome, Rome, Italy
| | - Isacco Desideri
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Carlo Greco
- Department of Radiation Oncology, Università Campus Bio-Medico di Roma, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | | | - Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Antonio Piras
- UO Radioterapia Oncologica, Villa Santa Teresa, Bagheria, Palermo, Italy.
| | - Viola Salvestrini
- Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
- Cyberknife Center, Istituto Fiorentino di Cura e Assistenza (IFCA), 50139, Florence, Italy
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Carr ME, Jelen U, Picton M, Batumalai V, Crawford D, Peng V, Twentyman T, de Leon J, Jameson MG. Towards simulation-free MR-linac treatment: utilizing male pelvis PSMA-PET/CT and population-based electron density assignments. Phys Med Biol 2023; 68:195012. [PMID: 37652043 DOI: 10.1088/1361-6560/acf5c6] [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: 04/05/2023] [Accepted: 08/31/2023] [Indexed: 09/02/2023]
Abstract
Objective. This study aimed to investigate the dosimetric impact of using population-based relative electron density (RED) overrides in lieu of simulation computerized tomography (CT) in a magnetic resonance linear accelerator (MRL) workflow for male pelvis patients. Additionally, the feasibility of using prostate specific membrane antigen positron emission tomography/CT (PSMA-PET/CT) scans to assess patients' eligibility for this proposed workflow was examined.Approach. In this study, 74 male pelvis patients treated on an Elekta Unity 1.5 T MRL were retrospectively selected. The patients' individual RED values for 8 organs of interest were extracted from their simulation-CT images to establish population-based RED values. These values were used to generate individual (IndD) and population-based (PopD) RED dose plans, representing current and proposed MRL workflows, respectively. Lastly, this study compared RED values obtained from CT and PET-CT scanners in a phantom and a subset of patients.Results. Population-based RED values were mostly within two standard deviations of ICRU Report 46 values. PopD plans were comparable to IndD plans, with the average %difference magnitudes of 0.5%, 0.6%, and 0.6% for mean dose (all organs), D0.1cm3(non-target organs) and D95%/D98% (target organs), respectively. Both phantom and patient PET-CT derived RED values had high agreement with corresponding CT-derived values, with correlation coefficients ≥ 0.9.Significance. Population-based RED values were considered suitable in a simulation-free MRL treatment workflow. Utilizing these RED values resulted in similar dosimetric uncertainties as per the current workflow. Initial findings also suggested that PET-CT scans may be used to assess prospective patients' eligibility for the proposed workflow. Future investigations will evaluate the clinical feasibility of implementing this workflow for prospective patients in the clinical setting. This is aimed to reduce patient burden during radiotherapy and increase department efficiencies.
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Affiliation(s)
- Madeline E Carr
- GenesisCare, New South Wales, Australia
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | | | | | - Vikneswary Batumalai
- GenesisCare, New South Wales, Australia
- School of Clinical Medicine, Medicine and Health, University of New South Wales, Australia
| | | | | | | | | | - Michael G Jameson
- GenesisCare, New South Wales, Australia
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
- School of Clinical Medicine, Medicine and Health, University of New South Wales, Australia
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Feng X, Tang B, Yao X, Liu M, Liao X, Yuan K, Peng Q, Orlandini LC. Effectiveness of bladder filling control during online MR-guided adaptive radiotherapy for rectal cancer. Radiat Oncol 2023; 18:136. [PMID: 37592338 PMCID: PMC10436664 DOI: 10.1186/s13014-023-02315-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/05/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND Magnetic resonance-guided adaptive radiotherapy (MRgART) treatment sessions at MR-Linac are time-consuming and changes in organs at risk volumes can impact the treatment dosimetry. This study aims to evaluate the feasibility to control bladder filling during the rectum MRgART online session and its effectiveness on plan dosimetry. METHODS A total of 109 online adaptive sessions of 24 rectum cancer patients treated at Unity 1.5 T MR-Linac with a short course radiotherapy (25 Gy, 5 Gy × 5) for whom the adaptive plan was optimized and recalculated online based on the daily magnetic resonance imaging (MRI) were analysed. Patients were fitted with a bladder catheter to control bladder filling; the bladder is emptied and then partially filled with a known amount of saline at the beginning and end of the online session. A first MRI ([Formula: see text]) acquired at the beginning of the session was used for plan adaptation and the second ([Formula: see text]) was acquired while approving the adapted plan and rigidly registered with the first to ensure the appropriateness of the isodoses on the ongoing delivery treatment. For each fraction, the time interval between the two MRIs and potential bladder changes were assessed with independent metrics, and the impact on the plan dosimetry was evaluated by comparing target and organs at risk dose volume histogram cut-off points of the plan adapted on [Formula: see text] and recalculated on [Formula: see text]. RESULTS Median bladder volume variations, DSC, and HD of 8.17%, 0.922, and 2.92 mm were registered within a median time of 38 min between [Formula: see text] and [Formula: see text]; dosimetric differences < 0.65% were registered for target coverage, and < 0.5% for bladder, small bowel and femoral heads constraints, with a p value > 0.05. CONCLUSION The use of a bladder filling control procedure can help ensure the dosimetric accuracy of the online adapted treatment delivered.
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Affiliation(s)
- Xi Feng
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Bin Tang
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Xinghong Yao
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Min Liu
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
- Institute of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, China
| | - Xiongfei Liao
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Ke Yuan
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Qian Peng
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Lucia Clara Orlandini
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
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