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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 2024:110387. [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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Emin S, Rossi E, Myrvold Rooth E, Dorniok T, Hedman M, Gagliardi G, Villegas F. Clinical implementation of a commercial synthetic computed tomography solution for radiotherapy treatment of glioblastoma. Phys Imaging Radiat Oncol 2024; 30:100589. [PMID: 38818305 PMCID: PMC11137592 DOI: 10.1016/j.phro.2024.100589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 05/12/2024] [Accepted: 05/13/2024] [Indexed: 06/01/2024] Open
Abstract
Background and Purpose Magnetic resonance (MR)-only radiotherapy (RT) workflow eliminates uncertainties due to computed tomography (CT)-MR image registration, by using synthetic CT (sCT) images generated from MR. This study describes the clinical implementation process, from retrospective commissioning to prospective validation stage of a commercial artificial intelligence (AI)-based sCT product. Evaluation of the dosimetric performance of the sCT is presented, with emphasis on the impact of voxel size differences between image modalities. Materials and methods sCT performance was assessed in glioblastoma RT planning. Dose differences for 30 patients in both commissioning and validation cohorts were calculated at various dose-volume-histogram (DVH) points for target and organs-at-risk (OAR). A gamma analysis was conducted on regridded image plans. Quality assurance (QA) guidelines were established based on commissioning phase results. Results Mean dose difference to target structures was found to be within ± 0.7 % regardless of image resolution and cohort. OARs' mean dose differences were within ± 1.3 % for plans calculated on regridded images for both cohorts, while differences were higher for plans with original voxel size, reaching up to -4.2 % for chiasma D2% in the commissioning cohort. Gamma passing rates for the brain structure using the criteria 1 %/1mm, 2 %/2mm and 3 %/3mm were 93.6 %/99.8 %/100 % and 96.6 %/99.9 %/100 % for commissioning and validation cohorts, respectively. Conclusions Dosimetric outcomes in both commissioning and validation stages confirmed sCT's equivalence to CT. The large patient cohort in this study aided in establishing a robust QA program for the MR-only workflow, now applied in glioblastoma RT at our center.
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Affiliation(s)
- Sevgi Emin
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | - Elia Rossi
- Department of Radiation Oncology, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | | | - Torsten Dorniok
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | - Mattias Hedman
- Department of Radiation Oncology, Karolinska University Hospital, 171 76 Stockholm, Sweden
- Department of Oncology-Pathology, Karolinska Institute, 171 77 Stockholm, Sweden
| | - Giovanna Gagliardi
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, 171 76 Stockholm, Sweden
- Department of Oncology-Pathology, Karolinska Institute, 171 77 Stockholm, Sweden
| | - Fernanda Villegas
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, 171 76 Stockholm, Sweden
- Department of Oncology-Pathology, Karolinska Institute, 171 77 Stockholm, Sweden
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Rossi E, Emin S, Gubanski M, Gagliardi G, Hedman M, Villegas F. Contouring practices and artefact management within a synthetic CT-based radiotherapy workflow for the central nervous system. Radiat Oncol 2024; 19:27. [PMID: 38424642 DOI: 10.1186/s13014-024-02422-9] [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: 10/04/2023] [Accepted: 02/19/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND The incorporation of magnetic resonance (MR) imaging in radiotherapy (RT) workflows improves contouring precision, yet it introduces geometrical uncertainties when registered with computed tomography (CT) scans. Synthetic CT (sCT) images could minimize these uncertainties and streamline the RT workflow. This study aims to compare the contouring capabilities of sCT images with conventional CT-based/MR-assisted RT workflows, with an emphasis on managing artefacts caused by surgical fixation devices (SFDs). METHODS The study comprised a commissioning cohort of 100 patients with cranial tumors treated using a conventional CT-based/MR-assisted RT workflow and a validation cohort of 30 patients with grade IV glioblastomas treated using an MR-only workflow. A CE-marked artificial-intelligence-based sCT product was utilized. The delineation accuracy comparison was performed using dice similarity coefficient (DSC) and average Hausdorff distance (AHD). Artefacts within the commissioning cohort were visually inspected, classified and an estimation of thickness was derived using Hausdorff distance (HD). For the validation cohort, boolean operators were used to extract artefact volumes adjacent to the target and contrasted to the planning treatment volume. RESULTS The combination of high DSC (0.94) and low AHD (0.04 mm) indicates equal target delineation capacity between sCT images and conventional CT scans. However, the results for organs at risk delineation were less consistent, likely because of voxel size differences between sCT images and CT scans and absence of standardized delineation routines. Artefacts observed in sCT images appeared as enhancements of cranial bone. When close to the target, they could affect its definition. Therefore, in the validation cohort the clinical target volume (CTV) was expanded towards the bone by 3.5 mm, as estimated by HD analysis. Subsequent analysis on cone-beam CT scans showed that the CTV adjustment was enough to provide acceptable target coverage. CONCLUSION The tested sCT product performed on par with conventional CT in terms of contouring capability. Additionally, this study provides both the first comprehensive classification of metal artefacts on a sCT product and a novel method to assess the clinical impact of artefacts caused by SFDs on target delineation. This methodology encourages similar analysis for other sCT products.
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Affiliation(s)
- Elia Rossi
- Department of Radiation Oncology, Karolinska University Hospital, Solna, Sweden
| | - Sevgi Emin
- Radiotherapy Physics and Engineering, Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Solna, Sweden
| | - Michael Gubanski
- Department of Radiation Oncology, Karolinska University Hospital, Solna, Sweden
- Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden
| | - Giovanna Gagliardi
- Radiotherapy Physics and Engineering, Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Solna, Sweden
- Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden
| | - Mattias Hedman
- Department of Radiation Oncology, Karolinska University Hospital, Solna, Sweden
- Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden
| | - Fernanda Villegas
- Radiotherapy Physics and Engineering, Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Solna, Sweden.
- Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden.
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McGee KP, Cao M, Das IJ, Yu V, Witte RJ, Kishan AU, Valle LF, Wiesinger F, De-Colle C, Cao Y, Breen WG, Traughber BJ. The Use of Magnetic Resonance Imaging in Radiation Therapy Treatment Simulation and Planning. J Magn Reson Imaging 2024. [PMID: 38265188 DOI: 10.1002/jmri.29246] [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: 09/05/2023] [Revised: 01/08/2024] [Accepted: 01/08/2024] [Indexed: 01/25/2024] Open
Abstract
Ever since its introduction as a diagnostic imaging tool the potential of magnetic resonance imaging (MRI) in radiation therapy (RT) treatment simulation and planning has been recognized. Recent technical advances have addressed many of the impediments to use of this technology and as a result have resulted in rapid and growing adoption of MRI in RT. The purpose of this article is to provide a broad review of the multiple uses of MR in the RT treatment simulation and planning process, identify several of the most used clinical scenarios in which MR is integral to the simulation and planning process, highlight existing limitations and provide multiple unmet needs thereby highlighting opportunities for the diagnostic MR imaging community to contribute and collaborate with our oncology colleagues. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 5.
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Affiliation(s)
- Kiaran P McGee
- Department of Radiology, Mayo Clinic & Foundation, Rochester, Minnesota, USA
| | - Minsong Cao
- Department of Radiation Oncology, University of California, Los Angeles, California, USA
| | - Indra J Das
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Victoria Yu
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Robert J Witte
- Department of Radiology, Mayo Clinic & Foundation, Rochester, Minnesota, USA
| | - Amar U Kishan
- Department of Radiation Oncology, University of California, Los Angeles, California, USA
| | - Luca F Valle
- Department of Radiation Oncology, University of California, Los Angeles, California, USA
| | | | - Chiara De-Colle
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - William G Breen
- Department of Radiation Oncology, Mayo Clinic & Foundation, Rochester, Minnesota, USA
| | - Bryan J Traughber
- Department of Radiation Oncology, Mayo Clinic & Foundation, Rochester, Minnesota, USA
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Ranta I, Wright P, Suilamo S, Kemppainen R, Schubert G, Kapanen M, Keyriläinen J. Clinical feasibility of a commercially available MRI-only method for radiotherapy treatment planning of the brain. J Appl Clin Med Phys 2023; 24:e14044. [PMID: 37345212 PMCID: PMC10476982 DOI: 10.1002/acm2.14044] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 01/19/2023] [Accepted: 04/25/2023] [Indexed: 06/23/2023] Open
Abstract
BACKGROUND Advancements in deep-learning based synthetic computed tomography (sCT) image conversion methods have enabled the development of magnetic resonance imaging (MRI)-only based radiotherapy treatment planning (RTP) of the brain. PURPOSE This study evaluates the clinical feasibility of a commercial, deep-learning based MRI-only RTP method with respect to dose calculation and patient positioning verification performance in RTP of the brain. METHODS Clinical validation of dose calculation accuracy was performed by a retrospective evaluation for 25 glioma and 25 brain metastasis patients. Dosimetric and image quality of the studied MRI-only RTP method was evaluated by a direct comparison of the sCT-based and computed tomography (CT)-based external beam radiation therapy (EBRT) images and treatment plans. Patient positioning verification accuracy of sCT images was evaluated retrospectively for 10 glioma and 10 brain metastasis patients based on clinical cone-beam computed tomography (CBCT) imaging. RESULTS An average mean dose difference of Dmean = 0.1% for planning target volume (PTV) and 0.6% for normal tissue (NT) structures were obtained for glioma patients. Respective results for brain metastasis patients were Dmean = 0.5% for PTVs and Dmean =1.0% for NTs. Global three-dimensional (3D) gamma pass rates using 2%/2 mm dose difference and distance-to-agreement (DTA) criterion were 98.0% for the glioma subgroup, and 95.2% for the brain metastasis subgroup using 1%/1 mm criterion. Mean distance differences of <1.0 mm were observed in all Cartesian directions between CT-based and sCT-based CBCT patient positioning in both subgroups. CONCLUSIONS In terms of dose calculation and patient positioning accuracy, the studied MRI-only method demonstrated its clinical feasibility for RTP of the brain. The results encourage the use of the studied method as part of a routine clinical workflow.
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Affiliation(s)
- Iiro Ranta
- Department of Physics and AstronomyUniversity of TurkuTurkuFinland
- Department of Medical PhysicsTurku University HospitalTurkuFinland
- Department of Oncology and RadiotherapyTurku University HospitalTurkuFinland
| | - Pauliina Wright
- Department of Medical PhysicsTurku University HospitalTurkuFinland
- Department of Oncology and RadiotherapyTurku University HospitalTurkuFinland
| | - Sami Suilamo
- Department of Medical PhysicsTurku University HospitalTurkuFinland
- Department of Oncology and RadiotherapyTurku University HospitalTurkuFinland
| | - Reko Kemppainen
- HUS Diagnostic CenterUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
| | | | - Mika Kapanen
- Department of Medical PhysicsMedical Imaging CenterTampere University HospitalTampereFinland
- Department of OncologyUnit of RadiotherapyTampere University HospitalTampereFinland
| | - Jani Keyriläinen
- Department of Physics and AstronomyUniversity of TurkuTurkuFinland
- Department of Medical PhysicsTurku University HospitalTurkuFinland
- Department of Oncology and RadiotherapyTurku University HospitalTurkuFinland
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Alzahrani N, Henry A, Clark A, Murray L, Nix M, Al-Qaisieh B. Geometric evaluations of CT and MRI based deep learning segmentation for brain OARs in radiotherapy. Phys Med Biol 2023; 68:175035. [PMID: 37579753 DOI: 10.1088/1361-6560/acf023] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 08/14/2023] [Indexed: 08/16/2023]
Abstract
Objective.Deep-learning auto-contouring (DL-AC) promises standardisation of organ-at-risk (OAR) contouring, enhancing quality and improving efficiency in radiotherapy. No commercial models exist for OAR contouring based on brain magnetic resonance imaging (MRI). We trained and evaluated computed tomography (CT) and MRI OAR autosegmentation models in RayStation. To ascertain clinical usability, we investigated the geometric impact of contour editing before training on model quality.Approach.Retrospective glioma cases were randomly selected for training (n= 32, 47) and validation (n= 9, 10) for MRI and CT, respectively. Clinical contours were edited using international consensus (gold standard) based on MRI and CT. MRI models were trained (i) using the original clinical contours based on planning CT and rigidly registered T1-weighted gadolinium-enhanced MRI (MRIu), (ii) as (i), further edited based on CT anatomy, to meet international consensus guidelines (MRIeCT), and (iii) as (i), further edited based on MRI anatomy (MRIeMRI). CT models were trained using: (iv) original clinical contours (CTu) and (v) clinical contours edited based on CT anatomy (CTeCT). Auto-contours were geometrically compared to gold standard validation contours (CTeCT or MRIeMRI) using Dice Similarity Coefficient, sensitivity, and mean distance to agreement. Models' performances were compared using paired Student's t-testing.Main results.The edited autosegmentation models successfully generated more segmentations than the unedited models. Paired t-testing showed editing pituitary, orbits, optic nerves, lenses, and optic chiasm on MRI before training significantly improved at least one geometry metric. MRI-based DL-AC performed worse than CT-based in delineating the lacrimal gland, whereas the CT-based performed worse in delineating the optic chiasm. No significant differences were found between the CTeCT and CTu except for optic chiasm.Significance.T1w-MRI DL-AC could segment all brain OARs except the lacrimal glands, which cannot be easily visualized on T1w-MRI. Editing contours on MRI before model training improved geometric performance. MRI DL-AC in RT may improve consistency, quality and efficiency but requires careful editing of training contours.
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Affiliation(s)
- Nouf Alzahrani
- King Abdulaziz University, Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
- University of Leeds, School of Medicine, Leeds, United Kingdom
- St James's University Hospital, Department of Medical Physics and Engineering, Leeds Cancer Centre, Leeds, United Kingdom
| | - Ann Henry
- University of Leeds, School of Medicine, Leeds, United Kingdom
- St James's University Hospital, Department of Medical Physics and Engineering, Leeds Cancer Centre, Leeds, United Kingdom
| | - Anna Clark
- St James's University Hospital, Department of Medical Physics and Engineering, Leeds Cancer Centre, Leeds, United Kingdom
| | - Louise Murray
- University of Leeds, School of Medicine, Leeds, United Kingdom
- St James's University Hospital, Department of Medical Physics and Engineering, Leeds Cancer Centre, Leeds, United Kingdom
| | - Michael Nix
- St James's University Hospital, Department of Medical Physics and Engineering, Leeds Cancer Centre, Leeds, United Kingdom
| | - Bashar Al-Qaisieh
- St James's University Hospital, Department of Medical Physics and Engineering, Leeds Cancer Centre, Leeds, United Kingdom
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