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Yan S, Maniscalco A, Wang B, Nguyen D, Jiang S, Shen C. Quality assurance for online adaptive radiotherapy: a secondary dose verification model with geometry-encoded U-Net. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2024; 5:045013. [PMID: 39399396 PMCID: PMC11467776 DOI: 10.1088/2632-2153/ad829e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 09/06/2024] [Accepted: 10/01/2024] [Indexed: 10/15/2024] Open
Abstract
In online adaptive radiotherapy (ART), quick computation-based secondary dose verification is crucial for ensuring the quality of ART plans while the patient is positioned on the treatment couch. However, traditional dose verification algorithms are generally time-consuming, reducing the efficiency of ART workflow. This study aims to develop an ultra-fast deep-learning (DL) based secondary dose verification algorithm to accurately estimate dose distributions using computed tomography (CT) and fluence maps (FMs). We integrated FMs into the CT image domain by explicitly resolving the geometry of treatment delivery. For each gantry angle, an FM was constructed based on the optimized multi-leaf collimator apertures and corresponding monitoring units. To effectively encode treatment beam configuration, the constructed FMs were back-projected to 30 cm away from the isocenter with respect to the exact geometry of the treatment machines. Then, a 3D U-Net was utilized to take the integrated CT and FM volume as input to estimate dose. Training and validation were performed on 381 prostate cancer cases, with an additional 40 testing cases for independent evaluation of model performance. The proposed model can estimate dose in ∼ 15 ms for each patient. The average γ passing rate ( 3 % / 2 mm , 10 % threshold) for the estimated dose was 99.9% ± 0.15% on testing patients. The mean dose differences for the planning target volume and organs at risk were 0.07 % ± 0.34 % and 0.48 % ± 0.72 % , respectively. We have developed a geometry-resolved DL framework for accurate dose estimation and demonstrated its potential in real-time online ART doses verification.
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Affiliation(s)
- Shunyu Yan
- The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Austen Maniscalco
- The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Biling Wang
- The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Dan Nguyen
- The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Steve Jiang
- The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Chenyang Shen
- The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
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Pierrard J, Heylen S, Vandermeulen A, Van Ooteghem G. Dealing with rectum motion during radiotherapy: How can we anticipate it? Tech Innov Patient Support Radiat Oncol 2024; 32:100277. [PMID: 39391230 PMCID: PMC11465212 DOI: 10.1016/j.tipsro.2024.100277] [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: 06/05/2024] [Revised: 09/02/2024] [Accepted: 09/20/2024] [Indexed: 10/12/2024] Open
Abstract
Introduction Intra- and inter-fraction rectum motion is important for pelvic radiotherapy (RT). This study assesses how RT session duration, the presence or the absence of an intra-rectal tumour, and the distance from the anorectal junction (ARJd) impact rectal motion. Materials and methods Analyses used cone-beam computed tomographies (CBCTs) from RT patients treated for rectal and prostate cancer. Three structures were evaluated: (1) the entire rectum in patients without a rectal tumour (RectumProstate); (2) the non-invaded portion (RectumRectum) and (3) the tumour-invaded portion (RectumTumour) in rectal cancer patients.Intrafraction motion was assessed using the Hausdorff distance 95% and the Mean distance-to-agreement between structures delineated on the first CBCT and the 2 subsequent CBCTs within a same RT session. Interfraction motion was quantified by comparing structures delineated on the planning-CT and the first CBCT of each session.Linear mixed model evaluated rectum motion in relation to time, tumour presence, and ARJd, respectively. Results We included 10 patients with and 10 without rectal cancer, collecting 385 CBCTs. A significant correlation (p < 0.05) between rectum motion and RT session duration was found. Intrafraction motion was significantly higher in prostate cancer patients (RectumProstate motion > RectumRectum and RectumTumour, p < 0.01). For interfraction motion, only the mean distance to agreement was significantly higher for RectumProstate (p < 0.05). Motion increased significantly with ARJd for all three structures (p < 0.001). Conclusions Session duration, absence of a tumour, and ARJd are associated with larger intra- and interfraction rectal motion. This highlights the need for tailored RT treatment, including online-adaptive RT, to manage intra- and interfraction variations. Rectal motion should be handled differently for patients with prostate cancer and those with rectal cancer.
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Affiliation(s)
- Julien Pierrard
- UCLouvain, Institut de Recherche Experimentale et Clinique (IREC), Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium
- Department of Radiation Oncology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Sofie Heylen
- Department of Radiation Oncology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Ad Vandermeulen
- Department of Radiation Oncology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Geneviève Van Ooteghem
- UCLouvain, Institut de Recherche Experimentale et Clinique (IREC), Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Brussels, Belgium
- Department of Radiation Oncology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
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Leino A, Heikkilä J, Virén T, Honkanen JTJ, Seppälä J, Korkalainen H. Deep learning-based prediction of the dose-volume histograms for volumetric modulated arc therapy of left-sided breast cancer. Med Phys 2024; 51:7986-7997. [PMID: 39291645 DOI: 10.1002/mp.17410] [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: 11/08/2023] [Revised: 07/01/2024] [Accepted: 08/17/2024] [Indexed: 09/19/2024] Open
Abstract
BACKGROUND The advancements in artificial intelligence and computational power have made deep learning an attractive tool for radiotherapy treatment planning. Deep learning has the potential to significantly simplify the trial-and-error process involved in inverse planning required by modern treatment techniques such as volumetric modulated arc therapy (VMAT). In this study, we explore the ability of deep learning to predict organ-at-risk (OAR) dose-volume histograms (DVHs) of left-sided breast cancer patients undergoing VMAT treatment based solely on their anatomical characteristics. The predicted DVHs could be used to derive patient-specific dose constraints and dose objectives, streamlining the treatment planning process, standardizing the quality of the plans, and personalizing the treatment planning. PURPOSE This study aimed to develop a deep learning-based framework for the prediction of organ-specific dose-volume histograms (DVH) based on structures delineated for left-sided breast cancer treatment. METHODS We used a dataset of 249 left-sided breast cancer patients treated with tangential VMAT fields. We extracted delineated structures and dose distributions for each patient and derived slice-by-slice DVHs for planning target volume (PTV) and organs-at-risk. The patients were divided into training (70%, n = 174), validation (10%, n = 24), and test (20%, n = 51) sets. Collected data were used to train a deep learning model for the prediction of the DVHs based on the delineated structures. The developed deep learning model comprised a modified DenseNet architecture followed by a recurrent neural network. RESULTS In the independent test set (n = 51), the point-wise differences in the slice-by-slice DVHs between the clinical and predicted DVHs were small; the mean squared errors were 3.53, 1.58, 2.28, 3.37, and 1.44 [×10-4] for PTV, heart, ipsilateral lung, contralateral lung, and contralateral breast, respectively. With the derived cumulative DVHs, the mean absolute difference ± standard deviation of mean doses between the clinical and the predicted DVH were 0.08 ± 0.04 Gy, 0.24 ± 0.22 Gy, 0.73 ± 0.46 Gy, 0.07 ± 0.06 Gy, and 0.14 ± 0.14 Gy for PTV, heart, ipsilateral lung, contralateral lung, and contralateral breast, respectively. CONCLUSIONS The deep learning-based approach enabled automatic and reliable prediction of the DVH based on delineated structures. The predicted DVHs could potentially serve as patient-specific clinical goals used to aid treatment planning and avoid suboptimal plans or to derive optimization objectives and constraints for automated treatment planning.
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Affiliation(s)
- Akseli Leino
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Center of Oncology, Kuopio University Hospital, Kuopio, Finland
- Eastern Finland Cancer Center (FICAN East), Kuopio University Hospital, Kuopio, Finland
| | - Janne Heikkilä
- Center of Oncology, Kuopio University Hospital, Kuopio, Finland
| | - Tuomas Virén
- Center of Oncology, Kuopio University Hospital, Kuopio, Finland
| | | | - Jan Seppälä
- Center of Oncology, Kuopio University Hospital, Kuopio, Finland
| | - Henri Korkalainen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Center of Oncology, Kuopio University Hospital, Kuopio, Finland
- Eastern Finland Cancer Center (FICAN East), Kuopio University Hospital, Kuopio, Finland
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Abdel-Wahab M, Giammarile F, Carrara M, Paez D, Hricak H, Ayati N, Li JJ, Mueller M, Aggarwal A, Al-Ibraheem A, Alkhatib S, Atun R, Bello A, Berger D, Delgado Bolton RC, Buatti JM, Burt G, Bjelac OC, Cordero-Mendez L, Dosanjh M, Eichler T, Fidarova E, Gondhowiardjo S, Gospodarowicz M, Grover S, Hande V, Harsdorf-Enderndorf E, Herrmann K, Hofman MS, Holmberg O, Jaffray D, Knoll P, Kunikowska J, Lewis JS, Lievens Y, Mikhail-Lette M, Ostwald D, Palta JR, Peristeris P, Rosa AA, Salem SA, Dos Santos MA, Sathekge MM, Shrivastava SK, Titovich E, Urbain JL, Vanderpuye V, Wahl RL, Yu JS, Zaghloul MS, Zhu H, Scott AM. Radiotherapy and theranostics: a Lancet Oncology Commission. Lancet Oncol 2024; 25:e545-e580. [PMID: 39362232 DOI: 10.1016/s1470-2045(24)00407-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 07/18/2024] [Accepted: 07/19/2024] [Indexed: 10/05/2024]
Abstract
Following on from the 2015 Lancet Oncology Commission on expanding global access to radiotherapy, Radiotherapy and theranostics: a Lancet Oncology Commission was created to assess the access and availability of radiotherapy to date and to address the important issue of access to the promising field of theranostics at a global level. A marked disparity in the availability of radiotherapy machines between high-income countries and low-income and middle-income countries (LMICs) has been identified previously and remains a major problem. The availability of a suitably trained and credentialled workforce has also been highlighted as a major limiting factor to effective implementation of radiotherapy, particularly in LMICs. We investigated initiatives that could mitigate these issues in radiotherapy, such as extended treatment hours, hypofractionation protocols, and new technologies. The broad implementation of hypofractionation techniques compared with conventional radiotherapy in prostate cancer and breast cancer was projected to provide radiotherapy for an additional 2·2 million patients (0·8 million patients with prostate cancer and 1·4 million patients with breast cancer) with existing resources, highlighting the importance of implementing new technologies in LMICs. A global survey undertaken for this Commission revealed that use of radiopharmaceutical therapy-other than 131I-was highly variable in high-income countries and LMICs, with supply chains, workforces, and regulatory issues affecting access and availability. The capacity for radioisotope production was highlighted as a key issue, and training and credentialling of health professionals involved in theranostics is required to ensure equitable access and availability for patient treatment. New initiatives-such as the International Atomic Energy Agency's Rays of Hope programme-and interest by international development banks in investing in radiotherapy should be supported by health-care systems and governments, and extended to accelerate the momentum generated by recognising global disparities in access to radiotherapy. In this Commission, we propose actions and investments that could enhance access to radiotherapy and theranostics worldwide, particularly in LMICs, to realise health and economic benefits and reduce the burden of cancer by accessing these treatments.
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Affiliation(s)
- May Abdel-Wahab
- Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria.
| | - Francesco Giammarile
- Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Mauro Carrara
- Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Diana Paez
- Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Hedvig Hricak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Molecular Pharmacology Program, Sloan Kettering Institute, New York, NY, USA; Department of Radiology, Weill Cornell Medical College, New York, NY, USA; Gerstner Sloan Kettering Graduate School of Biomedical Sciences, New York, NY, USA
| | - Nayyereh Ayati
- Centre for Health Economics, Monash Business School, Monash University, Melbourne, VIC, Australia
| | - Jing Jing Li
- Centre for Health Economics, Monash Business School, Monash University, Melbourne, VIC, Australia
| | | | - Ajay Aggarwal
- Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK
| | - Akram Al-Ibraheem
- Department of Nuclear Medicine, King Hussein Cancer Center, Amman, Jordan; Division of Nuclear Medicine, Department of Radiology and Nuclear Medicine, University of Jordan, Amman, Jordan
| | - Sondos Alkhatib
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, USA
| | - Rifat Atun
- Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA; Department of Health Policy and Management, Harvard T H Chan School of Public Health, Boston, MA, USA; Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | - Abubakar Bello
- National Hospital, Abuja and Federal University of Health Sciences, Azare, Nigeria
| | - Daniel Berger
- Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Roberto C Delgado Bolton
- Department of Diagnostic Imaging (Radiology) and Nuclear Medicine, University Hospital San Pedro and Centre for Biomedical Research of La Rioja, Logroño, Spain; Servicio Cántabro de Salud, Santander, Spain
| | - John M Buatti
- Department of Radiation Oncology, Holden Comprehensive Cancer Center, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | | | - Olivera Ciraj Bjelac
- Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Lisbeth Cordero-Mendez
- Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Manjit Dosanjh
- University of Oxford, Oxford, UK; European Organization for Nuclear Research, Geneva, Switzerland
| | - Thomas Eichler
- Department of Radiation Oncology, Massey Cancer Center Virginia Commonwealth University, Richmond, VA, USA
| | - Elena Fidarova
- Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
| | | | - Mary Gospodarowicz
- Radiation Oncology, University of Toronto, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Surbhi Grover
- Botswana-University of Pennsylvania Partnership, Gaborone, Botswana; Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Varsha Hande
- Department of Global Health, Medicine and Welfare, Atomic Bomb Disease Institute, Nagasaki University, Nagasaki, Japan
| | - Ekaterina Harsdorf-Enderndorf
- Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Ken Herrmann
- Department of Nuclear Medicine, University of Duisburg, Essen, Germany; German Cancer Consortium, University Hospital Essen, Essen, Germany
| | - Michael S Hofman
- Molecular Imaging and Therapeutic Nuclear Medicine, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Ola Holmberg
- Division of Radiation, Transport and Waste Safety, Department of Nuclear Safety and Security, International Atomic Energy Agency, Vienna, Austria
| | - David Jaffray
- Department of Radiation Physics and Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Peter Knoll
- Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Jolanta Kunikowska
- Nuclear Medicine Department, Medical University of Warsaw, Warsaw, Poland
| | - Jason S Lewis
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Molecular Pharmacology Program, Sloan Kettering Institute, New York, NY, USA; Department of Pharmacology, Weill Cornell Medical College, New York, NY, USA
| | - Yolande Lievens
- Department of Radiation Oncology, Ghent University Hospital and Ghent University, Ghent, Belgium
| | - Miriam Mikhail-Lette
- Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Dennis Ostwald
- WifOR Institute, Darmstadt, Germany; Steinbeis School of International Business and Entrepreneurship, Herrenberg, Germany
| | - Jatinder R Palta
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, USA
| | | | - Arthur A Rosa
- Radiation Oncology, Grupo Oncoclinicas, Salvador, Brazil
| | - Soha Ahmed Salem
- Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
| | | | - Mike M Sathekge
- Department of Nuclear Medicine, University of Pretoria, Pretoria, South Africa; Steve Biko Academic Hospital, Pretoria, South Africa; Nuclear Medicine Research Infrastructure, Pretoria, South Africa
| | | | - Egor Titovich
- Division of Human Health, Department of Nuclear Sciences and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Jean-Luc Urbain
- Department of Radiology, Division of Nuclear Medicine, Branford General Hospital, Ontario, Canada
| | - Verna Vanderpuye
- National Center for Radiotherapy Oncology and Nuclear Medicine Department of the Korlebu Teaching Hospital, Accra, Ghana
| | - Richard L Wahl
- Mallinckrodt Institute of Radiology, Department of Radiology, and Department of Radiation Oncology, Washington University School of Medicine, St Louis, MO, USA
| | - Jennifer S Yu
- Department of Radiation Oncology and Department of Cancer Biology, Cleveland Clinic, Cleveland, OH USA
| | - Mohamed Saad Zaghloul
- Radiation Oncology Department, National Cancer Institute, Cairo University & Children's Cancer Hospital, Cairo, Egypt
| | - Hongcheng Zhu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Andrew M Scott
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, VIC, Australia; Olivia Newton-John Cancer Research Institute, Melbourne, VIC, Australia; School of Cancer Medicine, La Trobe University, Melbourne, VIC, Australia; Faculty of Medicine, University of Melbourne, Melbourne, VIC, Australia.
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Cui X, Yang X, Li D, Dai X, Guo Y, Zhang W, Li Y, Wu X, Zhu L, Xu S, Zhuang H, Yang R, Geng L, Sui J. A StarGAN and transformer-based hybrid classification-regression model for multi-institution VMAT patient-specific quality assurance. Med Phys 2024. [PMID: 39484994 DOI: 10.1002/mp.17485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 08/30/2024] [Accepted: 09/28/2024] [Indexed: 11/03/2024] Open
Abstract
BACKGROUND The field of artificial intelligence (AI)-based patient-specific quality assurance (PSQA) for volumetric modulated arc therapy (VMAT) faces challenges in terms of developing general models across institutions due to the prevalence of multi-institution data collection and multivariate heterogeneity. Building a general model that is capable of handling diverse multi-institution data is critical for enabling large-scale integration and analysis. PURPOSE This study aims to develop a star generative adversarial network (StarGAN) and transformer-based hybrid classification-regression PSQA framework to address unification of heterogeneous data from different institutions. METHODS A StarGAN and transformer-based hybrid classification-regression model was developed as a general PSQA framework to predict gamma passing rates (GPRs) and classify quality assurance (QA) results as "Pass" or "Fail" at multiple institutions. A total of 1815 VMAT plans were collected from eight institutions to develop the general PSQA framework and perform clinical commissioning and implementation. Among them, 20 independent clinical plans from each of eight institutions, for a total of 160 plans, were used for the clinical commissioning, and 205 new clinical plans from eight institutions were used for clinical implementation. RESULTS For the 3%/3, 3%/2, and 2%/2 mm gamma criteria, the sensitivity of the proposed PSQA framework with pretraining was 90.13%, 92.03%, and 95.84%, respectively, while the specificity was 76.01%, 76.12%, and 85.34%, respectively. The mean absolute errors (MAEs) of the proposed PSQA framework with pretraining were 1.36%, 2.37%, and 3.96%, respectively, while the root-mean-square errors (RMSEs) were 2.31%, 3.89%, and 5.17%, respectively. The results demonstrated visible improvement at multiple institutions. For clinical commissioning, the deviations between the predicted and measured results were all within 3% for 3%/3 and 3%/2 mm at eight institutions. For clinical implementation, all failure plans were correctly identified by the proposed PSQA framework. CONCLUSIONS The general PSQA framework enables diverse clinical data sources to be handled to achieve enhanced model performance and generalizability, and provides a solution to the unification of heterogeneous data from different institutions to construct robust QA models. This approach can be clinically deployed for VMAT QA.
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Affiliation(s)
- Xiangxiang Cui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xueying Yang
- School of Physics, Beihang University, Beijing, China
- Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing, China
| | - Dingjie Li
- Department of Radiation Therapy, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Xiangkun Dai
- Department of Radiation Oncology, General Hospital of People's Liberation Army, Beijing, China
| | - Yuexin Guo
- Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Wei Zhang
- Department of Radiation Therapy, Yantai Yuhuangding Hospital, Yantai, Shandong, China
| | - Ying Li
- Department of Oncology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiangyang Wu
- Department of Radiotherapy, Shanxi Provincial Cancer Hospital, Xi'an, Shanxi, China
| | - Lihong Zhu
- Department of Radiotherapy, Beijing Obstetrics and Gynecology Hospital, Beijing, China
| | - Shouping Xu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongqing Zhuang
- Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing, China
| | - Ruijie Yang
- Department of Radiation Oncology, Cancer Center, Peking University Third Hospital, Beijing, China
| | - Lisheng Geng
- School of Physics, Beihang University, Beijing, China
- Peng Huanwu Collaborative Center for Research and Education, Beihang University, Beijing, China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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Marfisi D, Giannelli M, Marzi C, Del Meglio J, Barucci A, Masturzo L, Vignali C, Mascalchi M, Traino A, Casolo G, Diciotti S, Tessa C. Test-retest repeatability of myocardial radiomic features from quantitative cardiac magnetic resonance T1 and T2 mapping. Magn Reson Imaging 2024; 113:110217. [PMID: 39067653 DOI: 10.1016/j.mri.2024.110217] [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: 03/23/2024] [Revised: 06/14/2024] [Accepted: 07/23/2024] [Indexed: 07/30/2024]
Abstract
Radiomics of cardiac magnetic resonance (MR) imaging has proved to be potentially useful in the study of various myocardial diseases. Therefore, assessing the repeatability degree in radiomic features measurement is of fundamental importance. The aim of this study was to assess test-retest repeatability of myocardial radiomic features extracted from quantitative T1 and T2 maps. A representative group of 24 subjects (mean age 54 ± 18 years) referred for clinical cardiac MR imaging were enrolled in the study. For each subject, T1 and T2 mapping through MOLLI and T2-prepared TrueFISP acquisition sequences, respectively, were performed at 1.5 T. Then, 98 radiomic features of different classes (shape, first-order, second-order) were extracted from a region of interest encompassing the whole left ventricle myocardium in a short axis slice. The repeatability was assessed performing different and complementary analyses: intraclass correlation coefficient (ICC) and limits of agreement (LOA) (i.e., the interval within which 95% of the percentage differences between two repeated measures are expected to lie). Radiomic features were characterized by a relatively wide range of repeatability degree in terms of both ICC and LOA. Overall, 44.9% and 38.8% of radiomic features showed ICC values > 0.75 for T1 and T2 maps, respectively, while 25.5% and 23.4% of radiomic features showed LOA between ±10%. A subset of radiomic features for T1 (Mean, Median, 10Percentile, 90Percentile, RootMeanSquared, Imc2, RunLengthNonUniformityNormalized, RunPercentage and ShortRunEmphasis) and T2 (MaximumDiameter, RunLengthNonUniformityNormalized, RunPercentage, ShortRunEmphasis) maps presented both ICC > 0.75 and LOA between ±5%. Overall, radiomic features extracted from T1 maps showed better repeatability performance than those extracted from T2 maps, with shape features characterized by better repeatability than first-order and textural features. Moreover, only a limited subset of 9 and 4 radiomic features for T1 and T2 maps, respectively, showed high repeatability degree in terms of both ICC and LOA. These results confirm the importance of assessing test-retest repeatability degree in radiomic feature estimation and might be useful for a more effective/reliable use of myocardial T1 and T2 mapping radiomics in clinical or research studies.
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Affiliation(s)
- Daniela Marfisi
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", 56126 Pisa, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", 56126 Pisa, Italy.
| | - Chiara Marzi
- Department of Statistics, Computer Science, Applications "Giuseppe Parenti", University of Florence, 50134 Florence, Italy
| | - Jacopo Del Meglio
- Unit of Cardiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041 Lido di Camaiore, Italy
| | - Andrea Barucci
- Institute of Applied Physics "Nello Carrara" (IFAC), Council of National Research (CNR), 50019 Sesto Fiorentino, Italy
| | - Luigi Masturzo
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", 56126 Pisa, Italy
| | - Claudio Vignali
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041 Lido di Camaiore, Italy
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, 50121 Florence, Italy; Clinical Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50139 Florence, Italy
| | - Antonio Traino
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", 56126 Pisa, Italy
| | - Giancarlo Casolo
- Unit of Cardiology, Azienda USL Toscana Nord Ovest, Versilia Hospital, 55041 Lido di Camaiore, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 47522 Cesena, Italy
| | - Carlo Tessa
- Unit of Radiology, Azienda USL Toscana Nord Ovest, Apuane Hospital, 54100 Massa, Italy
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Liu X, Chen X, Chen D, Liu Y, Quan H, Gao L, Yan L, Dai J, Men K. A patient-specific auto-planning method for MRI-guided adaptive radiotherapy in prostate cancer. Radiother Oncol 2024; 200:110525. [PMID: 39245067 DOI: 10.1016/j.radonc.2024.110525] [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: 04/23/2024] [Revised: 08/29/2024] [Accepted: 09/03/2024] [Indexed: 09/10/2024]
Abstract
BACKGROUND AND PURPOSE Fast and automated generation of treatment plans is desirable for magnetic resonance imaging (MRI)-guided adaptive radiotherapy (MRIgART). This study proposed a novel patient-specific auto-planning method and validated its feasibility in improving the existing online planning workflow. MATERIALS AND METHODS Data from 40 patients with prostate cancer were collected retrospectively. A patient-specific auto-planning method was proposed to generate adaptive treatment plans. First, a population dose-prediction model (M0) was trained using data from previous patients. Second, a patient-specific model (Mps) was created for each new patient by fine-tuning M0 with the patient's data. Finally, an auto plan was optimized using the parameters derived from the predicted dose distribution by Mps. The auto plans were compared with manual plans in terms of plan quality, efficiency, dosimetric verification, and clinical evaluation. RESULTS The auto plans improved target coverage, reduced irradiation to the rectum, and provided comparable protection to other organs-at-risk. Target coverage for the planning target volume (+0.61 %, P = 0.023) and clinical target volume 4000 (+1.60 %, P < 0.001) increased. V2900cGy (-1.06 %, P = 0.004) and V1810cGy (-2.49 %, P < 0.001) to the rectal wall and V1810cGy (-2.82 %, P = 0.012) to the rectum were significantly reduced. The auto plans required less planning time (-3.92 min, P = 0.001), monitor units (-46.48, P = 0.003), and delivery time (-0.26 min, P = 0.004), and their gamma pass rates (3 %/2 mm) were higher (+0.47 %, P = 0.014). CONCLUSION The proposed patient-specific auto-planning method demonstrated a robust level of automation and was able to generate high-quality treatment plans in less time for MRIgART in prostate cancer.
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Affiliation(s)
- Xiaonan Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China; School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Xinyuan Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Deqi Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yuxiang Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Hong Quan
- School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Linrui Gao
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Lingling Yan
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
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8
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Trimpl MJ, Campbell S, Panakis N, Ajzensztejn D, Burke E, Ellis S, Johnstone P, Doyle E, Towers R, Higgins G, Bernard C, Hustinx R, Vallis KA, Stride EPJ, Gooding MJ. Deep learning-assisted interactive contouring of lung cancer: Impact on contouring time and consistency. Radiother Oncol 2024; 200:110500. [PMID: 39236985 DOI: 10.1016/j.radonc.2024.110500] [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: 06/10/2023] [Revised: 07/24/2024] [Accepted: 08/19/2024] [Indexed: 09/07/2024]
Abstract
BACKGROUND AND PURPOSE To evaluate the impact of a deep learning (DL)-assisted interactive contouring tool on inter-observer variability and the time taken to complete tumour contouring. MATERIALS AND METHODS Nine clinicians contoured the gross tumour volume (GTV) using the PET-CT scans of 10 non-small cell lung cancer (NSCLC) patients, either using DL-assisted or manual contouring tools. After contouring a case using one contouring method, the same case was contoured one week later using the other method. The contours and time taken were compared. RESULTS Use of the DL-assisted tool led to a statistically significant decrease in active contouring time of 23 % relative to the standard manual segmentation method (p < 0.01). The mean observation time for all clinicians and cases made up nearly 60 % of interaction time for both contouring approaches. On average the time spent contouring per case was reduced from 22 min to 19 min when using the DL-assisted tool. Additionally, the DL-assisted tool reduced contour variability in the parts of tumour where clinicians tended to disagree the most, while the consensus contour was similar whichever of the two contouring approaches was used. CONCLUSIONS A DL-assisted interactive contouring approach decreased active contouring time and local inter-observer variability when used to delineate lung cancer GTVs compared to a standard manual method. Integration of this tool into the clinical workflow could assist clinicians in contouring tasks and improve contouring efficiency.
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Affiliation(s)
- Michael J Trimpl
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK; Department of Oncology, University of Oxford, Oxford, UK; Mirada Medical Ltd, Oxford, UK.
| | - Sorcha Campbell
- Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK.
| | - Niki Panakis
- Oxford University Hospitals NHS Foundation Trust, UK.
| | | | - Emma Burke
- Oxford University Hospitals NHS Foundation Trust, UK.
| | - Shawn Ellis
- Oxford University Hospitals NHS Foundation Trust, UK.
| | | | - Emma Doyle
- Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK.
| | | | | | | | | | | | - Eleanor P J Stride
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Mark J Gooding
- Mirada Medical Ltd, Oxford, UK; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK; Inpictura Ltd, Abingdon, UK.
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9
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Agnoux E, Gehin W, Stefani A, Marchesi V, Martz N, Faivre JC. Reirradiation of bone metastasis: A narrative review of the literature. Cancer Radiother 2024; 28:568-575. [PMID: 39389841 DOI: 10.1016/j.canrad.2024.07.009] [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: 07/16/2024] [Accepted: 07/18/2024] [Indexed: 10/12/2024]
Abstract
Patients with bone metastasis are prevalent among those receiving palliative radiotherapy (RT), with approximately 20 % requiring reirradiation (reirradiation). The goal of bone reirradiation may be local control (oligoreoccurrence or oligoprogression of a previously treated lesion or in a previous treatment field) or symptomatic (threatening or painful progression). Published data on bone reirradiation indicate almost two-thirds of overall pain response. The primary organ at risk (especially for spine treatment) is the spinal cord. The risk of radiation myelitis is<1 % for cumulative doses of<50Gy. Intensity-modulated RT (IMRT) and stereotactic RT (SRT) appear to be safer than three-dimensional RT (3DRT), although randomized trials comparing these techniques in reirradiation are lacking. Reirradiation requires multidisciplinary assessment. Alternative treatments for bone metastases (surgery, interventional radiology, etc.) must be considered. Patients should have a performance status≤2, with at least a 1-month interval between treatments. The planning process involves reviewing previous RT plans, cautious dose adjustments, and precise target delineation and dose distribution to minimize toxicity. Cumulative dosimetry, patient consent, and vigilant post-treatment monitoring and dose reporting are crucial.
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Affiliation(s)
- Emma Agnoux
- Radiation Department, Institut de cancérologie de Lorraine, 54519 Vandœuvre-Lès-Nancy, France.
| | - William Gehin
- Radiation Department, Institut de cancérologie de Lorraine, 54519 Vandœuvre-Lès-Nancy, France
| | - Anaïs Stefani
- Radiation Department, Institut de cancérologie de Lorraine, 54519 Vandœuvre-Lès-Nancy, France
| | - Vincent Marchesi
- Medical Physics Department, Institut de cancérologie de Lorraine, 54519 Vandœuvre-Lès-Nancy, France
| | - Nicolas Martz
- Radiation Department, Institut de cancérologie de Lorraine, 54519 Vandœuvre-Lès-Nancy, France
| | - Jean-Christophe Faivre
- Radiation Department, Institut de cancérologie de Lorraine, 54519 Vandœuvre-Lès-Nancy, France
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10
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Cavinato S, Scaggion A, Paiusco M. Technical note: A software tool to extract complexity metrics from radiotherapy treatment plans. Med Phys 2024; 51:8602-8612. [PMID: 39186793 DOI: 10.1002/mp.17365] [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: 05/10/2024] [Revised: 08/08/2024] [Accepted: 08/09/2024] [Indexed: 08/28/2024] Open
Abstract
BACKGROUND Complexity metrics are mathematical quantities designed to quantify aspects of radiotherapy treatment plans that may affect both their deliverability and dosimetric accuracy. Despite numerous studies investigating their utility, there remains a notable absence of shared tools for their extraction. PURPOSE This study introduces UCoMX (Universal Complexity Metrics Extractor), a software package designed for the extraction of complexity metrics from the DICOM-RT plan files of radiotherapy treatments. METHODS UCoMX is developed around two extraction engines: VCoMX (VMAT Complexity Metrics Extractor) for VMAT/IMRT plans, and TCoMX (Tomotherapy Complexity Metrics Extractor) tailored for Helical Tomotherapy plans. The software, built using Matlab, is freely available in both Matlab-based and stand-alone versions. More than 90 complexity metrics, drawn from relevant literature, are implemented in the package: 43 for VMAT/IMRT and 51 for Helical Tomotherapy. RESULTS The package is designed to read DICOM-RT plan files generated by most commercially available Treatment Planning Systems (TPSs), across various treatment units. A reference dataset containing VMAT, IMRT, and Helical Tomotherapy plans is provided to serve as a reference for comparing UCoMX with other in-house systems available at other centers. CONCLUSION UCoMX offers a straightforward solution for extracting complexity metrics from radiotherapy plans. Its versatility is enhanced through different versions, including Matlab-based and stand-alone, and its compatibility with a wide range of commercially available TPSs and treatment units. UCoMX presents a free, user-friendly tool empowering researchers to compute the complexity of treatment plans efficiently.
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Affiliation(s)
- Samuele Cavinato
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Alessandro Scaggion
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Marta Paiusco
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
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11
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Robert C, Meyer P, Séroussi B, Leroy T, Bibault JE. Artificial intelligence and radiotherapy: Evolution or revolution? Cancer Radiother 2024; 28:503-509. [PMID: 39406605 DOI: 10.1016/j.canrad.2024.09.003] [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: 08/27/2024] [Revised: 08/31/2024] [Accepted: 09/01/2024] [Indexed: 11/03/2024]
Abstract
The integration of artificial intelligence, particularly deep learning algorithms, into radiotherapy represents a transformative shift in the field, enhancing accuracy, efficiency, and personalized care. This paper explores the multifaceted impact of artificial intelligence on radiotherapy, the evolution of the roles of radiation oncologists and medical physicists, and the associated practical challenges. The adoption of artificial intelligence promises to revolutionize the profession by automating repetitive tasks, improving diagnostic precision, and enabling adaptive radiotherapy. However, it also introduces significant risks, such as automation bias, verification failures, and the potential erosion of clinical skills. Ethical considerations, such as maintaining patient autonomy and addressing biases in artificial intelligence systems, are critical to ensuring the responsible use of artificial intelligence. Continuous training and development of robust quality assurance programs are required to mitigate these risks and maximize the benefits of artificial intelligence in radiotherapy.
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Affiliation(s)
- Charlotte Robert
- Inserm, U1030 Molecular Radiotherapy and Therapeutic Innovation, Université Paris-Saclay, Gustave-Roussy, Villejuif, France; Department of Radiation Oncology, Gustave-Roussy, Villejuif, France
| | - Philippe Meyer
- Department of Radiotherapy, Institut de Cancérologie Strasbourg Europe (ICANS), Strasbourg, France; Icube, CNRS UMR 7357, team Images, Strasbourg, France
| | - Brigitte Séroussi
- Inserm, Laboratoire d'informatique médicale et d'ingénierie des connaissances en e-santé (Limics), Sorbonne université, université Sorbonne Paris Nord, 75006 Paris, France; Hôpital Tenon, AP-HP, Paris, France; Délégation ministérielle du numérique en santé, ministère de la Santé, Paris, France
| | - Thomas Leroy
- Department of Radiation Oncology, Centre de Cancérologie des Dentellières, Valenciennes, France.
| | - Jean-Emmanuel Bibault
- Department of Radiation Oncology, Hôpital Europeen Georges-Pompidou, AP-HP, Université Paris-Cité, Paris, France; Institut du Cancer Paris Carpem, Université Paris-Cité, AP-HP, Paris, France
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12
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Rahman M, Iqbal Z, Parsons D, Salazar D, Visak J, Zhong X, Wang S, Stanley D, Godley A, Cai B, Sher D, Lin MH. Mitigating Risks in Cone Beam Computed Tomography Guided Online Adaptive Radiation Therapy: A Preventative Reference Planning Review Approach. Adv Radiat Oncol 2024; 9:101614. [PMID: 39399639 PMCID: PMC11470165 DOI: 10.1016/j.adro.2024.101614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 08/12/2024] [Indexed: 10/15/2024] Open
Abstract
Purpose Online adaptive radiation therapy (oART) treatment planning requires evaluating the temporal robustness of reference plans and anticipating the potential changes during treatment courses that may even lead to risks unique to the adaptive workflow. This study conducted a risk analysis of the cone beam computed tomography guided adaptive workflow and is the first to assess an adaptive-specific reference planning review that mitigates risk in the planning process to prevent events and treatment deficiencies during adaptation. Methods and Materials A quality management team of medical physicists, residents, physicians, and radiation therapists performed a fault tree analysis and failure mode and effects analysis. Fault trees were created for under/overdosing targets and treatment deficiencies and assisted in identifying failure modes for the failure mode and effects analysis. Treatment deficiency was defined as a nonideal oART plan resulting in treatment with a lower quality plan (either oART or scheduled plan), treatment delay, or canceling treatment for the day. A reference planning checklist was created to catch failure modes before reaching the patient. Risk priority numbers (RPNs = severity * detectability * occurrence) were scored with and without the reference planning checklist to quantify risk mitigation. A root cause analysis was conducted for an event where an adaptive plan failed to generate. Results The reference planning checklist (with items covering patient background, contouring/planning robustness for anatomy variability, and machine limitations) reduced the RPN for all failure modes. Only 1 failure mode with an RPN > 150 occurred with the reference planning checklist compared with 29 failure modes without, including 14 adaptive-specific failure modes. Contouring, planning, setup, scheduling, and documentation errors were identified during the fault tree analysis. Twenty-nine of 70 errors were adaptive-specific. The reference planning checklist could address 23 of 33 errors for over- or underdosing and 28 of 37 errors for treatment deficiency. The root cause analysis highlighted the need to check the setup prior to adaptive plan delivery and the time-out checklist. Conclusions The reference planning checklist improved the detection of the failure modes and improved the quality and robustness of the plans produced for oART. It is ideally performed before the physician plan review to prevent last-minute replan (before or after first adaptive treatment) and delay of patient start. The checklist presented can be modified based on failures specific to individual clinics and used at various planning steps based on available resources.
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Affiliation(s)
- Mahbubur Rahman
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - Zohaib Iqbal
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - David Parsons
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - Denise Salazar
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - Justin Visak
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - Xinran Zhong
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - Siqiu Wang
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - Dennis Stanley
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama
| | - Andrew Godley
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - Bin Cai
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - David Sher
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
| | - Mu-Han Lin
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas
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13
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de Leon J, Jelen U, Carr M, Crawford D, Picton M, Tran C, McKenzie L, Peng V, Twentyman T, Jameson MG, Batumalai V. Adapting outside the box: Simulation-free MR-guided stereotactic ablative radiotherapy for prostate cancer. Radiother Oncol 2024; 200:110527. [PMID: 39242030 DOI: 10.1016/j.radonc.2024.110527] [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: 07/07/2024] [Revised: 08/29/2024] [Accepted: 09/02/2024] [Indexed: 09/09/2024]
Abstract
BACKGROUND AND PURPOSE Magnetic resonance (MR)-guided radiotherapy (MRgRT) enhances treatment precision and adaptive capabilities, potentially supporting a simulation-free (sim-free) workflow. This work reports the first clinical implementation of a sim-free workflow using the MR-Linac for prostate cancer patients treated with stereotactic ablative radiotherapy (SABR). MATERIALS AND METHODS Fifteen patients who had undergone a prostate-specific membrane antigen positron emission tomography/CT (PSMA-PET/CT) scan as part of diagnostic workup were included in this work. Two reference plans were generated per patient: one using PSMA-PET/CT (sim-free plan) and the other using standard simulation CT (simCT plan). Dosimetric evaluations included comparisons between simCT, sim-free, and first fraction plans. Timing measurements were conducted to assess durations for both simCT and sim-free pre-treatment workflows. RESULTS All 15 patients underwent successful treatment using a sim-free workflow. Dosimetric differences between simCT, sim-free, and first fraction plans were minor and within acceptable clinical limits, with no major violations of standardised criteria. The sim-free workflow took on average 130 min, while the simCT workflow took 103 min. CONCLUSION This work demonstrates the feasibility and benefits of sim-free MR-guided adaptive radiotherapy for prostate SABR, representing the first reported clinical experience in an ablative setting. By eliminating traditional simulation scans, this approach reduces patient burden by minimising hospital visits and enhances treatment accessibility.
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Affiliation(s)
| | - Urszula Jelen
- GenesisCare, St Vincent's Hospital, Sydney, Australia
| | - Madeline Carr
- GenesisCare, St Vincent's Hospital, Sydney, Australia
| | | | | | - Charles Tran
- GenesisCare, St Vincent's Hospital, Sydney, Australia
| | | | - Valery Peng
- GenesisCare, St Vincent's Hospital, Sydney, Australia
| | | | - Michael G Jameson
- GenesisCare, St Vincent's Hospital, Sydney, Australia; School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Australia; Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - Vikneswary Batumalai
- GenesisCare, St Vincent's Hospital, Sydney, Australia; School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Australia; The George Institute for Global Health, UNSW Sydney, Sydney, NSW, Australia.
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14
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Camprodon G, Gabro A, El Ayachi Z, Chopra S, Nout R, Maingon P, Chargari C. Personalized strategies for brachytherapy of cervix cancer. Cancer Radiother 2024; 28:610-617. [PMID: 39395842 DOI: 10.1016/j.canrad.2024.09.006] [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: 09/16/2024] [Accepted: 09/17/2024] [Indexed: 10/14/2024]
Abstract
Among most tailored approaches in radiation oncology, the development of brachytherapy for the treatment of cervical cancer patients has benefited from various technological innovations. The development of 3D image-guided treatments was the first step for treatment personalization. This breakthrough preceded practice homogenization and validation of predictive dose and volume parameters and prognostic factors. We review some of the most significant strategies that emerged from the ongoing research in order to increase personalization in uterovaginal brachytherapy. A better stratification based on patients and tumors characteristics may lead to better discriminate candidates for intensification or de-escalation strategies, in order to still improve patient outcome while minimizing the risk of treatment-related side effects.
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Affiliation(s)
- Guillaume Camprodon
- Department of Radiation Oncology, hôpital Pitié Salpêtrière, Assistance publique-hôpitaux de Paris, Sorbonne université, Paris, France
| | - Alexandra Gabro
- Department of Radiation Oncology, hôpital Pitié Salpêtrière, Assistance publique-hôpitaux de Paris, Sorbonne université, Paris, France
| | - Zineb El Ayachi
- Department of Radiation Oncology, hôpital Pitié Salpêtrière, Assistance publique-hôpitaux de Paris, Sorbonne université, Paris, France
| | - Supriya Chopra
- Department of Radiation Oncology and Medical Physics, Advanced Centre for Treatment Research and Education in Cancer, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - Remi Nout
- Department of Radiation Oncology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Philippe Maingon
- Department of Radiation Oncology, hôpital Pitié Salpêtrière, Assistance publique-hôpitaux de Paris, Sorbonne université, Paris, France
| | - Cyrus Chargari
- Department of Radiation Oncology, hôpital Pitié Salpêtrière, Assistance publique-hôpitaux de Paris, Sorbonne université, Paris, France.
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15
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Deng L, Chen S, Li Y, Huang S, Yang X, Wang J. Synthetic CT generation based on multi-sequence MR using CycleGAN for head and neck MRI-only planning. Biomed Eng Lett 2024; 14:1319-1333. [PMID: 39465105 PMCID: PMC11502648 DOI: 10.1007/s13534-024-00402-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 06/10/2024] [Accepted: 06/11/2024] [Indexed: 10/29/2024] Open
Abstract
The purpose of this study is to investigate the influence of different magnetic resonance (MR) sequences on the accuracy of generating computed tomography (sCT) images for nasopharyngeal carcinoma based on CycleGAN. In this study, 143 patients' head and neck MR sequence (T1, T2, T1C, and T1DIXONC) and CT imaging data were acquired. The generator and discriminator of CycleGAN are improved to achieve the purpose of balance confrontation, and a cyclic consistent structure control domain is proposed in terms of loss function. Four different single-sequence MR images and one multi-sequence MR image were used to evaluate the accuracy of sCT. During the model testing phase, five testing scenarios were employed to further assess the mean absolute error, peak signal-to-noise ratio, structural similarity index, and root mean square error between the actual CT images and the sCT images generated by different models. T1 sequence-based sCT achieved better results in single-sequence MR-based sCT. Multi-sequence MR-based sCT achieved better results with T1 sequence-based sCT in terms of evaluation metrics. For metrological evaluation, the global gamma passage rate of sCT based on sequence MR was greater than 95% at 3%/3 mm, except for sCT based on T2 sequence MR. We developed a CycleGAN method to synthesize CT using different MR sequences, this method shows encouraging potential for dosimetric evaluation.
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Affiliation(s)
- Liwei Deng
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080 Heilongjiang China
- Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, 150080 Heilongjiang China
| | - Songyu Chen
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080 Heilongjiang China
| | - Yunfa Li
- Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, 150080 Heilongjiang China
| | - Sijuan Huang
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060 Guangdong China
| | - Xin Yang
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, 510060 Guangdong China
| | - Jing Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631 China
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16
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Fransson S, Strand R, Tilly D. Deep learning-based dose prediction for magnetic resonance-guided prostate radiotherapy. Med Phys 2024; 51:8087-8095. [PMID: 39106418 DOI: 10.1002/mp.17312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [ |