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Finnegan RN, Quinn A, Booth J, Belous G, Hardcastle N, Stewart M, Griffiths B, Carroll S, Thwaites DI. Cardiac substructure delineation in radiation therapy - A state-of-the-art review. J Med Imaging Radiat Oncol 2024. [PMID: 38757728 DOI: 10.1111/1754-9485.13668] [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: 01/24/2024] [Accepted: 04/29/2024] [Indexed: 05/18/2024]
Abstract
Delineation of cardiac substructures is crucial for a better understanding of radiation-related cardiotoxicities and to facilitate accurate and precise cardiac dose calculation for developing and applying risk models. This review examines recent advancements in cardiac substructure delineation in the radiation therapy (RT) context, aiming to provide a comprehensive overview of the current level of knowledge, challenges and future directions in this evolving field. Imaging used for RT planning presents challenges in reliably visualising cardiac anatomy. Although cardiac atlases and contouring guidelines aid in standardisation and reduction of variability, significant uncertainties remain in defining cardiac anatomy. Coupled with the inherent complexity of the heart, this necessitates auto-contouring for consistent large-scale data analysis and improved efficiency in prospective applications. Auto-contouring models, developed primarily for breast and lung cancer RT, have demonstrated performance comparable to manual contouring, marking a significant milestone in the evolution of cardiac delineation practices. Nevertheless, several key concerns require further investigation. There is an unmet need for expanding cardiac auto-contouring models to encompass a broader range of cancer sites. A shift in focus is needed from ensuring accuracy to enhancing the robustness and accessibility of auto-contouring models. Addressing these challenges is paramount for the integration of cardiac substructure delineation and associated risk models into routine clinical practice, thereby improving the safety of RT for future cancer patients.
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Affiliation(s)
- Robert N Finnegan
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
| | - Alexandra Quinn
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
| | - Jeremy Booth
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
| | - Gregg Belous
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, Queensland, Australia
| | - Nicholas Hardcastle
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Maegan Stewart
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Brooke Griffiths
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
| | - Susan Carroll
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - David I Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
- Radiotherapy Research Group, Leeds Institute of Medical Research, St James's Hospital and University of Leeds, Leeds, UK
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Vaugier L, Martin-Mervoyer E, Ah-Thiane L, Langé M, Ollivier L, Perennec T, Supiot S, Duvergé L, Lucia F, Trémolières P, Movassaghi R, Fresse-Warin K, Moignier A, Thillays F. How to contour the different heart subregions for future deep-learning modeling of the heart: A practical pictorial proposal for radiation oncologists. Clin Transl Radiat Oncol 2024; 45:100718. [PMID: 38204729 PMCID: PMC10776448 DOI: 10.1016/j.ctro.2023.100718] [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: 01/24/2023] [Revised: 12/14/2023] [Accepted: 12/16/2023] [Indexed: 01/12/2024] Open
Abstract
There are currently no accurate rules for manually delineating the subregions of the heart (cavities, vessels, aortic/mitral valves, Planning organ at Risk Volumes for coronary arteries) with the perspective of deep-learning based modeling. Our objective was to present a practical pictorial view for radiation oncologists, based on the RTOG atlas and anatomical complementary considerations for the cases where the RTOG guidelines are missing.
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Affiliation(s)
- Loig Vaugier
- Department of Radiation Oncology, Institut de Cancérologie de l’Ouest, 44800 Saint Herblain, France
| | - Elvire Martin-Mervoyer
- Department of Cardiology, Institut de Cancérologie de l’Ouest, 44800 Saint Herblain, France
| | - Loic Ah-Thiane
- Department of Radiation Oncology, Institut de Cancérologie de l’Ouest, 44800 Saint Herblain, France
| | - Martin Langé
- Department of Radiation Oncology, Institut de Cancérologie de l’Ouest, 44800 Saint Herblain, France
| | - Luc Ollivier
- Department of Radiation Oncology, Institut de Cancérologie de l’Ouest, 44800 Saint Herblain, France
| | - Tanguy Perennec
- Department of Radiation Oncology, Institut de Cancérologie de l’Ouest, 44800 Saint Herblain, France
| | - Stéphane Supiot
- Department of Radiation Oncology, Institut de Cancérologie de l’Ouest, 44800 Saint Herblain, France
- Laboratoire US2B, Unité en Sciences Biologiques et Biotechnologies, UMR CNRS 6286, UFR Sciences et Techniques, 2, rue de la Houssinière, 44322 Nantes, France
| | - Loig Duvergé
- Department of Radiation Oncology, Centre Eugène Marquis, 35000 Rennes, France
| | - François Lucia
- Department of Radiation Oncology, Centre Hospitalo-Universitaire (CHU), 29200 Brest, France
| | - Pierre Trémolières
- Department of Radiation Oncology, Institut de Cancérologie de l’Ouest, 49000 Angers, France
| | - Roshanack Movassaghi
- Department of Radiology, Institut de Cancérologie de l’Ouest, 44800 Saint Herblain, France
| | - Karine Fresse-Warin
- Department of Radiology – Non-invasive Cardiovascular Imaging, Centre Hospitalo-Universitaire (CHU), 44000 Nantes, France
| | - Alexandra Moignier
- Department of Medical Physics, Institut de Cancérologie de l’Ouest, 44800 Saint Herblain, France
| | - Francois Thillays
- Department of Radiation Oncology, Institut de Cancérologie de l’Ouest, 44800 Saint Herblain, France
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Chen X, Mumme RP, Corrigan KL, Mukai-Sasaki Y, Koutroumpakis E, Palaskas NL, Nguyen CM, Zhao Y, Huang K, Yu C, Xu T, Daniel A, Balter PA, Zhang X, Niedzielski JS, Shete SS, Deswal A, Court LE, Liao Z, Yang J. Deep learning-based automatic segmentation of cardiac substructures for lung cancers. Radiother Oncol 2024; 191:110061. [PMID: 38122850 DOI: 10.1016/j.radonc.2023.110061] [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: 09/26/2023] [Revised: 11/09/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023]
Abstract
PURPOSE Accurate and comprehensive segmentation of cardiac substructures is crucial for minimizing the risk of radiation-induced heart disease in lung cancer radiotherapy. We sought to develop and validate deep learning-based auto-segmentation models for cardiac substructures. MATERIALS AND METHODS Nineteen cardiac substructures (whole heart, 4 heart chambers, 6 great vessels, 4 valves, and 4 coronary arteries) in 100 patients treated for non-small cell lung cancer were manually delineated by two radiation oncologists. The valves and coronary arteries were delineated as planning risk volumes. An nnU-Net auto-segmentation model was trained, validated, and tested on this dataset with a split ratio of 75:5:20. The auto-segmented contours were evaluated by comparing them with manually drawn contours in terms of Dice similarity coefficient (DSC) and dose metrics extracted from clinical plans. An independent dataset of 42 patients was used for subjective evaluation of the auto-segmentation model by 4 physicians. RESULTS The average DSCs were 0.95 (+/- 0.01) for the whole heart, 0.91 (+/- 0.02) for 4 chambers, 0.86 (+/- 0.09) for 6 great vessels, 0.81 (+/- 0.09) for 4 valves, and 0.60 (+/- 0.14) for 4 coronary arteries. The average absolute errors in mean/max doses to all substructures were 1.04 (+/- 1.99) Gy and 2.20 (+/- 4.37) Gy. The subjective evaluation revealed that 94% of the auto-segmented contours were clinically acceptable. CONCLUSION We demonstrated the effectiveness of our nnU-Net model for delineating cardiac substructures, including coronary arteries. Our results indicate that this model has promise for studies regarding radiation dose to cardiac substructures.
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Affiliation(s)
- Xinru Chen
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, United States
| | - Raymond P Mumme
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Kelsey L Corrigan
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Yuki Mukai-Sasaki
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States; Advanced Medical Center, Shonan Kamakura General Hospital, Kamakura, Japan
| | - Efstratios Koutroumpakis
- Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Nicolas L Palaskas
- Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Callistus M Nguyen
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Yao Zhao
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, United States
| | - Kai Huang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, United States
| | - Cenji Yu
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, United States
| | - Ting Xu
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Aji Daniel
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Peter A Balter
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, United States
| | - Xiaodong Zhang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, United States
| | - Joshua S Niedzielski
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, United States
| | - Sanjay S Shete
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, United States; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Anita Deswal
- Department of Cardiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, United States
| | - Zhongxing Liao
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, United States.
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Olloni A, Lorenzen EL, Jeppesen SS, Diederichsen A, Finnegan R, Hoffmann L, Kristiansen C, Knap M, Milo MLH, Møller DS, Pøhl M, Persson G, Sand HMB, Sarup N, Thing RS, Brink C, Schytte T. An open source auto-segmentation algorithm for delineating heart and substructures - Development and validation within a multicenter lung cancer cohort. Radiother Oncol 2024; 191:110065. [PMID: 38122851 DOI: 10.1016/j.radonc.2023.110065] [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: 08/03/2023] [Revised: 11/27/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND AND PURPOSE Irradiation of the heart in thoracic cancers raises toxicity concerns. For accurate dose estimation, automated heart and substructure segmentation is potentially useful. In this study, a hybrid automatic segmentation is developed. The accuracy of delineation and dose predictions were evaluated, testing the method's potential within heart toxicity studies. MATERIALS AND METHODS The hybrid segmentation method delineated the heart, four chambers, three large vessels, and the coronary arteries. The method consisted of a nnU-net heart segmentation and partly atlas- and model-based segmentation of the substructures. The nnU-net training and atlas segmentation was based on lung cancer patients and was validated against a national consensus dataset of 12 patients with breast cancer. The accuracy of dose predictions between manual and auto-segmented heart and substructures was evaluated by transferring the dose distribution of 240 previously treated lung cancer patients to the consensus data set. RESULTS The hybrid auto-segmentation method performed well with a heart dice similarity coefficient (DSC) of 0.95, with no statistically significant difference between the automatic and manual delineations. The DSC for the chambers varied from 0.78-0.86 for the automatic segmentation and was comparable with the inter-observer variability. Most importantly, the automatic segmentation was as precise as the clinical experts in predicting the dose distribution to the heart and all substructures. CONCLUSION The hybrid segmentation method performed well in delineating the heart and substructures. The prediction of dose by the automatic segmentation was aligned with the manual delineations, enabling measurement of heart and substructure dose in large cohorts. The delineation algorithm will be available for download.
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Affiliation(s)
- Agon Olloni
- Department of Oncology, Odense University Hospital, Denmark; Department of Clinical Research, University of Southern Denmark, Denmark; Academy of Geriatric Cancer Research (AgeCare), Odense University Hospital, Denmark.
| | - Ebbe Laugaard Lorenzen
- Department of Clinical Research, University of Southern Denmark, Denmark; Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Denmark
| | - Stefan Starup Jeppesen
- Department of Oncology, Odense University Hospital, Denmark; Department of Clinical Research, University of Southern Denmark, Denmark; Academy of Geriatric Cancer Research (AgeCare), Odense University Hospital, Denmark
| | - Axel Diederichsen
- Department of Clinical Research, University of Southern Denmark, Denmark; Department of Cardiology, Odense University Hospital, Denmark
| | - Robert Finnegan
- Northern Sydney Cancer Centre, Royal North Shore Hospital, St Leonards, NSW, Australia
| | - Lone Hoffmann
- Department of Oncology, Aarhus University Hospital, Denmark; Department of Clinical Medicine, Faculty of Health Sciences, Aarhus University, Denmark
| | - Charlotte Kristiansen
- Department of Oncology, Vejle Hospital University Hospital of Southern Denmark, Denmark
| | - Marianne Knap
- Department of Oncology, Aarhus University Hospital, Denmark
| | | | - Ditte Sloth Møller
- Department of Oncology, Aarhus University Hospital, Denmark; Department of Clinical Medicine, Faculty of Health Sciences, Aarhus University, Denmark
| | - Mette Pøhl
- Department of Oncology, Rigshospitalet, Copenhagen University Hospital, Denmark
| | - Gitte Persson
- Department of Oncology, Copenhagen University Hospital, Herlev and Gentofte, Denmark; Department of Clinical Medicine, Copenhagen University, Denmark
| | - Hella M B Sand
- Department of Oncology, Aalborg University Hospital, Denmark
| | - Nis Sarup
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Denmark
| | - Rune Slot Thing
- Department of Oncology, Vejle Hospital University Hospital of Southern Denmark, Denmark
| | - Carsten Brink
- Department of Clinical Research, University of Southern Denmark, Denmark; Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Denmark
| | - Tine Schytte
- Department of Oncology, Odense University Hospital, Denmark; Department of Clinical Research, University of Southern Denmark, Denmark
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5
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Alnasser TN, Abdulaal L, Maiter A, Sharkey M, Dwivedi K, Salehi M, Garg P, Swift AJ, Alabed S. Advancements in cardiac structures segmentation: a comprehensive systematic review of deep learning in CT imaging. Front Cardiovasc Med 2024; 11:1323461. [PMID: 38317865 PMCID: PMC10839106 DOI: 10.3389/fcvm.2024.1323461] [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: 10/17/2023] [Accepted: 01/08/2024] [Indexed: 02/07/2024] Open
Abstract
Background Segmentation of cardiac structures is an important step in evaluation of the heart on imaging. There has been growing interest in how artificial intelligence (AI) methods-particularly deep learning (DL)-can be used to automate this process. Existing AI approaches to cardiac segmentation have mostly focused on cardiac MRI. This systematic review aimed to appraise the performance and quality of supervised DL tools for the segmentation of cardiac structures on CT. Methods Embase and Medline databases were searched to identify related studies from January 1, 2013 to December 4, 2023. Original research studies published in peer-reviewed journals after January 1, 2013 were eligible for inclusion if they presented supervised DL-based tools for the segmentation of cardiac structures and non-coronary great vessels on CT. The data extracted from eligible studies included information about cardiac structure(s) being segmented, study location, DL architectures and reported performance metrics such as the Dice similarity coefficient (DSC). The quality of the included studies was assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results 18 studies published after 2020 were included. The DSC scores median achieved for the most commonly segmented structures were left atrium (0.88, IQR 0.83-0.91), left ventricle (0.91, IQR 0.89-0.94), left ventricle myocardium (0.83, IQR 0.82-0.92), right atrium (0.88, IQR 0.83-0.90), right ventricle (0.91, IQR 0.85-0.92), and pulmonary artery (0.92, IQR 0.87-0.93). Compliance of studies with CLAIM was variable. In particular, only 58% of studies showed compliance with dataset description criteria and most of the studies did not test or validate their models on external data (81%). Conclusion Supervised DL has been applied to the segmentation of various cardiac structures on CT. Most showed similar performance as measured by DSC values. Existing studies have been limited by the size and nature of the training datasets, inconsistent descriptions of ground truth annotations and lack of testing in external data or clinical settings. Systematic Review Registration [www.crd.york.ac.uk/prospero/], PROSPERO [CRD42023431113].
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Affiliation(s)
- Turki Nasser Alnasser
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- College of Applied Medical Science, King Saud bin Abdulaziz University for Health Science, Riyadh, Saudi Arabia
| | - Lojain Abdulaal
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Ahmed Maiter
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Michael Sharkey
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Krit Dwivedi
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Mahan Salehi
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Pankaj Garg
- Norwich Medical School, Faculty of Medicine and Health Sciences, University of East Anglia, Norwich, United Kingdom
| | - Andrew James Swift
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Insigneo Institute, Faculty of Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Samer Alabed
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
- Insigneo Institute, Faculty of Engineering, The University of Sheffield, Sheffield, United Kingdom
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6
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Petit C, Escande A, Sarrade T, Vaugier L, Kirova Y, Tallet A. Radiation therapy in the thoracic region: Radio-induced cardiovascular disease, cardiac delineation and sparing, cardiac dose constraints, and cardiac implantable electronic devices. Cancer Radiother 2023; 27:588-598. [PMID: 37648559 DOI: 10.1016/j.canrad.2023.06.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 06/27/2023] [Indexed: 09/01/2023]
Abstract
Radiation therapy in the thoracic region may deliver incidental ionizing radiation to the surrounding healthy structures, including the heart. Radio-induced heart toxicity has long been a concern in breast cancer and Hodgkin's lymphoma and was deemed a long-term event. However, recent data highlight the need to limit the dose to the heart in less favorable thoracic cancers too, such as lung and esophageal cancers in which incidental irradiation led to increased mortality. This article will summarize available cardiac dose constraints in various clinical settings and the types of radio-induced cardiovascular diseases encountered as well as delineation of cardiac subheadings and management of cardiac devices. Although still not completely deciphered, heart dose constraints remain intensively investigated and the mean dose to the heart is no longer the only dosimetric parameter to consider since the left anterior descending artery as well as the left ventricle should also be part of dosimetry constraints.
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Affiliation(s)
- C Petit
- Radiation Oncology Department, institut Paoli-Calmettes, 232, boulevard Sainte-Marguerite, 13273 Marseille cedex 09, France
| | - A Escande
- Service de radiothérapie, centre Léonard-de-Vinci, Dechy, France; UMR 9189, laboratoire Cristal, université de Lille, Villeneuve-d'Ascq, France
| | - T Sarrade
- Department of Radiation Oncology, hôpital Tenon, Sorbonne université, 75020 Paris, France
| | - L Vaugier
- Department of Radiation Oncology, institut de cancérologie de l'Ouest, Saint-Herblain, France
| | - Y Kirova
- Department of Radiation Oncology, institut Curie, Paris, France
| | - A Tallet
- Radiation Oncology Department, institut Paoli-Calmettes, 232, boulevard Sainte-Marguerite, 13273 Marseille cedex 09, France; UMR 1068, CRCM Inserm, Marseille, France.
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7
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Finnegan RN, Chin V, Chlap P, Haidar A, Otton J, Dowling J, Thwaites DI, Vinod SK, Delaney GP, Holloway L. Open-source, fully-automated hybrid cardiac substructure segmentation: development and optimisation. Phys Eng Sci Med 2023; 46:377-393. [PMID: 36780065 PMCID: PMC10030448 DOI: 10.1007/s13246-023-01231-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 01/30/2023] [Indexed: 02/14/2023]
Abstract
Radiotherapy for thoracic and breast tumours is associated with a range of cardiotoxicities. Emerging evidence suggests cardiac substructure doses may be more predictive of specific outcomes, however, quantitative data necessary to develop clinical planning constraints is lacking. Retrospective analysis of patient data is required, which relies on accurate segmentation of cardiac substructures. In this study, a novel model was designed to deliver reliable, accurate, and anatomically consistent segmentation of 18 cardiac substructures on computed tomography (CT) scans. Thirty manually contoured CT scans were included. The proposed multi-stage method leverages deep learning (DL), multi-atlas mapping, and geometric modelling to automatically segment the whole heart, cardiac chambers, great vessels, heart valves, coronary arteries, and conduction nodes. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD), and volume ratio. Performance was reliable, with no errors observed and acceptable variation in accuracy between cases, including in challenging cases with imaging artefacts and atypical patient anatomy. The median DSC range was 0.81-0.93 for whole heart and cardiac chambers, 0.43-0.76 for great vessels and conduction nodes, and 0.22-0.53 for heart valves. For all structures the median MDA was below 6 mm, median HD ranged 7.7-19.7 mm, and median volume ratio was close to one (0.95-1.49) for all structures except the left main coronary artery (2.07). The fully automatic algorithm takes between 9 and 23 min per case. The proposed fully-automatic method accurately delineates cardiac substructures on radiotherapy planning CT scans. Robust and anatomically consistent segmentations, particularly for smaller structures, represents a major advantage of the proposed segmentation approach. The open-source software will facilitate more precise evaluation of cardiac doses and risks from available clinical datasets.
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Affiliation(s)
- Robert N Finnegan
- Northern Sydney Cancer Centre, Royal North Shore Hospital, St Leonards, NSW, Australia.
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia.
- Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia.
| | - Vicky Chin
- Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
- Liverpool Cancer Therapy Centre, South Western Sydney Local Health District, Liverpool, NSW, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - Phillip Chlap
- Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
- Liverpool Cancer Therapy Centre, South Western Sydney Local Health District, Liverpool, NSW, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - Ali Haidar
- Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
- Liverpool Cancer Therapy Centre, South Western Sydney Local Health District, Liverpool, NSW, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - James Otton
- South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - Jason Dowling
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia
- CSIRO Health and Biosecurity, The Australian e-Health and Research Centre, Herston, QLD, Australia
- School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, NSW, Australia
| | - David I Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia
- Radiotherapy Research Group, Leeds Institute of Medical Research, St James's Hospital and University of Leeds, Leeds, UK
| | - Shalini K Vinod
- Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
- Liverpool Cancer Therapy Centre, South Western Sydney Local Health District, Liverpool, NSW, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - Geoff P Delaney
- Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
- Liverpool Cancer Therapy Centre, South Western Sydney Local Health District, Liverpool, NSW, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - Lois Holloway
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
- Liverpool Cancer Therapy Centre, South Western Sydney Local Health District, Liverpool, NSW, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
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8
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Shen J, Gu P, Wang Y, Wang Z. Advances in automatic delineation of target volume and cardiac substructure in breast cancer radiotherapy (Review). Oncol Lett 2023; 25:110. [PMID: 36817059 PMCID: PMC9932716 DOI: 10.3892/ol.2023.13697] [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: 09/14/2022] [Accepted: 01/06/2023] [Indexed: 02/05/2023] Open
Abstract
Postoperative adjuvant radiotherapy plays an important role in the treatment of patients with breast cancer. With the continuous development of radiotherapeutic technologies, the requirements for radiotherapeutic accuracy are increasingly high. The accuracy of target volume and organ at risk delineation significantly affects the effect of radiotherapy. Automatic delineation software has been continuously developed for the automatic delineation of target areas and organs at risk. Automatic segmentation based on an atlas and deep learning is a hot topic in current clinical research. Automatic delineation can not only reduce the workload and delineation times, but also establish a uniform delineation standard and reduce inter-observer and intra-observer differences. In patients with breast cancer, especially in patients who undergo left breast radiotherapy, the protection of the heart is particularly important. Treating the whole heart as an organ at risk cannot meet the clinical needs, and it is necessary to limit the dose to specific cardiac substructures. The present review discusses the importance of automatic delineation of target volume and cardiac substructure in radiotherapy for patients with breast cancer.
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Affiliation(s)
- Jingjing Shen
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200438, P.R. China
| | - Peihua Gu
- Department of Oncology and Radiotherapy, Shidong Hospital Affiliated to University of Shanghai for Science and Technology, Shanghai 200438, P.R. China
| | - Yun Wang
- Department of Oncology and Radiotherapy, Shidong Hospital Affiliated to University of Shanghai for Science and Technology, Shanghai 200438, P.R. China
| | - Zhongming Wang
- Department of Oncology and Radiotherapy, Shidong Hospital Affiliated to University of Shanghai for Science and Technology, Shanghai 200438, P.R. China,Correspondence to: Dr Zhongming Wang, Department of Oncology and Radiotherapy, Shidong Hospital Affiliated to University of Shanghai for Science and Technology, 999 Shiguang Road, Shanghai 200438, P.R. China, E-mail:
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