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Refsgaard L, Holm Milo ML, Buhl ES, Jensen JM, Maae E, Berg M, Jensen I, Nielsen MH, Lorenzen EL, Thorsen LBJ, Korreman SS, Offersen BV. The effect of coronary artery calcifications and radiotherapy on the risk of coronary artery disease in high-risk breast cancer patients in the DBCG RT-Nation cohort. Radiother Oncol 2025; 204:110705. [PMID: 39725067 DOI: 10.1016/j.radonc.2024.110705] [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: 09/24/2024] [Revised: 12/18/2024] [Accepted: 12/20/2024] [Indexed: 12/28/2024]
Abstract
BACKGROUND AND PURPOSE Radiotherapy improves outcomes for breast cancer. However, prior studies have correlated the risk of coronary artery disease (CAD) to the mean heart dose (MHD), mean dose to the left anterior descending artery (LAD_mean) and the left ventricle V5Gy (LV5). Other studies showed an increased risk of CAD for patients with pronounced coronary artery calcification (CAC) at the time of radiotherapy. MATERIALS AND METHODS This cohort study included 3355 high-risk breast cancer patients treated in Western Denmark (2008-2016). We analysed CT scans, treatment plans, and dose distributions. CAC was measured using the Agatston score (AS). We examined the dose-response relationship between MHD, LV5 and LAD_mean and CAD, and the effect of CAC presence at radiotherapy. Secondary analysis assessed overall survival. RESULTS Of 3355 patients, 45 (1.2 %) developed CAD during follow-up. AS was a strong predictor of CAD risk with a hazard ratio of 9.51(CI95:5.16-17.53) for AS ≥ 100 versus AS < 100 and a 6.7 % difference in absolute cumulative CAD risk at ten years (7.7 % vs 1 %). For AS < 100 (97 % of patients) CAD risk increased with MHD, hazard ratio 1.25 (CI95:1.01-1.56) per Gy. ForAS ≥ 100, CAD risk was driven by CAC rather than radiation dose. CAC was associated with poorer overall survival. Median MHD for the whole cohort was 1.25 Gy (IQR:1.01-1.56). CONCLUSION AS from planning CT-scans predicted CAD risk and overall survival in breast cancer patients receiving radiotherapy. The MHD remained the strongest predictor in patients with low CAC. For patients with high CAC, the high baseline risk from CAC was a stronger risk factor than the dose-related risk.
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Affiliation(s)
- Lasse Refsgaard
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
| | - Marie Louise Holm Milo
- Department of Oncology, Aalborg University Hospital, Aalborg, Denmark; Department of Clinical Medicine, Faculty of Medicine, Aalborg University, Aalborg, Denmark
| | - Emma Skarsø Buhl
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | | | - Else Maae
- Department of Oncology, Vejle Hospital, University Hospital of Southern Denmark, Denmark
| | - Martin Berg
- Department of Oncology, Vejle Hospital, University Hospital of Southern Denmark, Denmark
| | - Ingelise Jensen
- Department of Oncology, Aalborg University Hospital, Aalborg, Denmark
| | | | - Ebbe Laugaard Lorenzen
- Department of Oncology, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Lise Bech Jellesmark Thorsen
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Stine Sofia Korreman
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Birgitte Vrou Offersen
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
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Guha A, Shah V, Nahle T, Singh S, Kunhiraman HH, Shehnaz F, Nain P, Makram OM, Mahmoudi M, Al-Kindi S, Madabhushi A, Shiradkar R, Daoud H. Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review. Curr Cardiol Rep 2025; 27:56. [PMID: 39969610 DOI: 10.1007/s11886-025-02215-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/06/2025] [Indexed: 02/20/2025]
Abstract
PURPOSE OF REVIEW This review explores the role of artificial intelligence (AI) in cardio-oncology, focusing on its latest application across problems in diagnosis, prognosis, risk stratification, and management of cardiovascular (CV) complications in cancer patients. It also highlights multi-omics analysis, explainable AI, and real-time decision-making, while addressing challenges like data heterogeneity and ethical concerns. RECENT FINDINGS AI can advance cardio-oncology by leveraging imaging, electronic health records (EHRs), electrocardiograms (ECG), and multi-omics data for early cardiotoxicity detection, stratification and long-term risk prediction. Novel AI-ECG models and imaging techniques improve diagnostic accuracy, while multi-omics analysis identifies biomarkers for personalized treatment. However, significant barriers, including data heterogeneity, lack of transparency, and regulatory challenges, hinder widespread adoption. AI significantly enhances early detection and intervention in cardio-oncology. Future efforts should address the impact of AI technologies on clinical outcomes, and ethical challenges, to enable broader clinical adoption and improve patient care.
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Affiliation(s)
- Avirup Guha
- Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA, USA.
- Cardio-Oncology Program, Medical College of Georgia at Augusta University, Augusta, GA, USA.
| | - Viraj Shah
- Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA, USA
- Cardio-Oncology Program, Medical College of Georgia at Augusta University, Augusta, GA, USA
| | - Tarek Nahle
- Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA, USA
- Cardio-Oncology Program, Medical College of Georgia at Augusta University, Augusta, GA, USA
| | - Shivam Singh
- Department of Internal Medicine, Reading Hospital, Tower Health, West Reading, PA, USA
| | - Harikrishnan Hyma Kunhiraman
- Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA, USA
- Cardio-Oncology Program, Medical College of Georgia at Augusta University, Augusta, GA, USA
| | - Fathima Shehnaz
- Department of Internal Medicine, Trinity Health Oakland, Wayne State University, Pontiac, MI, USA
| | - Priyanshu Nain
- Department of Internal Medicine, Advent Health, Rome, GA, USA
| | - Omar M Makram
- Division of Cardiology, Department of Medicine, Medical College of Georgia at Augusta University, Augusta, GA, USA
- Cardio-Oncology Program, Medical College of Georgia at Augusta University, Augusta, GA, USA
| | - Morteza Mahmoudi
- Department of Radiology and Precision Health Program, Michigan State University, East Lansing, MI, USA
| | - Sadeer Al-Kindi
- Division of Cardiovascular Prevention and Wellness, Houston Methodist DeBakey Heart and Vascular Center, Houston, TX, USA
| | - Anant Madabhushi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Rakesh Shiradkar
- Department of Biomedical Engineering and Informatics, Indiana University, Indianapolis, IN, USA
| | - Hisham Daoud
- School of Computer and Cyber Sciences, Augusta University, Augusta, GA, USA
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Chin V, Finnegan RN, Keall P, Otton J, Delaney GP, Vinod SK. Overview of cardiac toxicity from radiation therapy. J Med Imaging Radiat Oncol 2024; 68:987-1000. [PMID: 39301913 DOI: 10.1111/1754-9485.13757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 08/19/2024] [Indexed: 09/22/2024]
Abstract
Radiotherapy is an essential part of treatment for many patients with thoracic cancers. However, proximity of the heart to tumour targets can lead to cardiac side effects, with studies demonstrating link between cardiac radiation dose and adverse outcomes. Although reducing cardiac dose can reduce associated risks, most cardiac constraint recommendations in clinical use are generally based on dose to the whole heart, as dose assessment at cardiac substructure levels on individual patients has been limited historically. Furthermore, estimation of an individual's cardiac risk is complex and multifactorial, which includes radiation dose alongside baseline risk factors, and the impact of systemic therapies. This review gives an overview of the epidemiological impact of cancer and cardiac disease, risk factors contributing to radiation-related cardiotoxicity, the evidence for cardiac side effects and future directions in cardiotoxicity research. A better understanding of the interactions between risk factors, balancing treatment benefit versus toxicity and the ongoing management of cardiac risk is essential for optimal clinical care. The emerging field of cardio-oncology is thus a multidisciplinary collaborative effort to enable better understanding of cardiac risks and outcomes for better-informed patient management decisions.
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Affiliation(s)
- Vicky Chin
- Department of Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, New South Wales, Australia
- Image X Institute, University of Sydney, Sydney, New South Wales, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia
| | - Robert N Finnegan
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, New South Wales, Australia
- Institute of Medical Physics, University of Sydney, Sydney, New South Wales, Australia
| | - Paul Keall
- Image X Institute, University of Sydney, Sydney, New South Wales, Australia
| | - James Otton
- South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia
- Department of Cardiology, Liverpool Hospital, Sydney, New South Wales, Australia
| | - Geoff P Delaney
- Department of Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, New South Wales, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia
| | - Shalini K Vinod
- Department of Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, New South Wales, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia
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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; 68:914-949. [PMID: 38757728 PMCID: PMC11686467 DOI: 10.1111/1754-9485.13668] [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: 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 CentreRoyal North Shore HospitalSydneyNew South WalesAustralia
- Institute of Medical Physics, School of Physics, University of SydneySydneyNew South WalesAustralia
| | - Alexandra Quinn
- Northern Sydney Cancer CentreRoyal North Shore HospitalSydneyNew South WalesAustralia
| | - Jeremy Booth
- Northern Sydney Cancer CentreRoyal North Shore HospitalSydneyNew South WalesAustralia
- Institute of Medical Physics, School of Physics, University of SydneySydneyNew South WalesAustralia
| | - Gregg Belous
- Australian e‐Health Research CentreCommonwealth Scientific and Industrial Research OrganisationBrisbaneQueenslandAustralia
| | - Nicholas Hardcastle
- Department of Physical SciencesPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
- Sir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVictoriaAustralia
| | - Maegan Stewart
- Northern Sydney Cancer CentreRoyal North Shore HospitalSydneyNew South WalesAustralia
- School of Health Sciences, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
| | - Brooke Griffiths
- Northern Sydney Cancer CentreRoyal North Shore HospitalSydneyNew South WalesAustralia
| | - Susan Carroll
- Northern Sydney Cancer CentreRoyal North Shore HospitalSydneyNew South WalesAustralia
- School of Health Sciences, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
| | - David I Thwaites
- Institute of Medical Physics, School of Physics, University of SydneySydneyNew South WalesAustralia
- Radiotherapy Research GroupLeeds Institute of Medical Research, St James's Hospital and University of LeedsLeedsUK
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5
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Chin V, Finnegan RN, Chlap P, Holloway L, Thwaites DI, Otton J, Delaney GP, Vinod SK. Dosimetric Impact of Delineation and Motion Uncertainties on the Heart and Substructures in Lung Cancer Radiotherapy. Clin Oncol (R Coll Radiol) 2024; 36:420-429. [PMID: 38649309 DOI: 10.1016/j.clon.2024.04.002] [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: 07/17/2023] [Revised: 03/27/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024]
Abstract
AIMS Delineation variations and organ motion produce difficult-to-quantify uncertainties in planned radiation doses to targets and organs at risk. Similar to manual contouring, most automatic segmentation tools generate single delineations per structure; however, this does not indicate the range of clinically acceptable delineations. This study develops a method to generate a range of automatic cardiac structure segmentations, incorporating motion and delineation uncertainty, and evaluates the dosimetric impact in lung cancer. MATERIALS AND METHODS Eighteen cardiac structures were delineated using a locally developed auto-segmentation tool. It was applied to lung cancer planning CTs for 27 curative (planned dose ≥50 Gy) cases, and delineation variations were estimated by using ten mapping-atlases to provide separate substructure segmentations. Motion-related cardiac segmentation variations were estimated by auto-contouring structures on ten respiratory phases for 9/27 cases that had 4D-planning CTs. Dose volume histograms (DVHs) incorporating these variations were generated for comparison. RESULTS Variations in mean doses (Dmean), defined as the range in values across ten feasible auto-segmentations, were calculated for each cardiac substructure. Over the study cohort the median variations for delineation uncertainty and motion were 2.20-11.09 Gy and 0.72-4.06 Gy, respectively. As relative values, variations in Dmean were between 18.7%-65.3% and 7.8%-32.5% for delineation uncertainty and motion, respectively. Doses vary depending on the individual planned dose distribution, not simply on segmentation differences, with larger dose variations to cardiac structures lying within areas of steep dose gradient. CONCLUSION Radiotherapy dose uncertainties from delineation variations and respiratory-related heart motion were quantified using a cardiac substructure automatic segmentation tool. This predicts the 'dose range' where doses to structures are most likely to fall, rather than single DVH curves. This enables consideration of these uncertainties in cardiotoxicity research and for future plan optimisation. The tool was designed for cardiac structures, but similar methods are potentially applicable to other OARs.
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Affiliation(s)
- V Chin
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; University of Sydney, Image X Institute, Sydney, Australia.
| | - R N Finnegan
- Ingham Institute for Applied Medical Research, Sydney, Australia; University of Sydney, Institute of Medical Physics, Sydney, Australia; Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, Australia
| | - P Chlap
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia
| | - L Holloway
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia; University of Sydney, Institute of Medical Physics, Sydney, Australia
| | - D I Thwaites
- University of Sydney, Institute of Medical Physics, Sydney, Australia; St James's Hospital and University of Leeds, Leeds Institute of Medical Research, Radiotherapy Research Group, Leeds, United Kingdom
| | - J Otton
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Liverpool Hospital, Department of Cardiology, Sydney, Australia
| | - G P Delaney
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia
| | - S K Vinod
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Liverpool and Macarthur Cancer Therapy Centres, Department of Radiation Oncology, Sydney, Australia; Ingham Institute for Applied Medical Research, Sydney, Australia
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Rentiya ZS, Mandal S, Inban P, Vempalli H, Dabbara R, Ali S, Kaur K, Adegbite A, Intsiful TA, Jayan M, Odoma VA, Khan A. Revolutionizing Breast Cancer Detection With Artificial Intelligence (AI) in Radiology and Radiation Oncology: A Systematic Review. Cureus 2024; 16:e57619. [PMID: 38711711 PMCID: PMC11073588 DOI: 10.7759/cureus.57619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2024] [Indexed: 05/08/2024] Open
Abstract
The number one cause of cancer in women worldwide is breast cancer. Over the last three decades, the use of traditional screen-film mammography has increased, but in recent years, digital mammography and 3D tomosynthesis have become standard procedures for breast cancer screening. With the advancement of technology, the interpretation of images using automated algorithms has become a subject of interest. Initially, computer-aided detection (CAD) was introduced; however, it did not show any long-term benefit in clinical practice. With recent advances in artificial intelligence (AI) methods, these technologies are showing promising potential for more accurate and efficient automated breast cancer detection and treatment. While AI promises widespread integration in breast cancer detection and treatment, challenges such as data quality, regulatory, ethical implications, and algorithm validation are crucial. Addressing these is essential for fully realizing AI's potential in enhancing early diagnosis and improving patient outcomes in breast cancer management. In this review article, we aim to provide an overview of the latest developments and applications of AI in breast cancer screening and treatment. While the existing literature primarily consists of retrospective studies, ongoing and future prospective research is poised to offer deeper insights. Artificial intelligence is on the verge of widespread integration into breast cancer detection and treatment, holding the potential to enhance early diagnosis and improve patient outcomes.
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Affiliation(s)
- Zubir S Rentiya
- Radiation Oncology & Radiology, University of Virginia School of Medicine, Charlottesville, USA
| | - Shobha Mandal
- Neurology, Regional Neurological Associates, New York, USA
- Internal Medicine, Salem Internal Medicine, Primary Care (PC), Pennsville, USA
| | | | | | - Rishika Dabbara
- Internal Medicine, Kamineni Institute of Medical Sciences, Hyderabad, IND
| | - Sofia Ali
- Medicine, Peninsula Medical School, Plymouth, GBR
| | - Kirpa Kaur
- Medicine, Howard Community College, Ellicott City, USA
| | | | - Tarsha A Intsiful
- Radiology, College of Medicine, University of Ghana Medical Center, Accra, GHA
| | - Malavika Jayan
- Internal Medicine, Bangalore Medical College and Research Institute, Bangalore, IND
| | - Victor A Odoma
- Research, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
- Cardiovascular Medicine/Oncology (Acuity Adaptable Unit), Indiana University Health, Bloomington, USA
| | - Aadil Khan
- Trauma Surgery, Order of St. Francis (OSF) St Francis Medical Centre, University of Illinois Chicago, Peoria, USA
- Cardiology, University of Illinois at Chicago, Chicago, USA
- Internal Medicine, Lala Lajpat Rai (LLR) Hospital, Kanpur, IND
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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: 4] [Impact Index Per Article: 4.0] [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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Marchant T, Price G, McWilliam A, Henderson E, McSweeney D, van Herk M, Banfill K, Schmitt M, King J, Barker C, Faivre-Finn C. Assessment of heart-substructures auto-contouring accuracy for application in heart-sparing radiotherapy for lung cancer. BJR Open 2024; 6:tzae006. [PMID: 38737623 PMCID: PMC11087931 DOI: 10.1093/bjro/tzae006] [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: 06/29/2023] [Revised: 12/14/2023] [Accepted: 02/14/2024] [Indexed: 05/14/2024] Open
Abstract
Objectives We validated an auto-contouring algorithm for heart substructures in lung cancer patients, aiming to establish its accuracy and reliability for radiotherapy (RT) planning. We focus on contouring an amalgamated set of subregions in the base of the heart considered to be a new organ at risk, the cardiac avoidance area (CAA), to enable maximum dose limit implementation in lung RT planning. Methods The study validates a deep-learning model specifically adapted for auto-contouring the CAA (which includes the right atrium, aortic valve root, and proximal segments of the left and right coronary arteries). Geometric, dosimetric, quantitative, and qualitative validation measures are reported. Comparison with manual contours, including assessment of interobserver variability, and robustness testing over 198 cases are also conducted. Results Geometric validation shows that auto-contouring performance lies within the expected range of manual observer variability despite being slightly poorer than the average of manual observers (mean surface distance for CAA of 1.6 vs 1.2 mm, dice similarity coefficient of 0.86 vs 0.88). Dosimetric validation demonstrates consistency between plans optimized using auto-contours and manual contours. Robustness testing confirms acceptable contours in all cases, with 80% rated as "Good" and the remaining 20% as "Useful." Conclusions The auto-contouring algorithm for heart substructures in lung cancer patients demonstrates acceptable and comparable performance to human observers. Advances in knowledge Accurate and reliable auto-contouring results for the CAA facilitate the implementation of a maximum dose limit to this region in lung RT planning, which has now been introduced in the routine setting at our institution.
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Affiliation(s)
- Tom Marchant
- Christie Medical Physics & Engineering, The Christie NHS Foundation Trust, Manchester, M20 4BX, United Kingdom
- Division of Cancer Sciences, The University of Manchester, Manchester, M13 9PL, United Kingdom
| | - Gareth Price
- Division of Cancer Sciences, The University of Manchester, Manchester, M13 9PL, United Kingdom
- Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, M20 4BX, United Kingdom
| | - Alan McWilliam
- Division of Cancer Sciences, The University of Manchester, Manchester, M13 9PL, United Kingdom
- Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, M20 4BX, United Kingdom
| | - Edward Henderson
- Division of Cancer Sciences, The University of Manchester, Manchester, M13 9PL, United Kingdom
- Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, M20 4BX, United Kingdom
| | - Dónal McSweeney
- Division of Cancer Sciences, The University of Manchester, Manchester, M13 9PL, United Kingdom
- Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, M20 4BX, United Kingdom
| | - Marcel van Herk
- Division of Cancer Sciences, The University of Manchester, Manchester, M13 9PL, United Kingdom
- Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, M20 4BX, United Kingdom
| | - Kathryn Banfill
- Division of Cancer Sciences, The University of Manchester, Manchester, M13 9PL, United Kingdom
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, M20 4BX, United Kingdom
| | - Matthias Schmitt
- Division of Cardiovascular Sciences, The University of Manchester, Manchester, M13 9PL, United Kingdom
- Department of Cardiology, Manchester University NHS Foundation Trust, Manchester, M13 9WL, United Kingdom
| | - Jennifer King
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, M20 4BX, United Kingdom
| | - Claire Barker
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, M20 4BX, United Kingdom
| | - Corinne Faivre-Finn
- Division of Cancer Sciences, The University of Manchester, Manchester, M13 9PL, United Kingdom
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, M20 4BX, United Kingdom
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Chin V, Finnegan RN, Chlap P, Otton J, Haidar A, Holloway L, Thwaites DI, Dowling J, Delaney GP, Vinod SK. Validation of a Fully Automated Hybrid Deep Learning Cardiac Substructure Segmentation Tool for Contouring and Dose Evaluation in Lung Cancer Radiotherapy. Clin Oncol (R Coll Radiol) 2023; 35:370-381. [PMID: 36964031 DOI: 10.1016/j.clon.2023.03.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 01/05/2023] [Accepted: 03/07/2023] [Indexed: 03/12/2023]
Abstract
BACKGROUND AND PURPOSE Accurate and consistent delineation of cardiac substructures is challenging. The aim of this work was to validate a novel segmentation tool for automatic delineation of cardiac structures and subsequent dose evaluation, with potential application in clinical settings and large-scale radiation-related cardiotoxicity studies. MATERIALS AND METHODS A recently developed hybrid method for automatic segmentation of 18 cardiac structures, combining deep learning, multi-atlas mapping and geometric segmentation of small challenging substructures, was independently validated on 30 lung cancer cases. These included anatomical and imaging variations, such as tumour abutting heart, lung collapse and metal artefacts. Automatic segmentations were compared with manual contours of the 18 structures using quantitative metrics, including Dice similarity coefficient (DSC), mean distance to agreement (MDA) and dose comparisons. RESULTS A comparison of manual and automatic contours across all cases showed a median DSC of 0.75-0.93 and a median MDA of 2.09-3.34 mm for whole heart and chambers. The median MDA for great vessels, coronary arteries, cardiac valves, sinoatrial and atrioventricular conduction nodes was 3.01-8.54 mm. For the 27 cases treated with curative intent (planned target volume dose ≥50 Gy), the median dose difference was -1.12 to 0.57 Gy (absolute difference of 1.13-3.25%) for the mean dose to heart and chambers; and -2.25 to 4.45 Gy (absolute difference of 0.94-6.79%) for the mean dose to substructures. CONCLUSION The novel hybrid automatic segmentation tool reported high accuracy and consistency over a validation set with challenging anatomical and imaging variations. This has promising applications in substructure dose calculations of large-scale datasets and for future studies on long-term cardiac toxicity.
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Affiliation(s)
- V Chin
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Ingham Institute for Applied Medical Research, Radiation Oncology, Sydney, Australia; Department of Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia.
| | - R N Finnegan
- Ingham Institute for Applied Medical Research, Radiation Oncology, Sydney, Australia; School of Physics, Institute of Medical Physics, University of Sydney, Sydney, Australia; Northern Sydney Cancer Centre, Royal North Shore Hospital, St Leonards, Australia
| | - P Chlap
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Ingham Institute for Applied Medical Research, Radiation Oncology, Sydney, Australia; Department of Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - J Otton
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Department of Cardiology, Liverpool Hospital, Sydney, Australia
| | - A Haidar
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Ingham Institute for Applied Medical Research, Radiation Oncology, Sydney, Australia; Department of Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - L Holloway
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Ingham Institute for Applied Medical Research, Radiation Oncology, Sydney, Australia; Department of Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia; School of Physics, Institute of Medical Physics, University of Sydney, Sydney, Australia
| | - D I Thwaites
- School of Physics, Institute of Medical Physics, University of Sydney, Sydney, Australia; Radiotherapy Research Group, Leeds Institute of Medical Research, St James's Hospital and University of Leeds, Leeds, UK
| | - J Dowling
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; School of Physics, Institute of Medical Physics, University of Sydney, Sydney, Australia; CSIRO, Australian e-Health and Research Centre, Herston, Australia
| | - G P Delaney
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Ingham Institute for Applied Medical Research, Radiation Oncology, Sydney, Australia; Department of Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - S K Vinod
- University of New South Wales, South Western Sydney Clinical School, Sydney, Australia; Ingham Institute for Applied Medical Research, Radiation Oncology, Sydney, Australia; Department of Radiation Oncology, Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
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10
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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: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [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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11
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Walls GM, Ghita M, Queen R, Edgar KS, Gill EK, Kuburas R, Grieve DJ, Watson CJ, McWilliam A, Van Herk M, Williams KJ, Cole AJ, Jain S, Butterworth KT. Spatial Gene Expression Changes in the Mouse Heart After Base-Targeted Irradiation. Int J Radiat Oncol Biol Phys 2023; 115:453-463. [PMID: 35985456 DOI: 10.1016/j.ijrobp.2022.08.031] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 08/03/2022] [Accepted: 08/05/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE Radiation cardiotoxicity (RC) is a clinically significant adverse effect of treatment for patients with thoracic malignancies. Clinical studies in lung cancer have indicated that heart substructures are not uniformly radiosensitive, and that dose to the heart base drives RC. In this study, we aimed to characterize late changes in gene expression using spatial transcriptomics in a mouse model of base regional radiosensitivity. METHODS AND MATERIALS An aged female C57BL/6 mouse was irradiated with 16 Gy delivered to the cranial third of the heart using a 6 × 9 mm parallel opposed beam geometry on a small animal radiation research platform, and a second mouse was sham-irradiated. After echocardiography, whole hearts were collected at 30 weeks for spatial transcriptomic analysis to map gene expression changes occurring in different regions of the partially irradiated heart. Cardiac regions were manually annotated on the capture slides and the gene expression profiles compared across different regions. RESULTS Ejection fraction was reduced at 30 weeks after a 16 Gy irradiation to the heart base, compared with the sham-irradiated controls. There were markedly more significant gene expression changes within the irradiated regions compared with nonirradiated regions. Variation was observed in the transcriptomic effects of radiation on different cardiac base structures (eg, between the right atrium [n = 86 dysregulated genes], left atrium [n = 96 dysregulated genes], and the vasculature [n = 129 dysregulated genes]). Disrupted biological processes spanned extracellular matrix as well as circulatory, neuronal, and contractility activities. CONCLUSIONS This is the first study to report spatially resolved gene expression changes in irradiated tissues. Examination of the regional radiation response in the heart can help to further our understanding of the cardiac base's radiosensitivity and support the development of actionable targets for pharmacologic intervention and biologically relevant dose constraints.
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Affiliation(s)
- Gerard M Walls
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland; Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Belfast, Northern Ireland.
| | - Mihaela Ghita
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland
| | - Rachel Queen
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle-upon-Tyne, England
| | - Kevin S Edgar
- Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, Northern Ireland
| | - Eleanor K Gill
- Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, Northern Ireland; Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, England
| | - Refik Kuburas
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland
| | - David J Grieve
- Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, Northern Ireland
| | - Chris J Watson
- Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, Northern Ireland
| | - Alan McWilliam
- Division of Cancer Sciences, University of Manchester, Oglesby Building, Manchester, England; Department of Radiation Therapy Related Research, The Christie Foundation Trust, Manchester, England
| | - Marcel Van Herk
- Division of Cancer Sciences, University of Manchester, Oglesby Building, Manchester, England; Department of Radiation Therapy Related Research, The Christie Foundation Trust, Manchester, England
| | - Kaye J Williams
- Division of Pharmacy and Optometry, School of Health Science, Faculty of Biology Medicine and Health, University of Manchester, Manchester, England
| | - Aidan J Cole
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland; Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Belfast, Northern Ireland
| | - Suneil Jain
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland; Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Belfast, Northern Ireland
| | - Karl T Butterworth
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Northern Ireland
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Mackay K, Bernstein D, Glocker B, Kamnitsas K, Taylor A. A Review of the Metrics Used to Assess Auto-Contouring Systems in Radiotherapy. Clin Oncol (R Coll Radiol) 2023; 35:354-369. [PMID: 36803407 DOI: 10.1016/j.clon.2023.01.016] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 12/05/2022] [Accepted: 01/23/2023] [Indexed: 02/01/2023]
Abstract
Auto-contouring could revolutionise future planning of radiotherapy treatment. The lack of consensus on how to assess and validate auto-contouring systems currently limits clinical use. This review formally quantifies the assessment metrics used in studies published during one calendar year and assesses the need for standardised practice. A PubMed literature search was undertaken for papers evaluating radiotherapy auto-contouring published during 2021. Papers were assessed for types of metric and the methodology used to generate ground-truth comparators. Our PubMed search identified 212 studies, of which 117 met the criteria for clinical review. Geometric assessment metrics were used in 116 of 117 studies (99.1%). This includes the Dice Similarity Coefficient used in 113 (96.6%) studies. Clinically relevant metrics, such as qualitative, dosimetric and time-saving metrics, were less frequently used in 22 (18.8%), 27 (23.1%) and 18 (15.4%) of 117 studies, respectively. There was heterogeneity within each category of metric. Over 90 different names for geometric measures were used. Methods for qualitative assessment were different in all but two papers. Variation existed in the methods used to generate radiotherapy plans for dosimetric assessment. Consideration of editing time was only given in 11 (9.4%) papers. A single manual contour as a ground-truth comparator was used in 65 (55.6%) studies. Only 31 (26.5%) studies compared auto-contours to usual inter- and/or intra-observer variation. In conclusion, significant variation exists in how research papers currently assess the accuracy of automatically generated contours. Geometric measures are the most popular, however their clinical utility is unknown. There is heterogeneity in the methods used to perform clinical assessment. Considering the different stages of system implementation may provide a framework to decide the most appropriate metrics. This analysis supports the need for a consensus on the clinical implementation of auto-contouring.
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Affiliation(s)
- K Mackay
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK.
| | - D Bernstein
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK
| | - B Glocker
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - K Kamnitsas
- Department of Computing, Imperial College London, South Kensington Campus, London, UK; Department of Engineering Science, University of Oxford, Oxford, UK
| | - A Taylor
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK
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Krishnamurthy R, Mummudi N, Goda JS, Chopra S, Heijmen B, Swamidas J. Using Artificial Intelligence for Optimization of the Processes and Resource Utilization in Radiotherapy. JCO Glob Oncol 2022; 8:e2100393. [PMID: 36395438 PMCID: PMC10166445 DOI: 10.1200/go.21.00393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The radiotherapy (RT) process from planning to treatment delivery is a multistep, complex operation involving numerous levels of human-machine interaction and requiring high precision. These steps are labor-intensive and time-consuming and require meticulous coordination between professionals with diverse expertise. We reviewed and summarized the current status and prospects of artificial intelligence and machine learning relevant to the various steps in RT treatment planning and delivery workflow specifically in low- and middle-income countries (LMICs). We also searched the PubMed database using the search terms (Artificial Intelligence OR Machine Learning OR Deep Learning OR Automation OR knowledge-based planning AND Radiotherapy) AND (list of Low- and Middle-Income Countries as defined by the World Bank at the time of writing this review). The search yielded a total of 90 results, of which results with first authors from the LMICs were chosen. The reference lists of retrieved articles were also reviewed to search for more studies. No language restrictions were imposed. A total of 20 research items with unique study objectives conducted with the aim of enhancing RT processes were examined in detail. Artificial intelligence and machine learning can improve the overall efficiency of RT processes by reducing human intervention, aiding decision making, and efficiently executing lengthy, repetitive tasks. This improvement could permit the radiation oncologist to redistribute resources and focus on responsibilities such as patient counseling, education, and research, especially in resource-constrained LMICs.
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Affiliation(s)
- Revathy Krishnamurthy
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Naveen Mummudi
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Jayant Sastri Goda
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Supriya Chopra
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Ben Heijmen
- Division of Medical Physics, Department of Radiation Oncology, Erasmus MC Cancer Institute, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Jamema Swamidas
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
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14
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Walls GM, Giacometti V, Apte A, Thor M, McCann C, Hanna GG, O'Connor J, Deasy JO, Hounsell AR, Butterworth KT, Cole AJ, Jain S, McGarry CK. Validation of an established deep learning auto-segmentation tool for cardiac substructures in 4D radiotherapy planning scans. Phys Imaging Radiat Oncol 2022; 23:118-126. [PMID: 35941861 PMCID: PMC9356270 DOI: 10.1016/j.phro.2022.07.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 11/10/2022] Open
Abstract
Cardiotoxicity is a common complication of lung cancer radiotherapy. Segmentation of cardiac substructures is time-consuming and challenging. Deep learning segmentation tools can perform this task in 3D and 4D scans. Performance is high when assessed geometrically, dosimetrically and clinically. Auto-segmentation tools may accelerate clinical workflows and enable research.
Background Emerging data suggest that dose-sparing several key cardiac regions is prognostically beneficial in lung cancer radiotherapy. The cardiac substructures are challenging to contour due to their complex geometry, poor soft tissue definition on computed tomography (CT) and cardiorespiratory motion artefact. A neural network was previously trained to generate the cardiac substructures using three-dimensional radiotherapy planning CT scans (3D-CT). In this study, the performance of that tool on the average intensity projection from four-dimensional (4D) CT scans (4D-AVE), now commonly used in lung radiotherapy, was evaluated. Materials and Methods The 4D-AVE of n=20 patients completing radiotherapy for lung cancer 2015–2020 underwent manual and automated cardiac substructure segmentation. Manual and automated substructures were compared geometrically and dosimetrically. Two senior clinicians also qualitatively assessed the auto-segmentation tool’s output. Results Geometric comparison of the automated and manual segmentations exhibited high levels of similarity across parameters, including volume difference (11.8% overall) and Dice similarity coefficient (0.85 overall), and were consistent with 3D-CT performance. Differences in mean (median 0.2 Gy, range −1.6–0.3 Gy) and maximum (median 0.4 Gy, range −2.2–0.9 Gy) doses to substructures were generally small. Nearly all structures (99.5 %) were deemed to be appropriate for clinical use without further editing. Conclusions Cardiac substructure auto-segmentation using a deep learning-based tool trained on a 3D-CT dataset was feasible on the 4D-AVE scan, meaning this tool is suitable for use on 4D-CT radiotherapy planning scans. Application of this tool would increase the practicality of routine clinical cardiac substructure delineation, and enable further cardiac radiation effects research.
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15
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Should We Contour Cardiac Substructures in Routine Practice? How Autosegmentation Helped Us Get There (or Not). Int J Radiat Oncol Biol Phys 2022; 112:633-635. [DOI: 10.1016/j.ijrobp.2021.11.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 11/15/2021] [Indexed: 12/25/2022]
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