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Żydowicz WM, Skokowski J, Marano L, Polom K. Navigating the Metaverse: A New Virtual Tool with Promising Real Benefits for Breast Cancer Patients. J Clin Med 2024; 13:4337. [PMID: 39124604 PMCID: PMC11313674 DOI: 10.3390/jcm13154337] [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: 04/09/2024] [Revised: 05/22/2024] [Accepted: 07/22/2024] [Indexed: 08/12/2024] Open
Abstract
BC, affecting both women and men, is a complex disease where early diagnosis plays a crucial role in successful treatment and enhances patient survival rates. The Metaverse, a virtual world, may offer new, personalized approaches to diagnosing and treating BC. Although Artificial Intelligence (AI) is still in its early stages, its rapid advancement indicates potential applications within the healthcare sector, including consolidating patient information in one accessible location. This could provide physicians with more comprehensive insights into disease details. Leveraging the Metaverse could facilitate clinical data analysis and improve the precision of diagnosis, potentially allowing for more tailored treatments for BC patients. However, while this article highlights the possible transformative impacts of virtual technologies on BC treatment, it is important to approach these developments with cautious optimism, recognizing the need for further research and validation to ensure enhanced patient care with greater accuracy and efficiency.
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Affiliation(s)
- Weronika Magdalena Żydowicz
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
| | - Jaroslaw Skokowski
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
| | - Luigi Marano
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
| | - Karol Polom
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
- Department of Gastrointestinal Surgical Oncology, Greater Poland Cancer Centre, Garbary 15, 61-866 Poznan, Poland
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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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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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Shan G, Yu S, Lai Z, Xuan Z, Zhang J, Wang B, Ge Y. A Review of Artificial Intelligence Application for Radiotherapy. Dose Response 2024; 22:15593258241263687. [PMID: 38912333 PMCID: PMC11193352 DOI: 10.1177/15593258241263687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 05/03/2024] [Indexed: 06/25/2024] Open
Abstract
Background and Purpose Artificial intelligence (AI) is a technique which tries to think like humans and mimic human behaviors. It has been considered as an alternative in a lot of human-dependent steps in radiotherapy (RT), since the human participation is a principal uncertainty source in RT. The aim of this work is to provide a systematic summary of the current literature on AI application for RT, and to clarify its role for RT practice in terms of clinical views. Materials and Methods A systematic literature search of PubMed and Google Scholar was performed to identify original articles involving the AI applications in RT from the inception to 2022. Studies were included if they reported original data and explored the clinical applications of AI in RT. Results The selected studies were categorized into three aspects of RT: organ and lesion segmentation, treatment planning and quality assurance. For each aspect, this review discussed how these AI tools could be involved in the RT protocol. Conclusions Our study revealed that AI was a potential alternative for the human-dependent steps in the complex process of RT.
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Affiliation(s)
- Guoping Shan
- School of Electronic Science and Engineering, Nanjing University, Nanjing, China
- Zhejiang Cancer Hospital, Hangzhou, China
| | - Shunfei Yu
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Zhongjun Lai
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Zhiqiang Xuan
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Jie Zhang
- Zhejiang Cancer Hospital, Hangzhou, China
| | | | - Yun Ge
- School of Electronic Science and Engineering, Nanjing University, Nanjing, China
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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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Berrington de González A, Gibson TM, Lee C, Albert PS, Griffin KT, Kitahara CM, Liu D, Mille MM, Shin J, Bajaj BV, Flood TE, Gallotto SL, Paganetti H, Ahmed SK, Eaton BR, Indelicato DJ, Milgrom SA, Palmer JD, Baliga S, Poppe MM, Tsang DS, Wong K, Yock TI. The Pediatric Proton and Photon Therapy Comparison Cohort: Study Design for a Multicenter Retrospective Cohort to Investigate Subsequent Cancers After Pediatric Radiation Therapy. Adv Radiat Oncol 2023; 8:101273. [PMID: 38047226 PMCID: PMC10692298 DOI: 10.1016/j.adro.2023.101273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 05/08/2023] [Indexed: 12/05/2023] Open
Abstract
Purpose The physical properties of protons lower doses to surrounding normal tissues compared with photons, potentially reducing acute and long-term adverse effects, including subsequent cancers. The magnitude of benefit is uncertain, however, and currently based largely on modeling studies. Despite the paucity of directly comparative data, the number of proton centers and patients are expanding exponentially. Direct studies of the potential risks and benefits are needed in children, who have the highest risk of radiation-related subsequent cancers. The Pediatric Proton and Photon Therapy Comparison Cohort aims to meet this need. Methods and Materials We are developing a record-linkage cohort of 10,000 proton and 10,000 photon therapy patients treated from 2007 to 2022 in the United States and Canada for pediatric central nervous system tumors, sarcomas, Hodgkin lymphoma, or neuroblastoma, the pediatric tumors most frequently treated with protons. Exposure assessment will be based on state-of-the-art dosimetry facilitated by collection of electronic radiation records for all eligible patients. Subsequent cancers and mortality will be ascertained by linkage to state and provincial cancer registries in the United States and Canada, respectively. The primary analysis will examine subsequent cancer risk after proton therapy compared with photon therapy, adjusting for potential confounders and accounting for competing risks. Results For the primary aim comparing overall subsequent cancer rates between proton and photon therapy, we estimated that with 10,000 patients in each treatment group there would be 80% power to detect a relative risk of 0.8 assuming a cumulative incidence of subsequent cancers of 2.5% by 15 years after diagnosis. To date, 9 institutions have joined the cohort and initiated data collection; additional centers will be added in the coming year(s). Conclusions Our findings will affect clinical practice for pediatric patients with cancer by providing the first large-scale systematic comparison of the risk of subsequent cancers from proton compared with photon therapy.
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Affiliation(s)
| | - Todd M. Gibson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Choonsik Lee
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Paul S. Albert
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Keith T. Griffin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Cari Meinhold Kitahara
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Danping Liu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Matthew M. Mille
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Jungwook Shin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Benjamin V.M. Bajaj
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
| | - Tristin E. Flood
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
| | - Sara L. Gallotto
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
| | - Safia K. Ahmed
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota
| | - Bree R. Eaton
- Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
| | - Daniel J. Indelicato
- Department of Radiation Oncology, University of Florida College of Medicine, Jacksonville, Florida
| | - Sarah A. Milgrom
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Joshua D. Palmer
- Department of Radiation Oncology, James Cancer Hospital at the Ohio State University Wexner Medical Center and Nationwide Children's Hospital, Columbus, Ohio
| | - Sujith Baliga
- Department of Radiation Oncology, James Cancer Hospital at the Ohio State University Wexner Medical Center and Nationwide Children's Hospital, Columbus, Ohio
| | - Matthew M. Poppe
- Department of Radiation Oncology, University of Utah–Huntsman Cancer Institute, Salt Lake City, Utah
| | - Derek S. Tsang
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Kenneth Wong
- Radiation Oncology Program, Children's Hospital Los Angeles, Los Angeles, California
- Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Torunn I. Yock
- Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
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Cheng K, Wang J, Liu J, Zhang X, Shen Y, Su H. Public health implications of computer-aided diagnosis and treatment technologies in breast cancer care. AIMS Public Health 2023; 10:867-895. [PMID: 38187901 PMCID: PMC10764974 DOI: 10.3934/publichealth.2023057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 10/10/2023] [Indexed: 01/09/2024] Open
Abstract
Breast cancer remains a significant public health issue, being a leading cause of cancer-related mortality among women globally. Timely diagnosis and efficient treatment are crucial for enhancing patient outcomes, reducing healthcare burdens and advancing community health. This systematic review, following the PRISMA guidelines, aims to comprehensively synthesize the recent advancements in computer-aided diagnosis and treatment for breast cancer. The study covers the latest developments in image analysis and processing, machine learning and deep learning algorithms, multimodal fusion techniques and radiation therapy planning and simulation. The results of the review suggest that machine learning, augmented and virtual reality and data mining are the three major research hotspots in breast cancer management. Moreover, this paper discusses the challenges and opportunities for future research in this field. The conclusion highlights the importance of computer-aided techniques in the management of breast cancer and summarizes the key findings of the review.
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Affiliation(s)
- Kai Cheng
- Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264100, China
| | - Jiangtao Wang
- Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264100, China
| | - Jian Liu
- Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264100, China
| | - Xiangsheng Zhang
- Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264100, China
| | - Yuanyuan Shen
- Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264100, China
| | - Hang Su
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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8
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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: 3] [Impact Index Per Article: 3.0] [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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9
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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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10
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Haseltine JM, Apte A, Jackson A, Yorke E, Yu AF, Plodkowski A, Wu A, Peleg A, Al-Sadawi M, Iocolano M, Gelblum D, Shaverdian N, Simone CB, Rimner A, Gomez DR, Shepherd AF, Thor M. Association of cardiac calcium burden with overall survival after radiotherapy for non-small cell lung cancer. Phys Imaging Radiat Oncol 2023; 25:100410. [PMID: 36687507 PMCID: PMC9852638 DOI: 10.1016/j.phro.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 12/05/2022] [Accepted: 01/03/2023] [Indexed: 01/07/2023] Open
Abstract
Background and purpose Coronary calcifications are associated with coronary artery disease in patients undergoing radiotherapy (RT) for non-small cell lung cancer (NSCLC). We quantified calcifications in the coronary arteries and aorta and investigated their relationship with overall survival (OS) in patients treated with definitive RT (Def-RT) or post-operative RT (PORT). Materials and methods We analyzed 263 NSCLC patients treated from 2004 to 2017. Calcium burden was ascertained with a Hounsfield unit (HU) cutoff of > 130 in addition to a deep learning (DL) plaque estimator. The HU cutoff volumes were defined for coronary arteries (PlaqueCoro) and coronary arteries and aorta combined (PlaqueCoro+Ao), while the DL estimator ranged from 0 (no plaque) to 3 (high plaque). Patient and treatment characteristics were explored for association with OS. Results The median PlaqueCoro and PlaqueCoro+Ao was 0.75 cm3 and 0.87 cm3 in the Def-RT group and 0.03 cm3 and 0.52 cm3 in the PORT group. The median DL estimator was 2 in both cohorts. In Def-RT, large PlaqueCoro (HR:1.11 (95%CI:1.04-1.19); p = 0.008), and PlaqueCoro+Ao (HR:1.06 (95%CI:1.02-1.11); p = 0.03), and poor Karnofsky Performance Status (HR: 0.97 (95%CI: 0.94-0.99); p = 0.03) were associated with worse OS. No relationship was identified between the plaque volumes and OS in PORT, or between the DL plaque estimator and OS in either Def-RT or PORT. Conclusions Coronary artery calcification assessed from RT planning CT scans was significantly associated with OS in patients who underwent Def-RT for NSCLC. This HU thresholding method can be straightforwardly implemented such that the role of calcifications can be further explored.
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Affiliation(s)
- Justin M. Haseltine
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Aditya Apte
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Andrew Jackson
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ellen Yorke
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Anthony F. Yu
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Andrew Plodkowski
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Abraham Wu
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ariel Peleg
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Mohammed Al-Sadawi
- Department of Medicine, Stony Brook University Hospital, Stony Brook, NY 11794, USA
| | - Michelle Iocolano
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daphna Gelblum
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Narek Shaverdian
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Charles B. Simone
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Daniel R. Gomez
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Annemarie F. Shepherd
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Corresponding authors.
| | - Maria Thor
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Corresponding authors.
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11
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Automatic Cardiac Structure Contouring for Small Datasets with Cascaded Deep Learning Models. J Med Syst 2022; 46:22. [PMID: 35338425 PMCID: PMC8956542 DOI: 10.1007/s10916-022-01810-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 03/11/2022] [Indexed: 11/26/2022]
Abstract
Cardiac structure contouring is a time consuming and tedious manual activity used for radiotherapeutic dose toxicity planning. We developed an automatic cardiac structure segmentation pipeline for use in low-dose non-contrast planning CT based on deep learning algorithms for small datasets. Fifty CT scans were retrospectively selected and the whole heart, ventricles and atria were contoured. A two stage deep learning pipeline was trained on 41 non contrast planning CTs, tuned with 3 CT scans and validated on 6 CT scans. In the first stage, An InceptionResNetV2 network was used to identify the slices that contained cardiac structures. The second stage consisted of three deep learning models trained on the images containing cardiac structures to segment the structures. The three deep learning models predicted the segmentations/contours on axial, coronal and sagittal images and are combined to create the final prediction. The final accuracy of the pipeline was quantified on 6 volumes by calculating the Dice similarity coefficient (DC), 95% Hausdorff distance (95% HD) and volume ratios between predicted and ground truth volumes. Median DC and 95% HD of 0.96, 0.88, 0.92, 0.80 and 0.82, and 1.86, 2.98, 2.02, 6.16 and 6.46 were achieved for the whole heart, right and left ventricle, and right and left atria respectively. The median differences in volume were -4, -1, + 5, -16 and -20% for the whole heart, right and left ventricle, and right and left atria respectively. The automatic contouring pipeline achieves good results for whole heart and ventricles. Robust automatic contouring with deep learning methods seems viable for local centers with small datasets.
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12
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Milo MLH, Nyeng TB, Lorenzen EL, Hoffmann L, Møller DS, Offersen BV. Atlas-based auto-segmentation for delineating the heart and cardiac substructures in breast cancer radiation therapy. Acta Oncol 2022; 61:247-254. [PMID: 34427497 DOI: 10.1080/0284186x.2021.1967445] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND This study aimed to develop and validate an automatic multi-atlas segmentation method for delineating the heart and substructures in breast cancer radiation therapy (RT). MATERIAL AND METHODS The atlas database consisted of non-contrast-enhanced planning CT scans from 42 breast cancer patients, each with one manual delineation of the heart and 22 cardiac substructures. Half of the patients were scanned during free-breathing, the rest were scanned during a deep inspiration breath-hold. The auto-segmentation was developed in the MIM software system and validated geometrically and dosimetrically in two steps: The first validation in a small dataset to ensure consistency of the atlas. This was succeeded by a final test where multiple manual delineations in CT scans of 12 breast cancer patients were compared to the auto-segmentation. For geometric evaluation, the dice similarity coefficient (DSC) and the mean surface distance (MSD) were used. For dosimetric evaluation, the RT doses to each substructure in the manual and the automatic delineations were compared. RESULTS In the first validation, a high geometric and dosimetric performance between the automatic and manual delineations was observed for all substructures. The final test confirmed a high agreement between the automatic and manual delineations for the heart (DSC = 0.94) and the cardiac chambers (DSC: 0.75-0.86). The difference in MSD between the automatic and manual delineations was low (<4 mm) in all structures. Finally, a high correlation between mean RT doses for the automatic and the manual delineations was observed for the heart and substructures. CONCLUSIONS An automatic segmentation tool for delineation of the heart and substructures in breast cancer RT was developed and validated with a high correlation between the automatic and manual delineations. The atlas is pivotal for large-scale evaluations of radiation-associated heart disease.
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Affiliation(s)
- Marie Louise H. Milo
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Tine B. Nyeng
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - Ebbe L. Lorenzen
- Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark
| | - Lone Hoffmann
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Faculty of Health Sciences, Aarhus University, Denmark
| | - Ditte S. Møller
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Faculty of Health Sciences, Aarhus University, Denmark
| | - Birgitte V. Offersen
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
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13
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Jung JW, Mille MM, Ky B, Kenworthy W, Lee C, Yeom YS, Kwag A, Bosch W, MacDonald S, Cahlon O, Bekelman JE, Lee C. Application of an automatic segmentation method for evaluating cardiac structure doses received by breast radiotherapy patients. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2021; 19:138-144. [PMID: 34485719 PMCID: PMC8397890 DOI: 10.1016/j.phro.2021.08.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 08/12/2021] [Accepted: 08/13/2021] [Indexed: 12/16/2022]
Abstract
Atlas-based method for contouring heart substructures on breast radiotherapy CT. Excellent agreement between automatic and manual contours for most patients. Dice similarity coefficient for LAD was low (0.06) because a narrow, long structure. Doses derived from automatic and manual contours agree within observer variability. For left breast treatment, right ventricle and LAD dose most senstive to contour shift.
Background and purpose Quantifying radiation dose to cardiac substructures is important for research on the etiology and prevention of complications following radiotherapy; however, segmentation of substructures is challenging. In this study we demonstrate the application of our atlas-based automatic segmentation method to breast cancer radiotherapy plans for generating radiation doses in support of late effects research. Material and methods We applied our segmentation method to contour heart substructures on the computed tomography (CT) images of 70 breast cancer patients who received external photon radiotherapy. Two cardiologists provided manual segmentation of the whole heart (WH), left/right atria, left/right ventricles, and left anterior descending artery (LAD). The automatically contours were compared with manual delineations to evaluate similarity in terms of geometry and dose. Results The mean Dice similarity coefficient between manual and automatic segmentations was 0.96 for the WH, 0.65 to 0.82 for the atria and ventricles, and 0.06 for the LAD. The mean average surface distance was 1.2 mm for the WH, 3.4 to 4.1 mm for the atria and ventricles, and 6.4 mm for the LAD. We found the dose to the cardiac substructures based on our automatic segmentation agrees with manual segmentation within expected observer variability. For left breast patients, the mean absolute difference in mean dose was 0.1 Gy for the WH, 0.2 to 0.7 Gy for the atria and ventricles, and 1.8 Gy for the LAD. For right breast patients, these values were 0.0 Gy, 0.1 to 0.4 Gy, and 0.4 Gy, respectively. Conclusion Our automatic segmentation method will facilitate the development of radiotherapy prescriptive criteria for mitigating cardiovascular complications.
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Affiliation(s)
- Jae Won Jung
- Department of Physics, East Carolina University, Greenville, NC 27858, United States
| | - Matthew M. Mille
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, United States
| | - Bonnie Ky
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
| | - Walter Kenworthy
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
| | - Choonik Lee
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48109, United States
| | - Yeon Soo Yeom
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, United States
| | - Aaron Kwag
- Department of Neuroscience, Vanderbilt University, Nashville, TN 37240, United States
| | - Walter Bosch
- Department of Radiation Oncology, Washington University, St. Louis, MO 63130, United States
| | - Shannon MacDonald
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Oren Cahlon
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Justin E. Bekelman
- Department of Radiation Oncology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
| | - Choonsik Lee
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, United States
- Corresponding author at: Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Rockville, MD 20850, United States.
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14
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Loap P, Tkatchenko N, Goudjil F, Ribeiro M, Baron B, Fourquet A, Kirova Y. Cardiac substructure exposure in breast radiotherapy: a comparison between intensity modulated proton therapy and volumetric modulated arc therapy. Acta Oncol 2021; 60:1038-1044. [PMID: 33788665 DOI: 10.1080/0284186x.2021.1907860] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
INTRODUCTION Proton therapy for breast cancer treatment reduces cardiac radiation exposure. Left-sided breast cancer patients with indication for internal mammary chain (IMC) irradiation are most at risk of radiation-induced cardiotoxicity. This study aims to evaluate in this situation the potential dosimetric benefit of intensity modulated proton therapy (IMPT) over volumetric modulated arc therapy (VMAT) at the cardiac substructure level. MATERIALS AND METHODS Cardiac substructures were retrospectively delineated according to ESTRO guidelines on the simulation CT scans of fourteen left-sided breast cancer patients having undergone conserving surgery and adjuvant locoregional free-breathing (FB-) or deep inspiration breath-hold (DIBH-) VMAT with internal mammary chain irradiation. IMPT treatment was re-planned on the simulation CT scans. Mean doses to cardiac substructures were retrieved and compared between VMAT treatment plans and IMPT simulation plans. Pearson correlation coefficients were calculated between mean doses delivered to cardiac substructures using these two techniques. RESULTS Mean doses to all cardiac substructures were significantly lower with IMPT than with VMAT. Regardless of the irradiation technique, the most exposed cardiac substructure was the mid segment of the left anterior descending coronary artery (LADCA). Pearson correlation coefficients between mean doses to cardiac substructures were usually weak and statistically non-significant for IMPT; mean heart dose (MHD) only correlated with mean doses delivered to the right ventricle, to the mid segment of the right coronary artery (RCA) and, to a lesser extent, to the LADCA. CONCLUSION The dosimetric benefit of IMPT over conformal photon therapy was consistently observed for all cardiac substructures. MHD may not be a reliable dosimetric parameter for precise cardiac exposure evaluation when planning IMPT.
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Affiliation(s)
- Pierre Loap
- Institut Curie, Department of Radiation Oncology, Paris, France
| | | | - Farid Goudjil
- Institut Curie, Department of Radiation Oncology, Paris, France
| | - Madison Ribeiro
- Institut Curie, Department of Radiation Oncology, Paris, France
| | - Brian Baron
- Institut Curie, Department of Radiation Oncology, Paris, France
| | - Alain Fourquet
- Institut Curie, Department of Radiation Oncology, Paris, France
| | - Youlia Kirova
- Institut Curie, Department of Radiation Oncology, Paris, France
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15
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Spoor DS, Sijtsema NM, van den Bogaard VAB, van der Schaaf A, Brouwer CL, Ta BDP, Vliegenthart R, Kierkels RGJ, Langendijk JA, Maduro JH, Peters FBJ, Crijns APG. Validation of separate multi-atlases for auto segmentation of cardiac substructures in CT-scans acquired in deep inspiration breath hold and free breathing. Radiother Oncol 2021; 163:46-54. [PMID: 34343547 DOI: 10.1016/j.radonc.2021.07.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 07/23/2021] [Accepted: 07/24/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND PURPOSE Developing NTCP-models for cardiac complications after breast cancer (BC) radiotherapy requires cardiac dose-volume parameters for many patients. These can be obtained by using multi-atlas based automatic segmentation (MABAS) of cardiac structures in planning CT scans. We investigated the relevance of separate multi-atlases for deep inspiration breath hold (DIBH) and free breathing (FB) CT scans. MATERIALS AND METHODS BC patients scanned in DIBH (n = 10) and in FB (n = 20) were selected to create separate multi-atlases consisting of expert panel delineations of the whole heart, atria and ventricles. The accuracy of atlas-generated contours was validated with expert delineations in independent datasets (n = 10 for DIBH and FB) and reported as Dice coefficients, contour distances and dose-volume differences in relation to interobserver variability of manual contours. Dependency of MABAS contouring accuracy on breathing technique was assessed by validation of a FB atlas in DIBH patients and vice versa (cross-validation). RESULTS For all structures the FB and DIBH atlases resulted in Dice coefficients with their respective reference contours ≥ 0.8 and average contour distances ≤ 2 mm smaller than slice thickness of (CTs). No significant differences were found for dose-volume parameters in volumes receiving relevant dose levels (WH, LV and RV). Accuracy of the DIBH atlas was at least similar to, and for the ventricles better than, the interobserver variation in manual delineation. Cross-validation between breathing techniques showed a reduced MABAS performance. CONCLUSION Multi-atlas accuracy was at least similar to interobserver delineation variation. Separate atlases for scans made in DIBH and FB could benefit atlas performance because accuracy depends on breathing technique.
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Affiliation(s)
- Daan S Spoor
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Nanna M Sijtsema
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.
| | - Veerle A B van den Bogaard
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Arjen van der Schaaf
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Charlotte L Brouwer
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Bastiaan D P Ta
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Roel G J Kierkels
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - John H Maduro
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Femke B J Peters
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Anne P G Crijns
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
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16
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Patel B, Bianco C, Sengupta PP. Early Tracking of Radiation-Induced Cardiotoxicity: Paradigm, Pragmatism, and Possibilities. JACC CardioOncol 2021; 3:290-293. [PMID: 34396336 PMCID: PMC8352235 DOI: 10.1016/j.jaccao.2021.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Affiliation(s)
- Brijesh Patel
- Heart and Vascular Institute, West Virginia University, Morgantown, West Virginia, USA
| | - Christopher Bianco
- Heart and Vascular Institute, West Virginia University, Morgantown, West Virginia, USA
| | - Partho P. Sengupta
- Heart and Vascular Institute, West Virginia University, Morgantown, West Virginia, USA
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17
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Loap P, Beddok A, Cao KI, Goudjil F, Fourquet A, Dendale R, Kirova Y. Clinical practice of breast cancer protontherapy: A single-centre experience from selection to treatment. Cancer Radiother 2021; 25:358-365. [PMID: 33676830 DOI: 10.1016/j.canrad.2021.01.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 01/18/2021] [Accepted: 01/20/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE Breast protontherapy efficiently limits cardiac, lung and contralateral breast exposure, which may clinically translate into better late tolerance profile compared with classic photon techniques. While breast protontherapy is already implemented in the United States and in some European countries, clinical experience of breast cancer protontherapy is currently limited in France. The aim of this study is to evaluate the clinical practice of breast cancer protontherapy at the Institut Curie in order to implement this technique at a larger scale. MATERIALS AND METHODS Data from all breast cancer patients that have been addressed to the protontherapy centre of Orsay (CPO, Institut Curie) for adjuvant breast protontherapy were retrieved. We analysed why these patients were ultimately treated with protontherapy or not. RESULTS Between November 2019 and November 2020, eleven breast cancer patients have been evaluated for adjuvant protontherapy at the CPO. Two of them were ultimately treated with proton beams; adjuvant breast protontherapy therapy was well tolerated. The nine other patients were not treated with protontherapy due to lack of availability of protontherapy treatment rooms in acceptable time limits, at the time of patient evaluation. CONCLUSION Despite dosimetric advantages and excellent clinical tolerance, lack of availability of protontherapy machines currently limits wider implementation of breast protontherapy.
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Affiliation(s)
- P Loap
- Department of radiation oncology, Institut Curie, Paris, France
| | - A Beddok
- Department of radiation oncology, Institut Curie, Paris, France
| | - K I Cao
- Department of radiation oncology, Institut Curie, Paris, France
| | - F Goudjil
- Department of radiation oncology, Institut Curie, Paris, France
| | - A Fourquet
- Department of radiation oncology, Institut Curie, Paris, France
| | - R Dendale
- Department of radiation oncology, Institut Curie, Paris, France
| | - Y Kirova
- Department of radiation oncology, Institut Curie, Paris, France.
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18
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Loap P, Tkatchenko N, Kirova Y. Evaluation of a delineation software for cardiac atlas-based autosegmentation: An example of the use of artificial intelligence in modern radiotherapy. Cancer Radiother 2020; 24:826-833. [PMID: 33144062 DOI: 10.1016/j.canrad.2020.04.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 04/15/2020] [Accepted: 04/23/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE The primary objective of this work was to implement and evaluate a cardiac atlas-based autosegmentation technique based on the "Workflow Box" software (Mirada Medical, Oxford UK), in order to delineate cardiac substructures according to European Society of Therapeutic Radiation Oncology (ESTRO) guidelines; review and comparison with other cardiac atlas-based autosegmentation algorithms published to date. MATERIALS AND METHODS Of an atlas of data set from 20 breast cancer patients' CT scans with recontoured cardiac substructures creation according to the ESTRO guidelines. Performance evaluation on a validation data set consisting of 20 others CT scans acquired in the same treatment position: cardiac substructure were automatically contoured by the Mirada system, using the implemented cardiac atlas, and simultaneously manually contoured by a radiation oncologist. The Dice similarity coefficient was used to evaluate the concordance level between the manual and the automatic segmentations. RESULTS Dice similarity coefficient value was 0.95 for the whole heart and 0.80 for the four cardiac chambers. Average Dice similarity coefficient value for the left ventricle walls was 0.50, ranging between 0.34 for the apical wall and 0.70 for the lateral wall. Compared to manual contours, autosegmented substructure volumes were significantly smaller, with the exception of the left ventricle. Coronary artery segmentation was unsuccessful. Performances were overall similar to other published cardiac atlas-based autosegmentation algorithms. CONCLUSION The evaluated cardiac atlas-based autosegmentation technique, using the Mirada software, demonstrated acceptable performance for cardiac cavities delineation. However, algorithm improvement is still needed in order to develop efficient and trusted cardiac autosegmentation working tools for daily practice.
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Affiliation(s)
- P Loap
- Department of radiation oncology, institut Curie, 26, rue d'Ulm, 75006 Paris, France.
| | - N Tkatchenko
- Department of radiation oncology, institut Curie, 26, rue d'Ulm, 75006 Paris, France
| | - Y Kirova
- Department of radiation oncology, institut Curie, 26, rue d'Ulm, 75006 Paris, France
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Choi MS, Choi BS, Chung SY, Kim N, Chun J, Kim YB, Chang JS, Kim JS. Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer. Radiother Oncol 2020; 153:139-145. [PMID: 32991916 DOI: 10.1016/j.radonc.2020.09.045] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 09/22/2020] [Accepted: 09/23/2020] [Indexed: 12/21/2022]
Abstract
Manual segmentation is the gold standard method for radiation therapy planning; however, it is time-consuming and prone to inter- and intra-observer variation, giving rise to interests in auto-segmentation methods. We evaluated the feasibility of deep learning-based auto-segmentation (DLBAS) in comparison to commercially available atlas-based segmentation solutions (ABAS) for breast cancer radiation therapy. This study used contrast-enhanced planning computed tomography scans from 62 patients with breast cancer who underwent breast-conservation surgery. Contours of target volumes (CTVs), organs, and heart substructures were generated using two commercial ABAS solutions and DLBAS using fully convolutional DenseNet. The accuracy of the segmentation was assessed using 14 test patients using the Dice Similarity Coefficient and Hausdorff Distance referencing the expert contours. A sensitivity analysis was performed using non-contrast planning CT from 14 additional patients. Compared to ABAS, the proposed DLBAS model yielded more consistent results and the highest average Dice Similarity Coefficient values and lowest Hausdorff Distances, especially for CTVs and the substructures of the heart. ABAS showed limited performance in soft-tissue-based regions, such as the esophagus, cardiac arteries, and smaller CTVs. The results of sensitivity analysis between contrast and non-contrast CT test sets showed little difference in the performance of DLBAS and conversely, a large discrepancy for ABAS. The proposed DLBAS algorithm was more consistent and robust in its performance than ABAS across the majority of structures when examining both CTVs and normal organs. DLBAS has great potential to aid a key process in the radiation therapy workflow, helping optimise and reduce the clinical workload.
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Affiliation(s)
- Min Seo Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Byeong Su Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Seung Yeun Chung
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, South Korea
| | - Nalee Kim
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan School of Medicine, Seoul, South Korea
| | - Jaehee Chun
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Yong Bae Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Jee Suk Chang
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea.
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea.
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Redalen KR, Thorwarth D. Future directions on the merge of quantitative imaging and artificial intelligence in radiation oncology. Phys Imaging Radiat Oncol 2020; 15:44-45. [PMID: 33458325 PMCID: PMC7807640 DOI: 10.1016/j.phro.2020.06.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
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21
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Casares-Magaz O, Moiseenko V, Witte M, Rancati T, Muren LP. Towards spatial representations of dose distributions to predict risk of normal tissue morbidity after radiotherapy. Phys Imaging Radiat Oncol 2020; 15:105-107. [PMID: 33458334 PMCID: PMC7807547 DOI: 10.1016/j.phro.2020.08.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
- Oscar Casares-Magaz
- Department of Medical Physics - Oncology, Aarhus University/Aarhus University Hospital, Aarhus, Denmark
| | - Vitali Moiseenko
- Department of Radiation Medicine and Applied Science, University of California San Diego, La Jolla, CA, United States
| | - Marnix Witte
- Cluster Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Tiziana Rancati
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Ludvig P Muren
- Department of Medical Physics - Oncology, Aarhus University/Aarhus University Hospital, Aarhus, Denmark
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