1
|
Erdur AC, Rusche D, Scholz D, Kiechle J, Fischer S, Llorián-Salvador Ó, Buchner JA, Nguyen MQ, Etzel L, Weidner J, Metz MC, Wiestler B, Schnabel J, Rueckert D, Combs SE, Peeken JC. Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives. Strahlenther Onkol 2024:10.1007/s00066-024-02262-2. [PMID: 39105745 DOI: 10.1007/s00066-024-02262-2] [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: 01/21/2024] [Accepted: 06/13/2024] [Indexed: 08/07/2024]
Abstract
The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient journey. In this review article, we summarize contemporary AI techniques and explore the clinical applications of AI-based automated segmentation models in radiotherapy planning, focusing on delineation of organs at risk (OARs), the gross tumor volume (GTV), and the clinical target volume (CTV). Emphasizing the need for precise and individualized plans, we review various commercial and freeware segmentation tools and also state-of-the-art approaches. Through our own findings and based on the literature, we demonstrate improved efficiency and consistency as well as time savings in different clinical scenarios. Despite challenges in clinical implementation such as domain shifts, the potential benefits for personalized treatment planning are substantial. The integration of mathematical tumor growth models and AI-based tumor detection further enhances the possibilities for refining target volumes. As advancements continue, the prospect of one-stop-shop segmentation and radiotherapy planning represents an exciting frontier in radiotherapy, potentially enabling fast treatment with enhanced precision and individualization.
Collapse
Affiliation(s)
- Ayhan Can Erdur
- Institute for Artificial Intelligence and Informatics in Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany.
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany.
| | - Daniel Rusche
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Daniel Scholz
- Institute for Artificial Intelligence and Informatics in Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Department of Neuroradiology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Johannes Kiechle
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Institute for Computational Imaging and AI in Medicine, Technical University of Munich, Lichtenberg Str. 2a, 85748, Garching, Bavaria, Germany
- Munich Center for Machine Learning (MCML), Technical University of Munich, Arcisstraße 21, 80333, Munich, Bavaria, Germany
- Konrad Zuse School of Excellence in Reliable AI (relAI), Technical University of Munich, Walther-von-Dyck-Straße 10, 85748, Garching, Bavaria, Germany
| | - Stefan Fischer
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Institute for Computational Imaging and AI in Medicine, Technical University of Munich, Lichtenberg Str. 2a, 85748, Garching, Bavaria, Germany
- Munich Center for Machine Learning (MCML), Technical University of Munich, Arcisstraße 21, 80333, Munich, Bavaria, Germany
| | - Óscar Llorián-Salvador
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Department for Bioinformatics and Computational Biology - i12, Technical University of Munich, Boltzmannstraße 3, 85748, Garching, Bavaria, Germany
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz (JGU), Hüsch-Weg 15, 55128, Mainz, Rhineland-Palatinate, Germany
| | - Josef A Buchner
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Mai Q Nguyen
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Lucas Etzel
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum, Ingolstädter Landstraße 1, 85764, Oberschleißheim, Bavaria, Germany
| | - Jonas Weidner
- Institute for Artificial Intelligence and Informatics in Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Department of Neuroradiology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Marie-Christin Metz
- Department of Neuroradiology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
| | - Julia Schnabel
- Institute for Computational Imaging and AI in Medicine, Technical University of Munich, Lichtenberg Str. 2a, 85748, Garching, Bavaria, Germany
- Munich Center for Machine Learning (MCML), Technical University of Munich, Arcisstraße 21, 80333, Munich, Bavaria, Germany
- Konrad Zuse School of Excellence in Reliable AI (relAI), Technical University of Munich, Walther-von-Dyck-Straße 10, 85748, Garching, Bavaria, Germany
- Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Ingolstädter Landstraße 1, 85764, Neuherberg, Bavaria, Germany
- School of Biomedical Engineering & Imaging Sciences, King's College London, Strand, WC2R 2LS, London, London, UK
| | - Daniel Rueckert
- Institute for Artificial Intelligence and Informatics in Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Faculty of Engineering, Department of Computing, Imperial College London, Exhibition Rd, SW7 2BX, London, London, UK
| | - Stephanie E Combs
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum, Ingolstädter Landstraße 1, 85764, Oberschleißheim, Bavaria, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Bavaria, Germany
| | - Jan C Peeken
- Department of Radiation Oncology, TUM School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str., 81675, Munich, Bavaria, Germany
- Institute of Radiation Medicine (IRM), Helmholtz Zentrum, Ingolstädter Landstraße 1, 85764, Oberschleißheim, Bavaria, Germany
- Partner Site Munich, German Consortium for Translational Cancer Research (DKTK), Munich, Bavaria, Germany
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Rong Y, Chen Q, Fu Y, Yang X, Al-Hallaq HA, Wu QJ, Yuan L, Xiao Y, Cai B, Latifi K, Benedict SH, Buchsbaum JC, Qi XS. NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions. Int J Radiat Oncol Biol Phys 2024; 119:261-280. [PMID: 37972715 PMCID: PMC11023777 DOI: 10.1016/j.ijrobp.2023.10.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 09/16/2023] [Accepted: 10/14/2023] [Indexed: 11/19/2023]
Abstract
Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations.
Collapse
Affiliation(s)
- Yi Rong
- Mayo Clinic Arizona, Phoenix, AZ
| | - Quan Chen
- City of Hope Comprehensive Cancer Center Duarte, CA
| | - Yabo Fu
- Memorial Sloan Kettering Cancer Center, Commack, NY
| | | | | | | | - Lulin Yuan
- Virginia Commonwealth University, Richmond, VA
| | - Ying Xiao
- University of Pennsylvania/Abramson Cancer Center, Philadelphia, PA
| | - Bin Cai
- The University of Texas Southwestern Medical Center, Dallas, TX
| | | | - Stanley H Benedict
- University of California Davis Comprehensive Cancer Center, Sacramento, CA
| | | | - X Sharon Qi
- University of California Los Angeles, Los Angeles, CA
| |
Collapse
|
4
|
Le Bao V, Haworth A, Dowling J, Walker A, Arumugam S, Jameson M, Chlap P, Wiltshire K, Keats S, Cloak K, Sidhom M, Kneebone A, Holloway L. Evaluating the relationship between contouring variability and modelled treatment outcome for prostate bed radiotherapy. Phys Med Biol 2024; 69:085008. [PMID: 38471173 DOI: 10.1088/1361-6560/ad3325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 03/12/2024] [Indexed: 03/14/2024]
Abstract
Objectives.Contouring similarity metrics are often used in studies of inter-observer variation and automatic segmentation but do not provide an assessment of clinical impact. This study focused on post-prostatectomy radiotherapy and aimed to (1) identify if there is a relationship between variations in commonly used contouring similarity metrics and resulting dosimetry and (2) identify the variation in clinical target volume (CTV) contouring that significantly impacts dosimetry.Approach.The study retrospectively analysed CT scans of 10 patients from the TROG 08.03 RAVES trial. The CTV, rectum, and bladder were contoured independently by three experienced observers. Using these contours reference simultaneous truth and performance level estimation (STAPLE) volumes were established. Additional CTVs were generated using an atlas algorithm based on a single benchmark case with 42 manual contours. Volumetric-modulated arc therapy (VMAT) treatment plans were generated for the observer, atlas, and reference volumes. The dosimetry was evaluated using radiobiological metrics. Correlations between contouring similarity and dosimetry metrics were calculated using Spearman coefficient (Γ). To access impact of variations in planning target volume (PTV) margin, the STAPLE PTV was uniformly contracted and expanded, with plans created for each PTV volume. STAPLE dose-volume histograms (DVHs) were exported for plans generated based on the contracted/expanded volumes, and dose-volume metrics assessed.Mainresults. The study found no strong correlations between the considered similarity metrics and modelled outcomes. Moderate correlations (0.5 <Γ< 0.7) were observed for Dice similarity coefficient, Jaccard, and mean distance to agreement metrics and rectum toxicities. The observations of this study indicate a tendency for variations in CTV contraction/expansion below 5 mm to result in minor dosimetric impacts.Significance. Contouring similarity metrics must be used with caution when interpreting them as indicators of treatment plan variation. For post-prostatectomy VMAT patients, this work showed variations in contours with an expansion/contraction of less than 5 mm did not lead to notable dosimetric differences, this should be explored in a larger dataset to assess generalisability.
Collapse
Affiliation(s)
- Viet Le Bao
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
| | - Annette Haworth
- Institute of Medical Physics, School of Physics, University of Sydney, Australia
| | - Jason Dowling
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
| | - Amy Walker
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - Sankar Arumugam
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Michael Jameson
- St Vincent's Clinical School, University of New South Wales, Sydney, Australia
- GenesisCare, Sydney, NSW, Australia
| | - Phillip Chlap
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Kirsty Wiltshire
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
| | - Sarah Keats
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Kirrily Cloak
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | - Mark Sidhom
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
| | | | - Lois Holloway
- South Western Clinical School, University of New South Wales, Sydney, Australia
- Ingham Institute for Applied Medical Research, Sydney, Australia
- Institute of Medical Physics, School of Physics, University of Sydney, Australia
- Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| |
Collapse
|
5
|
Bibault JE, Giraud P. Deep learning for automated segmentation in radiotherapy: a narrative review. Br J Radiol 2024; 97:13-20. [PMID: 38263838 PMCID: PMC11027240 DOI: 10.1093/bjr/tqad018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 08/10/2023] [Accepted: 10/27/2023] [Indexed: 01/25/2024] Open
Abstract
The segmentation of organs and structures is a critical component of radiation therapy planning, with manual segmentation being a laborious and time-consuming task. Interobserver variability can also impact the outcomes of radiation therapy. Deep neural networks have recently gained attention for their ability to automate segmentation tasks, with convolutional neural networks (CNNs) being a popular approach. This article provides a descriptive review of the literature on deep learning (DL) techniques for segmentation in radiation therapy planning. This review focuses on five clinical sub-sites and finds that U-net is the most commonly used CNN architecture. The studies using DL for image segmentation were included in brain, head and neck, lung, abdominal, and pelvic cancers. The majority of DL segmentation articles in radiation therapy planning have concentrated on normal tissue structures. N-fold cross-validation was commonly employed, without external validation. This research area is expanding quickly, and standardization of metrics and independent validation are critical to benchmarking and comparing proposed methods.
Collapse
Affiliation(s)
- Jean-Emmanuel Bibault
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique—Hôpitaux de Paris, Université de Paris Cité, Paris, 75015, France
- INSERM UMR 1138, Centre de Recherche des Cordeliers, Paris, 75006, France
| | - Paul Giraud
- INSERM UMR 1138, Centre de Recherche des Cordeliers, Paris, 75006, France
- Radiation Oncology Department, Pitié Salpêtrière Hospital, Assistance Publique—Hôpitaux de Paris, Paris Sorbonne Universités, Paris, 75013, France
| |
Collapse
|
6
|
Rodríguez Outeiral R, Ferreira Silvério N, González PJ, Schaake EE, Janssen T, van der Heide UA, Simões R. A network score-based metric to optimize the quality assurance of automatic radiotherapy target segmentations. Phys Imaging Radiat Oncol 2023; 28:100500. [PMID: 37869474 PMCID: PMC10587515 DOI: 10.1016/j.phro.2023.100500] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/06/2023] [Accepted: 10/09/2023] [Indexed: 10/24/2023] Open
Abstract
Background and purpose Existing methods for quality assurance of the radiotherapy auto-segmentations focus on the correlation between the average model entropy and the Dice Similarity Coefficient (DSC) only. We identified a metric directly derived from the output of the network and correlated it with clinically relevant metrics for contour accuracy. Materials and Methods Magnetic Resonance Imaging auto-segmentations were available for the gross tumor volume for cervical cancer brachytherapy (106 segmentations) and for the clinical target volume for rectal cancer external-beam radiotherapy (77 segmentations). The nnU-Net's output before binarization was taken as a score map. We defined a metric as the mean of the voxels in the score map above a threshold (λ). Comparisons were made with the mean and standard deviation over the score map and with the mean over the entropy map. The DSC, the 95th Hausdorff distance, the mean surface distance (MSD) and the surface DSC were computed for segmentation quality. Correlations between the studied metrics and model quality were assessed with the Pearson correlation coefficient (r). The area under the curve (AUC) was determined for detecting segmentations that require reviewing. Results For both tasks, our metric (λ = 0.30) correlated more strongly with the segmentation quality than the mean over the entropy map (for surface DSC, r > 0.65 vs. r < 0.60). The AUC was above 0.84 for detecting MSD values above 2 mm. Conclusions Our metric correlated strongly with clinically relevant segmentation metrics and detected segmentations that required reviewing, indicating its potential for automatic quality assurance of radiotherapy target auto-segmentations.
Collapse
Affiliation(s)
- Roque Rodríguez Outeiral
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Nicole Ferreira Silvério
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Patrick J. González
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Eva E. Schaake
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Tomas Janssen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Uulke A. van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Rita Simões
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| |
Collapse
|
7
|
Feng X, Tang B, Yao X, Liu M, Liao X, Yuan K, Peng Q, Orlandini LC. Effectiveness of bladder filling control during online MR-guided adaptive radiotherapy for rectal cancer. Radiat Oncol 2023; 18:136. [PMID: 37592338 PMCID: PMC10436664 DOI: 10.1186/s13014-023-02315-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/05/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND Magnetic resonance-guided adaptive radiotherapy (MRgART) treatment sessions at MR-Linac are time-consuming and changes in organs at risk volumes can impact the treatment dosimetry. This study aims to evaluate the feasibility to control bladder filling during the rectum MRgART online session and its effectiveness on plan dosimetry. METHODS A total of 109 online adaptive sessions of 24 rectum cancer patients treated at Unity 1.5 T MR-Linac with a short course radiotherapy (25 Gy, 5 Gy × 5) for whom the adaptive plan was optimized and recalculated online based on the daily magnetic resonance imaging (MRI) were analysed. Patients were fitted with a bladder catheter to control bladder filling; the bladder is emptied and then partially filled with a known amount of saline at the beginning and end of the online session. A first MRI ([Formula: see text]) acquired at the beginning of the session was used for plan adaptation and the second ([Formula: see text]) was acquired while approving the adapted plan and rigidly registered with the first to ensure the appropriateness of the isodoses on the ongoing delivery treatment. For each fraction, the time interval between the two MRIs and potential bladder changes were assessed with independent metrics, and the impact on the plan dosimetry was evaluated by comparing target and organs at risk dose volume histogram cut-off points of the plan adapted on [Formula: see text] and recalculated on [Formula: see text]. RESULTS Median bladder volume variations, DSC, and HD of 8.17%, 0.922, and 2.92 mm were registered within a median time of 38 min between [Formula: see text] and [Formula: see text]; dosimetric differences < 0.65% were registered for target coverage, and < 0.5% for bladder, small bowel and femoral heads constraints, with a p value > 0.05. CONCLUSION The use of a bladder filling control procedure can help ensure the dosimetric accuracy of the online adapted treatment delivered.
Collapse
Affiliation(s)
- Xi Feng
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Bin Tang
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Xinghong Yao
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Min Liu
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
- Institute of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, China
| | - Xiongfei Liao
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Ke Yuan
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Qian Peng
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Lucia Clara Orlandini
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| |
Collapse
|
8
|
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: 10.0] [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.
Collapse
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
| |
Collapse
|
9
|
Chen X, Ma X, Yan X, Luo F, Yang S, Wang Z, Wu R, Wang J, Lu N, Bi N, Yi J, Wang S, Li Y, Dai J, Men K. Personalized auto-segmentation for magnetic resonance imaging guided adaptive radiotherapy of prostate cancer. Med Phys 2022; 49:4971-4979. [PMID: 35670079 DOI: 10.1002/mp.15793] [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: 12/24/2021] [Revised: 05/13/2022] [Accepted: 05/30/2022] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Fast and accurate delineation of organs on treatment-fraction images is critical in magnetic resonance imaging-guided adaptive radiotherapy (MRIgART). This study proposes a personalized auto-segmentation (AS) framework to assist online delineation of prostate cancer using MRIgART. METHODS Image data from 26 patients diagnosed with prostate cancer and treated using hypofractionated MRIgART (5 fractions per patient) were collected retrospectively. Daily pretreatment T2-weighted MRI was performed using a 1.5-T MRI system integrated into a Unity MR-linac. First-fraction image and contour data from 16 patients (80 image-sets) were used to train the population AS model, and the remaining 10 patients composed the test set. The proposed personalized AS framework contained two main steps. First, a convolutional neural network was employed to train the population model using the training set. Second, for each test patient, the population model was progressively fine-tuned with manually checked delineations of the patient's current and previous fractions to obtain a personalized model that was applied to the next fraction. RESULTS Compared with the population model, the personalized models substantially improved the mean Dice similarity coefficient from 0.79 to 0.93 for the prostate clinical target volume (CTV), 0.91 to 0.97 for the bladder, 0.82 to 0.92 for the rectum, 0.91 to 0.93 for the femoral heads, respectively. CONCLUSIONS The proposed method can achieve accurate segmentation and potentially shorten the overall online delineation time of MRIgART. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Xinyuan Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiangyu Ma
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xuena Yan
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Fei Luo
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Siran Yang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Zekun Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Runye Wu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jianyang Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Ningning Lu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Nan Bi
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Junlin Yi
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Shulian Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yexiong Li
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| |
Collapse
|
10
|
Vaassen F, Boukerroui D, Looney P, Canters R, Verhoeven K, Peeters S, Lubken I, Mannens J, Gooding MJ, van Elmpt W. Real-world analysis of manual editing of deep learning contouring in the thorax region. Phys Imaging Radiat Oncol 2022; 22:104-110. [PMID: 35602549 PMCID: PMC9115320 DOI: 10.1016/j.phro.2022.04.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 04/13/2022] [Accepted: 04/27/2022] [Indexed: 01/18/2023] Open
Abstract
Background and purpose User-adjustments after deep-learning (DL) contouring in radiotherapy were evaluated to get insight in real-world editing during clinical practice. This study assessed the amount, type and spatial regions of editing of auto-contouring for organs-at-risk (OARs) in routine clinical workflow for patients in the thorax region. Materials and methods A total of 350 lung cancer and 362 breast cancer patients, contoured between March 2020 and March 2021 using a commercial DL-contouring method followed by manual adjustments were retrospectively analyzed. Subsampling was performed for some OARs, using an inter-slice gap of 1-3 slices. Commonly-used whole-organ contouring assessment measures were calculated, and all cases were registered to a common reference shape per OAR to identify regions of manual adjustment. Results were expressed as the median, 10th-90th percentile of adjustment and visualized using 3D renderings. Results Per OAR, the median amount of editing was below 1 mm. However, large adjustments were found in some locations for most OARs. In general, enlarging of the auto-contours was needed. Subsampling DL-contours showed less adjustments were made in the interpolated slices compared to simulated no-subsampling for these OARs. Conclusion The real-world performance of automatic DL-contouring software was evaluated and proven useful in clinical practice. Specific regions-of-adjustment were identified per OAR in the thorax region, and separate models were found to be necessary for specific clinical indications different from training data. This analysis showed the need to perform routine clinical analysis especially when procedures or acquisition protocols change to have the best configuration of the workflow.
Collapse
Affiliation(s)
- Femke Vaassen
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | | | - Richard Canters
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Karolien Verhoeven
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Stephanie Peeters
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Indra Lubken
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Jolein Mannens
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | - Wouter van Elmpt
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| |
Collapse
|
11
|
Nourzadeh H, Hui C, Ahmad M, Sadeghzadehyazdi N, Watkins WT, Dutta SW, Alonso CE, Trifiletti DM, Siebers JV. Knowledge-based quality control of organ delineations in radiation therapy. Med Phys 2022; 49:1368-1381. [PMID: 35028948 DOI: 10.1002/mp.15458] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 10/17/2021] [Accepted: 12/17/2021] [Indexed: 11/08/2022] Open
Abstract
PURPOSE To reduce the likelihood of errors in organ delineations used for radiotherapy treatment planning, a knowledge-based quality control (KBQC) system, which discriminates between valid and anomalous delineations is developed. METHOD AND MATERIALS The KBQC is comprised of a group-wise inference system and anomaly detection modules trained using historical priors from 296 locally advanced lung and prostate cancer patient computational tomographies (CTs). The inference system discriminates different organs based on shape, relational, and intensity features. For a given delineated image set, the inference system solves a combinatorial optimization problem that results in an organ group whose relational features follow those of the training set considering the posterior probabilities obtained from support vector machine (SVM), discriminant subspace ensemble (DSE), and artificial neural network (ANN) classifiers. These classifiers are trained on nonrelational features with a 10-fold cross-validation scheme. The anomaly detection module is a bank of ANN autoencoders, each corresponding with an organ, trained on nonrelational features. A heuristic rule detects anomalous organs that exceed predefined organ-specific tolerances for the feature reconstruction error and the classifier's posterior probabilities. Independent data sets with anomalous delineations were used to test the overall performance of the KBQC system. The anomalous delineations were manually manipulated, computer-generated, or propagated based on a transformation obtained by imperfect registrations. Both peer-review-based scoring system and shape similarity coefficient (DSC) were used to label regions of interest (ROIs) as normal or anomalous in two independent test cohorts. RESULTS The accuracy of the classifiers was ≥ $\ge$ 99.8%, and the minimum per-class F1-scores were 0.99, 0.99, and 0.98 for SVM, DSE, and ANN, respectively. The group-wise inference system reduced the miss-classification likelihood for the test data set with anomalous delineations compared to each individual classifier and a fused classifier that used the average posterior probability of all classifiers. For 15 independent locally advanced lung patients, the system detected > $>$ 79% of the anomalous ROIs. For 1320 auto-segmented abdominopelvic organs, the anomaly detection system identified anomalous delineations, which also had low Dice similarity coefficient values with respect to manually delineated organs in the training data set. CONCLUSION The KBQC system detected anomalous delineations with superior accuracy compared to classification methods that judge only based on posterior probabilities.
Collapse
Affiliation(s)
- Hamidreza Nourzadeh
- Sidney Kimmel Cancer Center at Thomas Jefferson University, Philadelphia, Pennsylvania, USA
- Radiation Oncology Department, University of Virginia, Charlottesville, Virginia, USA
| | | | - Mahmoud Ahmad
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | | | - Sunil W Dutta
- Radiation Oncology Department, Emory University, Georgia, USA
| | | | | | - Jeffrey V Siebers
- Radiation Oncology Department, University of Virginia, Charlottesville, Virginia, USA
| |
Collapse
|
12
|
Marin T, Zhuo Y, Lahoud RM, Tian F, Ma X, Xing F, Moteabbed M, Liu X, Grogg K, Shusharina N, Woo J, Ma C, Chen YLE, El Fakhri G. Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas. Radiother Oncol 2022; 167:269-276. [PMID: 34808228 PMCID: PMC8934266 DOI: 10.1016/j.radonc.2021.09.034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 09/21/2021] [Accepted: 09/29/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND AND PURPOSE The delineation of the gross tumor volume (GTV) is a critical step for radiation therapy treatment planning. The delineation procedure is typically performed manually which exposes two major issues: cost and reproducibility. Delineation is a time-consuming process that is subject to inter- and intra-observer variability. While methods have been proposed to predict GTV contours, typical approaches ignore variability and therefore fail to utilize the valuable confidence information offered by multiple contours. MATERIALS AND METHODS In this work we propose an automatic GTV contouring method for soft-tissue sarcomas from X-ray computed tomography (CT) images, using deep learning by integrating inter- and intra-observer variability in the learned model. Sixty-eight patients with soft tissue and bone sarcomas were considered in this evaluation, all underwent pre-operative CT imaging used to perform GTV delineation. Four radiation oncologists and radiologists performed three contouring trials each for all patients. We quantify variability by defining confidence levels based on the frequency of inclusion of a given voxel into the GTV and use a deep convolutional neural network to learn GTV confidence maps. RESULTS Results were compared to confidence maps from the four readers as well as ground-truth consensus contours established jointly by all readers. The resulting continuous Dice score between predicted and true confidence maps was 87% and the Hausdorff distance was 14 mm. CONCLUSION Results demonstrate the ability of the proposed method to predict accurate contours while utilizing variability and as such it can be used to improve clinical workflow.
Collapse
Affiliation(s)
- Thibault Marin
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston MA, 02114, United States of America,Harvard Medical School, Boston MA, 02115, United States of America
| | - Yue Zhuo
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston MA, 02114, United States of America,Harvard Medical School, Boston MA, 02115, United States of America
| | - Rita Maria Lahoud
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston MA, 02114, United States of America,Harvard Medical School, Boston MA, 02115, United States of America
| | - Fei Tian
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston MA, 02114, United States of America,Harvard Medical School, Boston MA, 02115, United States of America
| | - Xiaoyue Ma
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston MA, 02114, United States of America,Harvard Medical School, Boston MA, 02115, United States of America
| | - Fangxu Xing
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston MA, 02114, United States of America,Harvard Medical School, Boston MA, 02115, United States of America
| | - Maryam Moteabbed
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston MA, 02114, United States of America,Department of Radiation Oncology, Massachusetts General Hospital, Boston MA, 02114, United States of America,Harvard Medical School, Boston MA, 02115, United States of America
| | - Xiaofeng Liu
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston MA, 02114, United States of America,Harvard Medical School, Boston MA, 02115, United States of America
| | - Kira Grogg
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston MA, 02114, United States of America,Harvard Medical School, Boston MA, 02115, United States of America
| | - Nadya Shusharina
- Department of Radiation Oncology, Massachusetts General Hospital, Boston MA, 02114, United States of America,Harvard Medical School, Boston MA, 02115, United States of America
| | - Jonghye Woo
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston MA, 02114, United States of America,Harvard Medical School, Boston MA, 02115, United States of America
| | - Chao Ma
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston MA, 02114, United States of America,Harvard Medical School, Boston MA, 02115, United States of America
| | - Yen-Lin E. Chen
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston MA, 02114, United States of America,Department of Radiation Oncology, Massachusetts General Hospital, Boston MA, 02114, United States of America,Harvard Medical School, Boston MA, 02115, United States of America
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston MA, 02114, United States of America,Harvard Medical School, Boston MA, 02115, United States of America,Corresponding author,
| |
Collapse
|
13
|
Li Y, Rao S, Chen W, Azghadi SF, Nguyen KNB, Moran A, Usera BM, Dyer BA, Shang L, Chen Q, Rong Y. Evaluating Automatic Segmentation for Swallowing-Related Organs for Head and Neck Cancer. Technol Cancer Res Treat 2022; 21:15330338221105724. [PMID: 35790457 PMCID: PMC9340321 DOI: 10.1177/15330338221105724] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Purpose: To evaluate the accuracy of deep-learning-based
auto-segmentation of the superior constrictor, middle constrictor, inferior
constrictor, and larynx in comparison with a traditional multi-atlas-based
method. Methods and Materials: One hundred and five computed
tomography image datasets from 83 head and neck cancer patients were
retrospectively collected and the superior constrictor, middle constrictor,
inferior constrictor, and larynx were analyzed for deep-learning versus
multi-atlas-based segmentation. Eighty-three computed tomography images (40
diagnostic computed tomography and 43 planning computed tomography) were used
for training the convolutional neural network, and for atlas-based model
training. The remaining 22 computed tomography datasets were used for validation
of the atlas-based auto-segmentation versus deep-learning-based
auto-segmentation contours, both of which were compared with the corresponding
manual contours. Quantitative measures included Dice similarity coefficient,
recall, precision, Hausdorff distance, 95th percentile of Hausdorff distance,
and mean surface distance. Dosimetric differences between the auto-generated
contours and manual contours were evaluated. Subjective evaluation was obtained
from 3 clinical observers to blindly score the autosegmented structures based on
the percentage of slices that require manual modification. Results:
The deep-learning-based auto-segmentation versus atlas-based auto-segmentation
results were compared for the superior constrictor, middle constrictor, inferior
constrictor, and larynx. The mean Dice similarity coefficient values for the 4
structures were 0.67, 0.60, 0.65, and 0.84 for deep-learning-based
auto-segmentation, whereas atlas-based auto-segmentation has Dice similarity
coefficient results at 0.45, 0.36, 0.50, and 0.70, respectively. The mean 95th
percentile of Hausdorff distance (cm) for the 4 structures were 0.41, 0.57,
0.59, and 0.54 for deep-learning-based auto-segmentation, but 0.78, 0.95, 0.96,
and 1.23 for atlas-based auto-segmentation results, respectively. Similar mean
dose differences were obtained from the 2 sets of autosegmented contours
compared to manual contours. The dose–volume discrepancies and the average
modification rates were higher with the atlas-based auto-segmentation contours.
Conclusion: Swallowing-related structures are more accurately
generated with DL-based versus atlas-based segmentation when compared with
manual contours.
Collapse
Affiliation(s)
- Yimin Li
- Department of Radiation Oncology, Xiamen Radiotherapy Quality
Control Center, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology,
The First Affiliated Hospital of Xiamen University, The Third Clinical Medical
College, Fujian Medical University, Xiamen, Fujian, China
- Department of Radiation Oncology, University of California Davis Medical
Center, Sacramento, CA, USA
| | - Shyam Rao
- Department of Radiation Oncology, University of California Davis Medical
Center, Sacramento, CA, USA
| | - Wen Chen
- Department of Radiation Oncology, Xiangya Hospital, Central South
University, Changsha, China
| | - Soheila F. Azghadi
- Department of Radiation Oncology, University of California Davis Medical
Center, Sacramento, CA, USA
| | - Ky Nam Bao Nguyen
- Department of Radiation Oncology, University of California Davis Medical
Center, Sacramento, CA, USA
| | - Angel Moran
- Department of Radiation Oncology, University of California Davis Medical
Center, Sacramento, CA, USA
| | - Brittni M Usera
- Department of Radiation Oncology, University of California Davis Medical
Center, Sacramento, CA, USA
| | - Brandon A Dyer
- Department of Radiation Oncology, Legacy Health, Portland, OR, USA
| | - Lu Shang
- Department of Radiation Oncology, University of California Davis Medical
Center, Sacramento, CA, USA
| | - Quan Chen
- Department of Radiation Oncology, City of Hope comprehensive Cancer
Center, Duarte, CA, USA
- Quan Chen, PhD, Department of Radiation
Oncology, City of Hope comprehensive cancer center, Duarte, CA 91010..
| | - Yi Rong
- Department of Radiation Oncology, University of California Davis Medical
Center, Sacramento, CA, USA
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA
- Yi Rong, PhD, Department of Radiation
Oncology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ 85054, USA.
| |
Collapse
|
14
|
Arends SR, Savenije MH, Eppinga WS, van der Velden JM, van den Berg CA, Verhoeff JJ. Clinical utility of convolutional neural networks for treatment planning in radiotherapy for spinal metastases. Phys Imaging Radiat Oncol 2022; 21:42-47. [PMID: 35243030 PMCID: PMC8857663 DOI: 10.1016/j.phro.2022.02.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 01/08/2022] [Accepted: 02/11/2022] [Indexed: 01/22/2023] Open
Abstract
We presented a CNN workflow for segmentation and labeling of vertebrae on CT. This approach proved to be robust in a majority of cases with spinal metastases. The presented workflow can save time in a clinical radiotherapy setting. The approach also allows for more advanced quantitative image analysis of vertebrae.
Background and purpose Spine delineation is essential for high quality radiotherapy treatment planning of spinal metastases. However, manual delineation is time-consuming and prone to interobserver variability. Automatic spine delineation, especially using deep learning, has shown promising results in healthy subjects. We aimed to evaluate the clinical utility of deep learning-based vertebral body delineations for radiotherapy planning purposes. Materials and methods A multi-scale convolutional neural network (CNN) was used for automatic segmentation and labeling. Two approaches were tested: the combined approach using one CNN for both segmentation and labeling, and the sequential approach using separate CNN’s for these tasks. Training and internal validation data included 580 vertebrae, external validation data included 202 vertebrae. For quantitative assessment, Dice similarity coefficient (DSC) and Hausdorff distance (HD) were used. Axial slices from external images were presented to radiation oncologists for subjective evaluation. Results Both approaches performed comparably during the internal validation (DSC: 96.7%, HD: 3.6 mm), but the sequential approach proved more robust during the external validation (DSC: 94.5% vs 94.4%, p < 0.001, HD: 4.5 vs 7.1 mm, p < 0.001). Subsequently, subjective evaluation of this sequential approach showed that experienced radiation oncologists could distinguish automatic from human-made contours in 63% of cases. They rated automatic contours clinically acceptable in 77% of cases, compared to 88% of human-made contours. Conclusion We present a feasible approach for automatic vertebral body delineation using two variants of a multi-scale CNN. This approach generates high quality automatic delineations, which can save time in a clinical radiotherapy workflow.
Collapse
|
15
|
Casà C, Piras A, D’Aviero A, Preziosi F, Mariani S, Cusumano D, Romano A, Boskoski I, Lenkowicz J, Dinapoli N, Cellini F, Gambacorta MA, Valentini V, Mattiucci GC, Boldrini L. The impact of radiomics in diagnosis and staging of pancreatic cancer. Ther Adv Gastrointest Endosc 2022; 15:26317745221081596. [PMID: 35342883 PMCID: PMC8943316 DOI: 10.1177/26317745221081596] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 02/02/2022] [Indexed: 02/05/2023] Open
Abstract
INTRODUCTION Pancreatic cancer (PC) is one of the most aggressive tumours, and better risk stratification among patients is required to provide tailored treatment. The meaning of radiomics and texture analysis as predictive techniques are not already systematically assessed. The aim of this study is to assess the role of radiomics in PC. METHODS A PubMed/MEDLINE and Embase systematic review was conducted to assess the role of radiomics in PC. The search strategy was 'radiomics [All Fields] AND ("pancreas" [MeSH Terms] OR "pancreas" [All Fields] OR "pancreatic" [All Fields])' and only original articles referred to PC in humans in the English language were considered. RESULTS A total of 123 studies and 183 studies were obtained using the mentioned search strategy on PubMed and Embase, respectively. After the complete selection process, a total of 56 papers were considered eligible for the analysis of the results. Radiomics methods were applied in PC for assessment technical feasibility and reproducibility aspects analysis, risk stratification, biologic or genomic status prediction and treatment response prediction. DISCUSSION Radiomics seems to be a promising approach to evaluate PC from diagnosis to treatment response prediction. Further and larger studies are required to confirm the role and allowed to include radiomics parameter in a comprehensive decision support system.
Collapse
Affiliation(s)
- Calogero Casà
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | | | | | - Francesco Preziosi
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Silvia Mariani
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Davide Cusumano
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Angela Romano
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Ivo Boskoski
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCSS, Rome, Italy
| | - Jacopo Lenkowicz
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Nicola Dinapoli
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Francesco Cellini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Maria Antonietta Gambacorta
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Vincenzo Valentini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Gian Carlo Mattiucci
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Luca Boldrini
- UOC Radioterapia Oncologica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| |
Collapse
|
16
|
Chen W, Wang C, Zhan W, Jia Y, Ruan F, Qiu L, Yang S, Li Y. A comparative study of auto-contouring softwares in delineation of organs at risk in lung cancer and rectal cancer. Sci Rep 2021; 11:23002. [PMID: 34836989 PMCID: PMC8626498 DOI: 10.1038/s41598-021-02330-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 11/11/2021] [Indexed: 11/09/2022] Open
Abstract
Radiotherapy requires the target area and the organs at risk to be contoured on the CT image of the patient. During the process of organs-at-Risk (OAR) of the chest and abdomen, the doctor needs to contour at each CT image. The delineations of large and varied shapes are time-consuming and laborious. This study aims to evaluate the results of two automatic contouring softwares on OARs definition of CT images of lung cancer and rectal cancer patients. The CT images of 15 patients with rectal cancer and 15 patients with lung cancer were selected separately, and the organs at risk were manually contoured by experienced physicians as reference structures. And then the same datasets were automatically contoured based on AiContour (version 3.1.8.0, Manufactured by Linking MED, Beijing, China) and Raystation (version 4.7.5.4, Manufactured by Raysearch, Stockholm, Sweden) respectively. Deep learning auto-segmentations and Atlas were respectively performed with AiContour and Raystation. Overlap index (OI), Dice similarity index (DSC) and Volume difference (Dv) were evaluated based on the auto-contours, and independent-sample t-test analysis is applied to the results. The results of deep learning auto-segmentations on OI and DSC were better than that of Atlas with statistical difference. There was no significant difference in Dv between the results of two software. With deep learning auto-segmentations, auto-contouring results of most organs in the chest and abdomen are good, and with slight modification, it can meet the clinical requirements for planning. With Atlas, auto-contouring results in most OAR is not as good as deep learning auto-segmentations, and only the auto-contouring results of some organs can be used clinically after modification.
Collapse
Affiliation(s)
- Weijun Chen
- Department of Radiation Therapy, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, People's Republic of China
| | - Cheng Wang
- Department of Nuclear Science and Technology, University of South China, Hengyang, 421001, Hunan, People's Republic of China
| | - Wenming Zhan
- Department of Radiation Therapy, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, People's Republic of China
| | - Yongshi Jia
- Department of Radiation Therapy, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, People's Republic of China
| | - Fangfang Ruan
- Department of Radiation Therapy, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, People's Republic of China
| | - Lingyun Qiu
- Department of Radiation Therapy, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, People's Republic of China
| | - Shuangyan Yang
- Department of Radiation Therapy, Shanghai Pulmonary Hospital, Shanghai, 200433, People's Republic of China
| | - Yucheng Li
- Department of Radiation Therapy, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, People's Republic of China.
| |
Collapse
|
17
|
Sherer MV, Lin D, Elguindi S, Duke S, Tan LT, Cacicedo J, Dahele M, Gillespie EF. Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review. Radiother Oncol 2021; 160:185-191. [PMID: 33984348 PMCID: PMC9444281 DOI: 10.1016/j.radonc.2021.05.003] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 05/01/2021] [Accepted: 05/03/2021] [Indexed: 12/18/2022]
Abstract
Advances in artificial intelligence-based methods have led to the development and publication of numerous systems for auto-segmentation in radiotherapy. These systems have the potential to decrease contour variability, which has been associated with poor clinical outcomes and increased efficiency in the treatment planning workflow. However, there are no uniform standards for evaluating auto-segmentation platforms to assess their efficacy at meeting these goals. Here, we review the most frequently used evaluation techniques which include geometric overlap, dosimetric parameters, time spent contouring, and clinical rating scales. These data suggest that many of the most commonly used geometric indices, such as the Dice Similarity Coefficient, are not well correlated with clinically meaningful endpoints. As such, a multi-domain evaluation, including composite geometric and/or dosimetric metrics with physician-reported assessment, is necessary to gauge the clinical readiness of auto-segmentation for radiation treatment planning.
Collapse
Affiliation(s)
- Michael V Sherer
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, United States
| | - Diana Lin
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Sharif Elguindi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Simon Duke
- Department of Oncology, Cambridge University Hospitals, United Kingdom
| | - Li-Tee Tan
- Department of Oncology, Cambridge University Hospitals, United Kingdom
| | - Jon Cacicedo
- Department of Radiation Oncology, Cruces University Hospital/BioCruces Health Research Institute, Osakidetza, Barakaldo, Spain
| | - Max Dahele
- Department of Radiation Oncology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Erin F Gillespie
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, United States.
| |
Collapse
|
18
|
Cusumano D, Boldrini L, Dhont J, Fiorino C, Green O, Güngör G, Jornet N, Klüter S, Landry G, Mattiucci GC, Placidi L, Reynaert N, Ruggieri R, Tanadini-Lang S, Thorwarth D, Yadav P, Yang Y, Valentini V, Verellen D, Indovina L. Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives. Phys Med 2021; 85:175-191. [PMID: 34022660 DOI: 10.1016/j.ejmp.2021.05.010] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/15/2021] [Accepted: 05/04/2021] [Indexed: 12/14/2022] Open
Abstract
Over the last years, technological innovation in Radiotherapy (RT) led to the introduction of Magnetic Resonance-guided RT (MRgRT) systems. Due to the higher soft tissue contrast compared to on-board CT-based systems, MRgRT is expected to significantly improve the treatment in many situations. MRgRT systems may extend the management of inter- and intra-fraction anatomical changes, offering the possibility of online adaptation of the dose distribution according to daily patient anatomy and to directly monitor tumor motion during treatment delivery by means of a continuous cine MR acquisition. Online adaptive treatments require a multidisciplinary and well-trained team, able to perform a series of operations in a safe, precise and fast manner while the patient is waiting on the treatment couch. Artificial Intelligence (AI) is expected to rapidly contribute to MRgRT, primarily by safely and efficiently automatising the various manual operations characterizing online adaptive treatments. Furthermore, AI is finding relevant applications in MRgRT in the fields of image segmentation, synthetic CT reconstruction, automatic (on-line) planning and the development of predictive models based on daily MRI. This review provides a comprehensive overview of the current AI integration in MRgRT from a medical physicist's perspective. Medical physicists are expected to be major actors in solving new tasks and in taking new responsibilities: their traditional role of guardians of the new technology implementation will change with increasing emphasis on the managing of AI tools, processes and advanced systems for imaging and data analysis, gradually replacing many repetitive manual tasks.
Collapse
Affiliation(s)
- Davide Cusumano
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | | | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milan, Italy
| | - Olga Green
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Görkem Güngör
- Acıbadem MAA University, School of Medicine, Department of Radiation Oncology, Maslak Istanbul, Turkey
| | - Núria Jornet
- Servei de Radiofísica i Radioprotecció, Hospital de la Santa Creu i Sant Pau, Spain
| | - Sebastian Klüter
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Munich, Germany
| | | | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy.
| | - Nick Reynaert
- Department of Medical Physics, Institut Jules Bordet, Belgium
| | - Ruggero Ruggieri
- Dipartimento di Radioterapia Oncologica Avanzata, IRCCS "Sacro cuore - don Calabria", Negrar di Valpolicella (VR), Italy
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tüebingen, Tübingen, Germany
| | - Poonam Yadav
- Department of Human Oncology School of Medicine and Public Heath University of Wisconsin - Madison, USA
| | - Yingli Yang
- Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, USA
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Dirk Verellen
- Department of Medical Physics, Iridium Cancer Network, Belgium; Faculty of Medicine and Health Sciences, Antwerp University, Antwerp, Belgium
| | - Luca Indovina
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| |
Collapse
|
19
|
Artificial intelligence in oncology. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00018-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
20
|
Artificial intelligence (AI) and interventional radiotherapy (brachytherapy): state of art and future perspectives. J Contemp Brachytherapy 2020; 12:497-500. [PMID: 33299440 PMCID: PMC7701925 DOI: 10.5114/jcb.2020.100384] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 07/16/2020] [Indexed: 11/17/2022] Open
Abstract
Purpose Artificial intelligence (AI) plays a central role in building decision supporting systems (DSS), and its application in healthcare is rapidly increasing. The aim of this study was to define the role of AI in healthcare, with main focus on radiation oncology (RO) and interventional radiotherapy (IRT, brachytherapy). Artificial intelligence in interventional radiation therapy AI in RO has a large impact in providing clinical decision support, data mining and advanced imaging analysis, automating repetitive tasks, optimizing time, and modelling patients and physicians' behaviors in heterogeneous contexts. Implementing AI and automation in RO and IRT can successfully facilitate all the steps of treatment workflow, such as patient consultation, target volume delineation, treatment planning, and treatment delivery. Conclusions AI may contribute to improve clinical outcomes through the application of predictive models and DSS optimization. This approach could lead to reducing time-consuming repetitive tasks, healthcare costs, and improving treatment quality assurance and patient's assistance in IRT.
Collapse
|
21
|
Brouwer CL, Boukerroui D, Oliveira J, Looney P, Steenbakkers RJ, Langendijk JA, Both S, Gooding MJ. Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy. Phys Imaging Radiat Oncol 2020; 16:54-60. [PMID: 33458344 PMCID: PMC7807591 DOI: 10.1016/j.phro.2020.10.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 09/30/2020] [Accepted: 10/01/2020] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND AND PURPOSE Auto-contouring performance has been widely studied in development and commissioning studies in radiotherapy, and its impact on clinical workflow assessed in that context. This study aimed to evaluate the manual adjustment of auto-contouring in routine clinical practice and to identify improvements regarding the auto-contouring model and clinical user interaction, to improve the efficiency of auto-contouring. MATERIALS AND METHODS A total of 103 clinical head and neck cancer cases, contoured using a commercial deep-learning contouring system and subsequently checked and edited for clinical use were retrospectively taken from clinical data over a twelve-month period (April 2019-April 2020). The amount of adjustment performed was calculated, and all cases were registered to a common reference frame for assessment purposes. The median, 10th and 90th percentile of adjustment were calculated and displayed using 3D renderings of structures to visually assess systematic and random adjustment. Results were also compared to inter-observer variation reported previously. Assessment was performed for both the whole structures and for regional sub-structures, and according to the radiation therapy technologist (RTT) who edited the contour. RESULTS The median amount of adjustment was low for all structures (<2 mm), although large local adjustment was observed for some structures. The median was systematically greater or equal to zero, indicating that the auto-contouring tends to under-segment the desired contour. CONCLUSION Auto-contouring performance assessment in routine clinical practice has identified systematic improvements required technically, but also highlighted the need for continued RTT training to ensure adherence to guidelines.
Collapse
Affiliation(s)
- Charlotte L. Brouwer
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
| | | | | | | | - Roel J.H.M. Steenbakkers
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
| | - Johannes A. Langendijk
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
| | - Stefan Both
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
| | | |
Collapse
|
22
|
Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance. Radiother Oncol 2020; 153:55-66. [PMID: 32920005 DOI: 10.1016/j.radonc.2020.09.008] [Citation(s) in RCA: 136] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 09/02/2020] [Accepted: 09/03/2020] [Indexed: 02/06/2023]
Abstract
Artificial Intelligence (AI) is currently being introduced into different domains, including medicine. Specifically in radiation oncology, machine learning models allow automation and optimization of the workflow. A lack of knowledge and interpretation of these AI models can hold back wide-spread and full deployment into clinical practice. To facilitate the integration of AI models in the radiotherapy workflow, generally applicable recommendations on implementation and quality assurance (QA) of AI models are presented. For commonly used applications in radiotherapy such as auto-segmentation, automated treatment planning and synthetic computed tomography (sCT) the basic concepts are discussed in depth. Emphasis is put on the commissioning, implementation and case-specific and routine QA of AI models needed for a methodical introduction in clinical practice.
Collapse
|
23
|
Brunenberg EJ, Steinseifer IK, van den Bosch S, Kaanders JH, Brouwer CL, Gooding MJ, van Elmpt W, Monshouwer R. External validation of deep learning-based contouring of head and neck organs at risk. Phys Imaging Radiat Oncol 2020; 15:8-15. [PMID: 33458320 PMCID: PMC7807543 DOI: 10.1016/j.phro.2020.06.006] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 05/29/2020] [Accepted: 06/27/2020] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND AND PURPOSE Head and neck (HN) radiotherapy can benefit from automatic delineation of tumor and surrounding organs because of the complex anatomy and the regular need for adaptation. The aim of this study was to assess the performance of a commercially available deep learning contouring (DLC) model on an external validation set. MATERIALS AND METHODS The CT-based DLC model, trained at the University Medical Center Groningen (UMCG), was applied to an independent set of 58 patients from the Radboud University Medical Center (RUMC). DLC results were compared to the RUMC manual reference using the Dice similarity coefficient (DSC) and 95th percentile of Hausdorff distance (HD95). Craniocaudal spatial information was added by calculating binned measures. In addition, a qualitative evaluation compared the acceptance of manual and DLC contours in both groups of observers. RESULTS Good correspondence was shown for the mandible (DSC 0.90; HD95 3.6 mm). Performance was reasonable for the glandular OARs, brainstem and oral cavity (DSC 0.78-0.85, HD95 3.7-7.3 mm). The other aerodigestive tract OARs showed only moderate agreement (DSC 0.53-0.65, HD95 around 9 mm). The binned measures displayed the largest deviations caudally and/or cranially. CONCLUSIONS This study demonstrates that the DLC model can provide a reasonable starting point for delineation when applied to an independent patient cohort. The qualitative evaluation did not reveal large differences in the interpretation of contouring guidelines between RUMC and UMCG observers.
Collapse
Affiliation(s)
- Ellen J.L. Brunenberg
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Isabell K. Steinseifer
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Sven van den Bosch
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Charlotte L. Brouwer
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - René Monshouwer
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| |
Collapse
|
24
|
Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring. Radiother Oncol 2020; 142:115-123. [DOI: 10.1016/j.radonc.2019.09.022] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 09/09/2019] [Accepted: 09/24/2019] [Indexed: 11/17/2022]
|
25
|
Hu Y, Byrne M, Archibald‐Heeren B, Thompson K, Fong A, Knesl M, Teh A, Tiong E, Foster R, Melnyk P, Burr M, Thompson A, Lim J, Moore L, Gordon F, Humble R, Hardy A, Williams S. Implementing user-defined atlas-based auto-segmentation for a large multi-centre organisation: the Australian Experience. J Med Radiat Sci 2019; 66:238-249. [PMID: 31657129 PMCID: PMC6920682 DOI: 10.1002/jmrs.359] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 09/09/2019] [Accepted: 09/11/2019] [Indexed: 11/21/2022] Open
Abstract
INTRODUCTION Contouring has become an increasingly important aspect of radiation therapy due to inverse planning, and yet is extremely time-consuming. To improve contouring efficiency and reduce potential inter-observer variation, the atlas-based auto-segmentation (ABAS) function in Velocity was introduced to ICON cancer centres (ICC) throughout Australia as a solution for automatic contouring. METHODS This paper described the implementation process of the ABAS function and the construction of user-defined atlas sets and compared the contouring efficiency before and after the introduction of ABAS. RESULTS The results indicate that the main limitation to the ABAS performance was Velocity's sub-optimal atlas selection method. Three user-defined atlas sets were constructed. Results suggested that the introduction of the ABAS saved at least 5 minutes of manual contouring time (P < 0.05), although further verification was required due to limitations in the data collection method. The pilot rollout adopting a 'champion' approach was successful and provided an opportunity to improve the user-defined atlases prior to the national implementation. CONCLUSION The implementation of user-defined ABAS for head and neck (H&N) and female thorax patients at ICCs was successful, which achieved at least 5 minutes of efficiency gain.
Collapse
Affiliation(s)
- Yunfei Hu
- ICON Cancer Centre GosfordGosfordNew South WalesAustralia
- Centre for Radiation Medical PhysicsUniversity of WollongongWollongongNew South WalesAustralia
| | - Mikel Byrne
- ICON Cancer Centre WahroongaSydney Adventist HospitalWahroongaNew South WalesAustralia
| | - Ben Archibald‐Heeren
- Centre for Radiation Medical PhysicsUniversity of WollongongWollongongNew South WalesAustralia
- ICON Cancer Centre WahroongaSydney Adventist HospitalWahroongaNew South WalesAustralia
| | | | - Andrew Fong
- ICON Cancer Centre WahroongaSydney Adventist HospitalWahroongaNew South WalesAustralia
| | - Marcel Knesl
- ICON Cancer Centre MaroochydoreMaroochydoreQueenslandAustralia
| | - Amy Teh
- ICON Cancer Centre GosfordGosfordNew South WalesAustralia
- ICON Cancer Centre WahroongaSydney Adventist HospitalWahroongaNew South WalesAustralia
- Sydney Adventist Hospital Clinical SchoolSydney Medical SchoolUniversity of SydneySydneyNew South WalesAustralia
| | - Eve Tiong
- ICON Cancer Centre MidlandMidlandWestern AustraliaAustralia
| | | | - Paul Melnyk
- ICON Cancer Centre GosfordGosfordNew South WalesAustralia
| | - Michelle Burr
- ICON Cancer Centre WahroongaSydney Adventist HospitalWahroongaNew South WalesAustralia
| | - Amelia Thompson
- ICON Cancer Centre WahroongaSydney Adventist HospitalWahroongaNew South WalesAustralia
| | - Jiy Lim
- ICON Cancer Centre WahroongaSydney Adventist HospitalWahroongaNew South WalesAustralia
| | - Luke Moore
- ICON Cancer Centre WahroongaSydney Adventist HospitalWahroongaNew South WalesAustralia
| | - Fiona Gordon
- ICON Cancer Centre WahroongaSydney Adventist HospitalWahroongaNew South WalesAustralia
| | - Rylie Humble
- ICON Cancer Centre CairnsLiz Plummer Cancer Care CentreCairnsQueenslandAustralia
| | - Anna Hardy
- ICON Cancer Centre SpringfieldLevel 1, Cancer Care CentreMater Private HospitalSpringfieldQueenslandAustralia
| | - Saul Williams
- ICON Cancer Centre RockinghamRockinghamWestern AustraliaAustralia
| |
Collapse
|
26
|
Frederick A, Roumeliotis M, Grendarova P, Craighead P, Abedin T, Watt E, Olivotto IA, Meyer T, Quirk S. A Framework for Clinical Validation of Automatic Contour Propagation: Standardizing Geometric and Dosimetric Evaluation. Pract Radiat Oncol 2019; 9:448-455. [DOI: 10.1016/j.prro.2019.06.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 06/11/2019] [Accepted: 06/25/2019] [Indexed: 10/26/2022]
|
27
|
Boldrini L, Bibault JE, Masciocchi C, Shen Y, Bittner MI. Deep Learning: A Review for the Radiation Oncologist. Front Oncol 2019; 9:977. [PMID: 31632910 PMCID: PMC6779810 DOI: 10.3389/fonc.2019.00977] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Accepted: 09/13/2019] [Indexed: 12/15/2022] Open
Abstract
Introduction: Deep Learning (DL) is a machine learning technique that uses deep neural networks to create a model. The application areas of deep learning in radiation oncology include image segmentation and detection, image phenotyping, and radiomic signature discovery, clinical outcome prediction, image dose quantification, dose-response modeling, radiation adaptation, and image generation. In this review, we explain the methods used in DL and perform a literature review using the Medline database to identify studies using deep learning in radiation oncology. The search was conducted in April 2018, and identified studies published between 1997 and 2018, strongly skewed toward 2015 and later. Methods: A literature review was performed using PubMed/Medline in order to identify important recent publications to be synthesized into a review of the current status of Deep Learning in radiation oncology, directed at a clinically-oriented reader. The search strategy included the search terms "radiotherapy" and "deep learning." In addition, reference lists of selected articles were hand searched for further potential hits of relevance to this review. The search was conducted in April 2018, and identified studies published between 1997 and 2018, strongly skewed toward 2015 and later. Results: Studies using DL for image segmentation were identified in Brain (n = 2), Head and Neck (n = 3), Lung (n = 6), Abdominal (n = 2), and Pelvic (n = 6) cancers. Use of Deep Learning has also been reported for outcome prediction, such as toxicity modeling (n = 3), treatment response and survival (n = 2), or treatment planning (n = 5). Conclusion: Over the past few years, there has been a significant number of studies assessing the performance of DL techniques in radiation oncology. They demonstrate how DL-based systems can aid clinicians in their daily work, be it by reducing the time required for or the variability in segmentation, or by helping to predict treatment outcomes and toxicities. It still remains to be seen when these techniques will be employed in routine clinical practice.
Collapse
Affiliation(s)
- Luca Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Jean-Emmanuel Bibault
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique—Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France
| | - Carlotta Masciocchi
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Yanting Shen
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Martin-Immanuel Bittner
- CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
28
|
Kosmin M, Ledsam J, Romera-Paredes B, Mendes R, Moinuddin S, de Souza D, Gunn L, Kelly C, Hughes C, Karthikesalingam A, Nutting C, Sharma R. Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer. Radiother Oncol 2019; 135:130-140. [DOI: 10.1016/j.radonc.2019.03.004] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 02/10/2019] [Accepted: 03/04/2019] [Indexed: 11/25/2022]
|
29
|
Finnegan R, Dowling J, Koh ES, Tang S, Otton J, Delaney G, Batumalai V, Luo C, Atluri P, Satchithanandha A, Thwaites D, Holloway L. Feasibility of multi-atlas cardiac segmentation from thoracic planning CT in a probabilistic framework. Phys Med Biol 2019; 64:085006. [PMID: 30856618 DOI: 10.1088/1361-6560/ab0ea6] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Toxicity to cardiac and coronary structures is an important late morbidity for patients undergoing left-sided breast radiotherapy. Many current studies have relied on estimates of cardiac doses assuming standardised anatomy, with a calculated increase in relative risk of 7.4% per Gy (mean heart dose). To provide individualised estimates for dose, delineation of various cardiac structures on patient images is required. Automatic multi-atlas based segmentation can provide a consistent, robust solution, however there are challenges to this method. We are aiming to develop and validate a cardiac atlas and segmentation framework, with a focus on the limitations and uncertainties in the process. We present a probabilistic approach to segmentation, which provides a simple method to incorporate inter-observer variation, as well as a useful tool for evaluating the accuracy and sources of error in segmentation. A dataset consisting of 20 planning computed tomography (CT) images of Australian breast cancer patients with delineations of 17 structures (including whole heart, four chambers, coronary arteries and valves) was manually contoured by three independent observers, following a protocol based on a published reference atlas, with verification by a cardiologist. To develop and validate the segmentation framework a leave-one-out cross-validation strategy was implemented. Performance of the automatic segmentations was evaluated relative to inter-observer variability in manually-derived contours; measures of volume and surface accuracy (Dice similarity coefficient (DSC) and mean absolute surface distance (MASD), respectively) were used to compare automatic segmentation to the consensus segmentation from manual contours. For the whole heart, the resulting segmentation achieved a DSC of [Formula: see text], with a MASD of [Formula: see text] mm. Quantitative results, together with the analysis of probabilistic labelling, indicate the feasibility of accurate and consistent segmentation of larger structures, whereas this is not the case for many smaller structures, where a major limitation in segmentation accuracy is the inter-observer variability in manual contouring.
Collapse
Affiliation(s)
- Robert Finnegan
- School of Physics, Institute of Medical Physics, University of Sydney, Sydney, Australia. Ingham Institute for Applied Medical Research, Liverpool, Australia. Author to whom all correspondence should be addressed
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
30
|
Lim TY, Gillespie E, Murphy J, Moore KL. Clinically Oriented Contour Evaluation Using Dosimetric Indices Generated From Automated Knowledge-Based Planning. Int J Radiat Oncol Biol Phys 2019; 103:1251-1260. [DOI: 10.1016/j.ijrobp.2018.11.048] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 11/16/2018] [Accepted: 11/21/2018] [Indexed: 12/09/2022]
|
31
|
Mercieca S, Belderbos JSA, van Baardwijk A, Delorme S, van Herk M. The impact of training and professional collaboration on the interobserver variation of lung cancer delineations: a multi-institutional study. Acta Oncol 2019; 58:200-208. [PMID: 30375905 DOI: 10.1080/0284186x.2018.1529422] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
BACKGROUND To assess the impact of training and interprofessional collaboration on the interobserver variation in the delineation of the lung gross tumor volume (GTVp) and lymph node (GTVln). MATERIAL AND METHODS Eight target volume delineations courses were organized between 2008 and 2013. Specialists and trainees in radiation oncology were asked to delineate the GTVp and GTVln on four representative CT images of a patient diagnosed with lung cancer individually prior each course (baseline), together as group (interprofessional collaboration) and post-training. The mean delineated volume and local standard deviation (local SD) between the contours for each course group were calculated and compared with the expert delineations. RESULTS A total 410 delineations were evaluated. The average local SD was lowest for the interprofessional collaboration (GTVp = 0.194 cm, GTVln = 0.371 cm) followed by the post-training (GTVp = 0.244 cm, GTVln = 0.607 cm) and baseline delineations (GTVp = 0.274 cm, GTVln: 0.718 cm). The mean delineated volume was smallest for the interprofessional (GTVp = 4.93 cm3, GTVln = 4.34 cm3) followed by the post-training (GTVp = 5.68 cm3, GTVln = 5.47 cm3) and baseline delineations (GTVp = 6.65 cm3, GTVln = 6.93 cm3). All delineations were larger than the expert for both GTVp and GTVln (p < .001). CONCLUSION Our findings indicate that image interpretational differences can lead to large interobserver variation particularly when delineating the GTVln. Interprofessional collaboration was found to have the greatest impact on reducing interobserver variation in the delineation of the GTVln. This highlights the need to develop a clinical workflow so as to ensure that difficult cases are reviewed routinely by a second radiation oncologist or radiologist so as to minimize the risk of geographical tumor miss and unnecessary irradiation to normal tissue.
Collapse
Affiliation(s)
- Susan Mercieca
- Faculty of Health Science, University of Malta. Msida, Malta
- Academisch Medisch Centrum Geneeskunde Amsterdam, Noord-Holland, The Netherlands
| | - José S. A. Belderbos
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Angela van Baardwijk
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Stefan Delorme
- German Cancer Research Center (Dkfz), Department of Radiology, Heidelberg, Germany
| | - Marcel van Herk
- Manchester Academic Health Centre, University of Manchester, The Christie NHS Foundation Trust, Manchester, UK
| |
Collapse
|
32
|
Boon IS, Au Yong TPT, Boon CS. Assessing the Role of Artificial Intelligence (AI) in Clinical Oncology: Utility of Machine Learning in Radiotherapy Target Volume Delineation. MEDICINES (BASEL, SWITZERLAND) 2018; 5:E131. [PMID: 30544901 PMCID: PMC6313566 DOI: 10.3390/medicines5040131] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 12/04/2018] [Accepted: 12/07/2018] [Indexed: 12/16/2022]
Abstract
The fields of radiotherapy and clinical oncology have been rapidly changed by the advances of technology. Improvement in computer processing power and imaging quality heralded precision radiotherapy allowing radiotherapy to be delivered efficiently, safely and effectively for patient benefit. Artificial intelligence (AI) is an emerging field of computer science which uses computer models and algorithms to replicate human-like intelligence and perform specific tasks which offers a huge potential to healthcare. We reviewed and presented the history, evolution and advancement in the fields of radiotherapy, clinical oncology and machine learning. Radiotherapy target delineation is a complex task of outlining tumour and organ at risks volumes to allow accurate delivery of radiotherapy. We discussed the radiotherapy planning, treatment delivery and reviewed how technology can help with this challenging process. We explored the evidence and clinical application of machine learning to radiotherapy. We concluded on the challenges, possible future directions and potential collaborations to achieve better outcome for cancer patients.
Collapse
Affiliation(s)
- Ian S Boon
- Department of Clinical Oncology, Leeds Cancer Centre, St James's Institute of Oncology, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK.
| | - Tracy P T Au Yong
- Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester WR5 1DD, UK.
| | - Cheng S Boon
- Worcestershire Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester WR5 1DD, UK.
| |
Collapse
|
33
|
Gooding MJ, Smith AJ, Tariq M, Aljabar P, Peressutti D, van der Stoep J, Reymen B, Emans D, Hattu D, van Loon J, de Rooy M, Wanders R, Peeters S, Lustberg T, van Soest J, Dekker A, van Elmpt W. Comparative evaluation of autocontouring in clinical practice: A practical method using the Turing test. Med Phys 2018; 45:5105-5115. [PMID: 30229951 DOI: 10.1002/mp.13200] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 09/10/2018] [Accepted: 09/10/2018] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Automated techniques for estimating the contours of organs and structures in medical images have become more widespread and a variety of measures are available for assessing their quality. Quantitative measures of geometric agreement, for example, overlap with a gold-standard delineation, are popular but may not predict the level of clinical acceptance for the contouring method. Therefore, surrogate measures that relate more directly to the clinical judgment of contours, and to the way they are used in routine workflows, need to be developed. The purpose of this study is to propose a method (inspired by the Turing Test) for providing contour quality measures that directly draw upon practitioners' assessments of manual and automatic contours. This approach assumes that an inability to distinguish automatically produced contours from those of clinical experts would indicate that the contours are of sufficient quality for clinical use. In turn, it is anticipated that such contours would receive less manual editing prior to being accepted for clinical use. In this study, an initial assessment of this approach is performed with radiation oncologists and therapists. METHODS Eight clinical observers were presented with thoracic organ-at-risk contours through a web interface and were asked to determine if they were automatically generated or manually delineated. The accuracy of the visual determination was assessed, and the proportion of contours for which the source was misclassified recorded. Contours of six different organs in a clinical workflow were for 20 patient cases. The time required to edit autocontours to a clinically acceptable standard was also measured, as a gold standard of clinical utility. Established quantitative measures of autocontouring performance, such as Dice similarity coefficient with respect to the original clinical contour and the misclassification rate accessed with the proposed framework, were evaluated as surrogates of the editing time measured. RESULTS The misclassification rates for each organ were: esophagus 30.0%, heart 22.9%, left lung 51.2%, right lung 58.5%, mediastinum envelope 43.9%, and spinal cord 46.8%. The time savings resulting from editing the autocontours compared to the standard clinical workflow were 12%, 25%, 43%, 77%, 46%, and 50%, respectively, for these organs. The median Dice similarity coefficients between the clinical contours and the autocontours were 0.46, 0.90, 0.98, 0.98, 0.94, and 0.86, respectively, for these organs. CONCLUSIONS A better correspondence with time saving was observed for the misclassification rate than the quantitative contour measures explored. From this, we conclude that the inability to accurately judge the source of a contour indicates a reduced need for editing and therefore a greater time saving overall. Hence, task-based assessments of contouring performance may be considered as an additional way of evaluating the clinical utility of autosegmentation methods.
Collapse
Affiliation(s)
- Mark J Gooding
- Mirada Medical Ltd, Oxford Centre for Innovation, New Road, Oxford, OX1 1BY, UK
| | - Annamarie J Smith
- Mirada Medical Ltd, Oxford Centre for Innovation, New Road, Oxford, OX1 1BY, UK
| | - Maira Tariq
- Mirada Medical Ltd, Oxford Centre for Innovation, New Road, Oxford, OX1 1BY, UK
| | - Paul Aljabar
- Mirada Medical Ltd, Oxford Centre for Innovation, New Road, Oxford, OX1 1BY, UK
| | - Devis Peressutti
- Mirada Medical Ltd, Oxford Centre for Innovation, New Road, Oxford, OX1 1BY, UK
| | - Judith van der Stoep
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Dr Tanslaan 12, 6229ET, Maastricht, The Netherlands
| | - Bart Reymen
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Dr Tanslaan 12, 6229ET, Maastricht, The Netherlands
| | - Daisy Emans
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Dr Tanslaan 12, 6229ET, Maastricht, The Netherlands
| | - Djoya Hattu
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Dr Tanslaan 12, 6229ET, Maastricht, The Netherlands
| | - Judith van Loon
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Dr Tanslaan 12, 6229ET, Maastricht, The Netherlands
| | - Maud de Rooy
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Dr Tanslaan 12, 6229ET, Maastricht, The Netherlands
| | - Rinus Wanders
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Dr Tanslaan 12, 6229ET, Maastricht, The Netherlands
| | - Stephanie Peeters
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Dr Tanslaan 12, 6229ET, Maastricht, The Netherlands
| | - Tim Lustberg
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Dr Tanslaan 12, 6229ET, Maastricht, The Netherlands
| | - Johan van Soest
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Dr Tanslaan 12, 6229ET, Maastricht, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Dr Tanslaan 12, 6229ET, Maastricht, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Dr Tanslaan 12, 6229ET, Maastricht, The Netherlands
| |
Collapse
|
34
|
Gambacorta MA, Boldrini L, Valentini C, Dinapoli N, Mattiucci GC, Chiloiro G, Pasini D, Manfrida S, Caria N, Minsky BD, Valentini V. Automatic segmentation software in locally advanced rectal cancer: READY (REsearch program in Auto Delineation sYstem)-RECTAL 02: prospective study. Oncotarget 2018; 7:42579-42584. [PMID: 27302924 PMCID: PMC5173157 DOI: 10.18632/oncotarget.9938] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Accepted: 05/17/2016] [Indexed: 11/25/2022] Open
Abstract
To validate autocontouring software (AS) in a clinical practice including a two steps delineation quality assurance (QA) procedure. The existing delineation agreement among experts for rectal cancer and the overlap and time criteria that have to be verified to allow the use of AS were defined. Median Dice Similarity Coefficient (MDSC), Mean slicewise Hausdorff Distances (MSHD) and Total-Time saving (TT) were analyzed. Two expert Radiation Oncologists reviewed CT-scans of 44 patients and agreed the reference-CTV: the first 14 consecutive cases were used to populate the software Atlas and 30 were used as Test. Each expert performed a manual (group A) and an automatic delineation (group B) of 15 Test patients. The delineations were compared with the reference contours. The overlap between the manual and automatic delineations with MDSC and MSHD and the TT were analyzed. Three acceptance criteria were set: MDSC ≥ 0.75, MSHD ≤1mm and TT sparing ≥ 50%. At least 2 criteria had to be met, one of which had to be TT saving, to validate the system. The MDSC was 0.75, MSHD 2.00 mm and the TT saving 55.5% between group A and group B. MDSC among experts was 0.84. Autosegmentation systems in rectal cancer partially met acceptability criteria with the present version.
Collapse
Affiliation(s)
- Maria A Gambacorta
- Department of Radiation Oncology, Sacred Heart Catholic University of Rome, Rome, Italy
| | - Luca Boldrini
- Department of Radiation Oncology, Sacred Heart Catholic University of Rome, Rome, Italy
| | - Chiara Valentini
- Department of Radiation Oncology, Sacred Heart Catholic University of Rome, Rome, Italy
| | - Nicola Dinapoli
- Department of Radiation Oncology, Sacred Heart Catholic University of Rome, Rome, Italy
| | - Gian C Mattiucci
- Department of Radiation Oncology, Sacred Heart Catholic University of Rome, Rome, Italy
| | - Giuditta Chiloiro
- Department of Radiation Oncology, Sacred Heart Catholic University of Rome, Rome, Italy
| | - Danilo Pasini
- Department of Radiation Oncology, Sacred Heart Catholic University of Rome, Rome, Italy
| | - Stefania Manfrida
- Department of Radiation Oncology, Sacred Heart Catholic University of Rome, Rome, Italy
| | - Nicola Caria
- Varian Medical Systems, Product Manager, Clinical Solutions, Palo Alto, CA, USA
| | - Bruce D Minsky
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Vincenzo Valentini
- Department of Radiation Oncology, Sacred Heart Catholic University of Rome, Rome, Italy
| |
Collapse
|
35
|
Yip E, Yun J, Gabos Z, Baker S, Yee D, Wachowicz K, Rathee S, Fallone BG. Evaluating performance of a user-trained MR lung tumor autocontouring algorithm in the context of intra- and interobserver variations. Med Phys 2017; 45:307-313. [DOI: 10.1002/mp.12687] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 10/17/2017] [Accepted: 11/10/2017] [Indexed: 12/25/2022] Open
Affiliation(s)
- Eugene Yip
- Department of Medical Physics; Cross Cancer Institute; 11560 University Avenue Edmonton AB T6G 1Z2 Canada
- Medical Physics Division; Department of Oncology; University of Alberta; 11560 University Avenue Edmonton AB T6G 1Z2 Canada
| | - Jihyun Yun
- Department of Medical Physics; Cross Cancer Institute; 11560 University Avenue Edmonton AB T6G 1Z2 Canada
- Medical Physics Division; Department of Oncology; University of Alberta; 11560 University Avenue Edmonton AB T6G 1Z2 Canada
| | - Zsolt Gabos
- Department of Radiation Oncology; Cross Cancer Institute; 11560 University Avenue Edmonton AB T6G 1Z2 Canada
- Radiation Oncology Division; Department of Oncology; University of Alberta; 11560 University Avenue Edmonton AB T6G 1Z2 Canada
| | - Sarah Baker
- Department of Radiation Oncology; Cross Cancer Institute; 11560 University Avenue Edmonton AB T6G 1Z2 Canada
- Radiation Oncology Division; Department of Oncology; University of Alberta; 11560 University Avenue Edmonton AB T6G 1Z2 Canada
| | - Don Yee
- Department of Radiation Oncology; Cross Cancer Institute; 11560 University Avenue Edmonton AB T6G 1Z2 Canada
- Radiation Oncology Division; Department of Oncology; University of Alberta; 11560 University Avenue Edmonton AB T6G 1Z2 Canada
| | - Keith Wachowicz
- Department of Medical Physics; Cross Cancer Institute; 11560 University Avenue Edmonton AB T6G 1Z2 Canada
- Medical Physics Division; Department of Oncology; University of Alberta; 11560 University Avenue Edmonton AB T6G 1Z2 Canada
| | - Satyapal Rathee
- Department of Medical Physics; Cross Cancer Institute; 11560 University Avenue Edmonton AB T6G 1Z2 Canada
- Medical Physics Division; Department of Oncology; University of Alberta; 11560 University Avenue Edmonton AB T6G 1Z2 Canada
| | - B. Gino Fallone
- Department of Medical Physics; Cross Cancer Institute; 11560 University Avenue Edmonton AB T6G 1Z2 Canada
- Medical Physics Division; Department of Oncology; University of Alberta; 11560 University Avenue Edmonton AB T6G 1Z2 Canada
| |
Collapse
|
36
|
Kristensen I, Nilsson K, Agrup M, Belfrage K, Embring A, Haugen H, Svärd AM, Knöös T, Nilsson P. A dose based approach for evaluation of inter-observer variations in target delineation. Tech Innov Patient Support Radiat Oncol 2017; 3-4:41-47. [PMID: 32095566 PMCID: PMC7033785 DOI: 10.1016/j.tipsro.2017.10.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Revised: 10/06/2017] [Accepted: 10/09/2017] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND AND PURPOSE Substantial inter-observer variations in target delineation have been presented previously. Target delineation for paediatric cases is difficult due to the small number of children, the variation in paediatric targets, the number of study protocols, and the individual patient's specific needs and demands. Uncertainties in target delineation might lead to under-dosage or over-dosage. The aim of this work is to apply the concept of a consensus volume and good quality treatment plans to visualise and quantify inter-observer target delineation variations in dosimetric terms in addition to conventional geometrically based volume concordance indices. MATERIAL AND METHODS Two paediatric cases were used to demonstrate the potential of adding dose metrics when evaluating target delineation diversity; Hodgkin's disease (case 1) and rhabdomyosarcoma of the parotid gland (case 2). The variability in target delineation (PTV delineations) between six centres was quantified using the generalised conformity index, CIgen, generated for volume overlap. The STAPLE algorithm, as implemented in CERR, was used for both cases to derive a consensus volumes. STAPLE is a probabilistic estimate of the true volume generated from all observers. Dose distributions created by each centre for the original target volumes were then applied to this consensus volume. RESULTS A considerable variation in target segmentation was seen in both cases. For case 1 the variation was 374-960 cm3 (average 669 cm3) and for case 2; 65-126 cm3 (average 109 cm3). CIgen were 0.53 and 0.70, respectively. The DVHs in absolute volume displayed for the delineated target volume as well as for the consensus volume adds information on both "compliant" target volumes as well as outliers which are hidden with just the use of concordance indices. CONCLUSIONS The DVHs in absolute volume add valuable and easily understood information to various indices for evaluating uniformity in target delineation.
Collapse
Affiliation(s)
- Ingrid Kristensen
- Department of Oncology, Clinical Sciences, Lund University, Lund, Sweden
- Department of Haematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Kristina Nilsson
- Department of Immunology, Genetics and Pathology, Experimental and Clinical Oncology, Clinical Oncology, Uppsala University Hospital, Uppsala, Sweden
| | - Måns Agrup
- Department of Oncology, Linköping University Hospital, Linköping, Sweden
| | - Karin Belfrage
- Department of Haematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Anna Embring
- Department of Oncology, Karolinska University Hospital, Stockholm, Sweden
| | - Hedda Haugen
- Department of Oncology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Anna-Maja Svärd
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Tommy Knöös
- Department of Haematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden
| | - Per Nilsson
- Department of Haematology, Oncology and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Medical Radiation Physics, Clinical Sciences, Lund University, Lund, Sweden
| |
Collapse
|
37
|
Kadoya N, Miyasaka Y, Yamamoto T, Kuroda Y, Ito K, Chiba M, Nakajima Y, Takahashi N, Kubozono M, Umezawa R, Dobashi S, Takeda K, Jingu K. Evaluation of rectum and bladder dose accumulation from external beam radiotherapy and brachytherapy for cervical cancer using two different deformable image registration techniques. JOURNAL OF RADIATION RESEARCH 2017; 58:720-728. [PMID: 28595311 PMCID: PMC5737357 DOI: 10.1093/jrr/rrx028] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Indexed: 05/12/2023]
Abstract
We evaluated dose-volume histogram (DVH) parameters based on deformable image registration (DIR) between brachytherapy (BT) and external beam radiotherapy (EBRT) that included a center-shielded (CS) plan. Eleven cervical cancer patients were treated with BT, and their pelvic and CS EBRT were studied. Planning CT images for EBRT and BT (except for the first BT, used as the reference image) were deformed with DIR to reference image. We used two DIR parameter settings: intensity-based and hybrid. Mean Dice similarity coefficients (DSCs) comparing EBRT with the reference for the uterus, rectum and bladder were 0.81, 0.77 and 0.83, respectively, for hybrid DIR and 0.47, 0.37 and 0.42, respectively, for intensity-based DIR (P < 0.05). D1 cm3 for hybrid DIR, intensity-based DIR and DVH addition were 75.1, 81.2 and 78.2 Gy, respectively, for the rectum, whereas they were 93.5, 92.3 and 94.3 Gy, respectively, for the bladder. D2 cm3 for hybrid DIR, intensity-based DIR and DVH addition were 70.1, 74.0 and 71.4 Gy, respectively, for the rectum, whereas they were 85.4, 82.8 and 85.4 Gy, respectively, for the bladder. Overall, hybrid DIR obtained higher DSCs than intensity-based DIR, and there were moderate differences in DVH parameters between the two DIR methods, although the results varied among patients. DIR is only experimental, and extra care should be taken when comparing DIR-based dose values with dose-effect curves established using DVH addition. Also, a true evaluation of DIR-based dose accumulation would require ground truth data (e.g. measurement with physical phantom).
Collapse
Affiliation(s)
- Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1–1 Seiryo-machi, Aoba-ku, Sendai, 980–8574, Japan
- Corresponding author. Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1–1 Seiryo-machi, Aoba-ku, Sendai, 980–8574, Japan. Tel: +81-22-717-7312; Fax: +81-22-717-7316;
| | - YuYa Miyasaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1–1 Seiryo-machi, Aoba-ku, Sendai, 980–8574, Japan
| | - Takaya Yamamoto
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1–1 Seiryo-machi, Aoba-ku, Sendai, 980–8574, Japan
| | - Yoshihiro Kuroda
- Department of Mechanical Science and Bioengineering, Graduate School of Engineering Science, Osaka University, Toyonaka, Japan
| | - Kengo Ito
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1–1 Seiryo-machi, Aoba-ku, Sendai, 980–8574, Japan
| | - Mizuki Chiba
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1–1 Seiryo-machi, Aoba-ku, Sendai, 980–8574, Japan
| | - Yujiro Nakajima
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1–1 Seiryo-machi, Aoba-ku, Sendai, 980–8574, Japan
| | - Noriyoshi Takahashi
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1–1 Seiryo-machi, Aoba-ku, Sendai, 980–8574, Japan
| | - Masaki Kubozono
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1–1 Seiryo-machi, Aoba-ku, Sendai, 980–8574, Japan
| | - Rei Umezawa
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1–1 Seiryo-machi, Aoba-ku, Sendai, 980–8574, Japan
| | - Suguru Dobashi
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Ken Takeda
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1–1 Seiryo-machi, Aoba-ku, Sendai, 980–8574, Japan
| |
Collapse
|
38
|
Beyond geometrical overlap: a Dosimetrical Evaluation of automated volumes Adaptation (DEA) in head and neck replanning. Tech Innov Patient Support Radiat Oncol 2017; 3-4:1-6. [PMID: 32095559 PMCID: PMC7033794 DOI: 10.1016/j.tipsro.2017.06.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Revised: 06/14/2017] [Accepted: 06/19/2017] [Indexed: 11/24/2022] Open
Abstract
Introduction Automated target volumes adaptation could be useful in H&N replanning, but its dosimetric impact has not been analyzed.Primary aim of this investigation is dose coverage assessment in fully automated and edited PTV adaptation settings, compared to manual benchmark. Materials and methods Ten IMRT patients were selected and replanning CTs were acquired.A deformable registration with PTV adaptation was performed defining PTVA.PTV B was obtained through manual editing and a benchmark PTV C was manually segmented by a delineation team.The Dice Similarity Index (DSI) and the mean Hausdorff Distance (mHD) were calculated between PTV A and PTV C, and between PTV B and PTV C.One IMRT plan was realized for each PTV: the plans optimized on PTV A and PTV B were proposed on PTV C to evaluate their dosimetric reliability compared to the benchmark plan in terms of PTV V95% dose coverage. Results The comparisons between PTV A with PTV C and PTV B with PTV C showed that the better DSI (high) and mHD values (low) are, the smaller difference when compared to PTV C V95% is described.Evaluating plan A and B, PTV C V95% reduced by 6.1 ± 3.0% and by 4.1 ± 2.3% respectively when compared to plan C PTV C V95%.PTV B reaches acceptable dose coverage values (PTV V95% >95%) when DSI is >0.91 and a mHD < 0.17 mm and it has better results when compared to PTV A in 70%. Discussion The results show a correlation between the DSI-mHD and the PTV V95% variation, in the comparisons PTV A and PTV B vs PTV C.Furthermore, we observed that PTV V95% coverage is higher in PTV B than in PTV A: the use of automated propagation may not be definitive and requires manual correction.
Collapse
|
39
|
Ciardo D, Gerardi MA, Vigorito S, Morra A, Dell'acqua V, Diaz FJ, Cattani F, Zaffino P, Ricotti R, Spadea MF, Riboldi M, Orecchia R, Baroni G, Leonardi MC, Jereczek-Fossa BA. Atlas-based segmentation in breast cancer radiotherapy: Evaluation of specific and generic-purpose atlases. Breast 2017; 32:44-52. [DOI: 10.1016/j.breast.2016.12.010] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Revised: 11/21/2016] [Accepted: 12/18/2016] [Indexed: 12/22/2022] Open
|
40
|
Eldesoky AR, Francolini G, Thomsen MS, Yates ES, Nyeng TB, Kirkove C, Kamby C, Blix ES, Nielsen MH, Taheri-Kadkhoda Z, Berg M, Offersen BV. Dosimetric assessment of an Atlas based automated segmentation for loco-regional radiation therapy of early breast cancer in the Skagen Trial 1: A multi-institutional study. Clin Transl Radiat Oncol 2017; 2:36-40. [PMID: 29657998 PMCID: PMC5893527 DOI: 10.1016/j.ctro.2017.01.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Revised: 01/08/2017] [Accepted: 01/10/2017] [Indexed: 11/29/2022] Open
Abstract
40 dose plans from the Skagen Trial 1 collected from Denmark, Belgium and Norway. Atlas-based automated segmentation of each CT scan was obtained using MIM Maestro™. DSC and difference in volume with manual segmentation were collected. HI, V95 and V90% measured on the two different segmentations were compared. Inter-observer variability was low and dose parameters were comparable.
The effect of Atlas-based automated segmentation (ABAS) on dose volume histogram (DVH) parameters compared to manual segmentation (MS) in loco-regional radiotherapy (RT) of early breast cancer was investigated in patients included in the Skagen Trial 1. This analysis supports implementation of ABAS in clinical practice and multi-institutional trials.
Collapse
Affiliation(s)
- Ahmed R Eldesoky
- Department of Oncology, Aarhus University Hospital, Norrebrogade 44, DK-8000 Aarhus C, Denmark.,Department of Clinical Oncology and Nuclear Medicine, Mansoura University, 60 Elgomhoria st, Mansoura, Egypt
| | - Giulio Francolini
- Department of Oncology, Aarhus University Hospital, Norrebrogade 44, DK-8000 Aarhus C, Denmark.,Department of Radiation Oncology, University of Florence, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Firenze, Florence, Italy
| | - Mette S Thomsen
- Department of Medical Physics, Aarhus University Hospital, Norrebrogade 44, DK-8000 Aarhus C, Denmark
| | - Esben S Yates
- Department of Medical Physics, Aarhus University Hospital, Norrebrogade 44, DK-8000 Aarhus C, Denmark
| | - Tine B Nyeng
- Department of Medical Physics, Aarhus University Hospital, Norrebrogade 44, DK-8000 Aarhus C, Denmark
| | - Carine Kirkove
- Department of Radiation Oncology, Catholic University of Louvain, 10 Ave Hippocrate, B-1200 Brussels, Belgium
| | - Claus Kamby
- Department of Oncology, Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen, Denmark
| | - Egil S Blix
- Department of Oncology, University Hospital of North Norway, Sykehusvegen 38, 9019 Tromsø, Norway
| | - Mette H Nielsen
- Department of Oncology, Odense University Hospital, Institute of Clinical Research, University of Southern Denmark, Odense, Winslowparken 19, 3, DK-5000 Odense C, Denmark
| | - Zahra Taheri-Kadkhoda
- Department of Oncology, Zealand University Hospital, Sygeshusvej 10, 4000 Roskilde, Denmark
| | - Martin Berg
- Department of Medical Physics, Hospital of Vejle, Kabbeltoft 25, 7100 Vejle, Denmark
| | - Birgitte V Offersen
- Department of Oncology, Aarhus University Hospital, Norrebrogade 44, DK-8000 Aarhus C, Denmark
| |
Collapse
|
41
|
Internal and external validation of an ESTRO delineation guideline – dependent automated segmentation tool for loco-regional radiation therapy of early breast cancer. Radiother Oncol 2016; 121:424-430. [DOI: 10.1016/j.radonc.2016.09.005] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Revised: 09/18/2016] [Accepted: 09/18/2016] [Indexed: 12/25/2022]
|
42
|
Hvid CA, Elstrøm UV, Jensen K, Alber M, Grau C. Accuracy of software-assisted contour propagation from planning CT to cone beam CT in head and neck radiotherapy. Acta Oncol 2016; 55:1324-1330. [PMID: 27556786 DOI: 10.1080/0284186x.2016.1185149] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
BACKGROUND Autocontouring improves workflow in computed tomography (CT)-based dose planning, but could also potentially play a role for optimal use of daily cone beam CT (CBCT) in adaptive radiotherapy. This study aims to determine the accuracy of a deformable image registration (DIR) algorithm for organs at risk (OAR) in the neck region, when applied to CBCT. MATERIAL AND METHODS For 30 head and neck cancer (HNC) patients 14 OARs including parotid glands, swallowing structures and spinal cord were delineated. Contours were propagated by DIR from CT to the CBCTs of the first and last treatment fraction. An indirect approach, propagating contours to the first CBCT and from there to the last CBCT was also tested. Propagated contours were compared to manually corrected contours by Dice similarity coefficient (DSC) and Hausdorff distance (HD). Dose was recalculated on CBCTs and dosimetric consequences of uncertainties in DIR were reviewed. RESULTS Mean DSC values of ≥0.8 were considered adequate and were achieved in tongue base (0.91), esophagus (0.85), glottic (0.81) and supraglottic larynx (0.83), inferior pharyngeal constrictor muscle (0.84), spinal cord (0.89) and all salivary glands in the first CBCT. For the last CBCT by direct propagation, adequate DSC values were achieved for tongue base (0.85), esophagus (0.84), spinal cord (0.87) and all salivary glands. Using indirect propagation only tongue base (0.80) and parotid glands (0.87) were ≥0.8. Mean relative dose difference between automated and corrected contours was within ±2.5% of planed dose except for esophagus inlet (-4.5%) and esophagus (5.0%) for the last CBCT using indirect propagation. CONCLUSION Compared to manually corrected contours, the DIR algorithm was accurate for use in CBCT images of HNC patients and the minor inaccuracies had little consequence for mean dose in most clinically relevant OAR. The method can thus enable a more automated segmentation of CBCT for use in adaptive radiotherapy.
Collapse
Affiliation(s)
- Christian A. Hvid
- Department of Oncology, Aarhus University Hospital, Aarhus C, Denmark
| | - Ulrik V. Elstrøm
- Department of Medical Physics, Aarhus University Hospital, Aarhus C, Denmark
| | - Kenneth Jensen
- Department of Oncology, Aarhus University Hospital, Aarhus C, Denmark
| | - Markus Alber
- Department of Medical Physics, Aarhus University Hospital, Aarhus C, Denmark
| | - Cai Grau
- Department of Oncology, Aarhus University Hospital, Aarhus C, Denmark
| |
Collapse
|
43
|
Doshi T, Wilson C, Paterson C, Lamb C, James A, MacKenzie K, Soraghan J, Petropoulakis L, Di Caterina G, Grose D. Validation of a Magnetic Resonance Imaging-based Auto-contouring Software Tool for Gross Tumour Delineation in Head and Neck Cancer Radiotherapy Planning. Clin Oncol (R Coll Radiol) 2016; 29:60-67. [PMID: 27780693 DOI: 10.1016/j.clon.2016.09.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Revised: 07/18/2016] [Accepted: 09/06/2016] [Indexed: 10/20/2022]
Abstract
AIMS To carry out statistical validation of a newly developed magnetic resonance imaging (MRI) auto-contouring software tool for gross tumour volume (GTV) delineation in head and neck tumours to assist in radiotherapy planning. MATERIALS AND METHODS Axial MRI baseline scans were obtained for 10 oropharyngeal and laryngeal cancer patients. GTV was present on 102 axial slices and auto-contoured using the modified fuzzy c-means clustering integrated with the level set method (FCLSM). Peer-reviewed (C-gold) manual contours were used as the reference standard to validate auto-contoured GTVs (C-auto) and mean manual contours (C-manual) from two expert clinicians (C1 and C2). Multiple geometric metrics, including the Dice similarity coefficient (DSC), were used for quantitative validation. A DSC≥0.7 was deemed acceptable. Inter- and intra-variabilities among the manual contours were also validated. The two-dimensional contours were then reconstructed in three dimensions for GTV volume calculation, comparison and three-dimensional visualisation. RESULTS The mean DSC between C-gold and C-auto was 0.79. The mean DSC between C-gold and C-manual was 0.79 and that between C1 and C2 was 0.80. The average time for GTV auto-contouring per patient was 8 min (range 6-13 min; mean 45 s per axial slice) compared with 15 min (range 6-23 min; mean 88 s per axial slice) for C1. The average volume concordance between C-gold and C-auto volumes was 86.51% compared with 74.16% between C-gold and C-manual. The average volume concordance between C1 and C2 volumes was 86.82%. CONCLUSIONS This newly designed MRI-based auto-contouring software tool shows initial acceptable results in GTV delineation of oropharyngeal and laryngeal tumours using FCLSM. This auto-contouring software tool may help reduce inter- and intra-variability and can assist clinical oncologists with time-consuming, complex radiotherapy planning.
Collapse
Affiliation(s)
- T Doshi
- Department of Electronic & Electrical Engineering, University of Strathclyde, Glasgow, UK.
| | - C Wilson
- Beatson West of Scotland Cancer Centre, Glasgow, UK
| | - C Paterson
- Beatson West of Scotland Cancer Centre, Glasgow, UK
| | - C Lamb
- Beatson West of Scotland Cancer Centre, Glasgow, UK
| | - A James
- Beatson West of Scotland Cancer Centre, Glasgow, UK
| | | | - J Soraghan
- Department of Electronic & Electrical Engineering, University of Strathclyde, Glasgow, UK
| | - L Petropoulakis
- Department of Electronic & Electrical Engineering, University of Strathclyde, Glasgow, UK
| | - G Di Caterina
- Department of Electronic & Electrical Engineering, University of Strathclyde, Glasgow, UK
| | - D Grose
- Beatson West of Scotland Cancer Centre, Glasgow, UK
| |
Collapse
|
44
|
Vinod SK, Jameson MG, Min M, Holloway LC. Uncertainties in volume delineation in radiation oncology: A systematic review and recommendations for future studies. Radiother Oncol 2016; 121:169-179. [PMID: 27729166 DOI: 10.1016/j.radonc.2016.09.009] [Citation(s) in RCA: 214] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Revised: 08/27/2016] [Accepted: 09/25/2016] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND PURPOSE Volume delineation is a well-recognised potential source of error in radiotherapy. Whilst it is important to quantify the degree of interobserver variability (IOV) in volume delineation, the resulting impact on dosimetry and clinical outcomes is a more relevant endpoint. We performed a literature review of studies evaluating IOV in target volume and organ-at-risk (OAR) delineation in order to analyse these with respect to the metrics used, reporting of dosimetric consequences, and use of statistical tests. METHODS AND MATERIALS Medline and Pubmed databases were queried for relevant articles using keywords. We included studies published in English between 2000 and 2014 with more than two observers. RESULTS 119 studies were identified covering all major tumour sites. CTV (n=47) and GTV (n=38) were most commonly contoured. Median number of participants and data sets were 7 (3-50) and 9 (1-132) respectively. There was considerable heterogeneity in the use of metrics and methods of analysis. Statistical analysis of results was reported in 68% (n=81) and dosimetric consequences in 21% (n=25) of studies. CONCLUSION There is a lack of consistency in conducting and reporting analyses from IOV studies. We suggest a framework to use for future studies evaluating IOV.
Collapse
Affiliation(s)
- Shalini K Vinod
- Cancer Therapy Centre, Liverpool Hospital, Australia; South Western Sydney Clinical School, University of New South Wales, Australia; Western Sydney University, Australia.
| | - Michael G Jameson
- Cancer Therapy Centre, Liverpool Hospital, Australia; Ingham Institute of Applied Medical Research, Liverpool Hospital, Australia; Centre for Medical Radiation Physics, University of Wollongong, Australia
| | - Myo Min
- Cancer Therapy Centre, Liverpool Hospital, Australia; South Western Sydney Clinical School, University of New South Wales, Australia; Ingham Institute of Applied Medical Research, Liverpool Hospital, Australia
| | - Lois C Holloway
- Cancer Therapy Centre, Liverpool Hospital, Australia; South Western Sydney Clinical School, University of New South Wales, Australia; Ingham Institute of Applied Medical Research, Liverpool Hospital, Australia; Centre for Medical Radiation Physics, University of Wollongong, Australia
| |
Collapse
|
45
|
Automatic segmentation of breast in prone position: Correlation of similarity indexes and breast pendulousness with dose/volume parameters. Radiother Oncol 2016; 120:124-7. [DOI: 10.1016/j.radonc.2016.04.041] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2015] [Revised: 04/25/2016] [Accepted: 04/26/2016] [Indexed: 11/19/2022]
|
46
|
Lim JY, Leech M. Use of auto-segmentation in the delineation of target volumes and organs at risk in head and neck. Acta Oncol 2016; 55:799-806. [PMID: 27248772 DOI: 10.3109/0284186x.2016.1173723] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
BACKGROUND Manual delineation of structures in head and neck cancers is an extremely time-consuming and labor-intensive procedure. With centers worldwide moving towards the use of intensity-modulated radiotherapy and adaptive radiotherapy, there is a need to explore and analyze auto-segmentation (AS) software, in the search for a faster yet accurate method of structure delineation. MATERIAL AND METHODS A search for studies published after 2005 comparing AS and manual delineation in contouring organ at risks (OARs) and target volume for head and neck patients was conducted. The reviewed results were then categorized into arguments proposing and opposing the review title. RESULTS Ten studies were reviewed and derived results were assessed in terms of delineation time-saving ability and extent of delineation accuracy. The influence of other external factors (observer variability, AS strategies adopted and stage of disease) were also considered. Results were conflicting with some studies demonstrating great potential in replacing manual delineation whereas other studies illustrated otherwise. Six of 10 studies investigated time saving; the largest time saving reported being 59%. However, one study found that additional time of 15.7% was required for AS. Four studies reported AS contours to be between 'reasonably good' and 'better quality' than the clinically used contours. Remaining studies cited lack of contrast, AS strategy used and the need for physician intervention as limitations in the standardized use of AS. DISCUSSION The studies demonstrated significant potential of AS as a useful delineation tool in contouring target volumes and OARs in head and neck cancers. However, it is evident that AS cannot totally replace manual delineation in contouring some structures in the head and neck and cannot be used independently without human intervention. It is also emphasized that delineation studies should be conducted locally so as to evaluate the true value of AS in head and neck cancers in a specific center.
Collapse
Affiliation(s)
- Jia Yi Lim
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity College, Dublin, Ireland
- Department of Radiation Oncology, National Cancer Centre, Singapore
| | - Michelle Leech
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity College, Dublin, Ireland
| |
Collapse
|
47
|
Pogson EM, Begg J, Jameson MG, Dempsey C, Latty D, Batumalai V, Lim A, Kandasamy K, Metcalfe PE, Holloway LC. A phantom assessment of achievable contouring concordance across multiple treatment planning systems. Radiother Oncol 2015; 117:438-41. [DOI: 10.1016/j.radonc.2015.09.022] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2014] [Revised: 09/01/2015] [Accepted: 09/18/2015] [Indexed: 11/26/2022]
|
48
|
Multi-subject atlas-based auto-segmentation reduces interobserver variation and improves dosimetric parameter consistency for organs at risk in nasopharyngeal carcinoma: A multi-institution clinical study. Radiother Oncol 2015; 115:407-11. [DOI: 10.1016/j.radonc.2015.05.012] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2014] [Revised: 05/12/2015] [Accepted: 05/12/2015] [Indexed: 11/22/2022]
|