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Perivolaris A, Adams-McGavin C, Madan Y, Kishibe T, Antoniou T, Mamdani M, Jung JJ. Quality of interaction between clinicians and artificial intelligence systems. A systematic review. Future Healthc J 2024; 11:100172. [PMID: 39281326 PMCID: PMC11399614 DOI: 10.1016/j.fhj.2024.100172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 07/15/2024] [Accepted: 08/04/2024] [Indexed: 09/18/2024]
Abstract
Introduction Artificial intelligence (AI) has the potential to improve healthcare quality when thoughtfully integrated into clinical practice. Current evaluations of AI solutions tend to focus solely on model performance. There is a critical knowledge gap in the assessment of AI-clinician interactions. We systematically reviewed existing literature to identify interaction traits that can be used to assess the quality of AI-clinician interactions. Methods We performed a systematic review of published studies to June 2022 that reported elements of interactions that impacted the relationship between clinicians and AI-enabled clinical decision support systems. Due to study heterogeneity, we conducted a narrative synthesis of the different interaction traits identified from this review. Two study authors categorised the AI-clinician interaction traits based on their shared constructs independently. After the independent categorisation, both authors engaged in a discussion to finalise the categories. Results From 34 included studies, we identified 210 interaction traits. The most common interaction traits included usefulness, ease of use, trust, satisfaction, willingness to use and usability. After removing duplicate or redundant traits, 90 unique interaction traits were identified. Unique interaction traits were then classified into seven categories: usability and user experience, system performance, clinician trust and acceptance, impact on patient care, communication, ethical and professional concerns, and clinician engagement and workflow. Discussion We identified seven categories of interaction traits between clinicians and AI systems. The proposed categories may serve as a foundation for a framework assessing the quality of AI-clinician interactions.
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Affiliation(s)
- Argyrios Perivolaris
- Institute of Medical Sciences, University of Toronto, Canada
- St. Michaels Hospital, Unity Health Toronto, Canada
| | - Chris Adams-McGavin
- Department of Surgery, Temetry Faculty of Medicine, University of Toronto, Canada
| | - Yasmine Madan
- Department of Health Sciences, McMaster University, Canada
| | | | - Tony Antoniou
- Department of Family and Community Medicine, St. Michael's Hospital, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Canada
- Department of Family and Community Medicine, University of Toronto, Canada
| | - Muhammad Mamdani
- St. Michaels Hospital, Unity Health Toronto, Canada
- Leslie Dan Faculty of Pharmacy, Temerty Faculty of Medicine, University of Toronto, Canada
- Dalla Lana School of Public Health, University of Toronto, Canada
| | - James J Jung
- Institute of Medical Sciences, University of Toronto, Canada
- St. Michaels Hospital, Unity Health Toronto, Canada
- Department of Surgery, Temetry Faculty of Medicine, University of Toronto, Canada
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Gooding MJ, Aluwini S, Guerrero Urbano T, McQuinlan Y, Om D, Staal FHE, Perennec T, Azzarouali S, Cardenas CE, Carver A, Korreman SS, Bibault JE. Fully automated radiotherapy treatment planning: A scan to plan challenge. Radiother Oncol 2024:110513. [PMID: 39222848 DOI: 10.1016/j.radonc.2024.110513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 08/19/2024] [Accepted: 08/26/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND AND PURPOSE Over the past decade, tools for automation of various sub-tasks in radiotherapy planning have been introduced, such as auto-contouring and auto-planning. The purpose of this study was to benchmark what degree of automation is possible. MATERIALS AND METHODS A challenge to perform automated treatment planning for prostate and prostate bed radiotherapy was set up. Participants were provided with simulation CTs and a treatment prescription and were asked to use automated tools to produce a deliverable radiotherapy treatment plan with as little human intervention as possible. Plans were scored for their adherence to the protocol when assessed using consensus expert contours. RESULTS Thirteen entries were received. The top submission adhered to 81.8% of the minimum objectives across all cases using the consensus contour, meeting all objectives in one of the ten cases. The same system met 89.5% of objectives when assessed with their own auto-contours, meeting all objectives in four of the ten cases. The majority of systems used in the challenge had regulatory clearance (Auto-contouring: 82.5%, Auto-planning: 77%). Despite the 'hard' rule that participants should not check or edit contours or plans, 69% reported looking at their results before submission. CONCLUSIONS Automation of the full planning workflow from simulation CT to deliverable treatment plan is possible for prostate and prostate bed radiotherapy. While many generated plans were found to require none or minor adjustment to be regarded as clinically acceptable, the result indicated there is still a lack of trust in such systems preventing full automation.
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Affiliation(s)
- Mark J Gooding
- Inpictura Ltd, 5 The Chambers, Vineyard, Abingdon OX14 3PX, The United Kingdom of Great Britain and Northern Ireland; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, M20 4BX Manchester, The United Kingdom of Great Britain and Northern Ireland.
| | - Shafak Aluwini
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
| | - Teresa Guerrero Urbano
- Department of Clinical Oncology Guy's and St Thomas' NHS Foundation Trust School of Cancer and Pharmaceutical Sciences King's College London, London, The United Kingdom of Great Britain and Northern Ireland.
| | - Yasmin McQuinlan
- Mirada Medical Ltd, Barclay House, 234 Botley Road OX2 0HP, The United Kingdom of Great Britain and Northern Ireland.
| | - Deborah Om
- Department of Medical Physics, Hôpital Européen Georges Pompidou, Université Paris Cité, 75015 Paris, France.
| | - Floor H E Staal
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, thte Netherlands.
| | - Tanguy Perennec
- Département de radiothérapie, Institut de Cancérologie de l'Ouest, Nantes, France.
| | - Sana Azzarouali
- Radiation Oncology, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.
| | - Carlos E Cardenas
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL, USA.
| | - Antony Carver
- Department of Medical Physics, University Hospitals Birmingham NHS Foundation Trust, Birmingham, The United Kingdom of Great Britain and Northern Ireland.
| | - Stine Sofia Korreman
- Department of Clinical Medicine, Aarhus University, Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark.
| | - Jean-Emmanuel Bibault
- Department of Radiation Oncology, Hôpital Européen Georges-Pompidou, Université Paris Cité, 75015 Paris, France.
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Nachbar M, Lo Russo M, Gani C, Boeke S, Wegener D, Paulsen F, Zips D, Roque T, Paragios N, Thorwarth D. Automatic AI-based contouring of prostate MRI for online adaptive radiotherapy. Z Med Phys 2024; 34:197-207. [PMID: 37263911 PMCID: PMC11156783 DOI: 10.1016/j.zemedi.2023.05.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 04/03/2023] [Accepted: 05/02/2023] [Indexed: 06/03/2023]
Abstract
BACKGROUND AND PURPOSE MR-guided radiotherapy (MRgRT) online plan adaptation accounts for tumor volume changes, interfraction motion and thus allows daily sparing of relevant organs at risk. Due to the high interfraction variability of bladder and rectum, patients with tumors in the pelvic region may strongly benefit from adaptive MRgRT. Currently, fast automatic annotation of anatomical structures is not available within the online MRgRT workflow. Therefore, the aim of this study was to train and validate a fast, accurate deep learning model for automatic MRI segmentation at the MR-Linac for future implementation in a clinical MRgRT workflow. MATERIALS AND METHODS For a total of 47 patients, T2w MRI data were acquired on a 1.5 T MR-Linac (Unity, Elekta) on five different days. Prostate, seminal vesicles, rectum, anal canal, bladder, penile bulb, body and bony structures were manually annotated. These training data consisting of 232 data sets in total was used for the generation of a deep learning based autocontouring model and validated on 20 unseen T2w-MRIs. For quantitative evaluation the validation set was contoured by a radiation oncologist as gold standard contours (GSC) and compared in MATLAB to the automatic contours (AIC). For the evaluation, dice similarity coefficients (DSC), and 95% Hausdorff distances (95% HD), added path length (APL) and surface DSC (sDSC) were calculated in a caudal-cranial window of ± 4 cm with respect to the prostate ends. For qualitative evaluation, five radiation oncologists scored the AIC on the possible usage within an online adaptive workflow as follows: (1) no modifications needed, (2) minor adjustments needed, (3) major adjustments/ multiple minor adjustments needed, (4) not usable. RESULTS The quantitative evaluation revealed a maximum median 95% HD of 6.9 mm for the rectum and minimum median 95% HD of 2.7 mm for the bladder. Maximal and minimal median DSC were detected for bladder with 0.97 and for penile bulb with 0.73, respectively. Using a tolerance level of 3 mm, the highest and lowest sDSC were determined for rectum (0.94) and anal canal (0.68), respectively. Qualitative evaluation resulted in a mean score of 1.2 for AICs over all organs and patients across all expert ratings. For the different autocontoured structures, the highest mean score of 1.0 was observed for anal canal, sacrum, femur left and right, and pelvis left, whereas for prostate the lowest mean score of 2.0 was detected. In total, 80% of the contours were rated be clinically acceptable, 16% to require minor and 4% major adjustments for online adaptive MRgRT. CONCLUSION In this study, an AI-based autocontouring was successfully trained for online adaptive MR-guided radiotherapy on the 1.5 T MR-Linac system. The developed model can automatically generate contours accepted by physicians (80%) or only with the need of minor corrections (16%) for the irradiation of primary prostate on the clinically employed sequences.
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Affiliation(s)
- Marcel Nachbar
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Monica Lo Russo
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Cihan Gani
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Simon Boeke
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Daniel Wegener
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Frank Paulsen
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Daniel Zips
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University of Tübingen, Tübingen, Germany; German Cancer Consortium (DKTK), partner site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Radiation Oncology, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | | | - Nikos Paragios
- TheraPanacea, Paris, France; CentraleSupelec, University of Paris-Saclay, Gif-sur-Yvette, France
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University of Tübingen, Tübingen, Germany; German Cancer Consortium (DKTK), partner site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany.
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Fink CA, Buchele C, Baumann L, Liermann J, Hoegen P, Ristau J, Regnery S, Sandrini E, König L, Rippke C, Bonekamp D, Schlemmer HP, Debus J, Koerber SA, Klüter S, Hörner-Rieber J. Dosimetric benefit of online treatment plan adaptation in stereotactic ultrahypofractionated MR-guided radiotherapy for localized prostate cancer. Front Oncol 2024; 14:1308406. [PMID: 38425342 PMCID: PMC10902126 DOI: 10.3389/fonc.2024.1308406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 01/24/2024] [Indexed: 03/02/2024] Open
Abstract
Background Apart from superior soft tissue contrast, MR-guided stereotactic body radiation therapy (SBRT) offers the chance for daily online plan adaptation. This study reports on the comparison of dose parameters before and after online plan adaptation in MR-guided SBRT of localized prostate cancer. Materials and methods 32 consecutive patients treated with ultrahypofractionated SBRT for localized prostate cancer within the prospective SMILE trial underwent a planning process for MR-guided radiotherapy with 37.5 Gy applied in 5 fractions. A base plan, derived from MRI simulation at an MRIdian Linac, was registered to daily MRI scans (predicted plan). Following target and OAR recontouring, the plan was reoptimized based on the daily anatomy (adapted plan). CTV and PTV coverage and doses at OAR were compared between predicted and adapted plans using linear mixed regression models. Results In 152 out of 160 fractions (95%), an adapted radiation plan was delivered. Mean CTV and PTV coverage increased by 1.4% and 4.5% after adaptation. 18% vs. 95% of the plans had a PTV coverage ≥95% before and after online adaptation, respectively. 78% vs. 100% of the plans had a CTV coverage ≥98% before and after online adaptation, respectively. The D0.2cc for both bladder and rectum were <38.5 Gy in 93% vs. 100% before and after online adaptation. The constraint at the urethra with a dose of <37.5 Gy was achieved in 59% vs. 93% before and after online adaptation. Conclusion Online adaptive plan adaptation improves target volume coverage and reduces doses to OAR in MR-guided SBRT of localized prostate cancer. Online plan adaptation could potentially further reduce acute and long-term side effects and improve local failure rates in MR-guided SBRT of localized prostate cancer.
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Affiliation(s)
- Christoph A. Fink
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Carolin Buchele
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Lukas Baumann
- Institute of Medical Biometry (IMBI), University of Heidelberg, Heidelberg, Germany
| | - Jakob Liermann
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Philipp Hoegen
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jonas Ristau
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Sebastian Regnery
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Elisabetta Sandrini
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Laila König
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Carolin Rippke
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - David Bonekamp
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), Partner Side Heidelberg, Heidelberg, Germany
| | | | - Juergen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), Partner Side Heidelberg, Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Center (HIT), Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Stefan A. Koerber
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
- Department of Radiation Oncology, Barmherzige Brueder Hospital Regensburg, Regensburg, Germany
| | - Sebastian Klüter
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Juliane Hörner-Rieber
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Winter JD, Reddy V, Li W, Craig T, Raman S. Impact of technological advances in treatment planning, image guidance, and treatment delivery on target margin design for prostate cancer radiotherapy: an updated review. Br J Radiol 2024; 97:31-40. [PMID: 38263844 PMCID: PMC11027310 DOI: 10.1093/bjr/tqad041] [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: 05/07/2023] [Revised: 08/22/2023] [Accepted: 11/21/2023] [Indexed: 01/25/2024] Open
Abstract
Recent innovations in image guidance, treatment delivery, and adaptive radiotherapy (RT) have created a new paradigm for planning target volume (PTV) margin design for patients with prostate cancer. We performed a review of the recent literature on PTV margin selection and design for intact prostate RT, excluding post-operative RT, brachytherapy, and proton therapy. Our review describes the increased focus on prostate and seminal vesicles as heterogenous deforming structures with further emergence of intra-prostatic GTV boost and concurrent pelvic lymph node treatment. To capture recent innovations, we highlight the evolution in cone beam CT guidance, and increasing use of MRI for improved target delineation and image registration and supporting online adaptive RT. Moreover, we summarize new and evolving image-guidance treatment platforms as well as recent reports of novel immobilization strategies and motion tracking. Our report also captures recent implementations of artificial intelligence to support image guidance and adaptive RT. To characterize the clinical impact of PTV margin changes via model-based risk estimates and clinical trials, we highlight recent high impact reports. Our report focusses on topics in the context of PTV margins but also showcase studies attempting to move beyond the PTV margin recipes with robust optimization and probabilistic planning approaches. Although guidelines exist for target margins conventional using CT-based image guidance, further validation is required to understand the optimal margins for online adaptation either alone or combined with real-time motion compensation to minimize systematic and random uncertainties in the treatment of patients with prostate cancer.
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Affiliation(s)
- Jeff D Winter
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Varun Reddy
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada
| | - Winnie Li
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Tim Craig
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
| | - Srinivas Raman
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON M5T 1P5, Canada
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Abstract
Magnetic resonance imaging-guided radiation therapy (MRIgRT) has improved soft tissue contrast over computed tomography (CT) based image-guided RT. Superior visualization of the target and surrounding radiosensitive structures has the potential to improve oncological outcomes partly due to safer dose-escalation and adaptive planning. In this review, we highlight the workflow of adaptive MRIgRT planning, which includes simulation imaging, daily MRI, identifying isocenter shifts, contouring, plan optimization, quality control, and delivery. Increased utilization of MRIgRT will depend on addressing technical limitations of this technology, while addressing treatment efficacy, cost-effectiveness, and workflow training.
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Affiliation(s)
- Cecil M Benitez
- Department of Radiation Oncology, UCLA Medical Center, Los Angeles, CA
| | - Michael D Chuong
- Department of Radiation Oncology, Miami Cancer Institute, Baptist Health South Florida; Miami, FL
| | - Luise A Künzel
- National Center for Tumor Diseases (NCT), Dresden; German Cancer Research Center (DKFZ), Heidelberg; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden; Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.; OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden Rossendorf, Dresden, Germany
| | - Daniela Thorwarth
- Department of Radiation Oncology, Section for Biomedical Physics, University of Tübingen, Tübingen, Germany..
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Tengler B, Künzel LA, Hagmüller M, Mönnich D, Boeke S, Wegener D, Gani C, Zips D, Thorwarth D. Full daily re-optimization improves plan quality during online adaptive radiotherapy. Phys Imaging Radiat Oncol 2024; 29:100534. [PMID: 38298884 PMCID: PMC10827578 DOI: 10.1016/j.phro.2024.100534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 02/02/2024] Open
Abstract
Background and purpose Daily online treatment plan adaptation requires a fast workflow and planning process. Current online planning consists of adaptation of a predefined reference plan, which might be suboptimal in cases of large anatomic changes. The aim of this study was to investigate plan quality differences between the current online re-planning approach and a complete re-optimization. Material and methods Magnetic resonance linear accelerator reference plans for ten prostate cancer patients were automatically generated using particle swarm optimization (PSO). Adapted plans were created for each fraction using (1) the current re-planning approach and (2) full PSO re-optimization and evaluated overall compliance with institutional dose-volume criteria compared to (3) clinically delivered fractions. Relative volume differences between reference and daily anatomy were assessed for planning target volumes (PTV60, PTV57.6), rectum and bladder and correlated with dose-volume results. Results The PSO approach showed significantly higher adherence to dose-volume criteria than the reference approach and clinical fractions (p < 0.001). In 74 % of PSO plans at most one criterion failed compared to 56 % in the reference approach and 41 % in clinical plans. A fair correlation between PTV60 D98% and relative bladder volume change was observed for the reference approach. Bladder volume reductions larger than 50 % compared to the reference plan recurrently decreased PTV60 D98% below 56 Gy. Conclusion Complete re-optimization maintained target coverage and organs at risk sparing even after large anatomic variations. Re-planning based on daily magnetic resonance imaging was sufficient for small variations, while large variations led to decreasing target coverage and organ-at-risk sparing.
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Affiliation(s)
- Benjamin Tengler
- Section for Biomedical Physics. Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Germany
| | - Luise A. Künzel
- National Center for Tumor Diseases (NCT), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany, Helmholtz-Zentrum Dresden - Rossendorf (HZDR), Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
| | - Markus Hagmüller
- Section for Biomedical Physics. Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Germany
| | - David Mönnich
- Section for Biomedical Physics. Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Germany
| | - Simon Boeke
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Germany
| | - Daniel Wegener
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Germany
| | - Cihan Gani
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Germany
| | - Daniel Zips
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Germany
| | - Daniela Thorwarth
- Section for Biomedical Physics. Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Germany
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Yamauchi R, Itazawa T, Kobayashi T, Kashiyama S, Akimoto H, Mizuno N, Kawamori J. Clinical evaluation of deep learning and atlas-based auto-segmentation for organs at risk delineation. Med Dosim 2023; 49:167-176. [PMID: 38061916 DOI: 10.1016/j.meddos.2023.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 09/03/2023] [Accepted: 11/02/2023] [Indexed: 08/04/2024]
Abstract
Manual delineation of organs at risk and clinical target volumes is essential in radiotherapy planning. Atlas-based auto-segmentation (ABAS) algorithms have become available and been shown to provide accurate contouring for various anatomical sites. Recently, deep learning auto-segmentation (DL-AS) algorithms have emerged as the state-of-the-art in medical image segmentation. This study aimed to evaluate the effect of auto-segmentation on the clinical workflow for contouring different anatomical sites of cancer, such as head and neck (H&N), breast, abdominal region, and prostate. Patients with H&N, breast, abdominal, and prostate cancer (n = 30 each) were enrolled in the study. Twenty-seven different organs at four sites were evaluated. RayStation was used to apply the ABAS. Siemens AI-Rad Companion Organs RT was used to apply the DL-AS. Evaluations were performed with similarity indices using geometric methods, time-evaluation, and qualitative scoring visual evaluations by radiation oncologists. The DL-AS algorithm was more accurate than ABAS algorithm on geometric indices for half of the structures. The qualitative scoring results of the two algorithms were significantly different, and DL-AS was more accurate on many contours. DL-AS had 41%, 29%, 86%, and 15% shorter edit times in the HnN, breast, abdomen, and prostate groups, respectively, than ABAS. There were no correlations between the geometric indices and visual assessments. The time required to edit the contours was considerably shorter for DL-AS than for ABAS. Auto-segmentation with deep learning could be the first step for clinical workflow optimization in radiotherapy.
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Affiliation(s)
- Ryohei Yamauchi
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan.
| | - Tomoko Itazawa
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan
| | - Takako Kobayashi
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan
| | - Shiho Kashiyama
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan; Department of Radiation Oncology, Japanese Red Cross Saitama Hospital, Saitama, Japan
| | - Hiroyoshi Akimoto
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan; Department of Radiation Oncology, Nippon Medical School Musashikosugi Hospital, Kanagawa, Japan
| | - Norifumi Mizuno
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan; Department of Radiation Oncology, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Jiro Kawamori
- Department of Radiation Oncology, St. Luke's International Hospital, Tokyo, Japan
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Towards real-time radiotherapy planning: The role of autonomous treatment strategies. Phys Imaging Radiat Oncol 2022; 24:136-137. [DOI: 10.1016/j.phro.2022.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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