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Berenato S, Williams M, Woodley O, Möhler C, Evans E, Millin AE, Wheeler PA. Novel dosimetric validation of a commercial CT scanner based deep learning automated contour solution for prostate radiotherapy. Phys Med 2024; 122:103339. [PMID: 38718703 DOI: 10.1016/j.ejmp.2024.103339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 03/19/2024] [Accepted: 03/25/2024] [Indexed: 06/13/2024] Open
Abstract
PURPOSE OAR delineation accuracy influences: (i) a patient's optimised dose distribution (PD), (ii) the reported doses (RD) presented at approval, which represent plan quality. This study utilised a novel dosimetric validation methodology, comprehensively evaluating a new CT-scanner-based AI contouring solution in terms of PD and RD within an automated planning workflow. METHODS 20 prostate patients were selected to evaluate AI contouring for rectum, bladder, and proximal femurs. Five planning 'pipelines' were considered; three using AI contours with differing levels of manual editing (nominally none (AIStd), minor editing in specific regions (AIMinEd), and fully corrected (AIFullEd)). Remaining pipelines were manual delineations from two observers (MDOb1, MDOb2). Automated radiotherapy plans were generated for each pipeline. Geometric and dosimetric agreement of contour sets AIStd, AIMinEd, AIFullEd and MDOb2 were evaluated against the reference set MDOb1. Non-inferiority of AI pipelines was assessed, hypothesising that compared to MDOb1, absolute deviations in metrics for AI contouring were no greater than that from MDOb2. RESULTS Compared to MDOb1, organ delineation time was reduced by 24.9 min (96 %), 21.4 min (79 %) and 12.2 min (45 %) for AIStd, AIMinEd and AIFullEd respectively. All pipelines exhibited generally good dosimetric agreement with MDOb1. For RD, median deviations were within ± 1.8 cm3, ± 1.7 % and ± 0.6 Gy for absolute volume, relative volume and mean dose metrics respectively. For PD, respective values were within ± 0.4 cm3, ± 0.5 % and ± 0.2 Gy. Statistically (p < 0.05), AIMinEd and AIFullEd were dosimetrically non-inferior to MDOb2. CONCLUSIONS This novel dosimetric validation demonstrated that following targeted minor editing (AIMinEd), AI contours were dosimetrically non-inferior to manual delineations, reducing delineation time by 79 %.
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Affiliation(s)
- Salvatore Berenato
- Velindre Cancer Centre, Radiotherapy Physics Department, Cardiff, Wales, United Kingdom
| | - Matthew Williams
- Velindre Cancer Centre, Radiotherapy Physics Department, Cardiff, Wales, United Kingdom
| | - Owain Woodley
- Velindre Cancer Centre, Radiotherapy Physics Department, Cardiff, Wales, United Kingdom
| | | | - Elin Evans
- Velindre Cancer Centre, Medical Directorate, Cardiff, Wales, United Kingdom
| | - Anthony E Millin
- Velindre Cancer Centre, Radiotherapy Physics Department, Cardiff, Wales, United Kingdom
| | - Philip A Wheeler
- Velindre Cancer Centre, Radiotherapy Physics Department, Cardiff, Wales, United Kingdom.
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Wheeler PA, West NS, Powis R, Maggs R, Chu M, Pearson RA, Willis N, Kurec B, Reed KL, Lewis DG, Staffurth J, Spezi E, Millin AE. Multi-institutional evaluation of a Pareto navigation guided automated radiotherapy planning solution for prostate cancer. Radiat Oncol 2024; 19:45. [PMID: 38589961 PMCID: PMC11003074 DOI: 10.1186/s13014-024-02404-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 01/15/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Current automated planning solutions are calibrated using trial and error or machine learning on historical datasets. Neither method allows for the intuitive exploration of differing trade-off options during calibration, which may aid in ensuring automated solutions align with clinical preference. Pareto navigation provides this functionality and offers a potential calibration alternative. The purpose of this study was to validate an automated radiotherapy planning solution with a novel multi-dimensional Pareto navigation calibration interface across two external institutions for prostate cancer. METHODS The implemented 'Pareto Guided Automated Planning' (PGAP) methodology was developed in RayStation using scripting and consisted of a Pareto navigation calibration interface built upon a 'Protocol Based Automatic Iterative Optimisation' planning framework. 30 previous patients were randomly selected by each institution (IA and IB), 10 for calibration and 20 for validation. Utilising the Pareto navigation interface automated protocols were calibrated to the institutions' clinical preferences. A single automated plan (VMATAuto) was generated for each validation patient with plan quality compared against the previously treated clinical plan (VMATClinical) both quantitatively, using a range of DVH metrics, and qualitatively through blind review at the external institution. RESULTS PGAP led to marked improvements across the majority of rectal dose metrics, with Dmean reduced by 3.7 Gy and 1.8 Gy for IA and IB respectively (p < 0.001). For bladder, results were mixed with low and intermediate dose metrics reduced for IB but increased for IA. Differences, whilst statistically significant (p < 0.05) were small and not considered clinically relevant. The reduction in rectum dose was not at the expense of PTV coverage (D98% was generally improved with VMATAuto), but was somewhat detrimental to PTV conformality. The prioritisation of rectum over conformality was however aligned with preferences expressed during calibration and was a key driver in both institutions demonstrating a clear preference towards VMATAuto, with 31/40 considered superior to VMATClinical upon blind review. CONCLUSIONS PGAP enabled intuitive adaptation of automated protocols to an institution's planning aims and yielded plans more congruent with the institution's clinical preference than the locally produced manual clinical plans.
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Affiliation(s)
- Philip A Wheeler
- Radiotherapy Physics Department, Velindre Cancer Centre, CF14 2TL, Cardiff, Wales, UK.
| | - Nicholas S West
- Northern Centre for Cancer Care, Cancer Services and Clinical Haematology, Newcastle upon Tyne, UK
| | - Richard Powis
- Worcester Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
| | - Rhydian Maggs
- Radiotherapy Physics Department, Velindre Cancer Centre, CF14 2TL, Cardiff, Wales, UK
| | - Michael Chu
- Radiotherapy Physics Department, Velindre Cancer Centre, CF14 2TL, Cardiff, Wales, UK
| | - Rachel A Pearson
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University Centre for Cancer, Newcastle University, Newcastle upon Tyne, UK
| | - Nick Willis
- Northern Centre for Cancer Care, Cancer Services and Clinical Haematology, Newcastle upon Tyne, UK
| | - Bartlomiej Kurec
- Worcester Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
| | - Katie L Reed
- Worcester Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
| | - David G Lewis
- Radiotherapy Physics Department, Velindre Cancer Centre, CF14 2TL, Cardiff, Wales, UK
| | - John Staffurth
- School of Medicine, Cardiff University, Cardiff, Wales, UK
- Velindre Cancer Centre, Medical Directorate, Cardiff, Wales, UK
| | - Emiliano Spezi
- School of Engineering, Cardiff University, Cardiff, Wales, UK
| | - Anthony E Millin
- Radiotherapy Physics Department, Velindre Cancer Centre, CF14 2TL, Cardiff, Wales, UK
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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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Wüthrich D, Zeverino M, Bourhis J, Bochud F, Moeckli R. Influence of optimisation parameters on directly deliverable Pareto fronts explored for prostate cancer. Phys Med 2023; 114:103139. [PMID: 37757500 DOI: 10.1016/j.ejmp.2023.103139] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/30/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
PURPOSE In inverse radiotherapy treatment planning, the Pareto front is the set of optimal solutions to the multi-criteria problem of adequately irradiating the planning target volume (PTV) while reducing dose to organs at risk (OAR). The Pareto front depends on the chosen optimisation parameters whose influence (clinically relevant versus not clinically relevant) is investigated in this paper. METHODS Thirty-one prostate cancer patients treated at our clinic were randomly selected. We developed an in-house Python script that controlled the commercial treatment planning system RayStation to calculate directly deliverable Pareto fronts. We calculated reference Pareto fronts for a given set of objective functions, varying the PTV coverage and the mean dose of the primary OAR (rectum) and fixing the mean doses of the secondary OARs (bladder and femoral heads). We calculated the fronts for different sets of objective functions and different mean doses to secondary OARs. We compared all fronts using a specific metric (clinical distance measure). RESULTS The in-house script was validated for directly deliverable Pareto front calculations in two and three dimensions. The Pareto fronts depended on the choice of objective functions and fixed mean doses to secondary OARs, whereas the parameters most influencing the front and leading to clinically relevant differences were the dose gradient around the PTV, the weight of the PTV objective function, and the bladder mean dose. CONCLUSIONS Our study suggests that for multi-criteria optimisation of prostate treatments using external therapy, dose gradient around the PTV and bladder mean dose are the most influencial parameters.
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Affiliation(s)
- Diana Wüthrich
- Institute of Radiation Physics, Lausanne University Hospital and Lausanne University, Rue du Grand-Pré 1, CH-1007 Lausanne, Switzerland.
| | - Michele Zeverino
- Institute of Radiation Physics, Lausanne University Hospital and Lausanne University, Rue du Grand-Pré 1, CH-1007 Lausanne, Switzerland.
| | - Jean Bourhis
- Department of Radiation Oncology, Lausanne University Hospital and Lausanne University, Rue du Bugnon 46, CH-1011 Lausanne, Switzerland.
| | - François Bochud
- Institute of Radiation Physics, Lausanne University Hospital and Lausanne University, Rue du Grand-Pré 1, CH-1007 Lausanne, Switzerland.
| | - Raphaël Moeckli
- Institute of Radiation Physics, Lausanne University Hospital and Lausanne University, Rue du Grand-Pré 1, CH-1007 Lausanne, Switzerland.
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Parkinson C, Matthams C, Foley K, Spezi E. Artificial intelligence in radiation oncology: A review of its current status and potential application for the radiotherapy workforce. Radiography (Lond) 2021; 27 Suppl 1:S63-S68. [PMID: 34493445 DOI: 10.1016/j.radi.2021.07.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 07/05/2021] [Accepted: 07/20/2021] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Radiation oncology is a continually evolving speciality. With the development of new imaging modalities and advanced imaging processing techniques, there is an increasing amount of data available to practitioners. In this narrative review, Artificial Intelligence (AI) is used as a reference to machine learning, and its potential, along with current problems in the field of radiation oncology, are considered from a technical position. KEY FINDINGS AI has the potential to harness the availability of data for improving patient outcomes, reducing toxicity, and easing clinical burdens. However, problems including the requirement of complexity of data, undefined core outcomes and limited generalisability are apparent. CONCLUSION This original review highlights considerations for the radiotherapy workforce, particularly therapeutic radiographers, as there will be an increasing requirement for their familiarity with AI due to their unique position as the interface between imaging technology and patients. IMPLICATIONS FOR PRACTICE Collaboration between AI experts and the radiotherapy workforce are required to overcome current issues before clinical adoption. The development of educational resources and standardised reporting of AI studies may help facilitate this.
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Affiliation(s)
- C Parkinson
- School of Engineering, Cardiff University, UK.
| | | | | | - E Spezi
- School of Engineering, Cardiff University, UK
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Kyroudi A, Petersson K, Ozsahin E, Bourhis J, Bochud F, Moeckli R. Exploration of clinical preferences in treatment planning of radiotherapy for prostate cancer using Pareto fronts and clinical grading analysis. Phys Imaging Radiat Oncol 2020; 14:82-86. [PMID: 33458319 PMCID: PMC7807626 DOI: 10.1016/j.phro.2020.05.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 05/26/2020] [Accepted: 05/29/2020] [Indexed: 12/25/2022] Open
Abstract
INTRODUCTION Radiotherapy treatment planning is a multi-criteria problem. Any optimization of the process produces a set of mathematically optimal solutions. These optimal plans are considered mathematically equal, but they differ in terms of the trade-offs involved. Since the various objectives are conflicting, the choice of the best plan for treatment is dependent on the preferences of the radiation oncologists or the medical physicists (decision makers).We defined a clinically relevant area on a prostate Pareto front which better represented clinical preferences and determined if there were differences among radiation oncologists and medical physicists. METHODS AND MATERIALS Pareto fronts of five localized prostate cancer patients were used to analyze and visualize the trade-off between the rectum sparing and the PTV under-dosage. Clinical preferences were evaluated with Clinical Grading Analysis by asking nine radiation oncologists and ten medical physicists to rate pairs of plans presented side by side. A choice of the optimal plan on the Pareto front was made by all decision makers. RESULTS The plans in the central region of the Pareto front (1-4% PTV under-dosage) received the best evaluations. Radiation oncologists preferred the organ at risk (OAR) sparing region (2.5-4% PTV under-dosage) while medical physicists preferred better PTV coverage (1-2.5% PTV under-dosage). When the Pareto fronts were additionally presented to the decisions makers they systematically chose the plan in the trade-off region (0.5-1% PTV under-dosage). CONCLUSION We determined a specific region on the Pareto front preferred by the radiation oncologists and medical physicists and found a difference between them.
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Affiliation(s)
- A. Kyroudi
- Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand-Pré 1, CH 1007 Lausanne, Switzerland
| | - K. Petersson
- Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand-Pré 1, CH 1007 Lausanne, Switzerland
| | - E. Ozsahin
- Department of Radiation Oncology, Lausanne University Hospital, Rue du Bugnon 46, CH 1011 Lausanne, Switzerland
| | - J. Bourhis
- Department of Radiation Oncology, Lausanne University Hospital, Rue du Bugnon 46, CH 1011 Lausanne, Switzerland
| | - F. Bochud
- Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand-Pré 1, CH 1007 Lausanne, Switzerland
| | - R. Moeckli
- Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand-Pré 1, CH 1007 Lausanne, Switzerland
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Wheeler PA, Chu M, Holmes R, Woodley OW, Jones CS, Maggs R, Staffurth J, Palaniappan N, Spezi E, Lewis DG, Campbell S, Fitzgibbon J, Millin AE. Evaluating the application of Pareto navigation guided automated radiotherapy treatment planning to prostate cancer. Radiother Oncol 2019; 141:220-226. [PMID: 31526670 DOI: 10.1016/j.radonc.2019.08.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 07/19/2019] [Accepted: 08/12/2019] [Indexed: 10/26/2022]
Abstract
BACKGROUND AND PURPOSE Current automated planning methods do not allow for the intuitive exploration of clinical trade-offs during calibration. Recently a novel automated planning solution, which is calibrated using Pareto navigation principles, has been developed to address this issue. The purpose of this work was to clinically validate the solution for prostate cancer patients with and without elective nodal irradiation. MATERIALS AND METHODS For 40 randomly selected patients (20 prostate and seminal vesicles (PSV) and 20 prostate and pelvic nodes (PPN)) automatically generated volumetric modulated arc therapy plans (VMATAuto) were compared against plans created by expert dosimetrists under clinical conditions (VMATClinical) and no time pressures (VMATIdeal). Plans were compared through quantitative comparison of dosimetric parameters and blind review by an oncologist. RESULTS Upon blind review 39/40 and 33/40 VMATAuto plans were considered preferable or equal to VMATClinical and VMATIdeal respectively, with all deemed clinically acceptable. Dosimetrically, VMATAuto, VMATClinical and VMATIdeal were similar, with observed differences generally of low clinical significance. Compared to VMATClinical, VMATAuto reduced hands-on planning time by 94% and 79% for PSV and PPN respectively. Total planning time was significantly reduced from 22.2 mins to 14.0 mins for PSV, with no significant reduction observed for PPN. CONCLUSIONS A novel automated planning solution has been evaluated, whose Pareto navigation based calibration enabled clinical decision-making on trade-off balancing to be intuitively incorporated into automated protocols. It was successfully applied to two sites of differing complexity and robustly generated high quality plans in an efficient manner.
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Affiliation(s)
- Philip A Wheeler
- Department of Medical Physics, Velindre Cancer Centre, Cardiff, United Kingdom.
| | - Michael Chu
- Department of Medical Physics, Velindre Cancer Centre, Cardiff, United Kingdom
| | - Rosemary Holmes
- Department of Medical Physics, Velindre Cancer Centre, Cardiff, United Kingdom
| | - Owain W Woodley
- Department of Medical Physics, Velindre Cancer Centre, Cardiff, United Kingdom
| | - Ceri S Jones
- Department of Medical Physics, Velindre Cancer Centre, Cardiff, United Kingdom
| | - Rhydian Maggs
- Department of Medical Physics, Velindre Cancer Centre, Cardiff, United Kingdom
| | - John Staffurth
- School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Nachi Palaniappan
- Department of Oncology, Velindre Cancer Centre, Cardiff, United Kingdom
| | - Emiliano Spezi
- School of Engineering, Cardiff University, Cardiff, United Kingdom
| | - David G Lewis
- Department of Medical Physics, Velindre Cancer Centre, Cardiff, United Kingdom
| | | | | | - Anthony E Millin
- Department of Medical Physics, Velindre Cancer Centre, Cardiff, United Kingdom
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Cozzi L. Advanced treatment planning strategies to enhance quality and efficiency of radiotherapy. Phys Imaging Radiat Oncol 2019; 11:69-70. [PMID: 33458281 PMCID: PMC7807646 DOI: 10.1016/j.phro.2019.09.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- Luca Cozzi
- Radiotherapy and Radiosurgery, Humanitas Clinical and Research Center, Rozzano (Milan), Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy
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