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Smolders A, Rivetti L, Vatterodt N, Korreman S, Lomax A, Sharma M, Studen A, Weber DC, Jeraj R, Albetini F. DiffuseRT: predicting likely anatomical deformations of patients undergoing radiotherapy. Phys Med Biol 2024; 69:155016. [PMID: 38986481 DOI: 10.1088/1361-6560/ad61b7] [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: 03/19/2024] [Accepted: 07/10/2024] [Indexed: 07/12/2024]
Abstract
Objective. Predicting potential deformations of patients can improve radiotherapy treatment planning. Here, we introduce new deep-learning models that predict likely anatomical changes during radiotherapy for head and neck cancer patients.Approach. Denoising diffusion probabilistic models (DDPMs) were developed to generate fraction-specific anatomical changes based on a reference cone-beam CT (CBCT), the fraction number and the dose distribution delivered. Three distinct DDPMs were developed: (1) theimage modelwas trained to directly generate likely future CBCTs, (2) the deformable vector field (DVF) model was trained to generate DVFs that deform a reference CBCT and (3) thehybrid modelwas trained similarly to the DVF model, but without relying on an external deformable registration algorithm. The models were trained on 9 patients with longitudinal CBCT images (224 CBCTs) and evaluated on 5 patients (152 CBCTs).Results. The generated images mainly exhibited random positioning shifts and small anatomical changes for early fractions. For later fractions, all models predicted weight losses in accordance with the training data. The distributions of volume and position changes of the body, esophagus, and parotids generated with the image and hybrid models were more similar to the ground truth distribution than the DVF model, evident from the lower Wasserstein distance achieved with the image (0.33) and hybrid model (0.30) compared to the DVF model (0.36). Generating several images for the same fraction did not yield the expected variability since the ground truth anatomical changes were only in 76% of the fractions within the 95% bounds predicted with the best model. Using the generated images for robust optimization of simplified proton therapy plans improved the worst-case clinical target volume V95 with 7% compared to optimizing with 3 mm set-up robustness while maintaining a similar integral dose.Significance. The newly developed DDPMs generate distributions similar to the real anatomical changes and have the potential to be used for robust anatomical optimization.
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Affiliation(s)
- A Smolders
- Paul Scherrer Institute, Center for Proton Therapy, Villigen, Switzerland
- Department of Physics, ETH Zurich, Switzerland
| | - L Rivetti
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - N Vatterodt
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - S Korreman
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - A Lomax
- Paul Scherrer Institute, Center for Proton Therapy, Villigen, Switzerland
- Department of Physics, ETH Zurich, Switzerland
| | - M Sharma
- Department of Radiation Oncology, University of California, San Francisco, CA, United States of America
| | - A Studen
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
- Jožef Stefan Institute, Ljubljana, Slovenia
| | - D C Weber
- Paul Scherrer Institute, Center for Proton Therapy, Villigen, Switzerland
- Department of Radiation Oncology, University Hospital Zurich, Zurich, Switzerland
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - R Jeraj
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
- Jožef Stefan Institute, Ljubljana, Slovenia
- University of Wisconsin-Madison, Madison, WI, United States of America
| | - F Albetini
- Paul Scherrer Institute, Center for Proton Therapy, Villigen, Switzerland
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Biswal NC, Zhang B, Nichols E, Witek ME, Regine WF, Yi B. Cone-Beam CT Images as an Indicator of QACT During Adaptive Proton Therapy of Extremity Sarcomas. Int J Part Ther 2024; 12:100017. [PMID: 39022119 PMCID: PMC11252065 DOI: 10.1016/j.ijpt.2024.100017] [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: 10/24/2023] [Revised: 04/09/2024] [Accepted: 04/17/2024] [Indexed: 07/20/2024] Open
Abstract
Purpose Periodic quality assurance CTs (QACTs) are routine in proton beam therapy. In this study, we tested whether the necessity for a QACT could be determined by evaluating the change in beam path length (BPL) on daily cone-beam CT (CBCT). Patients and Methods In this Institutional Review Board-approved study, we retrospectively analyzed 959 CBCT images from 78 patients with sarcomas treated with proton pencil-beam scanning. Plans on 17 QACTs out of a total of 243 were clinically determined to be replanned for various reasons. Daily CBCTs were retrospectively analyzed by automatic ray-tracing of each beam from the isocenter to the skin surface along the central axis. A script was developed for this purpose. Patterns of change in BPL on CBCT images were compared to those from adaptive planning using weekly QACTs. Results Sixteen of the 17 adaptive replans showed BPL changes ≥4 mm for at least 1 of the beams on 3 consecutive CBCT sessions. Similarly, 43 of 63 nonadaptively planned patients had BPL changes <4 mm for all of the beams. A new QACT criterium of a BPL change of any beam ≥4 mm on 3 consecutive CBCT sessions resulted in a sensitivity of 94.1% and a specificity of 68.3%. Had the BPL change been used as the QACT predictor, a total of 37 QACTs would have been performed rather than 243 QACTs in clinical practice. Conclusion The use of BPL changes on CBCT images represented a significant reduction (85%) in total QACT burden while maintaining treatment quality and accuracy. QACT can be performed only when it is needed, but not in a periodic manner. The benefits of reducing QACT frequency include reducing imaging dose and optimizing patient time and staff resources.
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Affiliation(s)
- Nrusingh C. Biswal
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201
- Maryland Proton Treatment Center, Baltimore, MD 21201
| | - Baoshe Zhang
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201
- Maryland Proton Treatment Center, Baltimore, MD 21201
| | - Elizabeth Nichols
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201
- Maryland Proton Treatment Center, Baltimore, MD 21201
| | - Matthew E. Witek
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201
- Maryland Proton Treatment Center, Baltimore, MD 21201
| | - William F. Regine
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201
- Maryland Proton Treatment Center, Baltimore, MD 21201
| | - ByongYong Yi
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201
- Maryland Proton Treatment Center, Baltimore, MD 21201
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Knäusl B, Belotti G, Bertholet J, Daartz J, Flampouri S, Hoogeman M, Knopf AC, Lin H, Moerman A, Paganelli C, Rucinski A, Schulte R, Shimizu S, Stützer K, Zhang X, Zhang Y, Czerska K. A review of the clinical introduction of 4D particle therapy research concepts. Phys Imaging Radiat Oncol 2024; 29:100535. [PMID: 38298885 PMCID: PMC10828898 DOI: 10.1016/j.phro.2024.100535] [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: 10/11/2023] [Revised: 12/12/2023] [Accepted: 01/04/2024] [Indexed: 02/02/2024] Open
Abstract
Background and purpose Many 4D particle therapy research concepts have been recently translated into clinics, however, remaining substantial differences depend on the indication and institute-related aspects. This work aims to summarise current state-of-the-art 4D particle therapy technology and outline a roadmap for future research and developments. Material and methods This review focused on the clinical implementation of 4D approaches for imaging, treatment planning, delivery and evaluation based on the 2021 and 2022 4D Treatment Workshops for Particle Therapy as well as a review of the most recent surveys, guidelines and scientific papers dedicated to this topic. Results Available technological capabilities for motion surveillance and compensation determined the course of each 4D particle treatment. 4D motion management, delivery techniques and strategies including imaging were diverse and depended on many factors. These included aspects of motion amplitude, tumour location, as well as accelerator technology driving the necessity of centre-specific dosimetric validation. Novel methodologies for X-ray based image processing and MRI for real-time tumour tracking and motion management were shown to have a large potential for online and offline adaptation schemes compensating for potential anatomical changes over the treatment course. The latest research developments were dominated by particle imaging, artificial intelligence methods and FLASH adding another level of complexity but also opportunities in the context of 4D treatments. Conclusion This review showed that the rapid technological advances in radiation oncology together with the available intrafractional motion management and adaptive strategies paved the way towards clinical implementation.
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Affiliation(s)
- Barbara Knäusl
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Gabriele Belotti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Jenny Bertholet
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Juliane Daartz
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Mischa Hoogeman
- Department of Medical Physics & Informatics, HollandPTC, Delft, The Netherlands
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, The Netherlands
| | - Antje C Knopf
- Institut für Medizintechnik und Medizininformatik Hochschule für Life Sciences FHNW, Muttenz, Switzerland
| | - Haibo Lin
- New York Proton Center, New York, NY, USA
| | - Astrid Moerman
- Department of Medical Physics & Informatics, HollandPTC, Delft, The Netherlands
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Antoni Rucinski
- Institute of Nuclear Physics Polish Academy of Sciences, PL-31342 Krakow, Poland
| | - Reinhard Schulte
- Division of Biomedical Engineering Sciences, School of Medicine, Loma Linda University
| | - Shing Shimizu
- Department of Carbon Ion Radiotherapy, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kristin Stützer
- OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz-Zentrum Dresden – Rossendorf, Institute of Radiooncology – OncoRay, Dresden, Germany
| | - Xiaodong Zhang
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
| | - Katarzyna Czerska
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
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Janson M, Glimelius L, Fredriksson A, Traneus E, Engwall E. Treatment planning of scanned proton beams in RayStation. Med Dosim 2023; 49:2-12. [PMID: 37996354 DOI: 10.1016/j.meddos.2023.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/17/2023] [Accepted: 10/22/2023] [Indexed: 11/25/2023]
Abstract
The use of scanned proton beams in external beam radiation therapy has seen a rapid development over the past decade. This technique places new demands on treatment planning, as compared to conventional photon-based radiation therapy. In this article, several proton specific functions as implemented in the treatment planning system RayStation are presented. We will cover algorithms for energy layer and spot selection, basic optimization including the handling of spot weight limits, optimization of the linear energy transfer (LET) distribution, robust optimization including the special case of 4D optimization, proton arc planning, and automatic planning using deep learning. We will further present the Monte Carlo (MC) proton dose engine in RayStation to some detail, from the material interpretation of the CT data, through the beam model parameterization, to the actual MC transport mechanism. Useful tools for plan evaluation, including robustness evaluation, and the versatile scripting interface are also described. The overall aim of the paper is to give an overview of some of the key proton planning functions in RayStation, with example usages, and at the same time provide the details about the underlying algorithms that previously have not been fully publicly available.
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Tsai P, Tseng YL, Shen B, Ackerman C, Zhai HA, Yu F, Simone CB, Choi JI, Lee NY, Kabarriti R, Lazarev S, Johnson CL, Liu J, Chen CC, Lin H. The Applications and Pitfalls of Cone-Beam Computed Tomography-Based Synthetic Computed Tomography for Adaptive Evaluation in Pencil-Beam Scanning Proton Therapy. Cancers (Basel) 2023; 15:5101. [PMID: 37894469 PMCID: PMC10605451 DOI: 10.3390/cancers15205101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 10/18/2023] [Accepted: 10/20/2023] [Indexed: 10/29/2023] Open
Abstract
PURPOSE The study evaluates the efficacy of cone-beam computed tomography (CBCT)-based synthetic CTs (sCT) as a potential alternative to verification CT (vCT) for enhanced treatment monitoring and early adaptation in proton therapy. METHODS Seven common treatment sites were studied. Two sets of sCT per case were generated: direct-deformed (DD) sCT and image-correction (IC) sCT. The image qualities and dosimetric impact of the sCT were compared to the same-day vCT. RESULTS The sCT agreed with vCT in regions of homogeneous tissues such as the brain and breast; however, notable discrepancies were observed in the thorax and abdomen. The sCT outliers existed for DD sCT when there was an anatomy change and for IC sCT in low-density regions. The target coverage exhibited less than a 5% variance in most DD and IC sCT cases when compared to vCT. The Dmax of serial organ-at-risk (OAR) in sCT plans shows greater deviation from vCT than small-volume dose metrics (D0.1cc). The parallel OAR volumetric and mean doses remained consistent, with average deviations below 1.5%. CONCLUSION The use of sCT enables precise treatment and prompt early adaptation for proton therapy. The quality assurance of sCT is mandatory in the early stage of clinical implementation.
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Affiliation(s)
- Pingfang Tsai
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | - Yu-Lun Tseng
- Proton Center, Taipei Medical University, Taipei 11031, Taiwan;
- Department of Radiation Oncology, Taipei Medical University, Taipei 11031, Taiwan
| | - Brian Shen
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | | | - Huifang A. Zhai
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | - Francis Yu
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | - Charles B. Simone
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | - J. Isabelle Choi
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | - Nancy Y. Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Rafi Kabarriti
- Department of Radiation Oncology, Montefiore Medical Center, Bronx, NY 10467, USA;
| | - Stanislav Lazarev
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Casey L. Johnson
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | - Jiayi Liu
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | - Chin-Cheng Chen
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
| | - Haibo Lin
- New York Proton Center, New York, NY 10035, USA; (P.T.); (B.S.); (H.A.Z.); (F.Y.); (C.B.S.II); (J.I.C.); (C.L.J.); (J.L.); (C.-C.C.)
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