1
|
Kelleter L, Marek L, Echner G, Ochoa-Parra P, Winter M, Harrabi S, Jakubek J, Jäkel O, Debus J, Martisikova M. An in-vivo treatment monitoring system for ion-beam radiotherapy based on 28 Timepix3 detectors. Sci Rep 2024; 14:15452. [PMID: 38965349 PMCID: PMC11224389 DOI: 10.1038/s41598-024-66266-9] [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: 04/11/2024] [Accepted: 07/01/2024] [Indexed: 07/06/2024] Open
Abstract
Ion-beam radiotherapy is an advanced cancer treatment modality offering steep dose gradients and a high biological effectiveness. These gradients make the therapy vulnerable to patient-setup and anatomical changes between treatment fractions, which may go unnoticed. Charged fragments from nuclear interactions of the ion beam with the patient tissue may carry information about the treatment quality. Currently, the fragments escape the patient undetected. Inter-fractional in-vivo treatment monitoring based on these charged nuclear fragments could make ion-beam therapy safer and more efficient. We developed an ion-beam monitoring system based on 28 hybrid silicon pixel detectors (Timepix3) to measure the distribution of fragment origins in three dimensions. The system design choices as well as the ion-beam monitoring performance measurements are presented in this manuscript. A spatial resolution of 4 mm along the beam axis was achieved for the measurement of individual fragment origins. Beam-range shifts of1.5 mm were identified in a clinically realistic treatment scenario with an anthropomorphic head phantom. The monitoring system is currently being used in a prospective clinical trial at the Heidelberg Ion Beam Therapy Centre for head-and-neck as well as central nervous system cancer patients.
Collapse
Affiliation(s)
- Laurent Kelleter
- Heidelberg Institute for Radiation Oncology (HIRO) and National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany.
- Division of Medical Physics in Radiation Oncology, German Cancer Research Centre (DKFZ), Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, A Partnership Between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany.
| | | | - Gernot Echner
- Heidelberg Institute for Radiation Oncology (HIRO) and National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany
- Division of Medical Physics in Radiation Oncology, German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | - Pamela Ochoa-Parra
- Heidelberg Institute for Radiation Oncology (HIRO) and National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany
- Division of Medical Physics in Radiation Oncology, German Cancer Research Centre (DKFZ), Heidelberg, Germany
- Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Marcus Winter
- Heidelberg Ion-Beam Therapy Centre (HIT), Department of Radiation Oncology Heidelberg University Hospital, Heidelberg, Germany
| | - Semi Harrabi
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Oliver Jäkel
- Heidelberg Institute for Radiation Oncology (HIRO) and National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany
- Division of Medical Physics in Radiation Oncology, German Cancer Research Centre (DKFZ), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, A Partnership Between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), Department of Radiation Oncology Heidelberg University Hospital, Heidelberg, Germany
| | - Jürgen Debus
- Division of Medical Physics in Radiation Oncology, German Cancer Research Centre (DKFZ), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, A Partnership Between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Centre (HIT), Department of Radiation Oncology Heidelberg University Hospital, Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Centre (DKFZ), Heidelberg, Germany
| | - Maria Martisikova
- Heidelberg Institute for Radiation Oncology (HIRO) and National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany
- Division of Medical Physics in Radiation Oncology, German Cancer Research Centre (DKFZ), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, A Partnership Between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| |
Collapse
|
2
|
Gambetta V, Fredriksson A, Menkel S, Richter C, Stützer K. The partial adaptation strategy for online-adaptive proton therapy: A proof of concept study in head and neck cancer patients. Med Phys 2024. [PMID: 38837396 DOI: 10.1002/mp.17178] [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: 11/22/2023] [Revised: 03/06/2024] [Accepted: 04/08/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND The accuracy of intensity-modulated proton therapy (IMPT) is greatly affected by anatomy variations that might occur during the treatment course. Online plan adaptations have been proposed as a solution to intervene promptly during a treatment session once the anatomy changes are detected. The implementation of online-adaptive proton therapy (OAPT) is still hindered by time-consuming tasks in the workflow. PURPOSE The study introduces the novel concept of partial adaptation and aims at investigating its feasibility as a potential solution to parallelize tasks during an OAPT workflow for saving valuable in-room time. METHODS The proof-of-principle simulation study includes datasets from six head and neck cancer (HNC) patients, each consisting of one planning CT (pCT) and three contoured control CTs (cCTs). Robust 3-field normo-fractionated initial IMPT plans were generated on the pCTs with a standardized field configuration, delivering 66 Gy and 54 Gy to the high-risk and low-risk clinical target volume (CTVHigh and CTVLow), respectively. For each cCT, a dose-mimicking-based partial adaptation was applied: two fields were adapted on the current anatomy taking into account the background dose of the first non-adapted field supposedly delivered in the meantime. Fraction doses on the cCTs resulting from partially adapted plans with different first (non-adapted) field assignments were compared against those from non-adapted and fully adapted plans regarding target coverage and organs at risk (OARs) sparing. The robustness of partially adapted plans was also evaluated. RESULTS Partially adapted plans showed comparable results to fully adapted plans and were superior to non-adapted plans for both target coverage and OAR sparing. Target coverage degradation in the non-adapted plans (median D98%: 95.9% and 97.5% for CTVLow and CTVHigh, respectively) was recovered by both partial (98.0% and 98.5%) and full adaptation (98.2% and 98.7%) in comparison to the initial plans (98.7% and 98.8%). The initial hotspot dose for the CTVHigh (median D2%: 101.8%) increased in the non-adapted plans (102.9%) and was recovered by the adaptive strategies (partial: 102.5%, full: 101.9%). The near-maximum dose (D0.01cc) to brainstem and spinal cord was within clinical constraints for all investigated dose distributions, but clearly increased for no adaptation and improved in the (both partially and fully) adapted plans with respect to the non-adapted ones. The parotids' median doses (D50) were mainly patient-specific depending on the proximity to the target region, but anyway lower for the partially and fully adapted plans compared to the non-adapted ones. OAR sparing was furthermore improved for the partially adapted plans in comparison to full adaptation. Robustness of the target dose metrics was preserved in all evaluated scenarios. CONCLUSIONS For OAPT of HNC patients, partial adaptation is able to generate plans of superior conformity to non-adapted plans and of comparable conformity as fully adapted plans, while having the potential to speed up the online-adaptive workflows. Thus, partial adaptation represents an intermediate approach until fast online adaptation workflows become available. Furthermore, it can be applied in workflows where online treatment verification stops the delivery and triggers an online adaptation for the remaining fraction.
Collapse
Affiliation(s)
- Virginia Gambetta
- 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
- Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany
| | | | - Stefan Menkel
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Christian Richter
- 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
- Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kristin Stützer
- 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
- Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany
| |
Collapse
|
3
|
Qiu Z, Depauw N, Gorissen BL, Madden T, Ajdari A, den Hertog D, Bortfeld T. A reference-point-method-based online proton treatment plan re-optimization strategy and a novel solution to planning constraint infeasibility problem. Phys Med Biol 2024; 69:125001. [PMID: 38729194 DOI: 10.1088/1361-6560/ad4a00] [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: 01/08/2024] [Accepted: 05/10/2024] [Indexed: 05/12/2024]
Abstract
Objective. Propose a highly automated treatment plan re-optimization strategy suitable for online adaptive proton therapy. The strategy includes a rapid re-optimization method that generates quality replans and a novel solution that efficiently addresses the planning constraint infeasibility issue that can significantly prolong the re-optimization process.Approach. We propose a systematic reference point method (RPM) model that minimizes the l-infinity norm from the initial treatment plan in the daily objective space for online re-optimization. This model minimizes the largest objective value deviation among the objectives of the daily replan from their reference values, leading to a daily replan similar to the initial plan. Whether a set of planning constraints is feasible with respect to the daily anatomy cannot be known before solving the corresponding optimization problem. The conventional trial-and-error-based relaxation process can cost a significant amount of time. To that end, we propose an optimization problem that first estimates the magnitude of daily violation of each planning constraint. Guided by the violation magnitude and clinical importance of the constraints, the constraints are then iteratively converted into objectives based on their priority until the infeasibility issue is solved.Main results.The proposed RPM-based strategy generated replans similar to the offline manual replans within the online time requirement for six head and neck and four breast patients. The average targetD95and relevant organ at risk sparing parameter differences between the RPM replans and clinical offline replans were -0.23, -1.62 Gy for head and neck cases and 0.29, -0.39 Gy for breast cases. The proposed constraint relaxation solution made the RPM problem feasible after one round of relaxation for all four patients who encountered the infeasibility issue.Significance. We proposed a novel RPM-based re-optimization strategy and demonstrated its effectiveness on complex cases, regardless of whether constraint infeasibility is encountered.
Collapse
Affiliation(s)
- Zihang Qiu
- Department of Business Analytics, University of Amsterdam, Amsterdam, The Netherlands
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States of America
| | - Nicolas Depauw
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States of America
| | - Bram L Gorissen
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Boston, MA, United States of America
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, United States of America
| | - Thomas Madden
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States of America
| | - Ali Ajdari
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States of America
| | - Dick den Hertog
- Department of Business Analytics, University of Amsterdam, Amsterdam, The Netherlands
| | - Thomas Bortfeld
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States of America
| |
Collapse
|
4
|
Rivetti L, Studen A, Sharma M, Chan J, Jeraj R. Uncertainty estimation and evaluation of deformation image registration based convolutional neural networks. Phys Med Biol 2024; 69:115045. [PMID: 38749468 DOI: 10.1088/1361-6560/ad4c4f] [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: 02/07/2024] [Accepted: 05/15/2024] [Indexed: 05/31/2024]
Abstract
Objective.Fast and accurate deformable image registration (DIR), including DIR uncertainty estimation, is essential for safe and reliable clinical deployment. While recent deep learning models have shown promise in predicting DIR with its uncertainty, challenges persist in proper uncertainty evaluation and hyperparameter optimization for these methods. This work aims to develop and evaluate a model that can perform fast DIR and predict its uncertainty in seconds.Approach.This study introduces a novel probabilistic multi-resolution image registration model utilizing convolutional neural networks to estimate a multivariate normal distributed dense displacement field (DDF) in a multimodal image registration problem. To assess the quality of the DDF distribution predicted by the model, we propose a new metric based on the Kullback-Leibler divergence. The performance of our approach was evaluated against three other DIR algorithms (VoxelMorph, Monte Carlo dropout, and Monte Carlo B-spline) capable of predicting uncertainty. The evaluation of the models included not only the quality of the deformation but also the reliability of the estimated uncertainty. Our application investigated the registration of a treatment planning computed tomography (CT) to follow-up cone beam CT for daily adaptive radiotherapy.Main results.The hyperparameter tuning of the models showed a trade-off between the estimated uncertainty's reliability and the deformation's accuracy. In the optimal trade-off, our model excelled in contour propagation and uncertainty estimation (p <0.05) compared to existing uncertainty estimation models. We obtained an average dice similarity coefficient of 0.89 and a KL-divergence of 0.15.Significance.By addressing challenges in DIR uncertainty estimation and evaluation, our work showed that both the DIR and its uncertainty can be reliably predicted, paving the way for safe deployment in a clinical environment.
Collapse
Affiliation(s)
- Luciano Rivetti
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - Andrej Studen
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
- Jožef Stefan Institute, Ljubljana, Slovenia
| | - Manju Sharma
- Department of Radiation Oncology, University of California, San Francisco, CA, United States of America
| | - Jason Chan
- Department of Radiation Oncology, University of California, San Francisco, CA, United States of America
| | - Robert Jeraj
- Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
- Jožef Stefan Institute, Ljubljana, Slovenia
- School of Medicine and Public Health, Department of Medical Physics, University of Wisconsin, Madison, WI, United States of America
| |
Collapse
|
5
|
Belotti G, Fattori G, Baroni G, Rit S. Extension of the cone-beam CT field-of-view using two complementary short scans. Med Phys 2024; 51:3391-3404. [PMID: 38043079 DOI: 10.1002/mp.16869] [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: 07/18/2023] [Revised: 10/10/2023] [Accepted: 11/09/2023] [Indexed: 12/05/2023] Open
Abstract
BACKGROUND Robotic C-arm cone-beam computed tomography (CBCT) scanners provide fast in-room imaging in radiotherapy. Their mobility extends beyond performing a gantry rotation, but they might encounter obstructions to their motion which limit the gantry angle range. The axial field-of-view (FOV) of a reconstructed CBCT image depends on the acquisition geometry. When imaging a large anatomical location, such as the thorax, abdomen, or pelvis, a centered cone beam might be insufficient to acquire untruncated projection images. Some CBCT scanners can laterally displace their detector and collimate the beam to increase the FOV, but the gantry must then perform a360 ∘ $360^{\circ}$ rotation to provide complete data for reconstruction. PURPOSE To extend the FOV of a CBCT image with a single short scan (gantry angle range of180 ∘ + $180^{\circ}+$ fan angle) using two complementary short scans. METHODS We defined an acquisition protocol using two short scans during which the source follows the same trajectory and where the detector has equal and opposite tilt and/or offset between the two scans, which we refer to as complementary scans. We created virtual acquisitions using a Monte Carlo simulator on a digital anthropomorphic phantom and on a computed tomography (CT) scan of a patient abdomen. For our proposed method, each simulation produced two complementary sets of projections, which were weighted for redundancies and used to reconstruct one CBCT image. We compared the resulting images to the ground truth phantoms and simulations of conventional scans. RESULTS Reconstruction artifacts were slightly more prominent in the complementary scans w.r.t. a complete scan with untruncated projections but matched those in a single short scan without truncation. When analyzing reconstructed scans from simulated projections with scatter and corrected with prior CT information, we found a global agreement between complementary and conventional scan approaches. CONCLUSIONS When dealing with a limited range of motion of the gantry of a CBCT scanner, two complementary short scans are a technically valid alternative to a full 360∘ $^{\circ}$ scan with equal FOV. This approach enables FOV extension without collisions or hardware upgrades.
Collapse
Affiliation(s)
- Gabriele Belotti
- Department of Electronics, Information and Bioengineering, CartCasLab, Politecnico di Milano (MI), Milan, Italy
| | - Giovanni Fattori
- Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, CartCasLab, Politecnico di Milano (MI), Milan, Italy
- Centro Nazionale di Adroterapia Oncologica (CNAO), Pavia (PV), Italy
| | - Simon Rit
- Univ Lyon, CREATIS, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR5220, U1294, Lyon, France
| |
Collapse
|
6
|
Chang C, Bohannon D, Tian Z, Wang Y, Mcdonald MW, Yu DS, Liu T, Zhou J, Yang X. A retrospective study on the investigation of potential dosimetric benefits of online adaptive proton therapy for head and neck cancer. J Appl Clin Med Phys 2024; 25:e14308. [PMID: 38368614 PMCID: PMC11087169 DOI: 10.1002/acm2.14308] [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: 05/03/2023] [Revised: 10/28/2023] [Accepted: 02/06/2024] [Indexed: 02/20/2024] Open
Abstract
PURPOSE Proton therapy is sensitive to anatomical changes, often occurring in head-and-neck (HN) cancer patients. Although multiple studies have proposed online adaptive proton therapy (APT), there is still a concern in the radiotherapy community about the necessity of online APT. We have performed a retrospective study to investigate the potential dosimetric benefits of online APT for HN patients relative to the current offline APT. METHODS Our retrospective study has a patient cohort of 10 cases. To mimic online APT, we re-evaluated the dose of the in-use treatment plan on patients' actual treatment anatomy captured by cone-beam CT (CBCT) for each fraction and performed a templated-based automatic replanning if needed, assuming that these were performed online before treatment delivery. Cumulative dose of the simulated online APT course was calculated and compared with that of the actual offline APT course and the designed plan dose of the initial treatment plan (referred to as nominal plan). The ProKnow scoring system was employed and adapted for our study to quantify the actual quality of both courses against our planning goals. RESULTS The average score of the nominal plans over the 10 cases is 41.0, while those of the actual offline APT course and our simulated online course is 25.8 and 37.5, respectively. Compared to the offline APT course, our online course improved dose quality for all cases, with the score improvement ranging from 0.4 to 26.9 and an average improvement of 11.7. CONCLUSION The results of our retrospective study have demonstrated that online APT can better address anatomical changes for HN cancer patients than the current offline replanning practice. The advanced artificial intelligence based automatic replanning technology presents a promising avenue for extending potential benefits of online APT.
Collapse
Affiliation(s)
- Chih‐Wei Chang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Duncan Bohannon
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Zhen Tian
- Department of Radiation and Cellular OncologyUniversity of ChicagoChicagoIllinoisUSA
| | - Yinan Wang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Mark W. Mcdonald
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - David S. Yu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Tian Liu
- Department of Radiation OncologyMount Sinai Medical CenterNew YorkNew YorkUSA
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGeorgiaUSA
| |
Collapse
|
7
|
Galapon AV, Thummerer A, Langendijk JA, Wagenaar D, Both S. Feasibility of Monte Carlo dropout-based uncertainty maps to evaluate deep learning-based synthetic CTs for adaptive proton therapy. Med Phys 2024; 51:2499-2509. [PMID: 37956266 DOI: 10.1002/mp.16838] [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: 06/02/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Deep learning has shown promising results to generate MRI-based synthetic CTs and to enable accurate proton dose calculations on MRIs. For clinical implementation of synthetic CTs, quality assurance tools that verify their quality and reliability are required but still lacking. PURPOSE This study aims to evaluate the predictive value of uncertainty maps generated with Monte Carlo dropout (MCD) for verifying proton dose calculations on deep-learning-based synthetic CTs (sCTs) derived from MRIs in online adaptive proton therapy. METHODS Two deep-learning models (DCNN and cycleGAN) were trained for CT image synthesis using 101 paired CT-MR images. sCT images were generated using MCD for each model by performing 10 inferences with activated dropout layers. The final sCT was obtained by averaging the inferred sCTs, while the uncertainty map was obtained from the HU variance corresponding to each voxel of 10 sCTs. The resulting uncertainty maps were compared to the observed HU-, range-, WET-, and dose-error maps between the sCT and planning CT. For range and WET errors, the generated uncertainty maps were projected along the 90-degree angle. To evaluate the dose distribution, a mask based on the 5%-isodose curve was applied to only include voxels along the beam paths. Pearson's correlation coefficients were calculated to determine the correlation between the uncertainty maps and HUs, range, WET, and dose errors. To evaluate the dosimetric accuracy of synthetic CTs, clinical proton treatment plans were recalculated and compared to the pCTs RESULTS: Evaluation of the correlation showed an average of r = 0.92 ± 0.03 and r = 0.92 ± 0.03 for errors between uncertainty-HU, r = 0.66 ± 0.09 and r = 0.62 ± 0.06 between uncertainty-range, r = 0.64 ± 0.06 and r = 0.58 ± 0.07 between uncertainty-WET, and r = 0.65 ± 0.09 and r = 0.67 ± 0.07 between uncertainty and dose difference for DCNN and cycleGAN model, respectively. Dosimetric comparison for target volumes showed an average 3%/3 mm gamma pass rate of 99.76 ± 0.43 (DCNN) and 99.10 ± 1.27 (cycleGAN). CONCLUSION The observed correlations between uncertainty maps and the various metrics (HU, range, WET, and dose errors) demonstrated the potential of MCD-based uncertainty maps as a reliable QA tool to evaluate the accuracy of deep learning-based sCTs.
Collapse
Affiliation(s)
- Arthur Villanueva Galapon
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Adrian Thummerer
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Germany
| | - Johannes Albertus Langendijk
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Dirk Wagenaar
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Stefan Both
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| |
Collapse
|
8
|
Nuyts S, Bollen H, Eisbruch A, Strojan P, Mendenhall WM, Ng SP, Ferlito A. Adaptive radiotherapy for head and neck cancer: Pitfalls and possibilities from the radiation oncologist's point of view. Cancer Med 2024; 13:e7192. [PMID: 38650546 PMCID: PMC11036082 DOI: 10.1002/cam4.7192] [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: 01/11/2024] [Revised: 03/19/2024] [Accepted: 04/03/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Patients with head and neck cancer (HNC) may experience substantial anatomical changes during the course of radiotherapy treatment. The implementation of adaptive radiotherapy (ART) proves effective in managing the consequent impact on the planned dose distribution. METHODS This narrative literature review comprehensively discusses the diverse strategies of ART in HNC and the documented dosimetric and clinical advantages associated with these approaches, while also addressing the current challenges for integration of ART into clinical practice. RESULTS AND CONCLUSION Although based on mainly non-randomized and retrospective trials, there is accumulating evidence that ART has the potential to reduce toxicity and improve quality of life and tumor control in HNC patients treated with RT. However, several questions remain regarding accurate patient selection, the ideal frequency and timing of replanning, and the appropriate way for image registration and dose calculation. Well-designed randomized prospective trials, with a predetermined protocol for both image registration and dose summation, are urgently needed to further investigate the dosimetric and clinical benefits of ART.
Collapse
Affiliation(s)
- Sandra Nuyts
- Laboratory of Experimental Radiotherapy, Department of OncologyKU LeuvenLeuvenBelgium
- Department of Radiation OncologyLeuven Cancer Institute, University Hospitals LeuvenLeuvenBelgium
| | - Heleen Bollen
- Laboratory of Experimental Radiotherapy, Department of OncologyKU LeuvenLeuvenBelgium
- Department of Radiation OncologyLeuven Cancer Institute, University Hospitals LeuvenLeuvenBelgium
| | - Avrahram Eisbruch
- Department of Radiation OncologyUniversity of MichiganAnn ArborMichiganUSA
| | - Primoz Strojan
- Department of Radiation Oncology Institute of OncologyUniversity of LjubljanaLjubljanaSlovenia
| | - William M. Mendenhall
- Department of Radiation OncologyUniversity of Florida College of MedicineGainesvilleFloridaUSA
| | - Sweet Ping Ng
- Department of Radiation OncologyOlivia Newton‐John Cancer and Wellness Centre, Austin HealthMelbourneAustralia
| | - Alfio Ferlito
- Coordinator International Head and Neck Scientific GroupUdineItaly
| |
Collapse
|
9
|
Kraan AC, Moglioni M, Battistoni G, Bersani D, Berti A, Carra P, Cerello P, Ciocca M, Ferrero V, Fiorina E, Mazzoni E, Morrocchi M, Muraro S, Orlandi E, Pennazio F, Retico A, Rosso V, Sportelli G, Vischioni B, Vitolo V, Bisogni MG. Using the gamma-index analysis for inter-fractional comparison of in-beam PET images for head-and-neck treatment monitoring in proton therapy: A Monte Carlo simulation study. Phys Med 2024; 120:103329. [PMID: 38492331 DOI: 10.1016/j.ejmp.2024.103329] [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: 05/15/2023] [Revised: 02/13/2024] [Accepted: 03/07/2024] [Indexed: 03/18/2024] Open
Abstract
GOAL In-beam Positron Emission Tomography (PET) is a technique for in-vivo non-invasive treatment monitoring for proton therapy. To detect anatomical changes in patients with PET, various analysis methods exist, but their clinical interpretation is problematic. The goal of this work is to investigate whether the gamma-index analysis, widely used for dose comparisons, is an appropriate tool for comparing in-beam PET distributions. Focusing on a head-and-neck patient, we investigate whether the gamma-index map and the passing rate are sensitive to progressive anatomical changes. METHODS/MATERIALS We simulated a treatment course of a proton therapy patient using FLUKA Monte Carlo simulations. Gradual emptying of the sinonasal cavity was modeled through a series of artificially modified CT scans. The in-beam PET activity distributions from three fields were evaluated, simulating a planar dual head geometry. We applied the 3D-gamma evaluation method to compare the PET images with a reference image without changes. Various tolerance criteria and parameters were tested, and results were compared to the CT-scans. RESULTS Based on 210 MC simulations we identified appropriate parameters for the gamma-index analysis. Tolerance values of 3 mm/3% and 2 mm/2% were suited for comparison of simulated in-beam PET distributions. The gamma passing rate decreased with increasing volume change for all fields. CONCLUSION The gamma-index analysis was found to be a useful tool for comparing simulated in-beam PET images, sensitive to sinonasal cavity emptying. Monitoring the gamma passing rate behavior over the treatment course is useful to detect anatomical changes occurring during the treatment course.
Collapse
Affiliation(s)
- Aafke Christine Kraan
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy
| | - Martina Moglioni
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy; Dipartimento di Fisica, Università di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy.
| | - Giuseppe Battistoni
- Istituto Nazionale di Fisica Nucleare, Sezione di Milano, Via Giovanni Celoria 16, Milano, 20133, Italy
| | - Davide Bersani
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Via Pietro Giuria 1, Torino, 10125, Italy
| | - Andrea Berti
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy; Dipartimento di Fisica, Università di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy
| | - Pietro Carra
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy; Dipartimento di Fisica, Università di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy
| | - Piergiorgio Cerello
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Via Pietro Giuria 1, Torino, 10125, Italy
| | - Mario Ciocca
- Centro Nazionale di Adroterapia Oncologica, Strada Privata Campeggi 53, Pavia, 27100, Italy
| | - Veronica Ferrero
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Via Pietro Giuria 1, Torino, 10125, Italy
| | - Elisa Fiorina
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Via Pietro Giuria 1, Torino, 10125, Italy
| | - Enrico Mazzoni
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy
| | - Matteo Morrocchi
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy; Dipartimento di Fisica, Università di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy
| | - Silvia Muraro
- Istituto Nazionale di Fisica Nucleare, Sezione di Milano, Via Giovanni Celoria 16, Milano, 20133, Italy
| | - Ester Orlandi
- Centro Nazionale di Adroterapia Oncologica, Strada Privata Campeggi 53, Pavia, 27100, Italy
| | - Francesco Pennazio
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Via Pietro Giuria 1, Torino, 10125, Italy
| | - Alessandra Retico
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy
| | - Valeria Rosso
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy; Dipartimento di Fisica, Università di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy
| | - Giancarlo Sportelli
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy; Dipartimento di Fisica, Università di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy
| | - Barbara Vischioni
- Centro Nazionale di Adroterapia Oncologica, Strada Privata Campeggi 53, Pavia, 27100, Italy
| | - Viviana Vitolo
- Centro Nazionale di Adroterapia Oncologica, Strada Privata Campeggi 53, Pavia, 27100, Italy
| | - Maria Giuseppina Bisogni
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy; Dipartimento di Fisica, Università di Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy
| |
Collapse
|
10
|
Moglioni M, Carra P, Arezzini S, Belcari N, Bersani D, Berti A, Bisogni MG, Calderisi M, Ceppa I, Cerello P, Ciocca M, Ferrero V, Fiorina E, Kraan AC, Mazzoni E, Morrocchi M, Pennazio F, Retico A, Rosso V, Sbolgi F, Vitolo V, Sportelli G. Synthetic CT imaging for PET monitoring in proton therapy: a simulation study. Phys Med Biol 2024; 69:065011. [PMID: 38373343 DOI: 10.1088/1361-6560/ad2a99] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 02/19/2024] [Indexed: 02/21/2024]
Abstract
Objective.This study addresses a fundamental limitation of in-beam positron emission tomography (IB-PET) in proton therapy: the lack of direct anatomical representation in the images it produces. We aim to overcome this shortcoming by pioneering the application of deep learning techniques to create synthetic control CT images (sCT) from combining IB-PET and planning CT scan data.Approach.We conducted simulations involving six patients who underwent irradiation with proton beams. Leveraging the architecture of a visual transformer (ViT) neural network, we developed a model to generate sCT images of these patients using the planning CT scans and the inter-fractional simulated PET activity maps during irradiation. To evaluate the model's performance, a comparison was conducted between the sCT images produced by the ViT model and the authentic control CT images-serving as the benchmark.Main results.The structural similarity index was computed at a mean value across all patients of 0.91, while the mean absolute error measured 22 Hounsfield Units (HU). Root mean squared error and peak signal-to-noise ratio values were 56 HU and 30 dB, respectively. The Dice similarity coefficient exhibited a value of 0.98. These values are comparable to or exceed those found in the literature. More than 70% of the synthetic morphological changes were found to be geometrically compatible with the ones reported in the real control CT scan.Significance.Our study presents an innovative approach to surface the hidden anatomical information of IB-PET in proton therapy. Our ViT-based model successfully generates sCT images from inter-fractional PET data and planning CT scans. Our model's performance stands on par with existing models relying on input from cone beam CT or magnetic resonance imaging, which contain more anatomical information than activity maps.
Collapse
Affiliation(s)
- Martina Moglioni
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
| | - Pietro Carra
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
| | - Silvia Arezzini
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
| | - Nicola Belcari
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
| | - Davide Bersani
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
| | - Andrea Berti
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
| | - Maria Giuseppina Bisogni
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
| | | | | | - Piergiorgio Cerello
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, I-10125 Torino, Italy
| | - Mario Ciocca
- Centro Nazionale di Adroterapia Oncologica, I-27100 Pavia, Italy
| | - Veronica Ferrero
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, I-10125 Torino, Italy
| | - Elisa Fiorina
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, I-10125 Torino, Italy
| | | | - Enrico Mazzoni
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
| | - Matteo Morrocchi
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
| | - Francesco Pennazio
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, I-10125 Torino, Italy
| | - Alessandra Retico
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
| | - Valeria Rosso
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
| | | | - Viviana Vitolo
- Centro Nazionale di Adroterapia Oncologica, I-27100 Pavia, Italy
| | - Giancarlo Sportelli
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, I-56127 Pisa, Italy
| |
Collapse
|
11
|
Kaushik S, Ödén J, Sharma DS, Fredriksson A, Toma-Dasu I. Generation and evaluation of anatomy-preserving virtual CT for online adaptive proton therapy. Med Phys 2024; 51:1536-1546. [PMID: 38230803 DOI: 10.1002/mp.16941] [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: 08/22/2023] [Revised: 11/24/2023] [Accepted: 12/31/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND Daily CTs generated by CBCT correction are required for daily replanning in online-adaptive proton therapy (APT) to effectively deal with inter-fractional changes. Out of the currently available methods, the suitability of a daily CT generation method for proton dose calculation also depends on the anatomical site. PURPOSE We propose an anatomy-preserving virtual CT (APvCT) method as a hybrid method of CBCT correction, which is especially suitable for large anatomy deformations. The accuracy of the hybrid method was assessed by comparison with the corrected CBCT (cCBCT) and virtual CT (vCT) methods in the context of online APT. METHODS Seventy-one daily CBCTs of four prostate cancer patients treated with intensity modulated proton therapy (IMPT) were converted to daily CTs using cCBCT, vCT, and the newly proposed APvCT method. In APvCT, planning CT (pCT) were mapped to CBCT geometry using deformable image registration with boundary conditions on controlling regions of interest (ROIs) created with deep learning segmentation on cCBCT. The relative frequency distribution (RFD) of HU, mass density and stopping power ratio (SPR) values were assessed and compared with the pCT. The ROIs in the APvCT and vCT were compared with cCBCT in terms of Dice similarity coefficient (DSC) and mean distance-to-agreement (mDTA). For each patient, a robustly optimized IMPT plan was created on the pCT and subsequent daily adaptive plans on daily CTs. For dose distribution comparison on the same anatomy, the daily adaptive plans on cCBCT and vCT were recalculated on the corresponding APvCT. The dose distributions were compared in terms of isodose volumes and 3D global gamma-index passing rate (GPR) at γ(2%, 2 mm) criterion. RESULTS For all patients, no noticeable difference in RFDs was observed amongst APvCT, vCT, and pCT except in cCBCT, which showed a noticeable difference. The minimum DSC value was 0.96 and 0.39 for contours in APvCT and vCT respectively. The average value of mDTA for APvCT was 0.01 cm for clinical target volume and ≤0.01 cm for organs at risk, which increased to 0.18 cm and ≤0.52 cm for vCT. The mean GPR value was 90.9%, 64.5%, and 67.0% for APvCT versus cCBCT, vCT versus cCBCT, and APvCT versus vCT, respectively. When recalculated on APvCT, the adaptive cCBCT and vCT plans resulted in mean GPRs of 89.5 ± 5.1% and 65.9 ± 19.1%, respectively. The mean DSC values for 80.0%, 90.0%, 95.0%, 98.0%, and 100.0% isodose volumes were 0.97, 0.97, 0.97, 0.95, and 0.91 for recalculated cCBCT plans, and 0.89, 0.88, 0.87, 0.85, and 0.81 for recalculated vCT plans. Hausdorff distance for the 100.0% isodose volume in some cases of recalculated cCBCT plans on APvCT exceeded 1.00 cm. CONCLUSIONS APvCT contours showed good agreement with reference contours of cCBCT which indicates anatomy preservation in APvCT. A vCT with erroneous anatomy can result in an incorrect adaptive plan. Further, slightly lower values of GPR between the APvCT and cCBCT-based adaptive plans can be explained by the difference in the cCBCT's SPR RFD from the pCT.
Collapse
Affiliation(s)
- Suryakant Kaushik
- RaySearch Laboratories AB (Publ), Stockholm, Sweden
- Department of Physics, Medical Radiation Physics, Stockholm University, Stockholm, Sweden
- Department of Oncology and Pathology, Medical Radiation Physics, Karolinska Institutet, Stockholm, Sweden
| | - Jakob Ödén
- RaySearch Laboratories AB (Publ), Stockholm, Sweden
| | | | | | - Iuliana Toma-Dasu
- Department of Physics, Medical Radiation Physics, Stockholm University, Stockholm, Sweden
- Department of Oncology and Pathology, Medical Radiation Physics, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
12
|
Li X, Bellotti R, Meier G, Bachtiary B, Weber D, Lomax A, Buhmann J, Zhang Y. Uncertainty-aware MR-based CT synthesis for robust proton therapy planning of brain tumour. Radiother Oncol 2024; 191:110056. [PMID: 38104781 DOI: 10.1016/j.radonc.2023.110056] [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: 05/01/2023] [Revised: 12/06/2023] [Accepted: 12/08/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND AND PURPOSE Deep learning techniques excel in MR-based CT synthesis, but missing uncertainty prediction limits its clinical use in proton therapy. We developed an uncertainty-aware framework and evaluated its efficiency in robust proton planning. MATERIALS AND METHODS A conditional generative-adversarial network was trained on 64 brain tumour patients with paired MR-CT images to generate synthetic CTs (sCT) from combined T1-T2 MRs of three orthogonal planes. A Bayesian neural network predicts Laplacian distributions for all voxels with parameters (μ, b). A robust proton plan was optimized using three sCTs of μ and μ±b. The dosimetric differences between the plan from sCT (sPlan) and the recalculated plan (rPlan) on planning CT (pCT) were quantified for each patient. The uncertainty-aware robust plan was compared to conventional robust (global ± 3 %) and non-robust plans. RESULTS In 8-fold cross-validation, sCT-pCT image differences (Mean-Absolute-Error) were 80.84 ± 9.84HU (body), 35.78 ± 6.07HU (soft tissues) and 221.88 ± 31.69HU (bones), with Dice scores of 90.33 ± 2.43 %, 95.13 ± 0.80 %, and 85.53 ± 4.16 %, respectively. The uncertainty distribution positively correlated with absolute prediction error (Correlation Coefficient: 0.62 ± 0.01). The uncertainty-conditioned robust optimisation improved the rPlan-sPlan agreement, e.g., D95 absolute difference (CTV) was 1.10 ± 1.24 % compared to conventional (1.64 ± 2.71 %) and non-robust (2.08 ± 2.96 %) optimisation. This trend was consistent across all target and organs-at-risk indexes. CONCLUSION The enhanced framework incorporates 3D uncertainty prediction and generates high-quality sCTs from MR images. The framework also facilitates conditioned robust optimisation, bolstering proton plan robustness against network prediction errors. The innovative feature of uncertainty visualisation and robust analyses contribute to evaluating sCT clinical utility for individual patients.
Collapse
Affiliation(s)
- Xia Li
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland; Department of Computer Science, ETH Zurich, Switzerland
| | - Renato Bellotti
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland; Department of Physics, ETH Zurich, Switzerland
| | - Gabriel Meier
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland
| | | | - Damien Weber
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland; Department of Radiation Oncology, University Hospital of Zurich, Switzerland; Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Antony Lomax
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland; Department of Physics, ETH Zurich, Switzerland
| | | | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institut, Switzerland.
| |
Collapse
|
13
|
Liu W, Feng H, Taylor PA, Kang M, Shen J, Saini J, Zhou J, Giap HB, Yu NY, Sio TS, Mohindra P, Chang JY, Bradley JD, Xiao Y, Simone CB, Lin L. Proton Pencil-Beam Scanning Stereotactic Body Radiation Therapy and Hypofractionated Radiation Therapy for Thoracic Malignancies: Patterns of Practice Survey and Recommendations for Future Development from NRG Oncology and PTCOG. ARXIV 2024:arXiv:2402.00489v1. [PMID: 38351927 PMCID: PMC10862926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Stereotactic body radiation therapy (SBRT) and hypofractionation using pencil-beam scanning (PBS) proton therapy (PBSPT) is an attractive option for thoracic malignancies. Combining the advantages of target coverage conformity and critical organ sparing from both PBSPT and SBRT, this new delivery technique has great potential to improve the therapeutic ratio, particularly for tumors near critical organs. Safe and effective implementation of PBSPT SBRT/hypofractionation to treat thoracic malignancies is more challenging than the conventionally-fractionated PBSPT due to concerns of amplified uncertainties at the larger dose per fraction. NRG Oncology and Particle Therapy Cooperative Group (PTCOG) Thoracic Subcommittee surveyed US proton centers to identify practice patterns of thoracic PBSPT SBRT/hypofractionation. From these patterns, we present recommendations for future technical development of proton SBRT/hypofractionation for thoracic treatment. Amongst other points, the recommendations highlight the need for volumetric image guidance and multiple CT-based robust optimization and robustness tools to minimize further the impact of uncertainties associated with respiratory motion. Advances in direct motion analysis techniques are urgently needed to supplement current motion management techniques.
Collapse
Affiliation(s)
- Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Hongying Feng
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Paige A. Taylor
- The Imaging and Radiation Oncology Core Houston Quality Assurance Center, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Minglei Kang
- New York Proton Center, New York City, New York, USA
| | - Jiajian Shen
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Jatinder Saini
- Seattle Cancer Care Alliance Proton Therapy Center and Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Huan B. Giap
- Department of Radiation Oncology, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Nathan Y. Yu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Terence S. Sio
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Pranshu Mohindra
- Department of Radiation Oncology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Joe Y. Chang
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston
| | - Jeffrey D. Bradley
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Liyong Lin
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| |
Collapse
|
14
|
Moteabbed M, Bobić M, Paganetti H, Efstathiou JA. The Role of Proton Therapy for Prostate Cancer in the Setting of Hip Prosthesis. Cancers (Basel) 2024; 16:330. [PMID: 38254818 PMCID: PMC10813677 DOI: 10.3390/cancers16020330] [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: 12/08/2023] [Revised: 01/02/2024] [Accepted: 01/05/2024] [Indexed: 01/24/2024] Open
Abstract
PURPOSE Given that the current standard of proton therapy (PT) for prostate cancer is through bilateral beams, this modality is typically avoided when it comes to treatment of patients with hip prosthesis. The purpose of this study was to evaluate whether novel PT methods, i.e., anterior proton beams and proton arc therapy (PArc), could be feasible options to treat this patient subpopulation. We evaluate PT methods in the context of dosimetry and robustness and compare with standard of practice volumetric modulated arc therapy (VMAT) to explore any potential benefits. METHODS Two PT and one VMAT treatment plans were retrospectively created for 10 patients who participated in a clinical trial with a weekly repeat CT (rCT) imaging component. All plans were robustly optimized and featured: (1) combination anterior oblique and lateral proton beams (AoL), (2) PArc, and (3) VMAT. All patients had hydrogel spacers in place, which enabled safe application of anterior proton beams. The planned dose was 70 Gy (RBE) to the entire prostate gland and 50 Gy (RBE) to the proximal seminal vesicles in 28 fractions. Along with plan dose-volume metrics, robustness to setup and interfractional variations were evaluated using the weekly rCT images. The linear energy transfer (LET)-weighted dose was evaluated for PArc plans to ensure urethra sparing given the typical high-LET region at the end of range. RESULTS Both PT methods were dosimetrically feasible and provided reduction of some key OAR metrics compared to VMAT except for penile bulb, while providing equally good target coverage. Significant differences in median rectum V35 (22-25%), penile bulb Dmean (5 Gy), rectum V61 (2%), right femoral head Dmean (5 Gy), and bladder V39 (4%) were found between PT and VMAT. All plans were equally robust to variations. LET-weighted dose in urethra was equivalent to the physical dose for PArc plans and hence no added urethral toxicity was expected. CONCLUSIONS PT for treatment of prostate cancer patients with hip prosthesis is feasible and equivalent or potentially superior to VMAT in quality in some cases. The choice of radiotherapy regimen can be personalized based on patient characteristics to achieve the best treatment outcome.
Collapse
Affiliation(s)
- Maryam Moteabbed
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA (J.A.E.)
| | - Mislav Bobić
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA (J.A.E.)
- Department of Physics, ETH Zurich, 8093 Zurich, Switzerland
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA (J.A.E.)
| | - Jason A. Efstathiou
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA (J.A.E.)
| |
Collapse
|
15
|
Bobić M, Christensen JB, Lee H, Choulilitsa E, Czerska K, Togno M, Safai S, Yukihara EG, Winey BA, Lomax AJ, Paganetti H, Albertini F, Nesteruk KP. Optically stimulated luminescence dosimeters for simultaneous measurement of point dose and dose-weighted LET in an adaptive proton therapy workflow. Front Oncol 2024; 13:1333039. [PMID: 38510267 PMCID: PMC10951997 DOI: 10.3389/fonc.2023.1333039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 12/18/2023] [Indexed: 03/22/2024] Open
Abstract
Purpose To demonstrate the suitability of optically stimulated luminescence detectors (OSLDs) for accurate simultaneous measurement of the absolute point dose and dose-weighted linear energy transfer (LETD) in an anthropomorphic phantom for experimental validation of daily adaptive proton therapy. Methods A clinically realistic intensity-modulated proton therapy (IMPT) treatment plan was created based on a CT of an anthropomorphic head-and-neck phantom made of tissue-equivalent material. The IMPT plan was optimized with three fields to deliver a uniform dose to the target volume covering the OSLDs. Different scenarios representing inter-fractional anatomical changes were created by modifying the phantom. An online adaptive proton therapy workflow was used to recover the daily dose distribution and account for the applied geometry changes. To validate the adaptive workflow, measurements were performed by irradiating Al2O3:C OSLDs inside the phantom. In addition to the measurements, retrospective Monte Carlo simulations were performed to compare the absolute dose and dose-averaged LET (LETD) delivered to the OSLDs. Results The online adaptive proton therapy workflow was shown to recover significant degradation in dose conformity resulting from large anatomical and positioning deviations from the reference plan. The Monte Carlo simulations were in close agreement with the OSLD measurements, with an average relative error of 1.4% for doses and 3.2% for LETD. The use of OSLDs for LET determination allowed for a correction for the ionization quenched response. Conclusion The OSLDs appear to be an excellent detector for simultaneously assessing dose and LET distributions in proton irradiation of an anthropomorphic phantom. The OSLDs can be cut to almost any size and shape, making them ideal for in-phantom measurements to probe the radiation quality and dose in a predefined region of interest. Although we have presented the results obtained in the experimental validation of an adaptive proton therapy workflow, the same approach can be generalized and used for a variety of clinical innovations and workflow developments that require accurate assessment of point dose and/or average LET.
Collapse
Affiliation(s)
- Mislav Bobić
- Department of Physics, ETH Zurich, Zurich, Switzerland
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | | | - Hoyeon Lee
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Evangelia Choulilitsa
- Department of Physics, ETH Zurich, Zurich, Switzerland
- Paul Scherrer Institute, Villigen, Switzerland
| | | | | | | | | | - Brian A. Winey
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Antony J. Lomax
- Department of Physics, ETH Zurich, Zurich, Switzerland
- Paul Scherrer Institute, Villigen, Switzerland
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | | | - Konrad P. Nesteruk
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
Hetzel R, Urbanevych V, Bolke A, Kasper J, Kercz M, Kołodziej M, Magiera A, Mueller F, Müller S, Rafecas M, Rusiecka K, Schug D, Schulz V, Stahl A, Weissler B, Wong ML, Wrońska A. Near-field coded-mask technique and its potential for proton therapy monitoring. Phys Med Biol 2023; 68:245028. [PMID: 37863101 DOI: 10.1088/1361-6560/ad05b2] [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: 02/10/2023] [Accepted: 10/20/2023] [Indexed: 10/22/2023]
Abstract
Objective.Prompt-gamma imaging encompasses several approaches to the online monitoring of the beam range or deposited dose distribution in proton therapy. We test one of the imaging techniques - a coded mask approach - both experimentally and via simulations.Approach.Two imaging setups have been investigated experimentally. Each of them comprised a structured tungsten collimator in the form of a modified uniformly redundant array mask and a LYSO:Ce scintillation detector of fine granularity. The setups differed in detector dimensions and operation mode (1D or 2D imaging). A series of measurements with radioactive sources have been conducted, testing the performance of the setups for near-field gamma imaging. Additionally, Monte Carlo simulations of a larger setup of the same type were conducted, investigating its performance with a realistic gamma source distribution occurring during proton therapy.Main results.The images of point-like sources reconstructed from two small-scale prototypes' data using the maximum-likelihood expectation maximisation algorithm constitute the experimental proof of principle for the near-field coded-mask imaging modality, both in the 1D and the 2D mode. Their precision allowed us to calibrate out certain systematic offsets appearing due to the limited alignment accuracy of setup elements. The simulation of the full-scale setup yielded a mean distal falloff retrieval precision of 0.72 mm in the studies for beam energy range 89.5-107.9 MeV and with 1 × 108protons (a typical number for distal spots). The implemented algorithm of image reconstruction is relatively fast-a typical procedure needs several seconds.Significance.Coded-mask imaging appears a valid option for proton therapy monitoring. The results of simulations let us conclude that the proposed full-scale setup is competitive with the knife-edge-shaped and the multi-parallel slit cameras investigated by other groups.
Collapse
Affiliation(s)
- Ronja Hetzel
- III. Physikalisches Institut B, RWTH Aachen University, Aachen, Germany
| | - Vitalii Urbanevych
- Marian Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland
| | - Andreas Bolke
- Institute of Medical Engineering, University of Lübeck, Lübeck, Germany
| | - Jonas Kasper
- III. Physikalisches Institut B, RWTH Aachen University, Aachen, Germany
| | - Monika Kercz
- Marian Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland
- Doctoral School of Exact and Natural Sciences, Jagiellonian University, Kraków, Poland
| | - Magdalena Kołodziej
- Marian Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland
- Doctoral School of Exact and Natural Sciences, Jagiellonian University, Kraków, Poland
| | - Andrzej Magiera
- Marian Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland
| | - Florian Mueller
- Physics of Molecular Imaging Systems, RWTH Aachen University, Aachen, Germany
| | - Sara Müller
- III. Physikalisches Institut B, RWTH Aachen University, Aachen, Germany
| | - Magdalena Rafecas
- Institute of Medical Engineering, University of Lübeck, Lübeck, Germany
| | - Katarzyna Rusiecka
- Marian Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland
| | - David Schug
- Physics of Molecular Imaging Systems, RWTH Aachen University, Aachen, Germany
- Hyperion Hybrid Imaging Systems GmbH, Aachen, Germany
| | - Volkmar Schulz
- III. Physikalisches Institut B, RWTH Aachen University, Aachen, Germany
- Physics of Molecular Imaging Systems, RWTH Aachen University, Aachen, Germany
- Hyperion Hybrid Imaging Systems GmbH, Aachen, Germany
| | - Achim Stahl
- III. Physikalisches Institut B, RWTH Aachen University, Aachen, Germany
| | - Bjoern Weissler
- Physics of Molecular Imaging Systems, RWTH Aachen University, Aachen, Germany
- Hyperion Hybrid Imaging Systems GmbH, Aachen, Germany
| | - Ming-Liang Wong
- Marian Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland
| | - Aleksandra Wrońska
- Marian Smoluchowski Institute of Physics, Jagiellonian University, Kraków, Poland
| |
Collapse
|
18
|
Nenoff L, Amstutz F, Murr M, Archibald-Heeren B, Fusella M, Hussein M, Lechner W, Zhang Y, Sharp G, Vasquez Osorio E. Review and recommendations on deformable image registration uncertainties for radiotherapy applications. Phys Med Biol 2023; 68:24TR01. [PMID: 37972540 PMCID: PMC10725576 DOI: 10.1088/1361-6560/ad0d8a] [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: 04/11/2023] [Revised: 10/30/2023] [Accepted: 11/15/2023] [Indexed: 11/19/2023]
Abstract
Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.
Collapse
Affiliation(s)
- Lena Nenoff
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
- 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
- Helmholtz-Zentrum Dresden—Rossendorf, Institute of Radiooncology—OncoRay, Dresden, Germany
| | - Florian Amstutz
- Department of Physics, ETH Zurich, Switzerland
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Martina Murr
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
| | | | - Marco Fusella
- Department of Radiation Oncology, Abano Terme Hospital, Italy
| | - Mohammad Hussein
- Metrology for Medical Physics, National Physical Laboratory, Teddington, United Kingdom
| | - Wolfgang Lechner
- Department of Radiation Oncology, Medical University of Vienna, Austria
| | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institute, Villigen PSI, Switzerland
| | - Greg Sharp
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
| | - Eliana Vasquez Osorio
- Division of Cancer Sciences, The University of Manchester, Manchester, United Kingdom
| |
Collapse
|
19
|
de Koster RJC, Thummerer A, Scandurra D, Langendijk JA, Both S. Technical note: Evaluation of deep learning based synthetic CTs clinical readiness for dose and NTCP driven head and neck adaptive proton therapy. Med Phys 2023; 50:8023-8033. [PMID: 37831597 DOI: 10.1002/mp.16782] [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: 03/27/2023] [Revised: 09/19/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND Adaptive proton therapy workflows rely on accurate imaging throughout the treatment course. Our centre currently utilizes weekly repeat CTs (rCTs) for treatment monitoring and plan adaptations. However, deep learning-based methods have recently shown to successfully correct CBCT images, which suffer from severe imaging artifacts, and generate high quality synthetic CT (sCT) images which enable CBCT-based proton dose calculations. PURPOSE To compare daily CBCT-based sCT images to planning CTs (pCT) and rCTs of head and neck (HN) cancer patients to investigate the dosimetric accuracy of CBCT-based sCTs in a scenario mimicking actual clinical practice. METHODS Data of 56 HN cancer patients, previously treated with proton therapy was used to generate 1.962 sCT images, using a previously developed and trained deep convolutional neural network. Clinical IMPT treatment plans were recalculated on the pCT, weekly rCTs and daily sCTs. The dosimetric accuracy of sCTs was compared to same day rCTs and the initial planning CT. As a reference, rCTs were also compared to pCTs. The dose difference between sCTs and rCTs/pCT was quantified by calculating the D98 difference for target volumes and Dmean difference for organs-at-risk. To investigate the clinical relevancy of possible dose differences, NTCP values were calculated for dysphagia and xerostomia. RESULTS For target volumes, only minor dose differences were found for sCT versus rCT and sCT versus pCT, with dose differences mostly within ±1.5%. Larger dose differences were observed in OARs, where a general shift towards positive differences was found, with the largest difference in the left parotid gland. Delta NTCP values for grade 2 dysphagia and xerostomia were within ±2.5% for 90% of the sCTs. CONCLUSIONS Target doses showed high similarity between rCTs and sCTs. Further investigations are required to identify the origin of the dose differences at OAR levels and its relevance in clinical decision making.
Collapse
Affiliation(s)
- Rutger J C de Koster
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Adrian Thummerer
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Daniel Scandurra
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Stefan Both
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| |
Collapse
|
20
|
Zhang X, Zhang H, Wang J, Ma Y, Liu X, Dai Z, He R, He P, Li Q. Deep learning-based fast denoising of Monte Carlo dose calculation in carbon ion radiotherapy. Med Phys 2023; 50:7314-7323. [PMID: 37656065 DOI: 10.1002/mp.16719] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 08/18/2023] [Accepted: 08/19/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND Plan verification is one of the important steps of quality assurance (QA) in carbon ion radiotherapy. Conventional methods of plan verification are based on phantom measurement, which is labor-intensive and time-consuming. Although the plan verification method based on Monte Carlo (MC) simulation provides a more accurate modeling of the physics, it is also time-consuming when simulating with a large number of particles. Therefore, how to ensure the accuracy of simulation results while reducing simulation time is the current difficulty and focus. PURPOSE The purpose of this work was to evaluate the feasibility of using deep learning-based MC denoising method to accelerate carbon-ion radiotherapy plan verification. METHODS Three models, including CycleGAN, 3DUNet and GhostUNet with Ghost module, were used to denoise the 1 × 106 carbon ions-based MC dose distribution to the accuracy of 1 × 108 carbon ions-based dose distribution. The CycleGAN's generator, 3DUNet and GhostUNet were all derived from the 3DUNet network. A total of 59 cases including 29 patients with head-and-neck cancers and 30 patients with lung cancers were collected, and 48 cases were randomly selected as the training set of the CycleGAN network and six cases as the test set. For the 3DUNet and GhostUNet models, the numbers of training set, validation set, and test set were 47, 6, and 6, respectively. Finally, the three models were evaluated qualitatively and quantitatively using RMSE and three-dimensional gamma analysis (3 mm, 3%). RESULTS The three end-to-end trained models could be used for denoising the 1 × 106 carbon ions-based dose distribution, and their generalization was proved. The GhostUNet obtained the lowest RMSE value of 0.075, indicating the smallest difference between its denoised and 1 × 108 carbon ions-based dose distributions. The average gamma passing rate (GPR) between the GhostUNet denoising-based versus 1 × 108 carbon ions-based dose distributions was 99.1%, higher than that of the CycleGAN at 94.3% and the 3DUNet at 96.2%. Among the three models, the GhostUNet model had the fewest parameters (4.27 million) and the shortest training time (99 s per epoch) but achieved the best denoising results. CONCLUSION The end-to-end deep network GhostUNet outperforms the CycleGAN, 3DUNet models in denoising MC dose distributions for carbon ion radiotherapy. The network requires less than 5 s to denoise a sample of MC simulation with few particles to obtain a qualitative and quantitative result comparable to the dose distribution simulated by MC with relatively large number particles, offering a significant reduction in computation time.
Collapse
Affiliation(s)
- Xinyang Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hui Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou, China
- Putian Lanhai Nuclear Medicine Research Center, Putian, China
| | - Jian Wang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yuanyuan Ma
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou, China
- Putian Lanhai Nuclear Medicine Research Center, Putian, China
| | - Xinguo Liu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou, China
- Putian Lanhai Nuclear Medicine Research Center, Putian, China
| | - Zhongying Dai
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou, China
- Putian Lanhai Nuclear Medicine Research Center, Putian, China
| | - Rui He
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
- School of Nuclear Science and Technology, Lanzhou University, Lanzhou, China
| | - Pengbo He
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou, China
- Putian Lanhai Nuclear Medicine Research Center, Putian, China
| | - Qiang Li
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou, China
- Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou, China
- University of Chinese Academy of Sciences, Beijing, China
- Putian Lanhai Nuclear Medicine Research Center, Putian, China
| |
Collapse
|
21
|
Yang B, Liu Y, Zhu J, Dai J, Men K. Deep learning framework to improve the quality of cone-beam computed tomography for radiotherapy scenarios. Med Phys 2023; 50:7641-7653. [PMID: 37345371 DOI: 10.1002/mp.16562] [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: 10/06/2022] [Revised: 06/01/2023] [Accepted: 06/03/2023] [Indexed: 06/23/2023] Open
Abstract
BACKGROUND The application of cone-beam computed tomography (CBCT) in image-guided radiotherapy and adaptive radiotherapy remains limited due to its poor image quality. PURPOSE In this study, we aim to develop a deep learning framework to generate high-quality CBCT images for therapeutic applications. METHODS The synthetic CT (sCT) generation from the CBCT was proposed using a transformer-based network with a hybrid loss function. The network was trained and validated using the data from 176 patients to produce a general model that can be extensively applied to enhance CBCT images. After the first therapy, each patient can receive paired CBCT/planning CT (pCT) scans, and the obtained data were used to fine-tune the general model for further improvement. For subsequent treatment, a patient-specific, personalized model was made available. In total, 34 patients were examined for general model testing, and another six patients who underwent rescanned pCT scan were used for personalized model training and testing. RESULTS The general model decreased the mean absolute error (MAE) from 135 HU to 59 HU as compared to the CBCT. The hybrid loss function demonstrated superior performance in CT number correction and noise/artifacts reduction. The proposed transformer-based network also showed superior power in CT number correction compared to the classical convolutional neural network. The personalized model showed improvement based on the general model in some details, and the MAE was reduced from 59 HU (for the general model) to 57 HU (p < 0.05 Wilcoxon signed-rank test). CONCLUSION We established a deep learning framework based on transformer for clinical needs. The deep learning model demonstrated potential for continuous improvement with the help of a suggested personalized training strategy compatible with the clinical workflow.
Collapse
Affiliation(s)
- Bining Yang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuxiang Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 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, 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, China
| |
Collapse
|
22
|
Berthold J, Pietsch J, Piplack N, Khamfongkhruea C, Thiele J, Hölscher T, Janssens G, Smeets J, Traneus E, Löck S, Stützer K, Richter C. Detectability of Anatomical Changes With Prompt-Gamma Imaging: First Systematic Evaluation of Clinical Application During Prostate-Cancer Proton Therapy. Int J Radiat Oncol Biol Phys 2023; 117:718-729. [PMID: 37160193 DOI: 10.1016/j.ijrobp.2023.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/02/2023] [Accepted: 05/02/2023] [Indexed: 05/11/2023]
Abstract
PURPOSE The development of online-adaptive proton therapy (PT) is essential to overcome limitations encountered by day-to-day variations of the patient's anatomy. Range verification could play an essential role in an online feedback loop for the detection of treatment deviations such as anatomical changes. Here, we present the results of the first systematic patient study regarding the detectability of anatomical changes by a prompt-gamma imaging (PGI) slit-camera system. METHODS AND MATERIALS For 15 patients with prostate cancer, PGI measurements were performed during 105 fractions (201 fields) with in-room control computed tomography (CT)acquisitions. Field-wise doses on control CT scans were manually classified as whether showing relevant or non-relevant anatomical changes. This manual classification of the treatment fields was then used to establish an automatic field-wise ground truth based on spot-wise dosimetric range shifts, which were retrieved from integrated depth-dose (IDD) profiles. To determine the detection capability of anatomical changes with PGI, spot-wise PGI-based range shifts were initially compared with corresponding dosimetric IDD range shifts. As final endpoint, the agreement of a developed field-wise PGI classification model with the field-wise ground truth was determined. Therefore, the PGI model was optimized and tested for a subcohort of 131 and 70 treatment fields, respectively. RESULTS The correlation between PGI and IDD range shifts was high, ρpearson = 0.67 (p < 0.01). Field-wise binary PGI classification resulted in an area under the curve of 0.72 and 0.80 for training and test cohorts, respectively. The model detected relevant anatomical changes in the independent test cohort, with a sensitivity and specificity of 74% and 79%, respectively. CONCLUSIONS For the first time, evidence of the detection capability of anatomical changes in prostate-cancer PT from clinically acquired PGI data is shown. This emphasizes the benefit of PGI-based range verification and demonstrates its potential for online-adaptive PT.
Collapse
Affiliation(s)
- Jonathan Berthold
- 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; Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany.
| | - Julian Pietsch
- 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; Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany
| | - Nick Piplack
- 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; Technische Universität Dresden, Faculty of Electrical and Computer Engineering, Dresden, Germany
| | - Chirasak Khamfongkhruea
- 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; Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Julia Thiele
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Tobias Hölscher
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | | | | | | | - Steffen Löck
- 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; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; German Cancer Consortium (DKTK), partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kristin Stützer
- 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; Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany
| | - Christian Richter
- 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; Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany; Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; German Cancer Consortium (DKTK), partner site Dresden, Germany and German Cancer Research Center (DKFZ), Heidelberg, Germany
| |
Collapse
|
23
|
Pang B, Si H, Liu M, Fu W, Zeng Y, Liu H, Cao T, Chang Y, Quan H, Yang Z. Comparison and evaluation of different deep learning models of synthetic CT generation from CBCT for nasopharynx cancer adaptive proton therapy. Med Phys 2023; 50:6920-6930. [PMID: 37800874 DOI: 10.1002/mp.16777] [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: 06/08/2023] [Revised: 08/09/2023] [Accepted: 09/17/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND Cone-beam computed tomography (CBCT) scanning is used for patient setup in image-guided radiotherapy. However, its inaccurate CT numbers limit its applicability in dose calculation and treatment planning. PURPOSE This study compares four deep learning methods for generating synthetic CT (sCT) to determine which method is more appropriate and offers potential for further clinical exploration in adaptive proton therapy for nasopharynx cancer. METHODS CBCTs and deformed planning CT (dCT) from 75 patients (60/5/10 for training, validation and testing) were used to compare cycle-consistent Generative Adversarial Network (cycleGAN), Unet, Unet+cycleGAN and conditionalGenerative Adversarial Network (cGAN) for sCT generation. The sCT images generated by each method were evaluated against dCT images using mean absolute error (MAE), structural similarity (SSIM), peak signal-to-noise ratio (PSNR), spatial non-uniformity (SNU) and radial averaging in the frequency domain. In addition, dosimetric accuracy was assessed through gamma analysis, differences in water equivalent thickness (WET), and dose-volume histogram metrics. RESULTS The cGAN model has demonstrated optimal performance in the four models across various indicators. In terms of image quality under global condition, the average MAE has been reduced to 16.39HU, SSIM has increased to 95.24%, and PSNR has increased to 28.98. Regarding dosimetric accuracy, the gamma passing rate (2%/2 mm) has reached 99.02%, and the WET difference is only 1.28 mm. The D95 value of CTVs coverage and Dmax value of spinal cord, brainstem show no significant differences between dCT and sCT generated by cGAN model. CONCLUSIONS The cGAN model has been shown to be a more suitable approach for generating sCT using CBCT, considering its characteristics and concepts. The resulting sCT has the potential for application in adaptive proton therapy.
Collapse
Affiliation(s)
- Bo Pang
- Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Hang Si
- Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Muyu Liu
- Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Wensheng Fu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Precision Radiation Oncology, Wuhan, China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yiling Zeng
- Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Hongyuan Liu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Precision Radiation Oncology, Wuhan, China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ting Cao
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Precision Radiation Oncology, Wuhan, China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Chang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Precision Radiation Oncology, Wuhan, China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hong Quan
- Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Zhiyong Yang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Precision Radiation Oncology, Wuhan, China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| |
Collapse
|
24
|
Nenoff L, Sudhyadhom A, Lau J, Sharp GC, Paganetti H, Pursley J. Comparing Predicted Toxicities between Hypofractionated Proton and Photon Radiotherapy of Liver Cancer Patients with Different Adaptive Schemes. Cancers (Basel) 2023; 15:4592. [PMID: 37760560 PMCID: PMC10526201 DOI: 10.3390/cancers15184592] [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: 07/28/2023] [Revised: 08/30/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023] Open
Abstract
With the availability of MRI linacs, online adaptive intensity modulated radiotherapy (IMRT) has become a treatment option for liver cancer patients, often combined with hypofractionation. Intensity modulated proton therapy (IMPT) has the potential to reduce the dose to healthy tissue, but it is particularly sensitive to changes in the beam path and might therefore benefit from online adaptation. This study compares the normal tissue complication probabilities (NTCPs) for liver and duodenal toxicity for adaptive and non-adaptive IMRT and IMPT treatments of liver cancer patients. Adaptive and non-adaptive IMRT and IMPT plans were optimized to 50 Gy (RBE = 1.1 for IMPT) in five fractions for 10 liver cancer patients, using the original MRI linac images and physician-drawn structures. Three liver NTCP models were used to predict radiation-induced liver disease, an increase in albumin-bilirubin level, and a Child-Pugh score increase of more than 2. Additionally, three duodenal NTCP models were used to predict gastric bleeding, gastrointestinal (GI) toxicity with grades >3, and duodenal toxicity grades 2-4. NTCPs were calculated for adaptive and non-adaptive IMRT and IMPT treatments. In general, IMRT showed higher NTCP values than IMPT and the differences were often significant. However, the differences between adaptive and non-adaptive treatment schemes were not significant, indicating that the NTCP benefit of adaptive treatment regimens is expected to be smaller than the expected difference between IMRT and IMPT.
Collapse
Affiliation(s)
- Lena Nenoff
- Harvard Medical School, Boston, MA 02114, USA (J.P.)
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Atchar Sudhyadhom
- Harvard Medical School, Boston, MA 02114, USA (J.P.)
- Radiation Oncology, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Jackson Lau
- Harvard Medical School, Boston, MA 02114, USA (J.P.)
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Gregory C. Sharp
- Harvard Medical School, Boston, MA 02114, USA (J.P.)
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Harald Paganetti
- Harvard Medical School, Boston, MA 02114, USA (J.P.)
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jennifer Pursley
- Harvard Medical School, Boston, MA 02114, USA (J.P.)
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA
| |
Collapse
|
25
|
Smolders A, Choulilitsa E, Czerska K, Bizzocchi N, Krcek R, Lomax A, Weber DC, Albertini F. Dosimetric comparison of autocontouring techniques for online adaptive proton therapy. Phys Med Biol 2023; 68:175006. [PMID: 37385266 DOI: 10.1088/1361-6560/ace307] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 06/29/2023] [Indexed: 07/01/2023]
Abstract
Objective.Anatomical and daily set-up uncertainties impede high precision delivery of proton therapy. With online adaptation, the daily plan is reoptimized on an image taken shortly before the treatment, reducing these uncertainties and, hence, allowing a more accurate delivery. This reoptimization requires target and organs-at-risk (OAR) contours on the daily image, which need to be delineated automatically since manual contouring is too slow. Whereas multiple methods for autocontouring exist, none of them are fully accurate, which affects the daily dose. This work aims to quantify the magnitude of this dosimetric effect for four contouring techniques.Approach.Plans reoptimized on automatic contours are compared with plans reoptimized on manual contours. The methods include rigid and deformable registration (DIR), deep-learning based segmentation and patient-specific segmentation.Main results.It was found that independently of the contouring method, the dosimetric influence of usingautomaticOARcontoursis small (<5% prescribed dose in most cases), with DIR yielding the best results. Contrarily, the dosimetric effect of using theautomatic target contourwas larger (>5% prescribed dose in most cases), indicating that manual verification of that contour remains necessary. However, when compared to non-adaptive therapy, the dose differences caused by automatically contouring the target were small and target coverage was improved, especially for DIR.Significance.The results show that manual adjustment of OARs is rarely necessary and that several autocontouring techniques are directly usable. Contrarily, manual adjustment of the target is important. This allows prioritizing tasks during time-critical online adaptive proton therapy and therefore supports its further clinical implementation.
Collapse
Affiliation(s)
- A Smolders
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
- Department of Physics, ETH Zurich, Switzerland
| | - E Choulilitsa
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
- Department of Physics, ETH Zurich, Switzerland
| | - K Czerska
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
| | - N Bizzocchi
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
| | - R Krcek
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - A Lomax
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
- Department of Physics, ETH Zurich, Switzerland
| | - D C Weber
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
- Department of Radiation Oncology, University Hospital Zurich, Switzerland
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - F Albertini
- Paul Scherrer Institute, Center for Proton Therapy, Switzerland
| |
Collapse
|
26
|
Taasti VT, Hattu D, Peeters S, van der Salm A, van Loon J, de Ruysscher D, Nilsson R, Andersson S, Engwall E, Unipan M, Canters R. Clinical evaluation of synthetic computed tomography methods in adaptive proton therapy of lung cancer patients. Phys Imaging Radiat Oncol 2023; 27:100459. [PMID: 37397874 PMCID: PMC10314284 DOI: 10.1016/j.phro.2023.100459] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/13/2023] [Accepted: 06/13/2023] [Indexed: 07/04/2023] Open
Abstract
Background and purpose Efficient workflows for adaptive proton therapy are of high importance. This study evaluated the possibility to replace repeat-CTs (reCTs) with synthetic CTs (sCTs), created based on cone-beam CTs (CBCTs), for flagging the need of plan adaptations in intensity-modulated proton therapy (IMPT) treatment of lung cancer patients. Materials and methods Forty-two IMPT patients were retrospectively included. For each patient, one CBCT and a same-day reCT were included. Two commercial sCT methods were applied; one based on CBCT number correction (Cor-sCT), and one based on deformable image registration (DIR-sCT). The clinical reCT workflow (deformable contour propagation and robust dose re-computation) was performed on the reCT as well as the two sCTs. The deformed target contours on the reCT/sCTs were checked by radiation oncologists and edited if needed. A dose-volume-histogram triggered plan adaptation method was compared between the reCT and the sCTs; patients needing a plan adaptation on the reCT but not on the sCT were denoted false negatives. As secondary evaluation, dose-volume-histogram comparison and gamma analysis (2%/2mm) were performed between the reCT and sCTs. Results There were five false negatives, two for Cor-sCT and three for DIR-sCT. However, three of these were only minor, and one was caused by tumour position differences between the reCT and CBCT and not by sCT quality issues. An average gamma pass rate of 93% was obtained for both sCT methods. Conclusion Both sCT methods were judged to be of clinical quality and valuable for reducing the amount of reCT acquisitions.
Collapse
Affiliation(s)
- Vicki Trier Taasti
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Djoya Hattu
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Stephanie Peeters
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Anke van der Salm
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Judith van Loon
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Dirk de Ruysscher
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | | | | | | | - Mirko Unipan
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Richard Canters
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands
| |
Collapse
|
27
|
Knäusl B, Taasti VT, Poulsen P, Muren LP. Surveying the clinical practice of treatment adaptation and motion management in particle therapy. Phys Imaging Radiat Oncol 2023; 27:100457. [PMID: 37361612 PMCID: PMC10285555 DOI: 10.1016/j.phro.2023.100457] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023] Open
Affiliation(s)
- Barbara Knäusl
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Vicki T Taasti
- Department of Radiation Oncology (Maastro), GROW - School for Oncology and Reproduction, Maastricht, University Medical Centre+, Maastricht, The Netherlands
| | - Per Poulsen
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Oncology, Aarhus University, Aarhus, Denmark
| | - Ludvig P Muren
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| |
Collapse
|
28
|
Taylor S, Lim P, Cantwell J, D’Souza D, Moinuddin S, Chang YC, Gaze MN, Gains J, Veiga C. Image guidance and interfractional anatomical variation in paediatric abdominal radiotherapy. Br J Radiol 2023; 96:20230058. [PMID: 37102707 PMCID: PMC10230397 DOI: 10.1259/bjr.20230058] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/22/2023] [Accepted: 03/23/2023] [Indexed: 04/28/2023] Open
Abstract
OBJECTIVES To identify variables predicting interfractional anatomical variations measured with cone-beam CT (CBCT) throughout abdominal paediatric radiotherapy, and to assess the potential of surface-guided radiotherapy (SGRT) to monitor these changes. METHODS Metrics of variation in gastrointestinal (GI) gas volume and separation of the body contour and abdominal wall were calculated from 21 planning CTs and 77 weekly CBCTs for 21 abdominal neuroblastoma patients (median 4 years, range: 2 - 19 years). Age, sex, feeding tubes, and general anaesthesia (GA) were explored as predictive variables for anatomical variation. Furthermore, GI gas variation was correlated with changes in body and abdominal wall separation, as well as simulated SGRT metrics of translational and rotational corrections between CT/CBCT. RESULTS GI gas volumes varied 74 ± 54 ml across all scans, while body and abdominal wall separation varied 2.0 ± 0.7 mm and 4.1 ± 1.5 mm from planning, respectively. Patients < 3.5 years (p = 0.04) and treated under GA (p < 0.01) experienced greater GI gas variation; GA was the strongest predictor in multivariate analysis (p < 0.01). Absence of feeding tubes was linked to greater body contour variation (p = 0.03). GI gas variation correlated with body (R = 0.53) and abdominal wall (R = 0.63) changes. The strongest correlations with SGRT metrics were found for anterior-posterior translation (R = 0.65) and rotation of the left-right axis (R = -0.36). CONCLUSIONS Young age, GA, and absence of feeding tubes were linked to stronger interfractional anatomical variation and are likely indicative of patients benefiting from adaptive/robust planning pathways. Our data suggest a role for SGRT to inform the need for CBCT at each treatment fraction in this patient group. ADVANCES IN KNOWLEDGE This is the first study to suggest the potential role of SGRT for the management of internal interfractional anatomical variation in paediatric abdominal radiotherapy.
Collapse
Affiliation(s)
- Sabrina Taylor
- University College London, Centre for Medical Image Computing, London, United Kingdom
| | - Pei Lim
- Department of Oncology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Jessica Cantwell
- Radiotherapy, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Derek D’Souza
- Radiotherapy, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Syed Moinuddin
- Radiotherapy, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Yen-Ching Chang
- Department of Oncology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Mark N Gaze
- Department of Oncology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Jennifer Gains
- Department of Oncology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Catarina Veiga
- University College London, Centre for Medical Image Computing, London, United Kingdom
| |
Collapse
|
29
|
Qiu Z, Olberg S, den Hertog D, Ajdari A, Bortfeld T, Pursley J. Online adaptive planning methods for intensity-modulated radiotherapy. Phys Med Biol 2023; 68:10.1088/1361-6560/accdb2. [PMID: 37068488 PMCID: PMC10637515 DOI: 10.1088/1361-6560/accdb2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 04/17/2023] [Indexed: 04/19/2023]
Abstract
Online adaptive radiation therapy aims at adapting a patient's treatment plan to their current anatomy to account for inter-fraction variations before daily treatment delivery. As this process needs to be accomplished while the patient is immobilized on the treatment couch, it requires time-efficient adaptive planning methods to generate a quality daily treatment plan rapidly. The conventional planning methods do not meet the time requirement of online adaptive radiation therapy because they often involve excessive human intervention, significantly prolonging the planning phase. This article reviews the planning strategies employed by current commercial online adaptive radiation therapy systems, research on online adaptive planning, and artificial intelligence's potential application to online adaptive planning.
Collapse
Affiliation(s)
- Zihang Qiu
- Department of Business Analytics, University of Amsterdam, The Netherlands
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Sven Olberg
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Dick den Hertog
- Department of Business Analytics, University of Amsterdam, The Netherlands
| | - Ali Ajdari
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Thomas Bortfeld
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Jennifer Pursley
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, United States of America
| |
Collapse
|
30
|
Meric I, Alagoz E, Hysing LB, Kögler T, Lathouwers D, Lionheart WRB, Mattingly J, Obhodas J, Pausch G, Pettersen HES, Ratliff HN, Rovituso M, Schellhammer SM, Setterdahl LM, Skjerdal K, Sterpin E, Sudac D, Turko JA, Ytre-Hauge KS. A hybrid multi-particle approach to range assessment-based treatment verification in particle therapy. Sci Rep 2023; 13:6709. [PMID: 37185591 PMCID: PMC10130067 DOI: 10.1038/s41598-023-33777-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 04/18/2023] [Indexed: 05/17/2023] Open
Abstract
Particle therapy (PT) used for cancer treatment can spare healthy tissue and reduce treatment toxicity. However, full exploitation of the dosimetric advantages of PT is not yet possible due to range uncertainties, warranting development of range-monitoring techniques. This study proposes a novel range-monitoring technique introducing the yet unexplored concept of simultaneous detection and imaging of fast neutrons and prompt-gamma rays produced in beam-tissue interactions. A quasi-monolithic organic detector array is proposed, and its feasibility for detecting range shifts in the context of proton therapy is explored through Monte Carlo simulations of realistic patient models and detector resolution effects. The results indicate that range shifts of [Formula: see text] can be detected at relatively low proton intensities ([Formula: see text] protons/spot) when spatial information obtained through imaging of both particle species are used simultaneously. This study lays the foundation for multi-particle detection and imaging systems in the context of range verification in PT.
Collapse
Affiliation(s)
- Ilker Meric
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, P.O. Box 7030, 5020, Bergen, Norway.
| | - Enver Alagoz
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, P.O. Box 7030, 5020, Bergen, Norway
| | - Liv B Hysing
- Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway
- Department of Physics and Technology, University of Bergen, P.O. Box 7803, 5020, Bergen, Norway
| | - Toni Kögler
- 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.
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, Dresden, Germany.
| | | | | | - John Mattingly
- Department of Nuclear Engineering, North Carolina State University, Raleigh, NC, USA
| | | | - Guntram Pausch
- Target Systemelektronik GmbH & Co. KG, Wuppertal, Germany
| | - Helge E S Pettersen
- Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway
| | - Hunter N Ratliff
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, P.O. Box 7030, 5020, Bergen, Norway
| | | | - Sonja M Schellhammer
- 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
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, Dresden, Germany
| | - Lena M Setterdahl
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, P.O. Box 7030, 5020, Bergen, Norway
| | - Kyrre Skjerdal
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, P.O. Box 7030, 5020, Bergen, Norway
| | - Edmond Sterpin
- Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Leuven, Belgium
| | | | - Joseph A Turko
- 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
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology-OncoRay, Dresden, Germany
| | - Kristian S Ytre-Hauge
- Department of Physics and Technology, University of Bergen, P.O. Box 7803, 5020, Bergen, Norway
| |
Collapse
|
31
|
Trnkova P, Zhang Y, Toshito T, Heijmen B, Richter C, Aznar MC, Albertini F, Bolsi A, Daartz J, Knopf AC, Bertholet J. A survey of practice patterns for adaptive particle therapy for interfractional changes. Phys Imaging Radiat Oncol 2023; 26:100442. [PMID: 37197154 PMCID: PMC10183663 DOI: 10.1016/j.phro.2023.100442] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 04/22/2023] [Accepted: 04/24/2023] [Indexed: 05/19/2023] Open
Abstract
Background and purpose Anatomical changes may compromise the planned target coverage and organs-at-risk dose in particle therapy. This study reports on the practice patterns for adaptive particle therapy (APT) to evaluate current clinical practice and wishes and barriers to further implementation. Materials and methods An institutional questionnaire was distributed to PT centres worldwide (7/2020-6/2021) asking which type of APT was used, details of the workflow, and what the wishes and barriers to implementation were. Seventy centres from 17 countries participated. A three-round Delphi consensus analysis (10/2022) among the authors followed to define recommendations on required actions and future vision. Results Out of the 68 clinically operational centres, 84% were users of APT for at least one treatment site with head and neck being most common. APT was mostly performed offline with only two online APT users (plan-library). No centre used online daily re-planning. Daily 3D imaging was used for APT by 19% of users. Sixty-eight percent of users had plans to increase their use or change their technique for APT. The main barrier was "lack of integrated and efficient workflows". Automation and speed, reliable dose deformation for dose accumulation and higher quality of in-room volumetric imaging were identified as the most urgent task for clinical implementation of online daily APT. Conclusion Offline APT was implemented by the majority of PT centres. Joint efforts between industry research and clinics are needed to translate innovations into efficient and clinically feasible workflows for broad-scale implementation of online APT.
Collapse
Affiliation(s)
- Petra Trnkova
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Corresponding author.
| | - Ye Zhang
- Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland
| | - Toshiyuki Toshito
- Nagoya Proton Therapy Center, Nagoya City University West Medical Center, Nagoya, Japan
| | - Ben Heijmen
- Department of Radiotherapy, Erasmus University Medical Center (Erasmus MC), Rotterdam, the Netherlands
| | - Christian Richter
- 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
| | - Marianne C. Aznar
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | | | - Alessandra Bolsi
- Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland
| | - Juliane Daartz
- Department of Radiation Oncology, Massachusetts General Hospital & Harvard Medical School, Boston, MA 02114, United States of America
| | - Antje C. Knopf
- Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland
- Institute for Medical Engineering and Medical Informatics, School of Life Science FHNW, Muttenz, Switzerland
| | - Jenny Bertholet
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, Bern, Switzerland
| |
Collapse
|
32
|
Bobić M, Lalonde A, Nesteruk KP, Lee H, Nenoff L, Gorissen BL, Bertolet A, Busse PM, Chan AW, Winey BA, Sharp GC, Verburg JM, Lomax AJ, Paganetti H. Large anatomical changes in head-and-neck cancers – a dosimetric comparison of online and offline adaptive proton therapy. Clin Transl Radiat Oncol 2023; 40:100625. [PMID: 37090849 PMCID: PMC10120292 DOI: 10.1016/j.ctro.2023.100625] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023] Open
Abstract
Purpose This work evaluates an online adaptive (OA) workflow for head-and-neck (H&N) intensity-modulated proton therapy (IMPT) and compares it with full offline replanning (FOR) in patients with large anatomical changes. Methods IMPT treatment plans are created retrospectively for a cohort of eight H&N cancer patients that previously required replanning during the course of treatment due to large anatomical changes. Daily cone-beam CTs (CBCT) are acquired and corrected for scatter, resulting in 253 analyzed fractions. To simulate the FOR workflow, nominal plans are created on the planning-CT and delivered until a repeated-CT is acquired; at this point, a new plan is created on the repeated-CT. To simulate the OA workflow, nominal plans are created on the planning-CT and adapted at each fraction using a simple beamlet weight-tuning technique. Dose distributions are calculated on the CBCTs with Monte Carlo for both delivery methods. The total treatment dose is accumulated on the planning-CT. Results Daily OA improved target coverage compared to FOR despite using smaller target margins. In the high-risk CTV, the median D98 degradation was 1.1 % and 2.1 % for OA and FOR, respectively. In the low-risk CTV, the same metrics yield 1.3 % and 5.2 % for OA and FOR, respectively. Smaller setup margins of OA reduced the dose to all OARs, which was most relevant for the parotid glands. Conclusion Daily OA can maintain prescription doses and constraints over the course of fractionated treatment, even in cases of large anatomical changes, reducing the necessity for manual replanning in H&N IMPT.
Collapse
|
33
|
Huiskes M, Astreinidou E, Kong W, Breedveld S, Heijmen B, Rasch C. Dosimetric impact of adaptive proton therapy in head and neck cancer - A review. Clin Transl Radiat Oncol 2023; 39:100598. [PMID: 36860581 PMCID: PMC9969246 DOI: 10.1016/j.ctro.2023.100598] [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: 12/12/2022] [Revised: 02/10/2023] [Accepted: 02/12/2023] [Indexed: 02/18/2023] Open
Abstract
Background Intensity Modulated Proton Therapy (IMPT) in head and neck cancer (HNC) is susceptible to anatomical changes and patient set-up inaccuracies during the radiotherapy course, which can cause discrepancies between planned and delivered dose. The discrepancies can be counteracted by adaptive replanning strategies. This article reviews the observed dosimetric impact of adaptive proton therapy (APT) and the timing to perform a plan adaptation in IMPT in HNC. Methods A literature search of articles published in PubMed/MEDLINE, EMBASE and Web of Science from January 2010 to March 2022 was performed. Among a total of 59 records assessed for possible eligibility, ten articles were included in this review. Results Included studies reported on target coverage deterioration in IMPT plans during the RT course, which was recovered with the application of an APT approach. All APT plans showed an average improved target coverage for the high- and low-dose targets as compared to the accumulated dose on the planned plans. Dose improvements up to 2.5 Gy (3.5 %) and up to 4.0 Gy (7.1 %) in the D98 of the high- and low dose targets were observed with APT. Doses to the organs at risk (OARs) remained equal or decreased slightly after APT was applied. In the included studies, APT was largely performed once, which resulted in the largest target coverage improvement, but eventual additional APT improved the target coverage further. There is no data showing what is the most appropriate timing for APT. Conclusion APT during IMPT for HNC patients improves target coverage. The largest improvement in target coverage was found with a single adaptive intervention, and an eventual second or more frequent APT application improved the target coverage further. Doses to the OARs remained equal or decreased slightly after applying APT. The most optimal timing for APT is yet to be determined.
Collapse
Affiliation(s)
- Merle Huiskes
- Department of Radiation Oncology, Leiden University Medical Center, Leiden, the Netherlands,Corresponding author at: Department of Radiation Oncology, Leiden University Medical Centre, Albinusdreef 2, P.O. Box 9600, Postal zone K1-P, 2300 RC Leiden, the Netherlands.
| | - Eleftheria Astreinidou
- Department of Radiation Oncology, Leiden University Medical Center, Leiden, the Netherlands
| | - Wens Kong
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, the Netherlands
| | - Sebastiaan Breedveld
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, the Netherlands
| | - Ben Heijmen
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, the Netherlands
| | - Coen Rasch
- Department of Radiation Oncology, Leiden University Medical Center, Leiden, the Netherlands,HollandPTC, Delft, the Netherlands
| |
Collapse
|
34
|
Zaki P, Chuong MD, Schaub SK, Lo SS, Ibrahim M, Apisarnthanarax S. Proton Beam Therapy and Photon-Based Magnetic Resonance Image-Guided Radiation Therapy: The Next Frontiers of Radiation Therapy for Hepatocellular Carcinoma. Technol Cancer Res Treat 2023; 22:15330338231206335. [PMID: 37908130 PMCID: PMC10621304 DOI: 10.1177/15330338231206335] [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: 08/17/2022] [Revised: 08/21/2023] [Accepted: 09/21/2023] [Indexed: 11/02/2023] Open
Abstract
External beam radiation therapy (EBRT) has increasingly been utilized in the treatment of hepatocellular carcinoma (HCC) due to technological advances with positive clinical outcomes. Innovations in EBRT include improved image guidance, motion management, treatment planning, and highly conformal techniques such as intensity-modulated radiation therapy (IMRT) and stereotactic body radiation therapy (SBRT). Moreover, proton beam therapy (PBT) and magnetic resonance image-guided radiation therapy (MRgRT) have expanded the capabilities of EBRT. PBT offers the advantage of minimizing low- and moderate-dose radiation to the surrounding normal tissue, thereby preserving uninvolved liver and allowing for dose escalation. MRgRT provides the advantage of improved soft tissue delineation compared to computerized tomography (CT) guidance. Additionally, MRgRT with online adaptive therapy is particularly useful for addressing motion not otherwise managed and reducing high-dose radiation to the normal tissue such as the stomach and bowel. PBT and online adaptive MRgRT are emerging technological advancements in EBRT that may provide a significant clinical benefit for patients with HCC.
Collapse
Affiliation(s)
- Peter Zaki
- Department of Radiation Oncology, University of Washington, Seattle, WA, USA
| | - Michael D. Chuong
- Department of Radiation Oncology, Miami Cancer Institute, Miami, FL, USA
| | - Stephanie K. Schaub
- Department of Radiation Oncology, University of Washington, Seattle, WA, USA
| | - Simon S. Lo
- Department of Radiation Oncology, University of Washington, Seattle, WA, USA
| | - Mariam Ibrahim
- School of Medicine, St. George's University, St. George's, Grenada
| | | |
Collapse
|
35
|
McNamara K, Schiavi A, Borys D, Brzezinski K, Gajewski J, Kopeć R, Rucinski A, Skóra T, Makkar S, Hrbacek J, Weber DC, Lomax AJ, Winterhalter C. GPU accelerated Monte Carlo scoring of positron emitting isotopes produced during proton therapy for PET verification. Phys Med Biol 2022; 67. [PMID: 36541512 DOI: 10.1088/1361-6560/aca515] [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: 05/03/2022] [Accepted: 11/22/2022] [Indexed: 11/23/2022]
Abstract
Objective.Verification of delivered proton therapy treatments is essential for reaping the many benefits of the modality, with the most widely proposedin vivoverification technique being the imaging of positron emitting isotopes generated in the patient during treatment using positron emission tomography (PET). The purpose of this work is to reduce the computational resources and time required for simulation of patient activation during proton therapy using the GPU accelerated Monte Carlo code FRED, and to validate the predicted activity against the widely used Monte Carlo code GATE.Approach.We implement a continuous scoring approach for the production of positron emitting isotopes within FRED version 5.59.9. We simulate treatment plans delivered to 95 head and neck patients at Centrum Cyklotronowe Bronowice using this GPU implementation, and verify the accuracy using the Monte Carlo toolkit GATE version 9.0.Main results.We report an average reduction in computational time by a factor of 50 when using a local system with 2 GPUs as opposed to a large compute cluster utilising between 200 to 700 CPU threads, enabling simulation of patient activity within an average of 2.9 min as opposed to 146 min. All simulated plans are in good agreement across the two Monte Carlo codes. The two codes agree within a maximum of 0.95σon a voxel-by-voxel basis for the prediction of 7 different isotopes across 472 simulated fields delivered to 95 patients, with the average deviation over all fields being 6.4 × 10-3σ.Significance.The implementation of activation calculations in the GPU accelerated Monte Carlo code FRED provides fast and reliable simulation of patient activation following proton therapy, allowing for research and development of clinical applications of range verification for this treatment modality using PET to proceed at a rapid pace.
Collapse
Affiliation(s)
- Keegan McNamara
- Centre for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland.,Physics Department, ETH Zürich, Zürich, Switzerland
| | - Angelo Schiavi
- Department of Basic and Applied Sciences for Engineering, Sapienza University of Rome, Rome, Italy
| | - Damian Borys
- Department of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland.,Institute of Nuclear Physics Polish Academy of Sciences, Kraków, Poland
| | - Karol Brzezinski
- Institute of Nuclear Physics Polish Academy of Sciences, Kraków, Poland
| | - Jan Gajewski
- Institute of Nuclear Physics Polish Academy of Sciences, Kraków, Poland
| | - Renata Kopeć
- Institute of Nuclear Physics Polish Academy of Sciences, Kraków, Poland
| | - Antoni Rucinski
- Institute of Nuclear Physics Polish Academy of Sciences, Kraków, Poland
| | - Tomasz Skóra
- Department of Radiotherapy, Maria Sklodowska-Curie National Research Institute of Oncology, Kraków Branch, Kraków, Poland
| | - Shubhangi Makkar
- Centre for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland.,Physics Department, ETH Zürich, Zürich, Switzerland
| | - Jan Hrbacek
- Centre for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland
| | - Damien C Weber
- Centre for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland.,Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Switzerland.,Department of Radiation Oncology, University Hospital of Zürich, Switzerland
| | - Antony J Lomax
- Centre for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland.,Physics Department, ETH Zürich, Zürich, Switzerland
| | - Carla Winterhalter
- Centre for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland.,Physics Department, ETH Zürich, Zürich, Switzerland
| |
Collapse
|
36
|
Zhang G, Zhou L, Han Z, Zhao W, Peng H. SWFT-Net: a deep learning framework for efficient fine-tuning spot weights towards adaptive proton therapy. Phys Med Biol 2022; 67. [PMID: 36541496 DOI: 10.1088/1361-6560/aca517] [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: 07/03/2022] [Accepted: 11/22/2022] [Indexed: 11/23/2022]
Abstract
Objective. One critical task for adaptive proton therapy is how to perform spot weight re-tuning and reoptimize plan, both of which are time-consuming and labor intensive. We proposed a deep learning framework (SWFT-Net) to speed up such a task, a starting point for us to move towards online adaptive proton therapy.Approach. For a H&N patient case, a reference intensity modulated proton therapy plan was generated. For data augmentation, spot weights were modified to generate three datasets (DS10, DS30, DS50), corresponding to different levels of weight adjustment. For each dataset, the samples were split into the training and testing groups at a ratio of 8:2 (6400 for training, 1706 for testing). To ease the difficulty of machine learning, the residuals of dose maps and spot weights (i.e. difference relative to a reference) were used as inputs and outputs, respectively. Quantitative analyses were performed in terms of normalized root mean square error (NRMSE) of spot weights, Gamma passing rate and dose difference within the PTV.Main results. The SWFT-Net is able to generate an adapted plan in less than a second with a NVIDIA GeForce RTX 3090 GPU. For the 1706 samples in the testing dataset, the NRMSE is 0.41% (DS10), 1.05% (DS30) and 2.04% (DS50), respectively. Cold/hot spots in the dose maps after adaptation are observed. The mean relative dose difference is 0.64% (DS10), 0.92% (DS30) and 0.88% (DS50), respectively. For all three datasets, the mean Gamma passing rate is consistently over 95% for both 1 mm/1% and 3 mm/3% settings.Significance. The proposed SWFT-Net is a promising tool to help realize adaptive proton therapy. It can be used as an alternative tool to other spot fine-tuning optimization algorithms, likely demonstrating superior performance in terms of speed, accuracy, robustness and minimum human interaction. This study lays down a foundation for us to move further incorporating other factors such as daily anatomical changes and propagated PTVs, and develop a truly online adaptive workflow in proton therapy.
Collapse
Affiliation(s)
- Guoliang Zhang
- Department of Medical Physics, School of Physics and Technology, Wuhan University, 430072, People's Republic of China
| | - Long Zhou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310020, People's Republic of China
| | - Zeng Han
- Department of Medical Physics, School of Physics and Technology, Wuhan University, 430072, People's Republic of China
| | - Wei Zhao
- School of Physics, Beihang University, Beijing, 100191, People's Republic of China
| | - Hao Peng
- Department of Medical Physics, School of Physics and Technology, Wuhan University, 430072, People's Republic of China.,Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, United States of America
| |
Collapse
|
37
|
An online adaptive plan library approach for intensity modulated proton therapy for head and neck cancer. Radiother Oncol 2022; 176:68-75. [PMID: 36150418 DOI: 10.1016/j.radonc.2022.09.011] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 08/25/2022] [Accepted: 09/13/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND AND PURPOSE In intensity modulated proton therapy (IMPT), the impact of setup errors and anatomical changes is commonly mitigated by robust optimization with population-based setup robustness (SR) settings and offline replanning. In this study we propose and evaluate an alternative approach based on daily plan selection from patient-specific pre-treatment established plan libraries (PLs). Clinical implementation of the PL strategy would be rather straightforward compared to daily online re-planning. MATERIALS AND METHODS For 15 head-and-neck cancer patients, the planning CT was used to generate a PL with 5 plans, robustly optimized for increasing SR: 0, 1, 2, 3, 5 mm, and 3% range robustness. Repeat CTs (rCTs) and realistic setup and range uncertainty distributions were used for simulation of treatment courses for the PL approach, treatments with fixed SR (fSR3) and a trigger-based offline adaptive schedule for 3 mm SR (fSR3OfA). Daily plan selection in the PL approach was based only on recomputed dose to the CTV on the rCT. RESULTS Compared to using fSR3 and fSR3OfA, the risk of xerostomia grade ≥ II & III and dysphagia ≥ grade III were significantly reduced with the PL. For 6/15 patients the risk of xerostomia and/or dysphagia ≥ grade II could be reduced by > 2% by using PL. For the other patients, adherence to target coverage constraints was often improved. fSR3OfA resulted in significantly improved coverage compared to PL for selected patients. CONCLUSION The proposed PL approach resulted in overall reduced NTCPs compared to fSR3 and fSR3OfA at limited cost in target coverage.
Collapse
|
38
|
Yao W, Zhang B, Han D, Polf J, Vedam S, Lasio G, Yi B. Use of CBCT plus plan robustness for reducing QACT frequency in intensity-modulated proton therapy: Head-and-neck cases. Med Phys 2022; 49:6794-6801. [PMID: 35933322 DOI: 10.1002/mp.15915] [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: 04/04/2022] [Revised: 06/20/2022] [Accepted: 08/01/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Anatomic variation has a significant dosimetric impact in intensity-modulated proton therapy. Weekly or biweekly computed tomography (CT) scans, called quality assurance CTs (QACTs), are used to monitor anatomic and resultant dose changes to determine whether adaptive plans are needed. Frequent CT scans result in unwanted QACT dose and increased clinical workloads. This study proposed utilizing patient setup cone-beam CTs (CBCTs) and treatment plan robustness to reduce the frequency of QACTs. METHODS We retrospectively analyzed data from 27 patients with head-and-neck cancer, including 594 CBCTs, 136 QACTs, and 19 adaptive plans. For each CBCT, water-equivalent thickness (WET) along the pencil-beam path was calculated. For each treatment plan, the WET of the first-day CBCT was used as the reference, and the mean WET changes (ΔWET) in each following CBCT was used as the surrogate of proton range change. Using CBCTs acquired prior to a QACT, we predicted the ΔWET on the QACT day by a linear regression model. The impact of range change on target dose was calculated as the predicted ΔWET weighted by the monitor units of each field. In addition, plan robustness was estimated from the robust dose-volume histograms (DVHs) and combined with ΔWET to reduce QACT frequency. Robustness was estimated from the distance between the DVH curves of the nominal and worst scenarios. RESULTS When the estimated mean ΔWET was <6.5 mm (or <7.5 mm if the robustness was >95%), the QACT could be skipped without missing any adaptive planning; otherwise a QACT was required. Overall, 41% of QACTs could be eliminated when ΔWET was <6.5 mm and 56% when ΔWET was <7.5 mm, and robustness was >95%. At least one QACT could have been omitted in 25 of the 27 cases under skipping thresholds at ΔWETs <7.5 mm and R > 95%. CONCLUSION This study suggests that the number of QACTs can be greatly reduced by calculating range change in patient setup CBCTs and can be further reduced by combining this information with analyses of plan robustness.
Collapse
Affiliation(s)
- Weiguang Yao
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Baoshe Zhang
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Dong Han
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Jerimy Polf
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Sastry Vedam
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Giovanni Lasio
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Byongyong Yi
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| |
Collapse
|
39
|
Thummerer A, Seller Oria C, Zaffino P, Visser S, Meijers A, Guterres Marmitt G, Wijsman R, Seco J, Langendijk JA, Knopf AC, Spadea MF, Both S. Deep learning-based 4D-synthetic CTs from sparse-view CBCTs for dose calculations in adaptive proton therapy. Med Phys 2022; 49:6824-6839. [PMID: 35982630 PMCID: PMC10087352 DOI: 10.1002/mp.15930] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/20/2022] [Accepted: 08/08/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Time-resolved 4D cone beam-computed tomography (4D-CBCT) allows a daily assessment of patient anatomy and respiratory motion. However, 4D-CBCTs suffer from imaging artifacts that affect the CT number accuracy and prevent accurate proton dose calculations. Deep learning can be used to correct CT numbers and generate synthetic CTs (sCTs) that can enable CBCT-based proton dose calculations. PURPOSE In this work, sparse view 4D-CBCTs were converted into 4D-sCT utilizing a deep convolutional neural network (DCNN). 4D-sCTs were evaluated in terms of image quality and dosimetric accuracy to determine if accurate proton dose calculations for adaptive proton therapy workflows of lung cancer patients are feasible. METHODS A dataset of 45 thoracic cancer patients was utilized to train and evaluate a DCNN to generate 4D-sCTs, based on sparse view 4D-CBCTs reconstructed from projections acquired with a 3D acquisition protocol. Mean absolute error (MAE) and mean error were used as metrics to evaluate the image quality of single phases and average 4D-sCTs against 4D-CTs acquired on the same day. The dosimetric accuracy was checked globally (gamma analysis) and locally for target volumes and organs-at-risk (OARs) (lung, heart, and esophagus). Furthermore, 4D-sCTs were also compared to 3D-sCTs. To evaluate CT number accuracy, proton radiography simulations in 4D-sCT and 4D-CTs were compared in terms of range errors. The clinical suitability of 4D-sCTs was demonstrated by performing a 4D dose reconstruction using patient specific treatment delivery log files and breathing signals. RESULTS 4D-sCTs resulted in average MAEs of 48.1 ± 6.5 HU (single phase) and 37.7 ± 6.2 HU (average). The global dosimetric evaluation showed gamma pass ratios of 92.3% ± 3.2% (single phase) and 94.4% ± 2.1% (average). The clinical target volume showed high agreement in D98 between 4D-CT and 4D-sCT, with differences below 2.4% for all patients. Larger dose differences were observed in mean doses of OARs (up to 8.4%). The comparison with 3D-sCTs showed no substantial image quality and dosimetric differences for the 4D-sCT average. Individual 4D-sCT phases showed slightly lower dosimetric accuracy. The range error evaluation revealed that lung tissues cause range errors about three times higher than the other tissues. CONCLUSION In this study, we have investigated the accuracy of deep learning-based 4D-sCTs for daily dose calculations in adaptive proton therapy. Despite image quality differences between 4D-sCTs and 3D-sCTs, comparable dosimetric accuracy was observed globally and locally. Further improvement of 3D and 4D lung sCTs could be achieved by increasing CT number accuracy in lung tissues.
Collapse
Affiliation(s)
- Adrian Thummerer
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Carmen Seller Oria
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Paolo Zaffino
- Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy
| | - Sabine Visser
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Arturs Meijers
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland
| | - Gabriel Guterres Marmitt
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Robin Wijsman
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Joao Seco
- Department of Biomedical Physics in Radiation Oncology, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany.,Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Johannes Albertus Langendijk
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Antje Christin Knopf
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Department I of Internal Medicine, Center for Integrated Oncology Cologne, University Hospital of Cologne, Cologne, Germany
| | - Maria Francesca Spadea
- Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy
| | - Stefan Both
- Department, of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| |
Collapse
|
40
|
A plan verification platform for online adaptive proton therapy using deep learning-based Monte–Carlo denoising. Phys Med 2022; 103:18-25. [DOI: 10.1016/j.ejmp.2022.09.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 09/27/2022] [Accepted: 09/28/2022] [Indexed: 11/21/2022] Open
|
41
|
Elhamiasl M, Salvo K, Poels K, Defraene G, Lambrecht M, Geets X, Sterpin E, Nuyts J. Low-dose CT allows for accurate proton therapy dose calculation and plan optimization. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac8dde] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 08/30/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. Protons offer a more conformal dose delivery compared to photons, yet they are sensitive to anatomical changes over the course of treatment. To minimize range uncertainties due to anatomical variations, a new CT acquisition at every treatment session would be paramount to enable daily dose calculation and subsequent plan adaptation. However, the series of CT scans results in an additional accumulated patient dose. Reducing CT radiation dose and thereby decreasing the potential risk of radiation exposure to patients is desirable, however, lowering the CT dose results in a lower signal-to-noise ratio and therefore in a reduced quality image. We hypothesized that the signal-to-noise ratio provided by conventional CT protocols is higher than needed for proton dose distribution estimation. In this study, we aim to investigate the effect of CT imaging dose reduction on proton therapy dose calculations and plan optimization. Approach. To verify our hypothesis, a CT dose reduction simulation tool has been developed and validated to simulate lower-dose CT scans from an existing standard-dose scan. The simulated lower-dose CTs were then used for proton dose calculation and plan optimization and the results were compared with those of the standard-dose scan. The same strategy was adopted to investigate the effect of CT dose reduction on water equivalent thickness (WET) calculation to quantify CT noise accumulation during integration along the beam. Main results. The similarity between the dose distributions acquired from the low-dose and standard-dose CTs was evaluated by the dose-volume histogram and the 3D Gamma analysis. The results on an anthropomorphic head phantom and three patient cases indicate that CT imaging dose reduction up to 90% does not have a significant effect on proton dose calculation and plan optimization. The relative error was employed to evaluate the similarity between WET maps and was found to be less than 1% after reducing the CT imaging dose by 90%. Significance. The results suggest the possibility of using low-dose CT for proton therapy dose estimation, since the dose distributions acquired from the standard-dose and low-dose CTs are clinically equivalent.
Collapse
|
42
|
Moglioni M, Kraan AC, Baroni G, Battistoni G, Belcari N, Berti A, Carra P, Cerello P, Ciocca M, De Gregorio A, De Simoni M, Del Sarto D, Donetti M, Dong Y, Embriaco A, Fantacci ME, Ferrero V, Fiorina E, Fischetti M, Franciosini G, Giraudo G, Laruina F, Maestri D, Magi M, Magro G, Malekzadeh E, Marafini M, Mattei I, Mazzoni E, Mereu P, Mirandola A, Morrocchi M, Muraro S, Orlandi E, Patera V, Pennazio F, Pullia M, Retico A, Rivetti A, Da Rocha Rolo MD, Rosso V, Sarti A, Schiavi A, Sciubba A, Sportelli G, Tampellini S, Toppi M, Traini G, Trigilio A, Valle SM, Valvo F, Vischioni B, Vitolo V, Wheadon R, Bisogni MG. In-vivo range verification analysis with in-beam PET data for patients treated with proton therapy at CNAO. Front Oncol 2022; 12:929949. [PMID: 36226070 PMCID: PMC9549776 DOI: 10.3389/fonc.2022.929949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
Morphological changes that may arise through a treatment course are probably one of the most significant sources of range uncertainty in proton therapy. Non-invasive in-vivo treatment monitoring is useful to increase treatment quality. The INSIDE in-beam Positron Emission Tomography (PET) scanner performs in-vivo range monitoring in proton and carbon therapy treatments at the National Center of Oncological Hadrontherapy (CNAO). It is currently in a clinical trial (ID: NCT03662373) and has acquired in-beam PET data during the treatment of various patients. In this work we analyze the in-beam PET (IB-PET) data of eight patients treated with proton therapy at CNAO. The goal of the analysis is twofold. First, we assess the level of experimental fluctuations in inter-fractional range differences (sensitivity) of the INSIDE PET system by studying patients without morphological changes. Second, we use the obtained results to see whether we can observe anomalously large range variations in patients where morphological changes have occurred. The sensitivity of the INSIDE IB-PET scanner was quantified as the standard deviation of the range difference distributions observed for six patients that did not show morphological changes. Inter-fractional range variations with respect to a reference distribution were estimated using the Most-Likely-Shift (MLS) method. To establish the efficacy of this method, we made a comparison with the Beam’s Eye View (BEV) method. For patients showing no morphological changes in the control CT the average range variation standard deviation was found to be 2.5 mm with the MLS method and 2.3 mm with the BEV method. On the other hand, for patients where some small anatomical changes occurred, we found larger standard deviation values. In these patients we evaluated where anomalous range differences were found and compared them with the CT. We found that the identified regions were mostly in agreement with the morphological changes seen in the CT scan.
Collapse
Affiliation(s)
- Martina Moglioni
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, Pisa, Italy
| | - Aafke Christine Kraan
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy
- *Correspondence: Aafke Christine Kraan,
| | - Guido Baroni
- Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
- Politecnico di Milano, Milano, Italy
| | | | - Nicola Belcari
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, Pisa, Italy
| | - Andrea Berti
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, Pisa, Italy
- Istituto di Scienza e Tecnologie dell’Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy
| | - Pietro Carra
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, Pisa, Italy
| | | | - Mario Ciocca
- Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | - Angelica De Gregorio
- Dipartimento di Fisica, Sapienza Università di Roma, Roma, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy
| | - Micol De Simoni
- Dipartimento di Fisica, Sapienza Università di Roma, Roma, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy
| | - Damiano Del Sarto
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, Pisa, Italy
| | - Marco Donetti
- Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | - Yunsheng Dong
- Istituto Nazionale di Fisica Nucleare, Sezione di Milano, Milano, Italy
- Dipartimento di Fisica, Università di Milano, Milano, Italy
| | - Alessia Embriaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Pavia, Pavia, Italy
| | - Maria Evelina Fantacci
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, Pisa, Italy
| | - Veronica Ferrero
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Torino, Italy
| | - Elisa Fiorina
- Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Torino, Italy
| | - Marta Fischetti
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy
- Dipartimento di Scienze di Base e Applicate per l’Ingegneria, Sapienza Universit `a di Roma, Roma, Italy
| | - Gaia Franciosini
- Dipartimento di Fisica, Sapienza Università di Roma, Roma, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy
| | - Giuseppe Giraudo
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Torino, Italy
| | - Francesco Laruina
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, Pisa, Italy
| | - Davide Maestri
- Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | - Marco Magi
- Dipartimento di Scienze di Base e Applicate per l’Ingegneria, Sapienza Universit `a di Roma, Roma, Italy
| | - Giuseppe Magro
- Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | - Etesam Malekzadeh
- Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
- Department of Medical Physics, Tarbiat Modares University, Teheran, Iran
| | - Michela Marafini
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy
- Museo Storico della Fisica e Centro Studi e Ricerche “E. Fermi”, Roma, Italy
| | - Ilaria Mattei
- Istituto Nazionale di Fisica Nucleare, Sezione di Milano, Milano, Italy
| | - Enrico Mazzoni
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy
| | - Paolo Mereu
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Torino, Italy
| | | | - Matteo Morrocchi
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, Pisa, Italy
| | - Silvia Muraro
- Istituto Nazionale di Fisica Nucleare, Sezione di Milano, Milano, Italy
| | - Ester Orlandi
- Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | - Vincenzo Patera
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy
- Dipartimento di Scienze di Base e Applicate per l’Ingegneria, Sapienza Universit `a di Roma, Roma, Italy
| | | | - Marco Pullia
- Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | | | - Angelo Rivetti
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Torino, Italy
| | | | - Valeria Rosso
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, Pisa, Italy
| | - Alessio Sarti
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy
- Dipartimento di Scienze di Base e Applicate per l’Ingegneria, Sapienza Universit `a di Roma, Roma, Italy
| | - Angelo Schiavi
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy
- Dipartimento di Scienze di Base e Applicate per l’Ingegneria, Sapienza Universit `a di Roma, Roma, Italy
| | - Adalberto Sciubba
- Dipartimento di Scienze di Base e Applicate per l’Ingegneria, Sapienza Universit `a di Roma, Roma, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione dei Laboratori di Frascati, Frascati, Italy
| | - Giancarlo Sportelli
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, Pisa, Italy
| | | | - Marco Toppi
- Dipartimento di Scienze di Base e Applicate per l’Ingegneria, Sapienza Universit `a di Roma, Roma, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione dei Laboratori di Frascati, Frascati, Italy
| | - Giacomo Traini
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy
- Museo Storico della Fisica e Centro Studi e Ricerche “E. Fermi”, Roma, Italy
| | - Antonio Trigilio
- Dipartimento di Fisica, Sapienza Università di Roma, Roma, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy
| | | | | | | | - Viviana Vitolo
- Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | - Richard Wheadon
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Torino, Italy
| | - Maria Giuseppina Bisogni
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, Pisa, Italy
| |
Collapse
|
43
|
Kusano Y, Katoh H, Minohara S, Fujii H, Miyasaka Y, Takayama Y, Imura K, Kusunoki T, Miyakawa S, Kamada T, Serizawa I, Takakusagi Y, Mizoguchi N, Tsuchida K, Yoshida D. Robust treatment planning in scanned carbon-ion radiotherapy for pancreatic cancer: Clinical verification using in-room computed tomography images. Front Oncol 2022; 12:974728. [PMID: 36106121 PMCID: PMC9465304 DOI: 10.3389/fonc.2022.974728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 08/03/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeCarbon-ion beam (C-beam) has a sharp dose distribution called the Bragg peak. Carbon-ion radiation therapy, such as stereotactic body radiotherapy in photon radiotherapy, can be completed in a short period by concentrating the radiation dose on the tumor while minimizing the dose to organs at-risk. However, the stopping position of C-beam is sensitive to density variations along the beam path and such variations can lower the tumor dose as well as cause the delivery of an unexpectedly high dose to the organs at risk. We evaluated the clinical efficacy of a robust planning technique considering gastrointestinal gas (G-gas) to deliver accurate radiation doses in carbon-ion radiotherapy for pancreatic cancer.Materials and methodsWe focused on the computed tomography (CT) value replacement method. Replacement signifies the overwriting of CT values in the CT images. The most effective replacement method for robust treatment planning was determined by verifying the effects of the three replacement patterns. We selected 10 consecutive patients. Pattern 1 replaces the CT value of the G-gas contours with the value of the region without G-gas (P1). This condition indicates a no-gas state. Pattern 2 replaces each gastrointestinal contour using the mean CT value of each contour (P2). The effect of G-gas was included in the replacement value. Pattern 3 indicates no replacement (P3). We analyzed variations in the target coverage (TC) and homogeneity index (HI) from the initial plan using in-room CT images. We then performed correlation analysis on the variations in G-gas, TC, and HI to evaluate the robustness against G-gas.ResultsAnalysis of variations in TC and HI revealed a significant difference between P1 and P3 and between P2 and P3. Although no statistically significant difference was observed between P1 and P2, variations, including the median, tended to be fewer in P2. The correlation analyses for G-gas, TC, and HI showed that P2 was less likely to be affected by G-gas.ConclusionFor a treatment plan that is robust to G-gas, P2 mean replacement method should be used. This method does not necessitate any particular software or equipment, and is convenient to implement in clinical practice.
Collapse
Affiliation(s)
- Yohsuke Kusano
- Section of Medical Physics and Engineering, Kanagawa Cancer Center, Yokohama, Japan
- *Correspondence: Yohsuke Kusano,
| | - Hiroyuki Katoh
- Department of Radiation Oncology, Kanagawa Cancer Center, Yokohama, Japan
| | - Shinichi Minohara
- Section of Medical Physics and Engineering, Kanagawa Cancer Center, Yokohama, Japan
| | - Hajime Fujii
- Accelerator Engineering Corporation, Kanagawa Office, Chiba, Japan
| | - Yuya Miyasaka
- Department of Heavy Particle Medical Science, Yamagata University Graduate School of Medical Science, Yamagata, Japan
| | - Yoshiki Takayama
- Section of Medical Physics and Engineering, Kanagawa Cancer Center, Yokohama, Japan
| | - Koh Imura
- Section of Medical Physics and Engineering, Kanagawa Cancer Center, Yokohama, Japan
| | - Terufumi Kusunoki
- Section of Medical Physics and Engineering, Kanagawa Cancer Center, Yokohama, Japan
| | - Shin Miyakawa
- Section of Medical Physics and Engineering, Kanagawa Cancer Center, Yokohama, Japan
| | - Tadashi Kamada
- Department of Radiation Oncology, Kanagawa Cancer Center, Yokohama, Japan
| | - Itsuko Serizawa
- Department of Radiation Oncology, Kanagawa Cancer Center, Yokohama, Japan
| | - Yosuke Takakusagi
- Department of Radiation Oncology, Kanagawa Cancer Center, Yokohama, Japan
| | - Nobutaka Mizoguchi
- Department of Radiation Oncology, Kanagawa Cancer Center, Yokohama, Japan
| | - Keisuke Tsuchida
- Department of Radiation Oncology, Kanagawa Cancer Center, Yokohama, Japan
| | - Daisaku Yoshida
- Department of Radiation Oncology, Kanagawa Cancer Center, Yokohama, Japan
| |
Collapse
|
44
|
Chen X, Liu Y, Yang B, Zhu J, Yuan S, Xie X, Liu Y, Dai J, Men K. A more effective CT synthesizer using transformers for cone-beam CT-guided adaptive radiotherapy. Front Oncol 2022; 12:988800. [PMID: 36091131 PMCID: PMC9454309 DOI: 10.3389/fonc.2022.988800] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeThe challenge of cone-beam computed tomography (CBCT) is its low image quality, which limits its application for adaptive radiotherapy (ART). Despite recent substantial improvement in CBCT imaging using the deep learning method, the image quality still needs to be improved for effective ART application. Spurred by the advantages of transformers, which employs multi-head attention mechanisms to capture long-range contextual relations between image pixels, we proposed a novel transformer-based network (called TransCBCT) to generate synthetic CT (sCT) from CBCT. This study aimed to further improve the accuracy and efficiency of ART.Materials and methodsIn this study, 91 patients diagnosed with prostate cancer were enrolled. We constructed a transformer-based hierarchical encoder–decoder structure with skip connection, called TransCBCT. The network also employed several convolutional layers to capture local context. The proposed TransCBCT was trained and validated on 6,144 paired CBCT/deformed CT images from 76 patients and tested on 1,026 paired images from 15 patients. The performance of the proposed TransCBCT was compared with a widely recognized style transferring deep learning method, the cycle-consistent adversarial network (CycleGAN). We evaluated the image quality and clinical value (application in auto-segmentation and dose calculation) for ART need.ResultsTransCBCT had superior performance in generating sCT from CBCT. The mean absolute error of TransCBCT was 28.8 ± 16.7 HU, compared to 66.5 ± 13.2 for raw CBCT, and 34.3 ± 17.3 for CycleGAN. It can preserve the structure of raw CBCT and reduce artifacts. When applied in auto-segmentation, the Dice similarity coefficients of bladder and rectum between auto-segmentation and oncologist manual contours were 0.92 and 0.84 for TransCBCT, respectively, compared to 0.90 and 0.83 for CycleGAN. When applied in dose calculation, the gamma passing rate (1%/1 mm criterion) was 97.5% ± 1.1% for TransCBCT, compared to 96.9% ± 1.8% for CycleGAN.ConclusionsThe proposed TransCBCT can effectively generate sCT for CBCT. It has the potential to improve radiotherapy accuracy.
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, China
- National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Chinese Academy of Medical Sciences, Langfang, China
| | - Yuxiang Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- School of Physics and Technology, Wuhan University, Wuhan, China
| | - Bining Yang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Siqi Yuan
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xuejie Xie
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yueping Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 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, 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, China
- *Correspondence: Kuo Men,
| |
Collapse
|
45
|
Nenoff L, Buti G, Bobić M, Lalonde A, Nesteruk KP, Winey B, Sharp GC, Sudhyadhom A, Paganetti H. Integrating Structure Propagation Uncertainties in the Optimization of Online Adaptive Proton Therapy Plans. Cancers (Basel) 2022; 14:cancers14163926. [PMID: 36010919 PMCID: PMC9406068 DOI: 10.3390/cancers14163926] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/10/2022] [Accepted: 08/12/2022] [Indexed: 01/11/2023] Open
Abstract
Currently, adaptive strategies require time- and resource-intensive manual structure corrections. This study compares different strategies: optimization without manual structure correction, adaptation with physician-drawn structures, and no adaptation. Strategies were compared for 16 patients with pancreas, liver, and head and neck (HN) cancer with 1-5 repeated images during treatment: 'reference adaptation', with structures drawn by a physician; 'single-DIR adaptation', using a single set of deformably propagated structures; 'multi-DIR adaptation', using robust planning with multiple deformed structure sets; 'conservative adaptation', using the intersection and union of all deformed structures; 'probabilistic adaptation', using the probability of a voxel belonging to the structure in the optimization weight; and 'no adaptation'. Plans were evaluated using reference structures and compared using a scoring system. The reference adaptation with physician-drawn structures performed best, and no adaptation performed the worst. For pancreas and liver patients, adaptation with a single DIR improved the plan quality over no adaptation. For HN patients, integrating structure uncertainties brought an additional benefit. If resources for manual structure corrections would prevent online adaptation, manual correction could be replaced by a fast 'plausibility check', and plans could be adapted with correction-free adaptation strategies. Including structure uncertainties in the optimization has the potential to make online adaptation more automatable.
Collapse
Affiliation(s)
- Lena Nenoff
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
- Correspondence:
| | - Gregory Buti
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
- Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Institute of Experimental and Clinical Research (IREC), Université Catholique de Louvain, 1200 Brussels, Belgium
| | - Mislav Bobić
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Physics, ETH Zurich, 8092 Zurich, Switzerland
| | - Arthur Lalonde
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Konrad P. Nesteruk
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Brian Winey
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Gregory Charles Sharp
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Atchar Sudhyadhom
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Harald Paganetti
- Harvard Medical School, Boston, MA 02115, USA
- Department of Radiation Oncology, Physics Division, Massachusetts General Hospital, Boston, MA 02114, USA
| |
Collapse
|
46
|
Cao W, Rocha H, Mohan R, Lim G, Goudarzi HM, Ferreira BC, Dias JM. Reflections on beam configuration optimization for intensity-modulated proton therapy. Phys Med Biol 2022; 67. [PMID: 35561700 DOI: 10.1088/1361-6560/ac6fac] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Presumably, intensity-modulated proton radiotherapy (IMPT) is the most powerful form of proton radiotherapy. In the current state of the art, IMPT beam configurations (i.e. the number of beams and their directions) are, in general, chosen subjectively based on prior experience and practicality. Beam configuration optimization (BCO) for IMPT could, in theory, significantly enhance IMPT’s therapeutic potential. However, BCO is complex and highly computer resource-intensive. Some algorithms for BCO have been developed for intensity-modulated photon therapy (IMRT). They are rarely used clinically mainly because the large number of beams typically employed in IMRT renders BCO essentially unnecessary. Moreover, in the newer form of IMRT, volumetric modulated arc therapy, there are no individual static beams. BCO is of greater importance for IMPT because it typically employs a very small number of beams (2-4) and, when the number of beams is small, BCO is critical for improving plan quality. However, the unique properties and requirements of protons, particularly in IMPT, make BCO challenging. Protons are more sensitive than photons to anatomic changes, exhibit variable relative biological effectiveness along their paths, and, as recently discovered, may spare the immune system. Such factors must be considered in IMPT BCO, though doing so would make BCO more resource intensive and make it more challenging to extend BCO algorithms developed for IMRT to IMPT. A limited amount of research in IMPT BCO has been conducted; however, considerable additional work is needed for its further development to make it truly effective and computationally practical. This article aims to provide a review of existing BCO algorithms, most of which were developed for IMRT, and addresses important requirements specific to BCO for IMPT optimization that necessitate the modification of existing approaches or the development of new effective and efficient ones.
Collapse
|
47
|
Nuyts S, Bollen H, Ng SP, Corry J, Eisbruch A, Mendenhall WM, Smee R, Strojan P, Ng WT, Ferlito A. Proton Therapy for Squamous Cell Carcinoma of the Head and Neck: Early Clinical Experience and Current Challenges. Cancers (Basel) 2022; 14:cancers14112587. [PMID: 35681568 PMCID: PMC9179360 DOI: 10.3390/cancers14112587] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/18/2022] [Accepted: 05/20/2022] [Indexed: 11/19/2022] Open
Abstract
Simple Summary Proton therapy is a promising type of radiation therapy used to destroy tumor cells. It has the potential to further improve the outcomes for patients with head and neck cancer since it allows to minimize the radiation dose to vital structures around the tumor, leading to less toxicity. This paper describes the current experience worldwide with proton therapy in head and neck cancer. Abstract Proton therapy (PT) is a promising development in radiation oncology, with the potential to further improve outcomes for patients with squamous cell carcinoma of the head and neck (HNSCC). By utilizing the finite range of protons, healthy tissue can be spared from beam exit doses that would otherwise be irradiated with photon-based treatments. Current evidence on PT for HNSCC is limited to comparative dosimetric analyses and retrospective single-institution series. As a consequence, the recognized indications for the reimbursement of PT remain scarce in most countries. Nevertheless, approximately 100 PT centers are in operation worldwide, and initial experiences for HNSCC are being reported. This review aims to summarize the results of the early clinical experience with PT for HNSCC and the challenges that are currently faced.
Collapse
Affiliation(s)
- Sandra Nuyts
- Laboratory of Experimental Radiotherapy, Department of Oncology, Katholieke Universiteit Leuven, 3000 Leuven, Belgium;
- Department of Oncology, Leuven Cancer Institute, Universitair Ziekenhuis Leuven, 3000 Leuven, Belgium
- Correspondence:
| | - Heleen Bollen
- Laboratory of Experimental Radiotherapy, Department of Oncology, Katholieke Universiteit Leuven, 3000 Leuven, Belgium;
- Department of Oncology, Leuven Cancer Institute, Universitair Ziekenhuis Leuven, 3000 Leuven, Belgium
| | - Sweet Ping Ng
- Department of Radiation Oncology, Austin Health, The University of Melbourne, Melbourne, VIC 3000, Australia;
| | - June Corry
- Division of Medicine, Department of Radiation Oncology, St. Vincent’s Hospital, The University of Melbourne, Melbourne, VIC 3000, Australia;
| | - Avraham Eisbruch
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48109, USA;
| | - William M Mendenhall
- Department of Radiation Oncology, College of Medicine, University of Florida, Gainesville, FL 32209, USA;
| | - Robert Smee
- Department of Radiation Oncology, The Prince of Wales Cancer Centre, Sydney, NSW 2031, Australia;
| | - Primoz Strojan
- Department of Radiation Oncology, Institute of Oncology, University of Ljubljana, 1000 Ljubljana, Slovenia;
| | - Wai Tong Ng
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China;
| | - Alfio Ferlito
- Coordinator of the International Head and Neck Scientific Group, 35125 Padua, Italy;
| |
Collapse
|
48
|
Pastor-Serrano O, Perkó Z. Millisecond speed deep learning based proton dose calculation with Monte Carlo accuracy. Phys Med Biol 2022; 67. [PMID: 35447605 DOI: 10.1088/1361-6560/ac692e] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 04/21/2022] [Indexed: 11/12/2022]
Abstract
Objective.Next generation online and real-time adaptive radiotherapy workflows require precise particle transport simulations in sub-second times, which is unfeasible with current analytical pencil beam algorithms (PBA) or Monte Carlo (MC) methods. We present a deep learning based millisecond speed dose calculation algorithm (DoTA) accurately predicting the dose deposited by mono-energetic proton pencil beams for arbitrary energies and patient geometries.Approach.Given the forward-scattering nature of protons, we frame 3D particle transport as modeling a sequence of 2D geometries in the beam's eye view. DoTA combines convolutional neural networks extracting spatial features (e.g. tissue and density contrasts) with a transformer self-attention backbone that routes information between the sequence of geometry slices and a vector representing the beam's energy, and is trained to predict low noise MC simulations of proton beamlets using 80 000 different head and neck, lung, and prostate geometries.Main results.Predicting beamlet doses in 5 ± 4.9 ms with a very high gamma pass rate of 99.37 ± 1.17% (1%, 3 mm) compared to the ground truth MC calculations, DoTA significantly improves upon analytical pencil beam algorithms both in precision and speed. Offering MC accuracy 100 times faster than PBAs for pencil beams, our model calculates full treatment plan doses in 10-15 s depending on the number of beamlets (800-2200 in our plans), achieving a 99.70 ± 0.14% (2%, 2 mm) gamma pass rate across 9 test patients.Significance.Outperforming all previous analytical pencil beam and deep learning based approaches, DoTA represents a new state of the art in data-driven dose calculation and can directly compete with the speed of even commercial GPU MC approaches. Providing the sub-second speed required for adaptive treatments, straightforward implementations could offer similar benefits to other steps of the radiotherapy workflow or other modalities such as helium or carbon treatments.
Collapse
Affiliation(s)
- Oscar Pastor-Serrano
- Delft University of Technology, Department of Radiation Science and Technology, Delft, The Netherlands
| | - Zoltán Perkó
- Delft University of Technology, Department of Radiation Science and Technology, Delft, The Netherlands
| |
Collapse
|
49
|
Feng H, Patel SH, Wong WW, Younkin JE, Penoncello GP, Morales DH, Stoker JB, Robertson DG, Fatyga M, Bues M, Schild SE, Foote RL, Liu W. GPU-accelerated Monte Carlo-based online adaptive proton therapy - a feasibility study. Med Phys 2022; 49:3550-3563. [PMID: 35443080 DOI: 10.1002/mp.15678] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/21/2022] [Accepted: 04/12/2022] [Indexed: 11/06/2022] Open
Abstract
PURPOSE To develop an online Graphic-Processing-Unit (GPU)-accelerated Monte-Carlo-based adaptive radiation therapy (ART) workflow for pencil beam scanning (PBS) proton therapy to address inter-fraction anatomical changes in patients treated with PBS. METHODS AND MATERIALS A four-step workflow was developed using our in-house developed GPU-accelerated Monte-Carlo-based treatment planning system to implement online Monte-Carlo-based ART for PBS. The first step conducts diffeomorphic demon-based deformable image registration (DIR) to propagate contours on the initial planning CT (pCT) to the verification CT (vCT) to form a new structure set. The second step performs forward dose calculation of the initial plan on the vCT with the propagated contours after manual approval (possible modifications involved). The third step triggers a re-optimization of the plan depending on whether the verification dose meets the clinical requirements or not. A robust evaluation will be done for both the verification plan in the second step and the re-opotimized plan in the third step. The fourth step involves a two-stage (before and after delivery) patient specific quality assurance (PSQA) of the re-optimized plan. The before-delivery PSQA is to compare the plan dose to the dose calculated using an independent fast open-source Monte Carlo code, MCsquare. The after-delivery PSQA is to compare the plan dose to the dose re-calculated using the log file (spot MU, spot position, and spot energy) collected during the delivery. Jaccard index (JI), Dice similarity coefficients (DSCs), and Hausdorff distance (HD) were used to assess the quality of the propagated contours in the first step. A commercial plan evaluation software, ClearCheck™, was integrated into the workflow to carry out efficient plan evaluation. 3D Gamma analysis was used during the fourth step to ensure the accuracy of the plan dose from re-optimization. Three patients with three different disease sites were chosen to evaluate the feasibility of the online ART workflow for PBS. RESULTS For all three patients, the propagated contours were found to have good volume conformance [JI (lowest-highest: 0.833-0.983) and DSC (0.909-0.992)] but sub-optimal boundary coincidence [HD (2.37-20.76 mm)] for organs at risk (OARs). The verification dose evaluated by ClearCheck™ showed significant degradation of the target coverage due to the inter-fractional anatomical changes. Re-optimization on the vCT resulted in great improvement of the plan quality to a clinically acceptable level. 3D Gamma analyses of PSQA confirmed the accuracy of the plan dose before delivery (mean Gamma index = 98.74% with a threshold of 2%/2 mm/10%), and after delivery based on the log files (mean Gamma index = 99.05% with a threshold of 2%/2 mm/10%). The average time cost for the complete execution of the workflow was around 858 seconds, excluding the time for manual intervention. CONCLUSION The proposed online ART workflow for PBS was demonstrated to be efficient and effective by generating a re-optimized plan that significantly improved the plan quality. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Hongying Feng
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Samir H Patel
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - William W Wong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - James E Younkin
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | | | | | - Joshua B Stoker
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | | | - Mirek Fatyga
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Martin Bues
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Steven E Schild
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Robert L Foote
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, 55902, USA
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| |
Collapse
|
50
|
Liu Y, Chen X, Zhu J, Yang B, Wei R, Xiong R, Quan H, Liu Y, Dai J, Men K. A two-step method to improve image quality of CBCT with phantom-based supervised and patient-based unsupervised learning strategies. Phys Med Biol 2022; 67. [PMID: 35354124 DOI: 10.1088/1361-6560/ac6289] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/30/2022] [Indexed: 11/12/2022]
Abstract
Objective.In this study, we aimed to develop deep learning framework to improve cone-beam computed tomography (CBCT) image quality for adaptive radiation therapy (ART) applications.Approach.Paired CBCT and planning CT images of 2 pelvic phantoms and 91 patients (15 patients for testing) diagnosed with prostate cancer were included in this study. First, well-matched images of rigid phantoms were used to train a U-net, which is the supervised learning strategy to reduce serious artifacts. Second, the phantom-trained U-net generated intermediate CT images from the patient CBCT images. Finally, a cycle-consistent generative adversarial network (CycleGAN) was trained with intermediate CT images and deformed planning CT images, which is the unsupervised learning strategy to learn the style of the patient images for further improvement. When testing or applying the trained model on patient CBCT images, the intermediate CT images were generated from the original CBCT image by U-net, and then the synthetic CT images were generated by the generator of CycleGAN with intermediate CT images as input. The performance was compared with conventional methods (U-net/CycleGAN alone trained with patient images) on the test set.Results.The proposed two-step method effectively improved the CBCT image quality to the level of CT scans. It outperformed conventional methods for region-of-interest contouring and HU calibration, which are important to ART applications. Compared with the U-net alone, it maintained the structure of CBCT. Compared with CycleGAN alone, our method improved the accuracy of CT number and effectively reduced the artifacts, making it more helpful for identifying the clinical target volume.Significance.This novel two-step method improves CBCT image quality by combining phantom-based supervised and patient-based unsupervised learning strategies. It has immense potential to be integrated into the ART workflow to improve radiotherapy accuracy.
Collapse
Affiliation(s)
- Yuxiang Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China.,School of Physics and Technology, Wuhan University, Wuhan 430072, People's Republic of China
| | - 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, People's Republic of China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Bining Yang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Ran Wei
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of China
| | - Rui Xiong
- School of Physics and Technology, Wuhan University, Wuhan 430072, People's Republic of China
| | - Hong Quan
- School of Physics and Technology, Wuhan University, Wuhan 430072, People's Republic of China
| | - Yueping Liu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People's Republic of 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, People's Republic of 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, People's Republic of China
| |
Collapse
|