201
|
Midroni J, Salunkhe R, Liu Z, Chow R, Boldt G, Palma D, Hoover D, Vinogradskiy Y, Raman S. Incorporation of Functional Lung Imaging Into Radiation Therapy Planning in Patients With Lung Cancer: A Systematic Review and Meta-Analysis. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00481-4. [PMID: 38631538 DOI: 10.1016/j.ijrobp.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/27/2024] [Accepted: 04/02/2024] [Indexed: 04/19/2024]
Abstract
Our purpose was to provide an understanding of current functional lung imaging (FLI) techniques and their potential to improve dosimetry and outcomes for patients with lung cancer receiving radiation therapy (RT). Excerpta Medica dataBASE (EMBASE), PubMed, and Cochrane Library were searched from 1990 until April 2023. Articles were included if they reported on FLI in one of: techniques, incorporation into RT planning for lung cancer, or quantification of RT-related outcomes for patients with lung cancer. Studies involving all RT modalities, including stereotactic body RT and particle therapy, were included. Meta-analyses were conducted to investigate differences in dose-function parameters between anatomic and functional RT planning techniques, as well as to investigate correlations of dose-function parameters with grade 2+ radiation pneumonitis (RP). One hundred seventy-eight studies were included in the narrative synthesis. We report on FLI modalities, dose-response quantification, functional lung (FL) definitions, FL avoidance techniques, and correlations between FL irradiation and toxicity. Meta-analysis results show that FL avoidance planning gives statistically significant absolute reductions of 3.22% to the fraction of well-ventilated lung receiving 20 Gy or more, 3.52% to the fraction of well-perfused lung receiving 20 Gy or more, 1.3 Gy to the mean dose to the well-ventilated lung, and 2.41 Gy to the mean dose to the well-perfused lung. Increases in the threshold value for defining FL are associated with decreases in functional parameters. For intensity modulated RT and volumetric modulated arc therapy, avoidance planning results in a 13% rate of grade 2+ RP, which is reduced compared with results from conventional planning cohorts. A trend of increased predictive ability for grade 2+ RP was seen in models using FL information but was not statistically significant. FLI shows promise as a method to spare FL during thoracic RT, but interventional trials related to FL avoidance planning are sparse. Such trials are critical to understanding the effect of FL avoidance planning on toxicity reduction and patient outcomes.
Collapse
Affiliation(s)
- Julie Midroni
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada
| | - Rohan Salunkhe
- Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Zhihui Liu
- Biostatistics, Princess Margaret Cancer Center, Toronto, Canada
| | - Ronald Chow
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
| | - Gabriel Boldt
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
| | - David Palma
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada; Ontario Institute for Cancer Research, Toronto, Canada
| | - Douglas Hoover
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, United States of America; Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, United States of America
| | - Srinivas Raman
- Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada.
| |
Collapse
|
202
|
Kuo HC, Mahmood U, Kirov AS, Mechalakos J, Della Biancia C, Cerviño LI, Lim SB. An automated technique for global noise level measurement in CT image with a conjunction of image gradient. Phys Med Biol 2024; 69:09NT01. [PMID: 38537310 DOI: 10.1088/1361-6560/ad3883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 03/27/2024] [Indexed: 04/16/2024]
Abstract
Automated assessment of noise level in clinical computed tomography (CT) images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical CT scans due to the fluctuation of x-ray quanta. Initially, a noise map is generated by sliding a 10 × 10 pixel for calculating Hounsfield unit (HU) standard deviation and the noise map is further combined with the gradient magnitude map. By employing Boolean operation, pixels with high gradients are excluded from the noise histogram generated with the noise map. By comparing the shape of the noise histogram from this method with Christianson's tissue-type global noise measurement algorithm, it was observed that the noise histogram computed in anthropomorphic phantoms had a similar shape with a close GNI value. In patient CT images, excluding the HU deviation due the structure change demonstrated to have consistent GNI values across the entire CT scan range with high heterogeneous tissue compared to the GNI values using Christianson's tissue-type method. The proposed GNI was evaluated in phantom scans and was found to be capable of comparing scan protocols between different scanners. The variation of GNI when using different reconstruction kernels in clinical CT images demonstrated a similar relationship between noise level and kernel sharpness as observed in uniform phantom: sharper kernel resulted in noisier images. This indicated that GNI was a suitable index for estimating the noise level in clinical CT images with either a smooth or grainy appearance. The study's results suggested that the algorithm can be effectively utilized to screen the noise level for a better CT image quality control.
Collapse
Affiliation(s)
- Hsiang-Chi Kuo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, United States of America
| | - Usman Mahmood
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, United States of America
| | - Assen S Kirov
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, United States of America
| | - James Mechalakos
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, United States of America
| | - Cesar Della Biancia
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, United States of America
| | - Laura I Cerviño
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, United States of America
| | - Seng Boh Lim
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY, United States of America
| |
Collapse
|
203
|
Philip MM, Watts J, Moeini SNM, Musheb M, McKiddie F, Welch A, Nath M. Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images. Phys Med Biol 2024; 69:095005. [PMID: 38530298 DOI: 10.1088/1361-6560/ad37ea] [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: 11/07/2023] [Accepted: 03/26/2024] [Indexed: 03/27/2024]
Abstract
Objective. Accurate and reproducible tumor delineation on positron emission tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation for tumor delineation in head and neck squamous cell carcinoma (HNSCC) PET images.Approach. We employed manual and six semi-automatic segmentation methods (just enough interaction (JEI), watershed, grow from seeds (GfS), flood filling (FF), 30% SUVmax and 40%SUVmax threshold) using 3D slicer software to extract 128 radiomic features from FDG-PET images of 100 HNSCC patients independently by three operators. We assessed the distributional properties of all features and considered 92 log-transformed features for subsequent analysis. For each paired comparison of a feature, we fitted a separate linear mixed effect model using the method (two levels; manual versus one semi-automatic method) as a fixed effect and the subject and the operator as the random effects. We estimated different statistics-the intraclass correlation coefficient agreement (aICC), limits of agreement (LoA), total deviation index (TDI), coverage probability (CP) and coefficient of individual agreement (CIA)-to evaluate the agreement between the manual and semi-automatic methods.Main results. Accounting for all statistics across 92 features, the JEI method consistently demonstrated acceptable agreement with the manual method, with median values of aICC = 0.86, TDI = 0.94, CP = 0.66, and CIA = 0.91.Significance. This study demonstrated that JEI method is a reliable semi-automatic method for tumor delineation on HNSCC PET images.
Collapse
Affiliation(s)
- Mahima Merin Philip
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen AB25 2ZD, United Kingdom
| | - Jessica Watts
- National Health Service Grampian, Aberdeen AB15 6RE, United Kingdom
| | | | - Mohammed Musheb
- National Health Service Highland, Inverness IV2 3BW, United Kingdom
| | - Fergus McKiddie
- National Health Service Grampian, Aberdeen AB15 6RE, United Kingdom
| | - Andy Welch
- Institute of Education in Healthcare and Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, United Kingdom
| | - Mintu Nath
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen AB25 2ZD, United Kingdom
| |
Collapse
|
204
|
Ahmed SBS, Naeem S, Khan AMH, Qureshi BM, Hussain A, Aydogan B, Muhammad W. Artificial neural network-assisted prediction of radiobiological indices in head and neck cancer. Front Artif Intell 2024; 7:1329737. [PMID: 38646416 PMCID: PMC11026659 DOI: 10.3389/frai.2024.1329737] [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: 11/01/2023] [Accepted: 03/25/2024] [Indexed: 04/23/2024] Open
Abstract
Background and purpose We proposed an artificial neural network model to predict radiobiological parameters for the head and neck squamous cell carcinoma patients treated with radiation therapy. The model uses the tumor specification, demographics, and radiation dose distribution to predict the tumor control probability and the normal tissue complications probability. These indices are crucial for the assessment and clinical management of cancer patients during treatment planning. Methods Two publicly available datasets of 31 and 215 head and neck squamous cell carcinoma patients treated with conformal radiation therapy were selected. The demographics, tumor specifications, and radiation therapy treatment parameters were extracted from the datasets used as inputs for the training of perceptron. Radiobiological indices are calculated by open-source software using dosevolume histograms from radiation therapy treatment plans. Those indices were used as output in the training of a single-layer neural network. The distribution of data used for training, validation, and testing purposes was 70, 15, and 15%, respectively. Results The best performance of the neural network was noted at epoch number 32 with the mean squared error of 0.0465. The accuracy of the prediction of radiobiological indices by the artificial neural network in training, validation, and test phases were determined to be 0.89, 0.87, and 0.82, respectively. We also found that the percentage volume of parotid inside the planning target volume is the significant parameter for the prediction of normal tissue complications probability. Conclusion We believe that the model has significant potential to predict radiobiological indices and help clinicians in treatment plan evaluation and treatment management of head and neck squamous cell carcinoma patients.
Collapse
Affiliation(s)
- Saad Bin Saeed Ahmed
- Department of Physics, Florida Atlantic University, Boca Raton, FL, United States
| | - Shahzaib Naeem
- Gamma Knife Radiosurgery Center, Dow University of Health Sciences, Karachi, Pakistan
| | | | | | | | - Bulent Aydogan
- Radiation and Cellular Oncology, University of Chicago, Chicago, IL, United States
| | - Wazir Muhammad
- Department of Physics, Florida Atlantic University, Boca Raton, FL, United States
| |
Collapse
|
205
|
Henry M, Templeton A, Smith R. A low-cost phantom design for evaluating spine SABR calculations in the presence of prosthetic vertebral stabilization. Phys Eng Sci Med 2024:10.1007/s13246-024-01412-1. [PMID: 38573488 DOI: 10.1007/s13246-024-01412-1] [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: 07/15/2023] [Accepted: 02/26/2024] [Indexed: 04/05/2024]
Abstract
Dose-perturbation characteristics are important to consider during the calculation of radiation therapy protocols for patients who are going to receive high doses that would reach the tolerance limits of the spinal cord [1]. Several studies have investigated dose perturbations introduced by metal implants in close proximity to spine SABR treatments [2-7]. However, there is a lack of work assessing this effect using the RayStation TPS [8]. We present an initial design for a low-cost phantom to evaluate spine stereotactic ablative radiotherapy (SABR) in the presence of prosthetic vertebral stabilization. The phantom is modular, allowing the prosthetic at the centre of the phantom to be removed by exchanging the central block. It also includes space to insert ion chamber and film. The agreement of the RayStation TPS (v8.0B) collapsed cone convolution (CCC) calculation and measurement was determined for phantom versions with and without prosthetic. There was little to no change in the agreement between the measured and calculated dose when introducing metallic hardware. This suggests that our Raystation-based SABR planning approach for patients with spinal hardware meets clinical expectations. Departments without access to anthropomorphic phantoms may find this design useful but should test their phantom design in typical clinical settings to ensure it is robust to real world situations.
Collapse
Affiliation(s)
- Michelle Henry
- Genesis Care - Fiona Stanley Hospital, Murdoch, WA, Australia.
| | | | - Ruth Smith
- Te Whatu Ora - Auckland City Hospital, Auckland, New Zealand
| |
Collapse
|
206
|
Edwards S, Luzzara M, Dell’Acqua V, Christodouleas J. The Radiation Therapy Technology Evidence Matrix: a framework to visualize evidence development for innovations in radiation therapy. Front Oncol 2024; 14:1351610. [PMID: 38628665 PMCID: PMC11018969 DOI: 10.3389/fonc.2024.1351610] [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: 12/06/2023] [Accepted: 03/19/2024] [Indexed: 04/19/2024] Open
Abstract
Clinical evidence is crucial in enabling the judicious adoption of technological innovations in radiation therapy (RT). Pharmaceutical evidence development frameworks are not useful for understanding how technical advances are maturing. In this paper, we introduce a new framework, the Radiation Therapy Technology Evidence Matrix (rtTEM), that helps visualize how the clinical evidence supporting new technologies is developing. The matrix is a unique 2D model based on the R-IDEAL clinical evaluation framework. It can be applied to clinical hypothesis testing trials, as well as publications reporting clinical treatment. We present the rtTEM and illustrate its application, using emerging and mature RT technologies as examples. The model breaks down the type of claim along the vertical axis and the strength of the evidence for that claim on the horizontal axis, both of which are inherent in clinical hypothesis testing. This simplified view allows for stakeholders to understand where the evidence is and where it is heading. Ultimately, the value of an innovation is typically demonstrated through superiority studies, which we have divided into three key categories - administrative, toxicity and control, to enable more detailed visibility of evidence development in that claim area. We propose the rtTEM can be used to track evidence development for new interventions in RT. We believe it will enable researchers and sponsors to identify gaps in evidence and to further direct evidence development. Thus, by highlighting evidence looked for by key policy decision makers, the rtTEM will support wider, timely patient access to high value technological advances.
Collapse
Affiliation(s)
- Sarah Edwards
- Medical Affairs and Clinical Research, Elekta AB, Stockholm, Sweden
| | | | | | | |
Collapse
|
207
|
Wallimann P, Piccirelli M, Nowakowska S, Armstrong T, Mayinger M, Boss A, Bink A, Guckenberger M, Tanadini-Lang S, Andratschke N, Pouymayou B. Validation of echo planar imaging based diffusion-weighted magnetic resonance imaging on a 0.35 T MR-Linac. Phys Imaging Radiat Oncol 2024; 30:100579. [PMID: 38707628 PMCID: PMC11068927 DOI: 10.1016/j.phro.2024.100579] [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: 12/05/2023] [Revised: 03/08/2024] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
Background and Purpose The feasibility of acquiring diffusion-weighted imaging (DWI) images on an MR-Linac for quantitative response assessment during radiotherapy was explored. DWI data obtained with a Spin Echo Echo Planar Imaging sequence adapted for a 0.35 T MR-Linac were examined and compared with DWI data from a conventional 3 T scanner. Materials and Methods Apparent diffusion coefficient (ADC) measurements and a distortion correction technique were investigated using DWI-calibrated phantoms and in the brains of seven volunteers. All DWI utilized two phase-encoding directions for distortion correction and off-resonance field estimation. ADC maps in the brain were analyzed for automatically segmented normal tissues. Results Phantom ADC measurements on the MR-Linac were within a 3 % margin of those recorded by the 3 T scanner. The maximum distortion observed in the phantom was 2.0 mm prior to correction and 1.1 mm post-correction on the MR-Linac, compared to 6.0 mm before correction and 3.6 mm after correction at 3 T. In vivo, the average ADC values for gray and white matter exhibited variations of 14 % and 4 %, respectively, for different selections of b-values on the MR-Linac. Distortions in brain images before correction, estimated through the off-resonance field, reached 2.7 mm on the MR-Linac and 12 mm at 3 T. Conclusion Accurate ADC measurements are achievable on a 0.35 T MR-Linac, both in phantom and in vivo. The selection of b-values significantly influences ADC values in vivo. DWI on the MR-Linac demonstrated lower distortion levels, with a maximum distortion reduced to 1.1 mm after correction.
Collapse
Affiliation(s)
- Philipp Wallimann
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Marco Piccirelli
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Sylwia Nowakowska
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Tess Armstrong
- ViewRay Inc., 2 Thermo Fisher Way, Oakwood Village, OH 44146, USA
| | - Michael Mayinger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andreas Boss
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| |
Collapse
|
208
|
Mody P, Huiskes M, Chaves-de-Plaza NF, Onderwater A, Lamsma R, Hildebrandt K, Hoekstra N, Astreinidou E, Staring M, Dankers F. Large-scale dose evaluation of deep learning organ contours in head-and-neck radiotherapy by leveraging existing plans. Phys Imaging Radiat Oncol 2024; 30:100572. [PMID: 38633281 PMCID: PMC11021837 DOI: 10.1016/j.phro.2024.100572] [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: 11/26/2023] [Revised: 03/21/2024] [Accepted: 03/21/2024] [Indexed: 04/19/2024] Open
Abstract
Background and purpose Retrospective dose evaluation for organ-at-risk auto-contours has previously used small cohorts due to additional manual effort required for treatment planning on auto-contours. We aimed to do this at large scale, by a) proposing and assessing an automated plan optimization workflow that used existing clinical plan parameters and b) using it for head-and-neck auto-contour dose evaluation. Materials and methods Our automated workflow emulated our clinic's treatment planning protocol and reused existing clinical plan optimization parameters. This workflow recreated the original clinical plan (P OG ) with manual contours (P MC ) and evaluated the dose effect (P OG - P MC ) on 70 photon and 30 proton plans of head-and-neck patients. As a use-case, the same workflow (and parameters) created a plan using auto-contours (P AC ) of eight head-and-neck organs-at-risk from a commercial tool and evaluated their dose effect (P MC - P AC ). Results For plan recreation (P OG - P MC ), our workflow had a median impact of 1.0% and 1.5% across dose metrics of auto-contours, for photon and proton respectively. Computer time of automated planning was 25% (photon) and 42% (proton) of manual planning time. For auto-contour evaluation (P MC - P AC ), we noticed an impact of 2.0% and 2.6% for photon and proton radiotherapy. All evaluations had a median Δ NTCP (Normal Tissue Complication Probability) less than 0.3%. Conclusions The plan replication capability of our automated program provides a blueprint for other clinics to perform auto-contour dose evaluation with large patient cohorts. Finally, despite geometric differences, auto-contours had a minimal median dose impact, hence inspiring confidence in their utility and facilitating their clinical adoption.
Collapse
Affiliation(s)
- Prerak Mody
- Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands
- HollandPTC consortium – Erasmus Medical Center, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center (LUMC), Leiden and Delft University of Technology, Delft, The Netherlands
| | - Merle Huiskes
- Department of Radiation Oncology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands
| | - Nicolas F. Chaves-de-Plaza
- HollandPTC consortium – Erasmus Medical Center, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center (LUMC), Leiden and Delft University of Technology, Delft, The Netherlands
- Computer Graphics and Visualization Group, EEMCS, TU Delft, Delft 2628 CD, The Netherlands
| | - Alice Onderwater
- Department of Radiation Oncology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands
| | - Rense Lamsma
- Department of Radiation Oncology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands
| | - Klaus Hildebrandt
- Computer Graphics and Visualization Group, EEMCS, TU Delft, Delft 2628 CD, The Netherlands
| | - Nienke Hoekstra
- Department of Radiation Oncology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands
| | - Eleftheria Astreinidou
- Department of Radiation Oncology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands
| | - Marius Staring
- Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands
- Department of Radiation Oncology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands
| | - Frank Dankers
- Department of Radiation Oncology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands
| |
Collapse
|
209
|
Ioakeim-Ioannidou M, Rose M, Chen YL, MacDonald SM. The Use of Proton and Carbon Ion Radiation Therapy for Sarcomas. Semin Radiat Oncol 2024; 34:207-217. [PMID: 38508785 DOI: 10.1016/j.semradonc.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
The unique physical and biological characteristics of proton and carbon ions allow for improved sparing of normal tissues, decreased integral dose to the body, and increased biological effect through high linear energy transfer. These properties are particularly useful for sarcomas given their histology, wide array of locations, and age of diagnosis. This review summarizes the literature and describes the clinical situations in which these heavy particles have advantages for treating sarcomas.
Collapse
Affiliation(s)
| | - Melanie Rose
- Department of Radiation Oncology, Dartmouth Hitchcock Medical Center, Lebanon, NH
| | - Yen-Lin Chen
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA
| | - Shannon M MacDonald
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA.
| |
Collapse
|
210
|
Iori F, Torelli N, Unkelbach J, Tanadini-Lang S, Christ SM, Guckenberger M. An in-silico planning study of stereotactic body radiation therapy for polymetastatic patients with more than ten extra-cranial lesions. Phys Imaging Radiat Oncol 2024; 30:100567. [PMID: 38516028 PMCID: PMC10950805 DOI: 10.1016/j.phro.2024.100567] [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: 11/22/2023] [Revised: 03/01/2024] [Accepted: 03/01/2024] [Indexed: 03/23/2024] Open
Abstract
Background and purpose Limited data is available about the feasibility of stereotactic body radiation therapy (SBRT) for treating more than five extra-cranial metastases, and almost no data for treating more than ten. The aim of this study was to investigate the feasibility of SBRT in this polymetatstatic setting. Materials and methods Consecutive metastatic melanoma patients with more than ten extra-cranial metastases and a maximum lesion diameter below 11 cm were selected from a single-center prospective registry for this in-silico planning study. For each patient, SBRT plans were generated to treat all metastases with a prescribed dose of 5x7Gy, and dose-limiting organs (OARs) were analyzed. A cell-kill based inverse planning approach was used to automatically determine the maximum deliverable dose to each lesion individually, while respecting all OARs constraints. Results A total of 23 polymetastatic patients with a medium of 17 metastases (range, 11-51) per patient were selected. SBRT plans with sufficient target coverage and respected OARs dose constraints were achieved in 16 out of 23 patients. In the remaining seven patients, the lungs V5Gy < 80 % and the liver D700 cm3 < 15Gy were most frequently the dose-limiting constraints. The cell-kill based planning approach allowed optimizing the dose administration depending on metastases total volume and location. Conclusion This retrospective planning study shows the feasibility of definitive SBRT for 70% of polymetastatic patients with more than ten extra-cranial lesions and proposes the cell-killing planning approach as an approach to individualize treatment planning in polymetastatic patients'.
Collapse
Affiliation(s)
- Federico Iori
- Radiation Oncology Unit, Azienda USL-IRCCS di Reggio Emilia, 42122 Reggio Emilia, Italy
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091 Zurich, Switzerland
- Clinical and Experimental Medicine PhD Program, Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Nathan Torelli
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091 Zurich, Switzerland
| | - Jan Unkelbach
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091 Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091 Zurich, Switzerland
| | - Sebastian M. Christ
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091 Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091 Zurich, Switzerland
| |
Collapse
|
211
|
Loebner HA, Bertholet J, Mackeprang PH, Volken W, Elicin O, Mueller S, Guyer G, Aebersold DM, Stampanoni MF, Fix MK, Manser P. Robustness analysis of dynamic trajectory radiotherapy and volumetric modulated arc therapy plans for head and neck cancer. Phys Imaging Radiat Oncol 2024; 30:100586. [PMID: 38808098 PMCID: PMC11130727 DOI: 10.1016/j.phro.2024.100586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/30/2024] Open
Abstract
Background and purpose Dynamic trajectory radiotherapy (DTRT) has been shown to improve healthy tissue sparing compared to volumetric arc therapy (VMAT). This study aimed to assess and compare the robustness of DTRT and VMAT treatment-plans for head and neck (H&N) cancer to patient-setup (PS) and machine-positioning uncertainties. Materials and methods The robustness of DTRT and VMAT plans previously created for 46 H&N cases, prescribed 50-70 Gy to 95 % of the planning-target-volume, was assessed. For this purpose, dose distributions were recalculated using Monte Carlo, including uncertainties in PS (translation and rotation) and machine-positioning (gantry-, table-, collimator-rotation and multi-leaf collimator (MLC)). Plan robustness was evaluated by the uncertainties' impact on normal tissue complication probabilities (NTCP) for xerostomia and dysphagia and on dose-volume endpoints. Differences between DTRT and VMAT plan robustness were compared using Wilcoxon matched-pair signed-rank test (α = 5 %). Results Average NTCP for moderate-to-severe xerostomia and grade ≥ II dysphagia was lower for DTRT than VMAT in the nominal scenario (0.5 %, p = 0.01; 2.1 %, p < 0.01) and for all investigated uncertainties, except MLC positioning, where the difference was not significant. Average differences compared to the nominal scenario were ≤ 3.5 Gy for rotational PS (≤ 3°) and machine-positioning (≤ 2°) uncertainties, <7 Gy for translational PS uncertainties (≤ 5 mm) and < 20 Gy for MLC-positioning uncertainties (≤ 5 mm). Conclusions DTRT and VMAT plan robustness to the investigated uncertainties depended on uncertainty direction and location of the structure-of-interest to the target. NTCP remained on average lower for DTRT than VMAT even when considering uncertainties.
Collapse
Affiliation(s)
- Hannes A. Loebner
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Jenny Bertholet
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Paul-Henry Mackeprang
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Werner Volken
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Olgun Elicin
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Silvan Mueller
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Gian Guyer
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Daniel M. Aebersold
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | | | - Michael K. Fix
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| | - Peter Manser
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland
| |
Collapse
|
212
|
Murr M, Bernchou U, Bubula-Rehm E, Ruschin M, Sadeghi P, Voet P, Winter JD, Yang J, Younus E, Zachiu C, Zhao Y, Zhong H, Thorwarth D. A multi-institutional comparison of retrospective deformable dose accumulation for online adaptive magnetic resonance-guided radiotherapy. Phys Imaging Radiat Oncol 2024; 30:100588. [PMID: 38883145 PMCID: PMC11176923 DOI: 10.1016/j.phro.2024.100588] [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: 01/16/2024] [Revised: 05/07/2024] [Accepted: 05/08/2024] [Indexed: 06/18/2024] Open
Abstract
Background and Purpose Application of different deformable dose accumulation (DDA) solutions makes institutional comparisons after online-adaptive magnetic resonance-guided radiotherapy (OA-MRgRT) challenging. The aim of this multi-institutional study was to analyze accuracy and agreement of DDA-implementations in OA-MRgRT. Material and Methods One gold standard (GS) case deformed with a biomechanical-model and five clinical cases consisting of prostate (2x), cervix, liver, and lymph node cancer, treated with OA-MRgRT, were analyzed. Six centers conducted DDA using institutional implementations. Deformable image registration (DIR) and DDA results were compared using the contour metrics Dice Similarity Coefficient (DSC), surface-DSC, Hausdorff-distance (HD95%), and accumulated dose-volume histograms (DVHs) analyzed via intraclass correlation coefficient (ICC) and clinical dosimetric criteria (CDC). Results For the GS, median DDA errors ranged from 0.0 to 2.8 Gy across contours and implementations. DIR of clinical cases resulted in DSC > 0.8 for up to 81.3% of contours and a variability of surface-DSC values depending on the implementation. Maximum HD95%=73.3 mm was found for duodenum in the liver case. Although DVH ICC > 0.90 was found after DDA for all but two contours, relevant absolute CDC differences were observed in clinical cases: Prostate I/II showed maximum differences in bladder V28Gy (10.2/7.6%), while for cervix, liver, and lymph node the highest differences were found for rectum D2cm3 (2.8 Gy), duodenum Dmax (7.1 Gy), and rectum D0.5cm3 (4.6 Gy). Conclusion Overall, high agreement was found between the different DIR and DDA implementations. Case- and algorithm-dependent differences were observed, leading to potentially clinically relevant results. Larger studies are needed to define future DDA-guidelines.
Collapse
Affiliation(s)
- Martina Murr
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
| | - Uffe Bernchou
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Laboratory of Radiation Physics, Odense University Hospital, Denmark
| | | | - Mark Ruschin
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Parisa Sadeghi
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | | | - Jeff D Winter
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Jinzhong Yang
- Department of Radiation Physics, the University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Eyesha Younus
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
- Department of Radiation Oncology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Cornel Zachiu
- University Medical Centre Utrecht, Department of Radiotherapy, 3584 CX Utrecht, the Netherlands
| | - Yao Zhao
- Department of Radiation Physics, the University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Hualiang Zhong
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
| |
Collapse
|
213
|
Castriconi R, Tudda A, Placidi L, Benecchi G, Cagni E, Dusi F, Ianiro A, Landoni V, Malatesta T, Mazzilli A, Meffe G, Oliviero C, Rambaldi Guidasci G, Scaggion A, Trojani V, Del Vecchio A, Fiorino C. Inter-institutional variability of knowledge-based plan prediction of left whole breast irradiation. Phys Med 2024; 120:103331. [PMID: 38484461 DOI: 10.1016/j.ejmp.2024.103331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 02/05/2024] [Accepted: 03/08/2024] [Indexed: 04/19/2024] Open
Abstract
PURPOSE Within a multi-institutional project, we aimed to assess the transferability of knowledge-based (KB) plan prediction models in the case of whole breast irradiation (WBI) for left-side breast irradiation with tangential fields (TF). METHODS Eight institutions set KB models, following previously shared common criteria. Plan prediction performance was tested on 16 new patients (2 pts per centre) extracting dose-volume-histogram (DVH) prediction bands of heart, ipsilateral lung, contralateral lung and breast. The inter-institutional variability was quantified by the standard deviations (SDint) of predicted DVHs and mean-dose (Dmean). The transferability of models, for the heart and the ipsilateral lung, was evaluated by the range of geometric Principal Component (PC1) applicability of a model to test patients of the other 7 institutions. RESULTS SDint of the DVH was 1.8 % and 1.6 % for the ipsilateral lung and the heart, respectively (20 %-80 % dose range); concerning Dmean, SDint was 0.9 Gy and 0.6 Gy for the ipsilateral lung and the heart, respectively (<0.2 Gy for contralateral organs). Mean predicted doses ranged between 4.3 and 5.9 Gy for the ipsilateral lung and 1.1-2.3 Gy for the heart. PC1 analysis suggested no relevant differences among models, except for one centre showing a systematic larger sparing of the heart, concomitant to a worse PTV coverage, due to high priority in sparing the left anterior descending coronary artery. CONCLUSIONS Results showed high transferability among models and low inter-institutional variability of 2% for plan prediction. These findings encourage the building of benchmark models in the case of TF-WBI.
Collapse
Affiliation(s)
- Roberta Castriconi
- Medical Physics Dept, IRCCS San Raffaele Scientific Institute, Milano, Italy.
| | - Alessia Tudda
- Medical Physics Dept, IRCCS San Raffaele Scientific Institute, Milano, Italy; Università Statale di Milano, Milano, Italy
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Giovanna Benecchi
- Medical Physics Dept, University Hospital of Parma AOUP, Parma, Italy
| | - Elisabetta Cagni
- Medical Physics Unit, Department of Advanced Technology, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Francesca Dusi
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Anna Ianiro
- IRCCS Istituto Nazionale dei Tumori Regina Elena, Rome, Italy
| | - Valeria Landoni
- IRCCS Istituto Nazionale dei Tumori Regina Elena, Rome, Italy
| | - Tiziana Malatesta
- UOC di Radioterapia Oncologica, Fatebenefratelli Isola Tiberina - Gemelli Isola, Roma, Italy
| | - Aldo Mazzilli
- Medical Physics Dept, University Hospital of Parma AOUP, Parma, Italy
| | - Guenda Meffe
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | | | | | - Alessandro Scaggion
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Valeria Trojani
- Medical Physics Unit, Department of Advanced Technology, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | | | - Claudio Fiorino
- Medical Physics Dept, IRCCS San Raffaele Scientific Institute, Milano, Italy
| |
Collapse
|
214
|
Witte M, Sonke JJ. A deep learning based dynamic arc radiotherapy photon dose engine trained on Monte Carlo dose distributions. Phys Imaging Radiat Oncol 2024; 30:100575. [PMID: 38644934 PMCID: PMC11031817 DOI: 10.1016/j.phro.2024.100575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 04/03/2024] [Accepted: 04/03/2024] [Indexed: 04/23/2024] Open
Abstract
Background and purpose Despite hardware acceleration, state-of-the-art Monte Carlo (MC) dose engines require considerable computation time to reduce stochastic noise. We developed a deep learning (DL) based dose engine reaching high accuracy at strongly reduced computation times. Materials and methods Radiotherapy treatment plans and computed tomography scans were collected for 350 treatments in a variety of tumor sites. Dose distributions were computed using a MC dose engine for ∼ 30,000 separate segments at 6 MV and 10 MV beam energies, both flattened and flattening filter free. For dynamic arcs these explicitly incorporated the leaf, jaw and gantry motions during dose delivery. A neural network was developed, combining two-dimensional convolution and recurrence using 64 hidden channels. Parameters were trained to minimize the mean squared log error loss between the MC computed dose and the model output. Full dose distributions were reconstructed for 100 additional treatment plans. Gamma analyses were performed to assess accuracy. Results DL dose evaluation was on average 82 times faster than MC computation at a 1 % accuracy setting. In voxels receiving at least 10 % of the maximum dose the overall global gamma pass rate using a 2 % and 2 mm criterion was 99.6 %, while mean local gamma values were accurate within 2 %. In the high dose region over 50 % of maximum the mean local gamma approached a 1 % accuracy. Conclusions A DL based dose engine was implemented, able to accurately reproduce MC computed dynamic arc radiotherapy dose distributions at high speed.
Collapse
Affiliation(s)
- Marnix Witte
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| |
Collapse
|
215
|
Zhou Y, Liu Y, Chen M, Fang J, Xiao L, Huang S, Qi Z, Deng X, Zhang J, Peng Y. Commissioning and clinical evaluation of a novel high-resolution quality assurance digital detector array for SRS and SBRT. J Appl Clin Med Phys 2024; 25:e14258. [PMID: 38175960 PMCID: PMC11005972 DOI: 10.1002/acm2.14258] [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: 07/08/2023] [Revised: 12/03/2023] [Accepted: 12/18/2023] [Indexed: 01/06/2024] Open
Abstract
PURPOSE We aimed to perform the commissioning and clinical evaluation of myQA SRS detector array for patient-specific quality assurance (PSQA) of stereotactic radiosurgery (SRS)/ stereotactic body radiotherapy (SBRT) plans. METHODS To perform the commissioning of myQA SRS, its dose linearity, dose-rate dependence, angular dependence, and field-size dependence were investigated. Ten SBRT plans were selected for clinical evaluation: 1) Common clinical deviations based on the original SBRT plan (Plan0), including multileaf collimator (MLC) positioning deviation and treatment positioning deviation were introduced. 2) Compared the performance of the myQA SRS and a high-resolution EPID dosimetry system in PSQA measurement for the SBRT plans. Evaluation parameters include gamma passing rate (GPR) and distance-to-agreement (DTA) pass rate (DPR). RESULTS The dose linearity, angle dependence, and field-size dependence of myQA SRS system exhibit excellent performance. The myQA SRS is highly sensitive in the detection of MLC deviations. The GPR of (3%/1 mm) decreases from 90.4% of the original plan to 72.7%/62.9% with an MLC outward/inward deviation of 3 mm. Additionally, when the setup error deviates by 1 mm in the X, Y, and Z directions with the GPR of (3%/1 mm) decreasing by an average of -20.9%, -25.7%, and -24.7%, respectively, and DPR (1 mm) decreasing by an average of -33.7%, -32.9%, and -29.8%. Additionally, the myQA SRS has a slightly higher GPR than EPID for PSQA, However, the difference is not statistically significant with the GPR of (3%/1 mm) of (average 90.4%% vs. 90.1%, p = 0.414). CONCLUSION Dosimetry characteristics of the myQA SRS device meets the accuracy and sensitivity requirement of PSQA for SRS/SBRT treatment. The dose rate dependence should be adequately calibrated before its application and a more stringent GPR (3%/1 mm) evaluation criterion is suggested when it is used for SRS/SBRT QA.
Collapse
Affiliation(s)
- Yang Zhou
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhouP. R. China
- Department of Radiation Oncology, Zhuzhou Hospital Affiliated to Xiangya School of MedicineCentral South UniversityZhuzhouP. R. China
| | - Yimei Liu
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhouP. R. China
| | - Meining Chen
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhouP. R. China
| | - Jianlan Fang
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhouP. R. China
| | - Liangjie Xiao
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhouP. R. China
| | - Shaomin Huang
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhouP. R. China
| | - Zhenyu Qi
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhouP. R. China
| | - Xiaowu Deng
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhouP. R. China
| | - Jun Zhang
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhouP. R. China
| | - Yinglin Peng
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for CancerSun Yat‐sen University Cancer CenterGuangzhouP. R. China
| |
Collapse
|
216
|
van den Dobbelsteen M, Hackett SL, van Asselen B, Oolbekkink S, Raaymakers BW, de Boer JC. Treatment planning evaluation and experimental validation of the magnetic resonance-based intrafraction drift correction. Phys Imaging Radiat Oncol 2024; 30:100580. [PMID: 38707627 PMCID: PMC11068926 DOI: 10.1016/j.phro.2024.100580] [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: 03/01/2024] [Revised: 04/17/2024] [Accepted: 04/18/2024] [Indexed: 05/07/2024] Open
Abstract
Background and purpose MRI-guided online adaptive treatments can account for interfractional variations, however intrafraction motion reduces treatment accuracy. Intrafraction plan adaptation methods, such as the Intrafraction Drift Correction (IDC) or sub-fractionation, are needed. IDC uses real-time automatic monitoring of the tumor position to initiate plan adaptations by repositioning segments. IDC is a fast adaptation method that occurs only when necessary and this method could enable margin reduction. This research provides a treatment planning evaluation and experimental validation of the IDC. Materials and methods An in silico treatment planning evaluation was performed for 13 prostate patients mid-treatment without and with intrafraction plan adaptation (IDC and sub-fractionation). The adaptation methods were evaluated using dose volume histogram (DVH) metrics. To experimentally verify IDC a treatment was mimicked whereby a motion phantom containing an EBT3 film moved mid-treatment, followed by repositioning of segments. In addition, the delivered treatment was irradiated on a diode array phantom for plan quality assurance purposes. Results The planning study showed benefits for using intrafraction adaptation methods relative to no adaptation, where the IDC and sub-fractionation showed consistently improved target coverage with median target coverages of 100.0%. The experimental results verified the IDC with high minimum gamma passing rates of 99.1% and small mean dose deviations of maximum 0.3%. Conclusion The straightforward and fast IDC technique showed DVH metrics consistent with the sub-fractionation method using segment weight re-optimization for prostate patients. The dosimetric and geometric accuracy was shown for a full IDC workflow using film and diode array dosimetry.
Collapse
Affiliation(s)
- Madelon van den Dobbelsteen
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Sara L. Hackett
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Bram van Asselen
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Stijn Oolbekkink
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Bas W. Raaymakers
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Johannes C.J. de Boer
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| |
Collapse
|
217
|
Takaoka T, Yanagi T, Takahashi S, Shibamoto Y, Imai Y, Okazaki D, Niwa M, Torii A, Kita N, Takano S, Tomita N, Hiwatashi A. Comparing different boost concepts and beam configurations for proton therapy of pancreatic cancer. Phys Imaging Radiat Oncol 2024; 30:100583. [PMID: 38711921 PMCID: PMC11070341 DOI: 10.1016/j.phro.2024.100583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 04/26/2024] [Accepted: 04/26/2024] [Indexed: 05/08/2024] Open
Abstract
Background and Purpose Interfractional geometrical and anatomical variations impact the accuracy of proton therapy for pancreatic cancer. This study investigated field-in-field (FIF) and simultaneous integrated boost (SIB) concepts for scanned proton therapy treatment with different beam configurations. Materials and Methods Robustly optimized treatment plans for fifteen patients were generated using FIF and SIB techniques with two, three, and four beams. The prescribed dose in 20 fractions was 60 Gy(RBE) for the internal gross tumor volume (IGTV) and 46 Gy(RBE) for the internal clinical target volume. Verification computed tomography (vCT) scans was performed on treatment days 1, 7, and 16. Initial treatment plans were recalculated on the rigidly registered vCTs. V100% and D95% for targets and D2cm3 for the stomach and duodenum were evaluated. Robustness evaluations (range uncertainty of 3.5 %) were performed to evaluate the stomach and duodenum dose-volume parameters. Results For all techniques, IGTV V100% and D95% decreased significantly when recalculating the dose on vCTs (p < 0.001). The median IGTV V100% and D95% over all vCTs ranged from 74.2 % to 90.2 % and 58.8 Gy(RBE) to 59.4 Gy(RBE), respectively. The FIF with two and three beams, and SIB with two beams maintained the highest IGTV V100% and D95%. In robustness evaluations, the ΔD2cm3 of stomach was highest in two beams plans, while the ΔD2cm3 of duodenum was highest in four beams plans, for both concepts. Conclusion Target coverage decreased when recalculating on CTs at different time for both concepts. The FIF with three beams maintained the highest IGTV coverage while sparing normal organs the most.
Collapse
Affiliation(s)
- Taiki Takaoka
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Takeshi Yanagi
- Department of Radiation Oncology, Narita Memorial Proton Center, Toyohashi, Japan
| | - Shinsei Takahashi
- Department of Radiation Oncology, Narita Memorial Proton Center, Toyohashi, Japan
| | - Yuta Shibamoto
- Department of Radiation Oncology, Narita Memorial Proton Center, Toyohashi, Japan
| | - Yuto Imai
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
- Department of Radiation Oncology, Narita Memorial Proton Center, Toyohashi, Japan
| | - Dai Okazaki
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Masanari Niwa
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Akira Torii
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Nozomi Kita
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Seiya Takano
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Natsuo Tomita
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Akio Hiwatashi
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| |
Collapse
|
218
|
Wüthrich D, Wang Z, Zeverino M, Bourhis J, Bochud F, Moeckli R. Comparison of volumetric modulated arc therapy and helical tomotherapy for prostate cancer using Pareto fronts. Med Phys 2024; 51:3010-3019. [PMID: 38055371 DOI: 10.1002/mp.16868] [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: 06/07/2023] [Revised: 11/02/2023] [Accepted: 11/14/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Studies comparing different radiotherapy treatment techniques-such as volumetric modulated arc therapy (VMAT) and helical tomotherapy (HT)-typically compare one treatment plan per technique. Often, some dose metrics favor one plan and others favor the other, so the final plan decision involves subjective preferences. Pareto front comparisons provide a more objective framework for comparing different treatment techniques. A Pareto front is the set of all treatment plans where improvement in one criterion is possible only by worsening another criterion. However, different Pareto fronts can be obtained depending on the chosen machine settings. PURPOSE To compare VMAT and HT using Pareto fronts and blind expert evaluation, to explain the observed differences, and to illustrate limitations of using Pareto fronts. METHODS We generated Pareto fronts for twenty-four prostate cancer patients treated at our clinic for VMAT and HT techniques using an in-house script that controlled a commercial treatment planning system. We varied the PTV under-coverage (100% - V95%) and the rectum mean dose, and fixed the mean doses to the bladder and femoral heads. In order to ensure a fair comparison, those fixed mean doses were the same for the two treatment techniques and the sets of objective functions were chosen so that the conformity indexes of the two treatment techniques were also the same. We used the same machine settings as are used in our clinic. Then, we compared the VMAT and HT Pareto fronts using a specific metric (clinical distance measure) and validated the comparison using a blinded expert evaluation of treatment plans on these fronts for all patients in the cohort. Furthermore, we investigated the observed differences between VMAT and HT and pointed out limitations of using Pareto fronts. RESULTS Both clinical distance and blind treatment plan comparison showed that VMAT Pareto fronts were better than HT fronts. VMAT fronts for 10 and 6 MV beam energy were almost identical. HT fronts improved with different machine settings, but were still inferior to VMAT fronts. CONCLUSIONS That VMAT Pareto fronts are better than HT fronts may be explained by the fact that the linear accelerator can rapidly vary the dose rate. This is an advantage in simple geometries that might vanish in more complex geometries. Furthermore, one should be cautious when speaking about Pareto optimal plans as the best possible plans, as their calculation depends on many parameters.
Collapse
Affiliation(s)
- Diana Wüthrich
- Institute of Radiation Physics, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Zirun Wang
- Institute of Radiation Physics, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Michele Zeverino
- Institute of Radiation Physics, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Jean Bourhis
- Department of Radiation Oncology, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - François Bochud
- Institute of Radiation Physics, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Raphaël Moeckli
- Institute of Radiation Physics, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| |
Collapse
|
219
|
Kolenbrander ID, Maspero M, Hendriksen AA, Pollitt R, van der Voort van Zyp JRN, van den Berg CAT, Pluim JPW, van Eijnatten MAJM. Deep-learning-based joint rigid and deformable contour propagation for magnetic resonance imaging-guided prostate radiotherapy. Med Phys 2024; 51:2367-2377. [PMID: 38408022 DOI: 10.1002/mp.17000] [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: 07/17/2023] [Revised: 12/05/2023] [Accepted: 02/04/2024] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND Deep learning-based unsupervised image registration has recently been proposed, promising fast registration. However, it has yet to be adopted in the online adaptive magnetic resonance imaging-guided radiotherapy (MRgRT) workflow. PURPOSE In this paper, we design an unsupervised, joint rigid, and deformable registration framework for contour propagation in MRgRT of prostate cancer. METHODS Three-dimensional pelvic T2-weighted MRIs of 143 prostate cancer patients undergoing radiotherapy were collected and divided into 110, 13, and 20 patients for training, validation, and testing. We designed a framework using convolutional neural networks (CNNs) for rigid and deformable registration. We selected the deformable registration network architecture among U-Net, MS-D Net, and LapIRN and optimized the training strategy (end-to-end vs. sequential). The framework was compared against an iterative baseline registration. We evaluated registration accuracy (the Dice and Hausdorff distance of the prostate and bladder contours), structural similarity index, and folding percentage to compare the methods. We also evaluated the framework's robustness to rigid and elastic deformations and bias field perturbations. RESULTS The end-to-end trained framework comprising LapIRN for the deformable component achieved the best median (interquartile range) prostate and bladder Dice of 0.89 (0.85-0.91) and 0.86 (0.80-0.91), respectively. This accuracy was comparable to the iterative baseline registration: prostate and bladder Dice of 0.91 (0.88-0.93) and 0.86 (0.80-0.92). The best models complete rigid and deformable registration in 0.002 (0.0005) and 0.74 (0.43) s (Nvidia Tesla V100-PCIe 32 GB GPU), respectively. We found that the models are robust to translations up to 52 mm, rotations up to 15∘ $^\circ$ , elastic deformations up to 40 mm, and bias fields. CONCLUSIONS Our proposed unsupervised, deep learning-based registration framework can perform rigid and deformable registration in less than a second with contour propagation accuracy comparable with iterative registration.
Collapse
Affiliation(s)
- Iris D Kolenbrander
- Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Eindhoven Artificial Intelligence Systems Institute, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Matteo Maspero
- Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Allard A Hendriksen
- Computational Imaging, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
| | - Ryan Pollitt
- Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Cornelis A T van den Berg
- Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Josien P W Pluim
- Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Eindhoven Artificial Intelligence Systems Institute, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Maureen A J M van Eijnatten
- Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Eindhoven Artificial Intelligence Systems Institute, Eindhoven University of Technology, Eindhoven, The Netherlands
| |
Collapse
|
220
|
Emin S, Rossi E, Myrvold Rooth E, Dorniok T, Hedman M, Gagliardi G, Villegas F. Clinical implementation of a commercial synthetic computed tomography solution for radiotherapy treatment of glioblastoma. Phys Imaging Radiat Oncol 2024; 30:100589. [PMID: 38818305 PMCID: PMC11137592 DOI: 10.1016/j.phro.2024.100589] [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: 11/13/2023] [Revised: 05/12/2024] [Accepted: 05/13/2024] [Indexed: 06/01/2024] Open
Abstract
Background and Purpose Magnetic resonance (MR)-only radiotherapy (RT) workflow eliminates uncertainties due to computed tomography (CT)-MR image registration, by using synthetic CT (sCT) images generated from MR. This study describes the clinical implementation process, from retrospective commissioning to prospective validation stage of a commercial artificial intelligence (AI)-based sCT product. Evaluation of the dosimetric performance of the sCT is presented, with emphasis on the impact of voxel size differences between image modalities. Materials and methods sCT performance was assessed in glioblastoma RT planning. Dose differences for 30 patients in both commissioning and validation cohorts were calculated at various dose-volume-histogram (DVH) points for target and organs-at-risk (OAR). A gamma analysis was conducted on regridded image plans. Quality assurance (QA) guidelines were established based on commissioning phase results. Results Mean dose difference to target structures was found to be within ± 0.7 % regardless of image resolution and cohort. OARs' mean dose differences were within ± 1.3 % for plans calculated on regridded images for both cohorts, while differences were higher for plans with original voxel size, reaching up to -4.2 % for chiasma D2% in the commissioning cohort. Gamma passing rates for the brain structure using the criteria 1 %/1mm, 2 %/2mm and 3 %/3mm were 93.6 %/99.8 %/100 % and 96.6 %/99.9 %/100 % for commissioning and validation cohorts, respectively. Conclusions Dosimetric outcomes in both commissioning and validation stages confirmed sCT's equivalence to CT. The large patient cohort in this study aided in establishing a robust QA program for the MR-only workflow, now applied in glioblastoma RT at our center.
Collapse
Affiliation(s)
- Sevgi Emin
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | - Elia Rossi
- Department of Radiation Oncology, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | | | - Torsten Dorniok
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | - Mattias Hedman
- Department of Radiation Oncology, Karolinska University Hospital, 171 76 Stockholm, Sweden
- Department of Oncology-Pathology, Karolinska Institute, 171 77 Stockholm, Sweden
| | - Giovanna Gagliardi
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, 171 76 Stockholm, Sweden
- Department of Oncology-Pathology, Karolinska Institute, 171 77 Stockholm, Sweden
| | - Fernanda Villegas
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, 171 76 Stockholm, Sweden
- Department of Oncology-Pathology, Karolinska Institute, 171 77 Stockholm, Sweden
| |
Collapse
|
221
|
Szkitsak J, Karius A, Fernolendt S, Schubert P, Speer S, Fietkau R, Bert C, Hofmann C. Optimized raw data selection for artifact reduction of breathing controlled four-dimensional sequence scanning. Phys Imaging Radiat Oncol 2024; 30:100584. [PMID: 38803466 PMCID: PMC11128500 DOI: 10.1016/j.phro.2024.100584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/10/2024] [Accepted: 05/02/2024] [Indexed: 05/29/2024] Open
Abstract
Background and purpose Even with most breathing-controlled four-dimensional computed tomography (4DCT) algorithms image artifacts caused by single significant longer breathing still occur, resulting in negative consequences for radiotherapy. Our study presents first phantom examinations of a new optimized raw data selection and binning algorithm, aiming to improve image quality and geometric accuracy without additional dose exposure. Materials and methods To validate the new approach, phantom measurements were performed to assess geometric accuracy (volume fidelity, root mean square error, Dice coefficient of volume overlap) for one- and three-dimensional tumor motion trajectories with and without considering motion hysteresis effects. Scans without significantly longer breathing cycles served as references. Results Median volume deviations between optimized approach and reference of at maximum 1% were obtained considering all movements. In comparison, standard reconstruction yielded median deviations of 9%, 21% and 12% for one-dimensional, three-dimensional, and hysteresis motion, respectively. Measurements in one- and three-dimensional directions reached a median Dice coefficient of 0.970 ± 0.013 and 0.975 ± 0.012, respectively, but only 0.918 ± 0.075 for hysteresis motions averaged over all measurements for the optimized selection. However, for the standard reconstruction median Dice coefficients were 0.845 ± 0.200, 0.868 ± 0.205 and 0.915 ± 0.075 for one- and three-dimensional as well as hysteresis motions, respectively. Median root mean square errors for the optimized algorithm were 30 ± 16 HU2 and 120 ± 90 HU2 for three-dimensional and hysteresis motions, compared to 212 ± 145 HU2 and 130 ± 131 HU2 for the standard reconstruction. Conclusions The algorithm was proven to reduce 4DCT-related artifacts due to missing projection data without further dose exposure. An improvement in radiotherapy treatment planning due to better image quality can be expected.
Collapse
Affiliation(s)
- Juliane Szkitsak
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Andre Karius
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | | | - Philipp Schubert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Stefan Speer
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Rainer Fietkau
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Christoph Bert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Christian Hofmann
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Siemens Healthcare GmbH, 91301 Forchheim, Germany
| |
Collapse
|
222
|
Piccardo AC, Gurdschinski S, Spieker S, Renner C, Czapiewski P, Wösle M, Ciernik IF. Repeated Radiation Therapy of Recurrent Solitary Fibrous Tumors of the Brain: A Medical Case History Over 20 Years. Adv Radiat Oncol 2024; 9:101426. [PMID: 38435964 PMCID: PMC10906171 DOI: 10.1016/j.adro.2023.101426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 11/20/2023] [Indexed: 03/05/2024] Open
Affiliation(s)
| | | | | | | | | | - Markus Wösle
- Radiotherapy and Radiation Oncology, Städtisches Klinikum Dessau, Brandenburg Medical School Theodor Fontane, Dessau, Germany
| | - I. Frank Ciernik
- University of Zurich (MeF), Zurich, Switzerland
- Radiotherapy and Radiation Oncology, Städtisches Klinikum Dessau, Brandenburg Medical School Theodor Fontane, Dessau, Germany
| |
Collapse
|
223
|
Renner A, Gulyas I, Buschmann M, Heilemann G, Knäusl B, Heilmann M, Widder J, Georg D, Trnková P. Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data. Phys Imaging Radiat Oncol 2024; 30:100594. [PMID: 38883146 PMCID: PMC11176922 DOI: 10.1016/j.phro.2024.100594] [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: 02/07/2024] [Revised: 05/17/2024] [Accepted: 05/25/2024] [Indexed: 06/18/2024] Open
Abstract
Background and purpose Active breathing motion management in radiotherapy consists of motion monitoring, quantification and mitigation. It is impacted by associated latencies of a few 100 ms. Artificial neural networks can successfully predict breathing motion and eliminate latencies. However, they require usually a large dataset for training. The objective of this work was to demonstrate that explicitly encoding the cyclic nature of the breathing signal into the training data enables significant reduction of training datasets which can be obtained from healthy volunteers. Material and methods Seventy surface scanner breathing signals from 25 healthy volunteers in anterior-posterior direction were used for training and validation (ratio 4:1) of long short-term memory models. The model performance was compared to a model using decomposition into phase, amplitude and a time-dependent baseline. Testing of the models was performed on 55 independent breathing signals in anterior-posterior direction from surface scanner (35 lung, 20 liver) of 30 patients with a mean breathing amplitude of (5.9 ± 6.7) mm. Results Using the decomposed breathing signal allowed for a reduction of the absolute root-mean square error (RMSE) from 0.34 mm to 0.12 mm during validation. Testing using patient data yielded an average absolute RMSE of the breathing signal of (0.16 ± 0.11) mm with a prediction horizon of 500 ms. Conclusion It was demonstrated that a motion prediction model can be trained with less than 100 datasets of healthy volunteers if breathing cycle parameters are considered. Applied to 55 patients, the model predicted breathing motion with a high accuracy.
Collapse
Affiliation(s)
- Andreas Renner
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University of Vienna, Austria
| | - Ingo Gulyas
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- MedAustron Ion Therapy Center, Wiener Neustadt, Austria
| | - Martin Buschmann
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Gerd Heilemann
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University of Vienna, Austria
| | - Barbara Knäusl
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University of Vienna, Austria
- MedAustron Ion Therapy Center, Wiener Neustadt, Austria
| | - Martin Heilmann
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Joachim Widder
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Dietmar Georg
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Image and Knowledge Driven Precision Radiation Oncology, Department of Radiation Oncology, Medical University of Vienna, Austria
- MedAustron Ion Therapy Center, Wiener Neustadt, Austria
| | - Petra Trnková
- Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| |
Collapse
|
224
|
Khodabakhshi Z, Gabrys H, Wallimann P, Guckenberger M, Andratschke N, Tanadini-Lang S. Magnetic resonance imaging radiomic features stability in brain metastases: Impact of image preprocessing, image-, and feature-level harmonization. Phys Imaging Radiat Oncol 2024; 30:100585. [PMID: 38799810 PMCID: PMC11127267 DOI: 10.1016/j.phro.2024.100585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 04/23/2024] [Accepted: 05/02/2024] [Indexed: 05/29/2024] Open
Abstract
Background and purpose Magnetic resonance imaging (MRI) scans are highly sensitive to acquisition and reconstruction parameters which affect feature stability and model generalizability in radiomic research. This work aims to investigate the effect of image pre-processing and harmonization methods on the stability of brain MRI radiomic features and the prediction performance of radiomic models in patients with brain metastases (BMs). Materials and methods Two T1 contrast enhanced brain MRI data-sets were used in this study. The first contained 25 BMs patients with scans at two different time points and was used for features stability analysis. The effect of gray level discretization (GLD), intensity normalization (Z-score, Nyul, WhiteStripe, and in house-developed method named N-Peaks), and ComBat harmonization on features stability was investigated and features with intraclass correlation coefficient >0.8 were considered as stable. The second data-set containing 64 BMs patients was used for a classification task to investigate the informativeness of stable features and the effects of harmonization methods on radiomic model performance. Results Applying fixed bin number (FBN) GLD, resulted in higher number of stable features compare to fixed bin size (FBS) discretization (10 ± 5.5 % higher). `Harmonization in feature domain improved the stability for non-normalized and normalized images with Z-score and WhiteStripe methods. For the classification task, keeping the stable features resulted in good performance only for normalized images with N-Peaks along with FBS discretization. Conclusions To develop a robust MRI based radiomic model we recommend using an intensity normalization method based on a reference tissue (e.g N-Peaks) and then using FBS discretization.
Collapse
Affiliation(s)
- Zahra Khodabakhshi
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Hubert Gabrys
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Philipp Wallimann
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| |
Collapse
|
225
|
Jiang W, Shi X, Zhang X, Li Z, Yue J. Feasibility and safety of contrast-enhanced magnetic resonance-guided adaptive radiotherapy for upper abdominal tumors: A preliminary exploration. Phys Imaging Radiat Oncol 2024; 30:100582. [PMID: 38765880 PMCID: PMC11099332 DOI: 10.1016/j.phro.2024.100582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/22/2024] Open
Abstract
This study investigates the use of contrast-enhanced magnetic resonance (MR) in MR-guided adaptive radiotherapy (MRgART) for upper abdominal tumors. Contrast-enhanced T1-weighted MR (cT1w MR) using half doses of gadoterate was used to guide daily adaptive radiotherapy for tumors poorly visualized without contrast. The use of gadoterate was found to be feasible and safe in 5-fraction MRgART and could improve the contrast-to-noise ratio of MR images. And the use of cT1w MR could reduce the interobserver variation of adaptive tumor delineation compared to plain T1w MR (4.41 vs. 6.58, p < 0.001) and T2w MR (4.41 vs. 7.42, p < 0.001).
Collapse
Affiliation(s)
- Wenheng Jiang
- Department of Graduate, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xihua Shi
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xiang Zhang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Zhenjiang Li
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jinbo Yue
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| |
Collapse
|
226
|
Singh PK, Verma R, Tripathi D, Singh S, Bhushan M, Kumar L, Barik S, Gairola M. Evaluation of the Treatment Planning and Delivery for Hip Implant Cases on Tomotherapy. J Med Phys 2024; 49:270-278. [PMID: 39131420 PMCID: PMC11309148 DOI: 10.4103/jmp.jmp_182_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 02/16/2024] [Accepted: 02/21/2024] [Indexed: 08/13/2024] Open
Abstract
Purpose The metal present in the implant creates artifacts during the treatment simulation, which impacts the treatment planning and delivery of the prescribed dose to the target and sparing normal tissues. This retrospective study evaluated the uncertainties in the planning and delivery of doses for prosthesis cases with dedicated phantom. Materials and Methods In this retrospective study, 11 patients with a hip prosthesis having cervix carcinoma were selected. Two treatment plans were generated on treatment planning system (TPS) for each case. Plan_No_Res was without any beam restriction, and Plan_exit_only was the plan with restricted beam entry through the metallic implant. An indigenous phantom was utilized to verify the accuracy of the treatment. In the phantom, some groves were present, which could be filled by implants that mimic the patient's geometries, like left, right and bilateral femur implants. The delivered doses were recorded using optically stimulated luminescence dosimeters (OSLDs), which were placed at different positions in the phantom. The plans were further calculated using megavoltage computed tomography (MVCT) scans acquired during treatment. Results The patient data showed no significant dose changes between the two planning methods. The treatment time increases from 412.18 ± 86.65 to 427.36 ± 104.80 with P = 0.03 for Plan_No_Res and Plan_exit_only, respectively. The difference between planned and delivered doses of various points across phantom geometries was within ± 9.5% in each case as left, right, and bilateral implant. The variations between OSLDs and MVCT calculated doses were also within ± 10.8%. Conclusion The study showed the competency of tomotherapy planning for hip prosthesis cases. The phantom measurements demonstrate the errors in dosimetry near the implant material, suggesting the need for precise methods to deal with artifact-related issues.
Collapse
Affiliation(s)
- Pawan Kumar Singh
- Department of Physics, Amity Institute of Applied Sciences, Amity University (AUUP), Noida, India
- Department of Radiation Oncology, Vardhman Mahavir Medical College and Safdarjung Hospital, Delhi, India
| | - Rohit Verma
- Department of Physics, Amity Institute of Applied Sciences, Amity University (AUUP), Noida, India
| | - Deepak Tripathi
- Department of Physics, USAR, Guru Gobind Singh Indraprastha University, East Campus, Delhi, India
| | - Sukhvir Singh
- Radiation Safety Group, Institute of Nuclear Medicine and Allied Sciences, Defence Research and Development Organisation, New Delhi, India
| | - Manindra Bhushan
- Department of Radiation Oncology and Division of Medical Physics, Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, India
| | - Lalit Kumar
- Department of Radiation Oncology, Max Super Speciality Hospital, New Delhi, India
| | - Soumitra Barik
- Department of Radiation Oncology and Division of Medical Physics, Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, India
| | - Munish Gairola
- Department of Radiation Oncology and Division of Medical Physics, Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, India
| |
Collapse
|
227
|
Hassan SP, de Leon J, Batumalai V, Moutrie Z, Hogan L, Ge Y, Stricker P, Jameson MG. Magnetic resonance guided adaptive post prostatectomy radiotherapy: Accumulated dose comparison of different workflows. J Appl Clin Med Phys 2024; 25:e14253. [PMID: 38394627 PMCID: PMC11005979 DOI: 10.1002/acm2.14253] [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: 05/17/2023] [Revised: 09/26/2023] [Accepted: 12/06/2023] [Indexed: 02/25/2024] Open
Abstract
PURPOSE The aim of this study was to assess the use of magnetic resonance guided adaptive radiotherapy (MRgART) in the post-prostatectomy setting; comparing dose accumulation for our initial seven patients treated with fully adaptive workflow on the Unity MR-Linac (MRL) and with non-adaptive plans generated offline. Additionally, we analyzed toxicity in patients receiving treatment. METHODS Seven patients were treated with MRgART. The prescription was 70-72 Gy in 35-36 fractions. Patients were treated with an adapt to shape (ATS) technique. For each clinically delivered plan, a non-adaptive plan based upon the reference plan was generated and compared to the associated clinically delivered plan. A total of 468 plans were analyzed. Concordance Index of target and Organs at Risk (OARs) for each fraction with reference contours was analyzed. Acute toxicity was then assessed at six-months following completion of treatment with Common Terminology for Adverse Events (CTCAE) Toxicity Criteria. RESULTS A total of 246 fractions were clinically delivered to seven patients; 234 fractions were delivered via MRgART and 12 fractions delivered via a traditional linear accelerator due to machine issues. Pre-treatment reference plans met CTV and OAR criteria. PTV coverage satisfaction was higher in the clinically delivered adaptive plans than non-adaptive comparison plans; 42.93% versus 7.27% respectively. Six-month CTCAE genitourinary and gastrointestinal toxicity was absent in most patients, and mild-to-moderate in a minority of patients (Grade 1 GU toxicity in one patient and Grade 2 GI toxicity in one patient). CONCLUSIONS Daily MRgART treatment consistently met planning criteria. Target volume variability in prostate bed treatment can be mitigated by using MRgART and deliver satisfactory coverage of CTV whilst minimizing dose to adjacent OARs and reducing toxicity.
Collapse
Affiliation(s)
- Sean P. Hassan
- GenesisCareSt Vincent's HospitalSydneyNew South WalesAustralia
| | | | - Vikneswary Batumalai
- GenesisCareSt Vincent's HospitalSydneyNew South WalesAustralia
- Faculty of MedicineUniversity of New South WalesSydneyNew South WalesAustralia
| | - Zoe Moutrie
- GenesisCareSt Vincent's HospitalSydneyNew South WalesAustralia
- Faculty of MedicineUniversity of New South WalesSydneyNew South WalesAustralia
- South Western Sydney Cancer ServicesNew South Wales HealthSydneyAustralia
- Ingham Institute for Applied Medical ResearchSydneyAustralia
| | - Louise Hogan
- GenesisCareSt Vincent's HospitalSydneyNew South WalesAustralia
- GenesisCareMurdochWestern AustraliaAustralia
| | - Yuanyuan Ge
- GenesisCareSt Vincent's HospitalSydneyNew South WalesAustralia
| | - Phillip Stricker
- Faculty of MedicineUniversity of New South WalesSydneyNew South WalesAustralia
- Western Sydney UniversityPenrithNew South WalesAustralia
- St Vincent's Prostate Cancer Research CentreDarlinghurstNew South WalesAustralia
- Garvan Institute, DarlinghurstSydneyNew South WalesAustralia
- University of SydneyCamperdownNew South WalesAustralia
| | - Michael G. Jameson
- GenesisCareSt Vincent's HospitalSydneyNew South WalesAustralia
- Faculty of MedicineUniversity of New South WalesSydneyNew South WalesAustralia
- Centre for Medical Radiation PhysicsUniversity of WollongongWollongongAustralia
| |
Collapse
|
228
|
Fjellanger K, Heijmen BJ, Breedveld S, Sandvik IM, Hysing LB. Comparison of deep inspiration breath hold and free breathing intensity modulated proton therapy of locally advanced lung cancer. Phys Imaging Radiat Oncol 2024; 30:100590. [PMID: 38827886 PMCID: PMC11140793 DOI: 10.1016/j.phro.2024.100590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 05/14/2024] [Accepted: 05/14/2024] [Indexed: 06/05/2024] Open
Abstract
Background and purpose For locally advanced non-small cell lung cancer (LA-NSCLC), intensity-modulated proton therapy (IMPT) can reduce organ at risk (OAR) doses compared to intensity-modulated radiotherapy (IMRT). Deep inspiration breath hold (DIBH) reduces OAR doses compared to free breathing (FB) in IMRT. In IMPT, differences in dose distributions and robustness between DIBH and FB are unclear. In this study, we compare DIBH to FB in IMPT, and IMPT to IMRT. Materials and methods Fortyone LA-NSCLC patients were prospectively included. 4D computed tomography images (4DCTs) and DIBH CTs were acquired for treatment planning and during weeks 1 and 3 of treatment. A new system for automated robust planning was developed and used to generate a FB and a DIBH IMPT plan for each patient. Plans were compared in terms of dose-volume parameters and normal tissue complication probabilities (NTCPs). Dose recalculations on repeat CTs were used to compare inter-fraction plan robustness. Results In IMPT, DIBH reduced median lungs Dmean from 9.3 Gy(RBE) to 8.0 Gy(RBE) compared to FB, and radiation pneumonitis NTCP from 10.9 % to 9.4 % (p < 0.001). Inter-fraction plan robustness for DIBH and FB was similar. Median NTCPs for radiation pneumonitis and mortality were around 9 percentage points lower with IMPT than IMRT (p < 0.001). These differences were much larger than between FB and DIBH within each modality. Conclusion DIBH IMPT resulted in reduced lung dose and radiation pneumonitis NTCP compared to FB IMPT. Inter-fraction robustness was comparable. OAR doses were far lower in IMPT than IMRT.
Collapse
Affiliation(s)
- Kristine Fjellanger
- Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway
- Institute of Physics and Technology, University of Bergen, Bergen, Norway
| | - Ben J.M. Heijmen
- Department of Radiotherapy, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Sebastiaan Breedveld
- Department of Radiotherapy, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Inger Marie Sandvik
- Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway
| | - Liv B. Hysing
- Department of Oncology and Medical Physics, Haukeland University Hospital, Bergen, Norway
- Institute of Physics and Technology, University of Bergen, Bergen, Norway
| |
Collapse
|
229
|
Gao Y, Yoon S, Ma TM, Yang Y, Sheng K, Low DA, Ballas L, Steinberg ML, Kishan AU, Cao M. Intra-fractional geometric and dose/volume metric variations of magnetic resonance imaging-guided stereotactic radiotherapy of prostate bed after radical prostatectomy. Phys Imaging Radiat Oncol 2024; 30:100573. [PMID: 38585371 PMCID: PMC10997948 DOI: 10.1016/j.phro.2024.100573] [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: 11/06/2023] [Revised: 03/22/2024] [Accepted: 03/22/2024] [Indexed: 04/09/2024] Open
Abstract
Background and purpose Magnetic Resonance Imaging (MRI)-guided Stereotactic body radiotherapy (SBRT) treatment to prostate bed after radical prostatectomy has garnered growing interests. The aim of this study is to evaluate intra-fractional anatomic and dose/volume metric variations for patients receiving this treatment. Materials and methods Nineteen patients who received 30-34 Gy in 5 fractions on a 0.35T MR-Linac were included. Pre- and post-treatment MRIs were acquired for each fraction (total of 75 fractions). The Clinical Target Volume (CTV), bladder, rectum, and rectal wall were contoured on all images. Volumetric changes, Hausdorff distance, Mean Distance to Agreement (MDA), and Dice similarity coefficient (DSC) for each structure were calculated. Median value and Interquartile range (IQR) were recorded. Changes in target coverage and Organ at Risk (OAR) constraints were compared and evaluated using Wilcoxon rank sum tests at a significant level of 0.05. Results Bladder had the largest volumetric changes, with a median volume increase of 48.9 % (IQR 28.9-76.8 %) and a median MDA of 5.1 mm (IQR 3.4-7.1 mm). Intra-fractional CTV volume remained stable with a median volume change of 1.2 % (0.0-4.8 %). DSC was 0.97 (IQR 0.94-0.99). For the dose/volume metrics, there were no statistically significant changes observed except for an increase in bladder hotspot and a decrease of bladder V32.5 Gy and mean dose. The CTV V95% changed from 99.9 % (IQR 98.8-100 %) to 99.6 % (IQR 93.9-100 %). Conclusion Despite intra-fractional variations of OARs, CTV coverage remained stable during MRI-guided SBRT treatments for the prostate bed.
Collapse
Affiliation(s)
- Yu Gao
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA
| | - Stephanie Yoon
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiation Oncology, City of Hope, Duarte, CA, USA
| | - Ting Martin Ma
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiation Oncology, University of Washington, Seattle, WA, USA
| | - Yingli Yang
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiation Oncology, Shanghai Ruijin Hospital, China
| | - Ke Sheng
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA
| | - Daniel A. Low
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Leslie Ballas
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Michael L. Steinberg
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Amar U Kishan
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Minsong Cao
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, USA
| |
Collapse
|
230
|
Akdag O, Borman PTS, Mandija S, Woodhead PL, Uijtewaal P, Raaymakers BW, Fast MF. Experimental demonstration of real-time cardiac physiology-based radiotherapy gating for improved cardiac radioablation on an MR-linac. Med Phys 2024; 51:2354-2366. [PMID: 38477841 DOI: 10.1002/mp.17024] [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: 09/28/2023] [Revised: 02/09/2024] [Accepted: 02/23/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Cardiac radioablation is a noninvasive stereotactic body radiation therapy (SBRT) technique to treat patients with refractory ventricular tachycardia (VT) by delivering a single high-dose fraction to the VT isthmus. Cardiorespiratory motion induces position uncertainties resulting in decreased dose conformality. Electocardiograms (ECG) are typically used during cardiac MRI (CMR) to acquire images in a predefined cardiac phase, thus mitigating cardiac motion during image acquisition. PURPOSE We demonstrate real-time cardiac physiology-based radiotherapy beam gating within a preset cardiac phase on an MR-linac. METHODS MR images were acquired in healthy volunteers (n = 5, mean age = 29.6 years, mean heart-rate (HR) = 56.2 bpm) on the 1.5 T Unity MR-linac (Elekta AB, Stockholm, Sweden) after obtaining written informed consent. The images were acquired using a single-slice balance steady-state free precession (bSSFP) sequence in the coronal or sagittal plane (TR/TE = 3/1.48 ms, flip angle = 48∘ $^{\circ }$ , SENSE = 1.5,field-of-view = 400 × 207 $\text{field-of-view} = {400}\times {207}$ mm 2 ${\text{mm}}^{2}$ , voxel size =3 × 3 × 15 $3\times 3\times 15$ mm 3 ${\rm mm}^{3}$ , partial Fourier factor = 0.65, frame rate = 13.3 Hz). In parallel, a 4-lead ECG-signal was acquired using MR-compatible equipment. The feasibility of ECG-based beam gating was demonstrated with a prototype gating workflow using a Quasar MRI4D motion phantom (IBA Quasar, London, ON, Canada), which was deployed in the bore of the MR-linac. Two volunteer-derived combined ECG-motion traces (n = 2, mean age = 26 years, mean HR = 57.4 bpm, peak-to-peak amplitude = 14.7 mm) were programmed into the phantom to mimic dose delivery on a cardiac target in breath-hold. Clinical ECG-equipment was connected to the phantom for ECG-voltage-streaming in real-time using research software. Treatment beam gating was performed in the quiescent phase (end-diastole). System latencies were compensated by delay time correction. A previously developed MRI-based gating workflow was used as a benchmark in this study. A 15-beam intensity-modulated radiotherapy (IMRT) plan (1 × 6.25 ${1}\times {6.25}$ Gy) was delivered for different motion scenarios onto radiochromic films. Next, cardiac motion was then estimated at the basal anterolateral myocardial wall via normalized cross-correlation-based template matching. The estimated motion signal was temporally aligned with the ECG-signal, which were then used for position- and ECG-based gating simulations in the cranial-caudal (CC), anterior-posterior (AP), and right-left (RL) directions. The effect of gating was investigated by analyzing the differences in residual motion at 30, 50, and 70% treatment beam duty cycles. RESULTS ECG-based (MRI-based) beam gating was performed with effective duty cycles of 60.5% (68.8%) and 47.7% (50.4%) with residual motion reductions of 62.5% (44.7%) and 43.9% (59.3%). Local gamma analyses (1%/1 mm) returned pass rates of 97.6% (94.1%) and 90.5% (98.3%) for gated scenarios, which exceed the pass rates of 70.3% and 82.0% for nongated scenarios, respectively. In average, the gating simulations returned maximum residual motion reductions of 88%, 74%, and 81% at 30%, 50%, and 70% duty cycles, respectively, in favor of MRI-based gating. CONCLUSIONS Real-time ECG-based beam gating is a feasible alternative to MRI-based gating, resulting in improved dose delivery in terms of highγ -pass $\gamma {\text{-pass}}$ rates, decreased dose deposition outside the PTV and residual motion reduction, while by-passing cardiac MRI challenges.
Collapse
Affiliation(s)
- Osman Akdag
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Pim T S Borman
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Stefano Mandija
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
- Computational Imaging Group for MR Diagnostics and Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Peter L Woodhead
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
- Elekta AB, Stockholm, Sweden
| | - Prescilla Uijtewaal
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Bas W Raaymakers
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Martin F Fast
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|
231
|
Yoon S, Shim YS, Park SH, Sung J, Nickel MD, Kim YJ, Lee HY, Kim HJ. Hepatobiliary phase imaging in cirrhotic patients using compressed sensing and controlled aliasing in parallel imaging results in higher acceleration. Eur Radiol 2024; 34:2233-2243. [PMID: 37731096 DOI: 10.1007/s00330-023-10226-w] [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: 02/26/2023] [Revised: 07/19/2023] [Accepted: 07/27/2023] [Indexed: 09/22/2023]
Abstract
OBJECTIVE We aimed to compare the image quality and focal lesion detection ability of hepatobiliary phase (HBP) images obtained using compressed sensing (CS) and controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) in patients with liver cirrhosis. MATERIALS AND METHODS We retrospectively included 244 gadoxetic acid-enhanced liver MRI from 244 patients with cirrhosis obtained by two HBP images using CS and CAIPIRINHA from July 2020 to December 2020. The optimized resolution and scan time for CS-HBP and CAIPIRINHA-HBP were 0.9 × 0.9 × 1.5 mm3 and 15 s and 1.3 × 1.3 × 3 mm3 and 16 s, respectively. We compared the image quality between the two sets of images in 244 patients and focal lesion (n = 294) analyses for 112 patients. RESULTS CS-HBP showed comparable overall image quality (3.7 ± 0.9 vs. 3.6 ± 0.8, p = 0.680), superior liver edge sharpness (3.9 ± 0.6 vs. 3.6 ± 0.5, p < 0.001), and fewer respiratory motion artifacts (4.0 ± 0.7 vs. 3.8 ± 0.5, p < 0.001), but higher non-respiratory artifacts (3.4 ± 0.7 vs. 3.6 ± 0.6, p < 0.001) and subjective image noise (3.5 ± 0.8 vs. 3.6 ± 0.7, p = 0.014) than CAIPIRINHA-HBP. CS-HBP showed a higher signal-to-noise ratio in the liver than CAIPIRINHA-HBP (20.9 ± 9.0 vs. 18.9 ± 7.1, p = 0.008). The pooled sensitivity, specificity, and AUC were 90.0%, 77.5%, and 0.84 for CS-HBP and 73.5%, 82.4%, and 0.78 for CAIPIRINHA-HBP, respectively. CONCLUSIONS CS-HBP showed better focal lesion detection ability, comparable overall image quality, and fewer respiratory motion artifacts, but higher non-respiratory artifacts and noise compared to CAIPIRINHA-HBP. Thus, CS-HBP could be recommended for liver MRI in patients with cirrhosis to improve diagnostic performance. CLINICAL RELEVANCE STATEMENT Thin-slice CS-HBP may be useful for detecting sub-centimeter hepatocellular carcinoma in cirrhotic patients with Child-Pugh classification A while maintaining comparable subjective image quality. KEY POINTS • Compared with controlled aliasing in parallel imaging results in higher acceleration, compressed sensing hepatobiliary phase yielded thinner slices and shorter scan time at a higher accelerating factor. • Compressed sensing hepatobiliary phase showed comparable overall image quality, superior liver edge sharpness, and fewer respiratory motion artifacts, but higher non-respiratory artifacts and subjective image noise than controlled aliasing in parallel imaging results in higher acceleration-hepatobiliary phase. • Compressed sensing hepatobiliary phase can detect sub-centimeter hepatocellular carcinoma in cirrhotic patients with Child-Pugh classification A.
Collapse
Affiliation(s)
- Sungjin Yoon
- Department of Radiology, Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774 Beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea
| | - Young Sup Shim
- Department of Radiology, Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774 Beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea
| | - So Hyun Park
- Department of Radiology, Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774 Beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea.
| | - Jaekon Sung
- Siemens Healthineers Ltd., Seoul, Republic of Korea
| | | | - Ye Jin Kim
- Department of Radiology, Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774 Beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea
| | - Hee Young Lee
- Department of Radiology, Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774 Beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea
| | - Hwa Jung Kim
- Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
| |
Collapse
|
232
|
Oonsiri S, Kingkaew S, Vimolnoch M, Chatchumnan N, Plangpleng N, Oonsiri P. Effectiveness of multi-criteria optimization in combination with knowledge-based modeling in radiotherapy of left-sided breast including regional nodes. Phys Imaging Radiat Oncol 2024; 30:100595. [PMID: 38872709 PMCID: PMC11169521 DOI: 10.1016/j.phro.2024.100595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 05/27/2024] [Accepted: 05/27/2024] [Indexed: 06/15/2024] Open
Abstract
Multi-criteria optimization (MCO) is a method that was added to treatment planning to create high-quality treatment plans. This study aimed to investigate the effectiveness of MCO in combination with knowledge-based planning (KBP) in radiotherapy for left-sided breasts, including regional nodes. Dose/volume parameters were evaluated for manual plans (MP), KBP, and KBP + MCO. Planning target volume doses of MP had better coverage while KBP + MCO plans demonstrated the lowest organ at risk doses. KBP and KBP + MCO plans had increasing complexity as expressed in the number of monitor units.
Collapse
Affiliation(s)
- Sornjarod Oonsiri
- Division of Radiation Oncology, Department of Radiology, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
| | - Sakda Kingkaew
- Division of Radiation Oncology, Department of Radiology, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
| | - Mananchaya Vimolnoch
- Division of Radiation Oncology, Department of Radiology, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
| | - Nichakan Chatchumnan
- Division of Radiation Oncology, Department of Radiology, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
| | - Nuttha Plangpleng
- Division of Radiation Oncology, Department of Radiology, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
| | - Puntiwa Oonsiri
- Division of Radiation Oncology, Department of Radiology, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
| |
Collapse
|
233
|
Balagopal A, Dohopolski M, Suk Kwon Y, Montalvo S, Morgan H, Bai T, Nguyen D, Liang X, Zhong X, Lin MH, Desai N, Jiang S. Deep learning based automatic segmentation of the Internal Pudendal Artery in definitive radiotherapy treatment planning of localized prostate cancer. Phys Imaging Radiat Oncol 2024; 30:100577. [PMID: 38707629 PMCID: PMC11068618 DOI: 10.1016/j.phro.2024.100577] [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: 12/12/2023] [Revised: 04/06/2024] [Accepted: 04/08/2024] [Indexed: 05/07/2024] Open
Abstract
Background and purpose Radiation-induced erectile dysfunction (RiED) commonly affects prostate cancer patients, prompting clinical trials across institutions to explore dose-sparing to internal-pudendal-arteries (IPA) for preserving sexual potency. IPA, challenging to segment, isn't conventionally considered an organ-at-risk (OAR). This study proposes a deep learning (DL) auto-segmentation model for IPA, using Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) or CT alone to accommodate varied clinical practices. Materials and methods A total of 86 patients with CT and MRI images and noisy IPA labels were recruited in this study. We split the data into 42/14/30 for model training, testing, and a clinical observer study, respectively. There were three major innovations in this model: 1) we designed an architecture with squeeze-and-excite blocks and modality attention for effective feature extraction and production of accurate segmentation, 2) a novel loss function was used for training the model effectively with noisy labels, and 3) modality dropout strategy was used for making the model capable of segmentation in the absence of MRI. Results Test dataset metrics were DSC 61.71 ± 7.7 %, ASD 2.5 ± .87 mm, and HD95 7.0 ± 2.3 mm. AI segmented contours showed dosimetric similarity to expert physician's contours. Observer study indicated higher scores for AI contours (mean = 3.7) compared to inexperienced physicians' contours (mean = 3.1). Inexperienced physicians improved scores to 3.7 when starting with AI contours. Conclusion The proposed model achieved good quality IPA contours to improve uniformity of segmentation and to facilitate introduction of standardized IPA segmentation into clinical trials and practice.
Collapse
Affiliation(s)
- Anjali Balagopal
- Medical Artificial Intelligence and Automation (MAIA) Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Michael Dohopolski
- Medical Artificial Intelligence and Automation (MAIA) Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Young Suk Kwon
- Medical Artificial Intelligence and Automation (MAIA) Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Steven Montalvo
- Medical Artificial Intelligence and Automation (MAIA) Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Howard Morgan
- Medical Artificial Intelligence and Automation (MAIA) Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ti Bai
- Medical Artificial Intelligence and Automation (MAIA) Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xiao Liang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xinran Zhong
- Medical Artificial Intelligence and Automation (MAIA) Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Mu-Han Lin
- Medical Artificial Intelligence and Automation (MAIA) Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Neil Desai
- Medical Artificial Intelligence and Automation (MAIA) Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Steve Jiang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| |
Collapse
|
234
|
Thwaites DI, Prokopovich DA, Garrett RF, Haworth A, Rosenfeld A, Ahern V. The rationale for a carbon ion radiation therapy facility in Australia. J Med Radiat Sci 2024; 71 Suppl 2:59-76. [PMID: 38061984 PMCID: PMC11011608 DOI: 10.1002/jmrs.744] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 11/17/2023] [Indexed: 04/13/2024] Open
Abstract
Australia has taken a collaborative nationally networked approach to achieve particle therapy capability. This supports the under-construction proton therapy facility in Adelaide, other potential proton centres and an under-evaluation proposal for a hybrid carbon ion and proton centre in western Sydney. A wide-ranging overview is presented of the rationale for carbon ion radiation therapy, applying observations to the case for an Australian facility and to the clinical and research potential from such a national centre.
Collapse
Affiliation(s)
- David I. Thwaites
- Institute of Medical Physics, School of PhysicsUniversity of SydneySydneyNew South WalesAustralia
- Department of Radiation OncologySydney West Radiation Oncology NetworkWestmeadNew South WalesAustralia
- Radiotherapy Research Group, Institute of Medical ResearchSt James's Hospital and University of LeedsLeedsUK
| | | | - Richard F. Garrett
- Australian Nuclear Science and Technology OrganisationLucas HeightsNew South WalesAustralia
| | - Annette Haworth
- Institute of Medical Physics, School of PhysicsUniversity of SydneySydneyNew South WalesAustralia
- Department of Radiation OncologySydney West Radiation Oncology NetworkWestmeadNew South WalesAustralia
| | - Anatoly Rosenfeld
- Centre for Medical Radiation Physics, School of PhysicsUniversity of WollongongSydneyNew South WalesAustralia
| | - Verity Ahern
- Department of Radiation OncologySydney West Radiation Oncology NetworkWestmeadNew South WalesAustralia
- Westmead Clinical School, Faculty of Medicine and HealthUniversity of SydneySydneyNew South WalesAustralia
| |
Collapse
|
235
|
Sherwani MK, Gopalakrishnan S. A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy. FRONTIERS IN RADIOLOGY 2024; 4:1385742. [PMID: 38601888 PMCID: PMC11004271 DOI: 10.3389/fradi.2024.1385742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 03/11/2024] [Indexed: 04/12/2024]
Abstract
The aim of this systematic review is to determine whether Deep Learning (DL) algorithms can provide a clinically feasible alternative to classic algorithms for synthetic Computer Tomography (sCT). The following categories are presented in this study: ∙ MR-based treatment planning and synthetic CT generation techniques. ∙ Generation of synthetic CT images based on Cone Beam CT images. ∙ Low-dose CT to High-dose CT generation. ∙ Attenuation correction for PET images. To perform appropriate database searches, we reviewed journal articles published between January 2018 and June 2023. Current methodology, study strategies, and results with relevant clinical applications were analyzed as we outlined the state-of-the-art of deep learning based approaches to inter-modality and intra-modality image synthesis. This was accomplished by contrasting the provided methodologies with traditional research approaches. The key contributions of each category were highlighted, specific challenges were identified, and accomplishments were summarized. As a final step, the statistics of all the cited works from various aspects were analyzed, which revealed that DL-based sCTs have achieved considerable popularity, while also showing the potential of this technology. In order to assess the clinical readiness of the presented methods, we examined the current status of DL-based sCT generation.
Collapse
Affiliation(s)
- Moiz Khan Sherwani
- Section for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | | |
Collapse
|
236
|
Storm KS, Åström LM, Sibolt P, Behrens CP, Persson GF, Serup-Hansen E. ROAR-A: re-optimization based Online Adaptive Radiotherapy of anal cancer, a prospective phase II trial protocol. BMC Cancer 2024; 24:374. [PMID: 38528456 DOI: 10.1186/s12885-024-12111-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 03/12/2024] [Indexed: 03/27/2024] Open
Abstract
BACKGROUND Chemo-radiotherapy with curative intent for anal cancer has high complete remission rates, but acute treatment-related gastrointestinal (GI) toxicity is significant. Toxicity occurs due to irradiation of surrounding normal tissue. Current radiotherapy requires the addition of large planning margins to the radiation field to ensure target coverage regardless of the considerable organ motion in the pelvic region. This increases the irradiated volume and radiation dose to the surrounding normal tissue and thereby toxicity. Online adaptive radiotherapy uses artificial intelligence to adjust the treatment to the anatomy of the day. This allows for the reduction of planning margins, minimizing the irradiated volume and thereby radiation to the surrounding normal tissue.This study examines if cone beam computed tomography (CBCT)-guided oART with daily automated treatment re-planning can reduce acute gastrointestinal toxicity in patients with anal cancer. METHODS/DESIGN The study is a prospective, single-arm, phase II trial conducted at Copenhagen University Hospital, Herlev and Gentofte, Denmark. 205 patients with local only or locally advanced anal cancer, referred for radiotherapy with or without chemotherapy with curative intent, are planned for inclusion. Toxicity and quality of life are reported with Common Terminology Criteria of Adverse Events and patient-reported outcome questionnaires, before, during, and after treatment. The primary endpoint is a reduction in the incidence of acute treatment-related grade ≥ 2 diarrhea from 36 to 25% after daily online adaptive radiotherapy compared to standard radiotherapy. Secondary endpoints include all acute and late toxicity, overall survival, and reduction in treatment interruptions. RESULTS Accrual began in January 2022 and is expected to finish in January 2026. Primary endpoint results are expected to be available in April 2026. DISCUSSION This is the first study utilizing online adaptive radiotherapy to treat anal cancer. We hope to determine whether there is a clinical benefit for the patients, with significant reductions in acute GI toxicity without compromising treatment efficacy. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT05438836. Danish Ethical Committee: H-21028093.
Collapse
Affiliation(s)
- Katrine Smedegaard Storm
- Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte, Herlev, Denmark.
- Department of Clinical Medicine, Faculty of Health Sciences, University of Copenhagen, København, Denmark.
| | - Lina M Åström
- Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte, Herlev, Denmark
- Department of Health Technology, Technical University of Denmark, Roskilde, Denmark
| | - Patrik Sibolt
- Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte, Herlev, Denmark
| | - Claus P Behrens
- Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte, Herlev, Denmark
- Department of Health Technology, Technical University of Denmark, Roskilde, Denmark
| | - Gitte F Persson
- Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte, Herlev, Denmark
- Department of Clinical Medicine, Faculty of Health Sciences, University of Copenhagen, København, Denmark
| | - Eva Serup-Hansen
- Department of Oncology, Copenhagen University Hospital - Herlev and Gentofte, Herlev, Denmark
| |
Collapse
|
237
|
Halilaj I, Ankolekar A, Lenaers A, Chatterjee A, Oberije CJG, Eppings L, Smit HJM, Hendriks LEL, Jochems A, Lieverse RIY, van Timmeren JE, Wind A, Lambin P. Improving shared decision making for lung cancer treatment by developing and validating an open-source web based patient decision aid for stage I-II non-small cell lung cancer. Front Digit Health 2024; 5:1303261. [PMID: 38586126 PMCID: PMC10995236 DOI: 10.3389/fdgth.2023.1303261] [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: 09/28/2023] [Accepted: 11/20/2023] [Indexed: 04/09/2024] Open
Abstract
The aim of this study was to develop and evaluate a proof-of-concept open-source individualized Patient Decision Aid (iPDA) with a group of patients, physicians, and computer scientists. The iPDA was developed based on the International Patient Decision Aid Standards (IPDAS). A previously published questionnaire was adapted and used to test the user-friendliness and content of the iPDA. The questionnaire contained 40 multiple-choice questions, and answers were given on a 5-point Likert Scale (1-5) ranging from "strongly disagree" to "strongly agree." In addition to the questionnaire, semi-structured interviews were conducted with patients. We performed a descriptive analysis of the responses. The iPDA was evaluated by 28 computer scientists, 21 physicians, and 13 patients. The results demonstrate that the iPDA was found valuable by 92% (patients), 96% (computer scientists), and 86% (physicians), while the treatment information was judged useful by 92%, 96%, and 95%, respectively. Additionally, the tool was thought to be motivating for patients to actively engage in their treatment by 92%, 93%, and 91% of the above respondents groups. More multimedia components and less text were suggested by the respondents as ways to improve the tool and user interface. In conclusion, we successfully developed and tested an iPDA for patients with stage I-II Non-Small Cell Lung Cancer (NSCLC).
Collapse
Affiliation(s)
- Iva Halilaj
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands
- Health Innovation Ventures, Maastricht, Netherlands
| | - Anshu Ankolekar
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands
| | - Anouk Lenaers
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands
| | - Avishek Chatterjee
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands
| | | | - Lisanne Eppings
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands
| | | | - Lizza E. L. Hendriks
- Department of Pulmonary Diseases, GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, Netherlands
| | - Arthur Jochems
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands
| | - Relinde I. Y. Lieverse
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands
- Department of Internal Medicine, Catharina Hospital, Eindhoven, Netherlands
| | - Janita E. van Timmeren
- Department of Radiation Oncology, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Anke Wind
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Netherlands
| |
Collapse
|
238
|
Rijs Z, Kawsar KA, Saha P, van de Sande M, Lui D. Evaluation of computed tomography artefacts of carbon-fiber and titanium implants in patients with spinal oligometastatic disease undergoing stereotactic ablative radiotherapy. Sci Rep 2024; 14:6700. [PMID: 38509154 PMCID: PMC10954645 DOI: 10.1038/s41598-024-52498-2] [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: 04/11/2023] [Accepted: 01/19/2024] [Indexed: 03/22/2024] Open
Abstract
This study evaluated artefacts on computed tomography (CT) images using Hounsfield units (HU) in patients with spinal oligometastatic disease who received carbon-fiber (CF; n = 11) or titanium (n = 11) spine implants and underwent stereotactic ablative radiotherapy (SABR). Pre- and postoperative HU were measured at the vertebral body, pedicle, and spinal cord at three different levels: the lower instrumented vertebra, the level of metastatic spinal cord compression, and an uninvolved level. Areas measured at each level were delicately matched pre- and postoperatively. Significant differences in HU were observed at the vertebral body, the pedicle, and the spinal cord at the lowest instrumented vertebra level for both CF and titanium (average increase 1.54-fold and 5.11-fold respectively). At the metastatic spinal cord compression level, a trend towards a higher HU-increase was observed in titanium compared with CF treated patients (average increase 2.51-fold and 1.43-fold respectively). The relatively high postoperative HU-increase after insertion of titanium implants indicated CT artefacts, while the relatively low HU-increase of CF implants was not associated with artefacts. Less CT artefacts could facilitate an easier contouring phase in radiotherapy planning. In addition, we propose a CT artefact grading system based on postoperative HU-increase. This system could serve as a valuable tool in future research to assess if less CT artefacts lead to time savings during radiotherapy treatment planning and, potentially, to better tumoricidal effects and less adverse effects if particle therapy would be administered.
Collapse
Affiliation(s)
- Zeger Rijs
- Department of Orthopedic Surgery, Leiden University Medical Center, Leiden, The Netherlands.
| | | | - Priyanshu Saha
- Department of Orthopedic and Spinal Surgery, St. George's Hospital, London, UK
| | - Michiel van de Sande
- Department of Orthopedic Surgery, Leiden University Medical Center, Leiden, The Netherlands
| | - Darren Lui
- Department of Orthopedic and Spinal Surgery, St. George's Hospital, London, UK
| |
Collapse
|
239
|
Felchle H, Brunner V, Groll T, Walther CN, Nefzger SM, Zaurito AE, Silva MG, Gissibl J, Topping GJ, Lansink Rotgerink L, Saur D, Steiger K, Combs SE, Tschurtschenthaler M, Fischer JC. Novel Tumor Organoid-Based Mouse Model to Study Image Guided Radiation Therapy of Rectal Cancer After Noninvasive and Precise Endoscopic Implantation. Int J Radiat Oncol Biol Phys 2024; 118:1094-1104. [PMID: 37875245 DOI: 10.1016/j.ijrobp.2023.10.008] [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: 06/23/2023] [Accepted: 10/08/2023] [Indexed: 10/26/2023]
Abstract
PURPOSE Preoperative (neoadjuvant) radiation therapy (RT) is an essential part of multimodal rectal cancer therapy. Recently, total neoadjuvant therapy (TNT), which combines simultaneous radiochemotherapy with additional courses of chemotherapy, has emerged as an effective approach. TNT achieves a pathologic complete remission in approximately 30% of resected patients, opening avenues for treatment strategies that avoid radical organ resection. Furthermore, recent studies have demonstrated that anti-programmed cell death protein 1 immunotherapy can induce clinical complete responses in patients with specific genetic alterations. There is significant potential to enhance outcomes through intensifying, personalizing, and de-escalating treatment approaches. However, the heterogeneous response rates to RT or TNT and strategies to sensitize patients without specific genetic changes to immunotherapy remain poorly understood. METHODS AND MATERIALS We developed a novel orthotopic mouse model of rectal cancer based on precisely defined endoscopic injections of tumor organoids that reflect tumor heterogeneity. Subsequently, we employed endoscopic- and computed tomography-guided RT and validated rectal tumor growth and response rates to therapy using small-animal magnetic resonance imaging and endoscopic follow-up. RESULTS Rectal tumor formation was successfully induced in all mice after 2 organoid injections. Clinically relevant RT regimens with 5 × 5 Gy significantly delayed clinical signs of tumor progression and significantly improved survival. Consistent with human disease, rectal tumor progression correlated with the development of liver and lung metastases. Notably, long-term survivors after RT showed no evidence of tumor recurrence, as demonstrated by in vivo radiologic tumor staging and histopathologic examination. CONCLUSIONS Our novel mouse model combines orthotopic tumor growth via noninvasive and precise rectal organoid injection and small-animal RT. This model holds significant promise for investigating the effect of tumor cell-intrinsic aspects, genetic alterations of the host, and exogenous factors (eg, nutrition or microbiota) on RT outcomes. Furthermore, it allows for the exploration of combination therapies involving chemotherapy, immunotherapy, or novel targeted therapies.
Collapse
Affiliation(s)
- Hannah Felchle
- Department of Radiation Oncology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Valentina Brunner
- Translational Cancer Research and Institute of Experimental Cancer Therapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany; Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Tanja Groll
- Comparative Experimental Pathology, School of Medicine, Technical University of Munich, Munich, Germany; Institute of Pathology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Caroline N Walther
- Department of Radiation Oncology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Sophie M Nefzger
- Department of Radiation Oncology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Antonio E Zaurito
- Translational Cancer Research and Institute of Experimental Cancer Therapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany; Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Miguel G Silva
- Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany; Institute of Molecular Oncology and Functional Genomics, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Julia Gissibl
- Department of Radiation Oncology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Geoffrey J Topping
- Department of Nuclear Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Laura Lansink Rotgerink
- Department of Radiation Oncology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Dieter Saur
- Translational Cancer Research and Institute of Experimental Cancer Therapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany; Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner-site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Katja Steiger
- Comparative Experimental Pathology, School of Medicine, Technical University of Munich, Munich, Germany; Institute of Pathology, School of Medicine, Technical University of Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner-site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner-site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany; Helmholtz Zentrum München, Institute of Radiation Medicine, Neuherberg, Germany
| | - Markus Tschurtschenthaler
- Translational Cancer Research and Institute of Experimental Cancer Therapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany; Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner-site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Julius C Fischer
- Department of Radiation Oncology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.
| |
Collapse
|
240
|
Kierkels RGJ, Hernandez V, Saez J, Angerud A, Hilgers GC, Surmann K, Schuring D, Minken AWH. Multileaf collimator characterization and modeling for a 1.5 T MR-linac using static synchronous and asynchronous sweeping gaps. Phys Med Biol 2024; 69:075004. [PMID: 38412538 DOI: 10.1088/1361-6560/ad2d7d] [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: 11/20/2023] [Accepted: 02/27/2024] [Indexed: 02/29/2024]
Abstract
Objective.The Elekta unity MR-linac delivers step-and-shoot intensity modulated radiotherapy plans using a multileaf collimator (MLC) based on the Agility MLC used on conventional Elekta linacs. Currently, details of the physical Unity MLC and the computational model within its treatment planning system (TPS)Monacoare lacking in published literature. Recently, a novel approach to characterize the physical properties of MLCs was introduced using dynamic synchronous and asynchronous sweeping gap (aSG) tests. Our objective was to develop a step-and-shoot version of the dynamic aSG test to characterize the Unity MLC and the computational MLC models in theMonacoandRayStationTPSs.Approach.Dynamic aSG were discretized into a step-and-shoot aSG by investigating the number of segments/sweep and the minimal number of monitor units (MU) per segment. The step-and-shoot aSG tests were compared to the dynamic aSG tests on a conventional linac at a source-to-detector distance of 143.5 cm, mimicking the Unity configuration. the step-and-shoot aSG tests were used to characterize the Unity MLC through measurements and dose calculations in both TPSs.Main results.The step-and-shoot aSGs tests with 100 segments and 5 MU/segment gave results very similar to the dynamic aSG experiments. The effective tongue-and-groove width of the Unity gradually increased up to 1.4 cm from the leaf tip end. The MLC models inRayStationandMonacoagreed with experimental data within 2.0% and 10%, respectively. The largest discrepancies inMonacowere found for aSG tests with >10 mm leaf interdigitation, which are non-typical for clinical plans.Significance.The step-and-shoot aSG tests accurately characterize the MLC in step-and-shoot delivery mode. The MLC model inRayStation2023B accurately describes the tongue-and-groove and leaf tip effects whereasMonacooverestimates the tongue-and-groove shadowing further away from the leaf tip end.
Collapse
Affiliation(s)
| | - Victor Hernandez
- Hospital Sant Joan de Reus, Department of Medical Physics, Reus, Spain
| | - Jordi Saez
- Hospital Clínic de Barcelona, Department of Radiation Oncology, Barcelona, Spain
| | | | | | | | | | | |
Collapse
|
241
|
Oud M, Breedveld S, Rojo-Santiago J, Giżyńska MK, Kroesen M, Habraken S, Perkó Z, Heijmen B, Hoogeman M. A fast and robust constraint-based online re-optimization approach for automated online adaptive intensity modulated proton therapy in head and neck cancer. Phys Med Biol 2024; 69:075007. [PMID: 38373350 DOI: 10.1088/1361-6560/ad2a98] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 02/19/2024] [Indexed: 02/21/2024]
Abstract
Objective. In head-and-neck cancer intensity modulated proton therapy, adaptive radiotherapy is currently restricted to offline re-planning, mitigating the effect of slow changes in patient anatomies. Daily online adaptations can potentially improve dosimetry. Here, a new, fully automated online re-optimization strategy is presented. In a retrospective study, this online re-optimization approach was compared to our trigger-based offline re-planning (offlineTBre-planning) schedule, including extensive robustness analyses.Approach. The online re-optimization method employs automated multi-criterial re-optimization, using robust optimization with 1 mm setup-robustness settings (in contrast to 3 mm for offlineTBre-planning). Hard planning constraints and spot addition are used to enforce adequate target coverage, avoid prohibitively large maximum doses and minimize organ-at-risk doses. For 67 repeat-CTs from 15 patients, fraction doses of the two strategies were compared for the CTVs and organs-at-risk. Per repeat-CT, 10.000 fractions with different setup and range robustness settings were simulated using polynomial chaos expansion for fast and accurate dose calculations.Main results. For 14/67 repeat-CTs, offlineTBre-planning resulted in <50% probability ofD98%≥ 95% of the prescribed dose (Dpres) in one or both CTVs, which never happened with online re-optimization. With offlineTBre-planning, eight repeat-CTs had zero probability of obtainingD98%≥ 95%Dpresfor CTV7000, while the minimum probability with online re-optimization was 81%. Risks of xerostomia and dysphagia grade ≥ II were reduced by 3.5 ± 1.7 and 3.9 ± 2.8 percentage point [mean ± SD] (p< 10-5for both). In online re-optimization, adjustment of spot configuration followed by spot-intensity re-optimization took 3.4 min on average.Significance. The fast online re-optimization strategy always prevented substantial losses of target coverage caused by day-to-day anatomical variations, as opposed to the clinical trigger-based offline re-planning schedule. On top of this, online re-optimization could be performed with smaller setup robustness settings, contributing to improved organs-at-risk sparing.
Collapse
Affiliation(s)
- Michelle Oud
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, The Netherlands
- HollandPTC, Department of Medical Physics & Informatics, Delft, The Netherlands
| | - Sebastiaan Breedveld
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, The Netherlands
| | - Jesús Rojo-Santiago
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, The Netherlands
- HollandPTC, Department of Medical Physics & Informatics, Delft, The Netherlands
| | | | - Michiel Kroesen
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, The Netherlands
- HollandPTC, Department of Radiation Oncology, Delft, The Netherlands
| | - Steven Habraken
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, The Netherlands
- HollandPTC, Department of Medical Physics & Informatics, Delft, The Netherlands
| | - Zoltán Perkó
- Delft University of Technology, Faculty of Applied Sciences, Department of Radiation Science and Technology, The Netherlands
| | - Ben Heijmen
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, The Netherlands
| | - Mischa Hoogeman
- Erasmus MC Cancer Institute, University Medical Center Rotterdam, Department of Radiotherapy, Rotterdam, The Netherlands
- HollandPTC, Department of Medical Physics & Informatics, Delft, The Netherlands
| |
Collapse
|
242
|
Pascuzzo R, Garattini SK, Doniselli FM. Clinical Application of Radiomics in Oncology: Where Do We Stand? J Magn Reson Imaging 2024. [PMID: 38477019 DOI: 10.1002/jmri.29340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 02/24/2024] [Indexed: 03/14/2024] Open
Affiliation(s)
- Riccardo Pascuzzo
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Silvio Ken Garattini
- Department of Medical Oncology, Azienda Sanitaria Universitaria Friuli Centrale (ASUFC), Udine, Italy
| | - Fabio M Doniselli
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| |
Collapse
|
243
|
Grigo J, Szkitsak J, Höfler D, Fietkau R, Putz F, Bert C. "sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy. Radiat Oncol 2024; 19:33. [PMID: 38459584 PMCID: PMC10924348 DOI: 10.1186/s13014-024-02428-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 02/29/2024] [Indexed: 03/10/2024] Open
Abstract
BACKGROUND Radiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images are used for RT treatment planning whereas magnetic resonance imaging (MRI) images are used for tumor delineation. Therefore, MRI and CT need to be registered, which is an error prone process. The purpose of this clinical study is to investigate the clinical feasibility of a deep learning-based MRI-only workflow for brain radiotherapy, that eliminates the registration uncertainty through calculation of a synthetic CT (sCT) from MRI data. METHODS A total of 54 patients with an indication for radiation treatment of the brain and stereotactic mask immobilization will be recruited. All study patients will receive standard therapy and imaging including both CT and MRI. All patients will receive dedicated RT-MRI scans in treatment position. An sCT will be reconstructed from an acquired MRI DIXON-sequence using a commercially available deep learning solution on which subsequent radiotherapy planning will be performed. Through multiple quality assurance (QA) measures and reviews during the course of the study, the feasibility of an MRI-only workflow and comparative parameters between sCT and standard CT workflow will be investigated holistically. These QA measures include feasibility and quality of image guidance (IGRT) at the linear accelerator using sCT derived digitally reconstructed radiographs in addition to potential dosimetric deviations between the CT and sCT plan. The aim of this clinical study is to establish a brain MRI-only workflow as well as to identify risks and QA mechanisms to ensure a safe integration of deep learning-based sCT into radiotherapy planning and delivery. DISCUSSION Compared to CT, MRI offers a superior soft tissue contrast without additional radiation dose to the patients. However, up to now, even though the dosimetrical equivalence of CT and sCT has been shown in several retrospective studies, MRI-only workflows have still not been widely adopted. The present study aims to determine feasibility and safety of deep learning-based MRI-only radiotherapy in a holistic manner incorporating the whole radiotherapy workflow. TRIAL REGISTRATION NCT06106997.
Collapse
Affiliation(s)
- Johanna Grigo
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Juliane Szkitsak
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Daniel Höfler
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Rainer Fietkau
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Florian Putz
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany
| | - Christoph Bert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Universitätsstraße 27, DE- 91054, Erlangen, Germany.
- Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
| |
Collapse
|
244
|
Rabe M, Dietrich O, Forbrig R, Niyazi M, Belka C, Corradini S, Landry G, Kurz C. Repeatability quantification of brain diffusion-weighted imaging for future clinical implementation at a low-field MR-linac. Radiat Oncol 2024; 19:31. [PMID: 38448888 PMCID: PMC10916154 DOI: 10.1186/s13014-024-02424-7] [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: 01/31/2024] [Accepted: 02/26/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND Longitudinal assessments of apparent diffusion coefficients (ADCs) derived from diffusion-weighted imaging (DWI) during intracranial radiotherapy at magnetic resonance imaging-guided linear accelerators (MR-linacs) could enable early response assessment by tracking tumor diffusivity changes. However, DWI pulse sequences are currently unavailable in clinical practice at low-field MR-linacs. Quantifying the in vivo repeatability of ADC measurements is a crucial step towards clinical implementation of DWI sequences but has not yet been reported on for low-field MR-linacs. This study assessed ADC measurement repeatability in a phantom and in vivo at a 0.35 T MR-linac. METHODS Eleven volunteers and a diffusion phantom were imaged on a 0.35 T MR-linac. Two echo-planar imaging DWI sequence variants, emphasizing high spatial resolution ("highRes") and signal-to-noise ratio ("highSNR"), were investigated. A test-retest study with an intermediate outside-scanner-break was performed to assess repeatability in the phantom and volunteers' brains. Mean ADCs within phantom vials, cerebrospinal fluid (CSF), and four brain tissue regions were compared to literature values. Absolute relative differences of mean ADCs in pre- and post-break scans were calculated for the diffusion phantom, and repeatability coefficients (RC) and relative RC (relRC) with 95% confidence intervals were determined for each region-of-interest (ROI) in volunteers. RESULTS Both DWI sequence variants demonstrated high repeatability, with absolute relative deviations below 1% for water, dimethyl sulfoxide, and polyethylene glycol in the diffusion phantom. RelRCs were 7% [5%, 12%] (CSF; highRes), 12% [9%, 22%] (CSF; highSNR), 9% [8%, 12%] (brain tissue ROIs; highRes), and 6% [5%, 7%] (brain tissue ROIs; highSNR), respectively. ADCs measured with the highSNR variant were consistent with literature values for volunteers, while smaller mean values were measured for the diffusion phantom. Conversely, the highRes variant underestimated ADCs compared to literature values, indicating systematic deviations. CONCLUSIONS High repeatability of ADC measurements in a diffusion phantom and volunteers' brains were measured at a low-field MR-linac. The highSNR variant outperformed the highRes variant in accuracy and repeatability, at the expense of an approximately doubled voxel volume. The observed high in vivo repeatability confirms the potential utility of DWI at low-field MR-linacs for early treatment response assessment.
Collapse
Affiliation(s)
- Moritz Rabe
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
| | - Olaf Dietrich
- Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Robert Forbrig
- Institute of Neuroradiology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Maximilian Niyazi
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, a Partnership Between DKFZ and LMU University Hospital Munich, Munich, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
- Department of Radiation Oncology, University of Tübingen, Tübingen, Germany
| | - Claus Belka
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, a Partnership Between DKFZ and LMU University Hospital Munich, Munich, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| |
Collapse
|
245
|
Zhang L, Liu Z, Zhang L, Wu Z, Yu X, Holmes J, Feng H, Dai H, Li X, Li Q, Wong WW, Vora SA, Zhu D, Liu T, Liu W. Technical Note: Generalizable and Promptable Artificial Intelligence Model to Augment Clinical Delineation in Radiation Oncology. Med Phys 2024; 51:2187-2199. [PMID: 38319676 PMCID: PMC10939804 DOI: 10.1002/mp.16965] [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: 08/23/2023] [Revised: 12/29/2023] [Accepted: 01/14/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep learning-based auto-segmentation of OARs has shown promising results and is increasingly being used in radiation therapy. However, existing deep learning-based auto-segmentation approaches face two challenges in clinical practice: generalizability and human-AI interaction. A generalizable and promptable auto-segmentation model, which segments OARs of multiple disease sites simultaneously and supports on-the-fly human-AI interaction, can significantly enhance the efficiency of radiation therapy treatment planning. PURPOSE Meta's segment anything model (SAM) was proposed as a generalizable and promptable model for next-generation natural image segmentation. We further evaluated the performance of SAM in radiotherapy segmentation. METHODS Computed tomography (CT) images of clinical cases from four disease sites at our institute were collected: prostate, lung, gastrointestinal, and head & neck. For each case, we selected the OARs important in radiotherapy treatment planning. We then compared both the Dice coefficients and Jaccard indices derived from three distinct methods: manual delineation (ground truth), automatic segmentation using SAM's 'segment anything' mode, and automatic segmentation using SAM's 'box prompt' mode that implements manual interaction via live prompts during segmentation. RESULTS Our results indicate that SAM's segment anything mode can achieve clinically acceptable segmentation results in most OARs with Dice scores higher than 0.7. SAM's box prompt mode further improves Dice scores by 0.1∼0.5. Similar results were observed for Jaccard indices. The results show that SAM performs better for prostate and lung, but worse for gastrointestinal and head & neck. When considering the size of organs and the distinctiveness of their boundaries, SAM shows better performance for large organs with distinct boundaries, such as lung and liver, and worse for smaller organs with less distinct boundaries, like parotid and cochlea. CONCLUSIONS Our results demonstrate SAM's robust generalizability with consistent accuracy in automatic segmentation for radiotherapy. Furthermore, the advanced box-prompt method enables the users to augment auto-segmentation interactively and dynamically, leading to patient-specific auto-segmentation in radiation therapy. SAM's generalizability across different disease sites and different modalities makes it feasible to develop a generic auto-segmentation model in radiotherapy.
Collapse
Affiliation(s)
- Lian Zhang
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Zhengliang Liu
- School of Computing, University of Georgia, Athens, GA 30602, USA
| | - Lu Zhang
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
| | - Zihao Wu
- School of Computing, University of Georgia, Athens, GA 30602, USA
| | - Xiaowei Yu
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
| | - Jason Holmes
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Hongying Feng
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Haixing Dai
- School of Computing, University of Georgia, Athens, GA 30602, USA
| | - Xiang Li
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Quanzheng Li
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - William W. Wong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Sujay A. Vora
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Dajiang Zhu
- School of Computing, University of Georgia, Athens, GA 30602, USA
| | - Tianming Liu
- School of Computing, University of Georgia, Athens, GA 30602, USA
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| |
Collapse
|
246
|
Fog LS, Webb LK, Barber J, Jennings M, Towns S, Olivera S, Shakeshaft J. ACPSEM position paper: pre-treatment patient specific plan checks and quality assurance in radiation oncology. Phys Eng Sci Med 2024; 47:7-15. [PMID: 38315415 DOI: 10.1007/s13246-023-01367-9] [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: 12/05/2023] [Accepted: 12/06/2023] [Indexed: 02/07/2024]
Abstract
The Australasian College of Physical Scientists and Engineers in Medicine (ACPSEM) has not previously made recommendations outlining the requirements for physics plan checks in Australia and New Zealand. A recent workforce modelling exercise, undertaken by the ACPSEM, revealed that the workload of a clinical radiation oncology medical physicist can comprise of up to 50% patient specific quality assurance activities. Therefore, in 2022 the ACPSEM Radiation Oncology Specialty Group (ROSG) set up a working group to address this issue. This position paper authored by ROSG endorses the recommendations of the American Association of Physicists in Medicine (AAPM) Task Group 218, 219 and 275 reports with some contextualisation for the Australia and New Zealand settings. A few recommendations from other sources are also endorsed to complete the position.
Collapse
Affiliation(s)
- Lotte S Fog
- Alfred Health Radiation Oncology, Melbourne, VIC, Australia.
| | | | - Jeffrey Barber
- Sydney West Radiation Oncology Network, Blacktown Hospital, Blacktown, NSW, 2148, Australia
| | - Matthew Jennings
- ICON Cancer Care, Cordelia St, South Brisbane, QLD, 4101, Australia
| | - Sam Towns
- Alfred Health Radiation Oncology, Melbourne, VIC, Australia
| | - Susana Olivera
- ICON Cancer Care, Liz Plummer Cancer Centre, Cairns, QLD, 4870, Australia
| | - John Shakeshaft
- ICON Cancer Care, Gold Coast University Hospital, 1 Hospital Blvd, Southport, QLD, 4215, Australia
| |
Collapse
|
247
|
Walls GM, McCann C, O'Connor J, O'Sullivan A, I Johnston D, McAleese J, McGarry CK, Cole AJ, Jain S, Butterworth KT, Hanna GG. Pulmonary vein dose and risk of atrial fibrillation in patients with non-small cell lung cancer following definitive radiotherapy: An NI-HEART analysis. Radiother Oncol 2024; 192:110085. [PMID: 38184145 DOI: 10.1016/j.radonc.2024.110085] [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: 10/16/2023] [Revised: 12/22/2023] [Accepted: 01/02/2024] [Indexed: 01/08/2024]
Abstract
BACKGROUND AND PURPOSE Symptomatic arrhythmia is common following radiotherapy for non-small cell lung cancer (NSCLC), frequently resulting in morbidity and hospitalization. Modern treatment planning technology theoretically allows sparing of cardiac substructures. Atrial fibrillation (AF) comprises the majority of post-radiotherapy arrhythmias, but efforts to prevent this cardiotoxicity have been limited as the causative cardiac substructure is not known. In this study we investigated if incidental radiation dose to the pulmonary veins (PVs) is associated with AF. MATERIAL AND METHODS A single-centre study of patients completing contemporary (chemo)radiation for NSCLC, with modern planning techniques. Oncology, cardiology and death records were examined, and AF events were verified by a cardiologist. Cardiac substructures were contoured on planning scans for retrospective dose analysis. RESULTS In 420 eligible patients with NSCLC treated with intensity-modulated (70%) or 3D-conformal (30%) radiotherapy with a median OS of 21.8 months (IQR 10.8-35.1), there were 26 cases of new AF (6%). All cases were grade 3 except two cases of grade 4. Dose metrics for both the left (V55) and right (V10) PVs were associated with the incidence of new AF. Metrics remained statistically significant after accounting for the competing risk of death and cardiovascular covariables for both the left (HR 1.02, 95%CI 1.00-1.03, p = 0.005) and right (HR 1.01 (95%CI 1.00-1.02, p = 0.033) PVs. CONCLUSION Radiation dose to the PVs during treatment of NSCLC was associated with the onset of AF. Actively sparing the PVs during treatment planning could reduce the incidence of AF during follow-up, and screening for AF may be warranted for select cases.
Collapse
Affiliation(s)
- Gerard M Walls
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Jubilee Road, Belfast, Northern Ireland.
| | - Conor McCann
- Department of Cardiology, Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland
| | - John O'Connor
- School of Engineering, University of Ulster, York Street, Belfast, Northern Ireland
| | - Anna O'Sullivan
- School of Medicine, University College Dublin, Belfield Dublin 4, Ireland
| | - David I Johnston
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Jubilee Road, Belfast, Northern Ireland
| | - Jonathan McAleese
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland
| | - Conor K McGarry
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Jubilee Road, Belfast, Northern Ireland
| | - Aidan J Cole
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland
| | - Suneil Jain
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Jubilee Road, Belfast, Northern Ireland
| | - Karl T Butterworth
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Jubilee Road, Belfast, Northern Ireland
| | - Gerard G Hanna
- Cancer Centre Belfast City Hospital, Belfast Health & Social Care Trust, Lisburn Road, Belfast, Northern Ireland; Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Jubilee Road, Belfast, Northern Ireland
| |
Collapse
|
248
|
Wang B, Liu Y, Zhang J, Yin S, Liu B, Ding S, Qiu B, Deng X. Evaluating contouring accuracy and dosimetry impact of current MRI-guided adaptive radiation therapy for brain metastases: a retrospective study. J Neurooncol 2024; 167:123-132. [PMID: 38300388 PMCID: PMC10978730 DOI: 10.1007/s11060-024-04583-9] [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: 12/27/2023] [Accepted: 01/22/2024] [Indexed: 02/02/2024]
Abstract
BACKGROUND Magnetic resonance imaging (MRI) guided adaptive radiotherapy (MRgART) has gained increasing attention, showing clinical advantages over conventional radiotherapy. However, there are concerns regarding online target delineation and modification accuracy. In our study, we aimed to investigate the accuracy of brain metastases (BMs) contouring and its impact on dosimetry in 1.5 T MRI-guided online adaptive fractionated stereotactic radiotherapy (FSRT). METHODS Eighteen patients with 64 BMs were retrospectively evaluated. Pre-treatment 3.0 T MRI scans (gadolinium contrast-enhanced T1w, T1c) and initial 1.5 T MR-Linac scans (non-enhanced online-T1, T2, and FLAIR) were used for gross target volume (GTV) contouring. Five radiation oncologists independently contoured GTVs on pre-treatment T1c and initial online-T1, T2, and FLAIR images. We assessed intra-observer and inter-observer variations and analysed the dosimetry impact through treatment planning based on GTVs generated by online MRI, simulating the current online adaptive radiotherapy practice. RESULTS The average Dice Similarity Coefficient (DSC) for inter-observer comparison were 0.79, 0.54, 0.59, and 0.64 for pre-treatment T1c, online-T1, T2, and FLAIR, respectively. Inter-observer variations were significantly smaller for the 3.0 T pre-treatment T1c than for the contrast-free online 1.5 T MR scans (P < 0.001). Compared to the T1c contours, the average DSC index of intra-observer contouring was 0.52‒0.55 for online MRIs. For BMs larger than 3 cm3, visible on all image sets, the average DSC indices were 0.69, 0.71 and 0.64 for online-T1, T2, and FLAIR, respectively, compared to the pre-treatment T1c contour. For BMs < 3 cm3, the average visibility rates were 22.3%, 41.3%, and 51.8% for online-T1, T2, and FLAIR, respectively. Simulated adaptive planning showed an average prescription dose coverage of 63.4‒66.9% when evaluated by ground truth planning target volumes (PTVs) generated on pre-treatment T1c, reducing it from over 99% coverage by PTVs generated on online MRIs. CONCLUSIONS The accuracy of online target contouring was unsatisfactory for the current MRI-guided online adaptive FSRT. Small lesions had poor visibility on 1.5 T non-contrast-enhanced MR-Linac images. Contour inaccuracies caused a one-third drop in prescription dose coverage for the target volume. Future studies should explore the feasibility of contrast agent administration during daily treatment in MRI-guided online adaptive FSRT procedures.
Collapse
Affiliation(s)
- Bin Wang
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, 651 East Dongfeng Road, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Yimei Liu
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, 651 East Dongfeng Road, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Jun Zhang
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, 651 East Dongfeng Road, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Shaohan Yin
- Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Biaoshui Liu
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, 651 East Dongfeng Road, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Shouliang Ding
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, 651 East Dongfeng Road, Guangzhou, Guangdong, 510060, People's Republic of China
| | - Bo Qiu
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, 651 East Dongfeng Road, Guangzhou, Guangdong, 510060, People's Republic of China.
| | - Xiaowu Deng
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, 651 East Dongfeng Road, Guangzhou, Guangdong, 510060, People's Republic of China.
| |
Collapse
|
249
|
Salahuddin S, Buzdar SA, Iqbal K, Azam MA, Strigari L. Efficient quality assurance for isocentric stability in stereotactic body radiation therapy using machine learning. Radiol Phys Technol 2024; 17:219-229. [PMID: 38160437 DOI: 10.1007/s12194-023-00768-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024]
Abstract
This study aims to predict isocentric stability for stereotactic body radiation therapy (SBRT) treatments using machine learning (ML), covers the challenges of manual assessment and computational time for quality assurance (QA), and supports medical physicists to enhance accuracy. The isocentric parameters for collimator (C), gantry (G), and table (T) tests were conducted with the RUBY phantom during QA using TrueBeam linac for SBRT. This analysis combined statistical features from the IsoCheck EPID software. Five ML models, including logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB), and support vector machines (SVM), were used to predict the outcome of the QA procedure. 247 Winston-Lutz (WL) tests were collected from 2020 to 2022. In our study, both DT and RF achieved the highest score on test accuracy (Acc. test) ranging from 93.5% to 99.4%, and area under curve (AUC) values from 90 to 100% on three modes (C, G, and T). The precision, recall, and F1 scores indicate the DT model consistently outperforms other ML models in predicting isocenter stability deviation in QA. The QA assessment using ML models can assist error prediction early to avoid potential harm during SBRT and ensure safe and effective patient treatments.
Collapse
Affiliation(s)
- Sana Salahuddin
- Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138, Bologna, Italy.
| | - Saeed Ahmad Buzdar
- Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Khalid Iqbal
- Medical Physics Department Shaukat Khanum Memorial Cancer Hospital & Research Center, Lahore, Pakistan
| | - Muhammad Adeel Azam
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
- Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS), University of Genoa, Genoa, Italy
| | - Lidia Strigari
- Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138, Bologna, Italy
| |
Collapse
|
250
|
Zhang Y, Shen Z, Jiao R. Segment anything model for medical image segmentation: Current applications and future directions. Comput Biol Med 2024; 171:108238. [PMID: 38422961 DOI: 10.1016/j.compbiomed.2024.108238] [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: 08/07/2023] [Revised: 02/06/2024] [Accepted: 02/25/2024] [Indexed: 03/02/2024]
Abstract
Due to the inherent flexibility of prompting, foundation models have emerged as the predominant force in the fields of natural language processing and computer vision. The recent introduction of the Segment Anything Model (SAM) signifies a noteworthy expansion of the prompt-driven paradigm into the domain of image segmentation, thereby introducing a plethora of previously unexplored capabilities. However, the viability of its application to medical image segmentation remains uncertain, given the substantial distinctions between natural and medical images. In this work, we provide a comprehensive overview of recent endeavors aimed at extending the efficacy of SAM to medical image segmentation tasks, encompassing both empirical benchmarking and methodological adaptations. Additionally, we explore potential avenues for future research directions in SAM's role within medical image segmentation. While direct application of SAM to medical image segmentation does not yield satisfactory performance on multi-modal and multi-target medical datasets so far, numerous insights gleaned from these efforts serve as valuable guidance for shaping the trajectory of foundational models in the realm of medical image analysis. To support ongoing research endeavors, we maintain an active repository that contains an up-to-date paper list and a succinct summary of open-source projects at https://github.com/YichiZhang98/SAM4MIS.
Collapse
Affiliation(s)
- Yichi Zhang
- School of Data Science, Fudan University, Shanghai, China.
| | - Zhenrong Shen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Rushi Jiao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|