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Jiang J, Min Seo Choi C, Deasy JO, Rimner A, Thor M, Veeraraghavan H. Artificial intelligence-based automated segmentation and radiotherapy dose mapping for thoracic normal tissues. Phys Imaging Radiat Oncol 2024; 29:100542. [PMID: 38369989 PMCID: PMC10869275 DOI: 10.1016/j.phro.2024.100542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/17/2024] [Accepted: 01/24/2024] [Indexed: 02/20/2024] Open
Abstract
Background and purpose Objective assessment of delivered radiotherapy (RT) to thoracic organs requires fast and accurate deformable dose mapping. The aim of this study was to implement and evaluate an artificial intelligence (AI) deformable image registration (DIR) and organ segmentation-based AI dose mapping (AIDA) applied to the esophagus and the heart. Materials and methods AIDA metrics were calculated for 72 locally advanced non-small cell lung cancer patients treated with concurrent chemo-RT to 60 Gy in 2 Gy fractions in an automated pipeline. The pipeline steps were: (i) automated rigid alignment and cropping of planning CT to week 1 and week 2 cone-beam CT (CBCT) field-of-views, (ii) AI segmentation on CBCTs, and (iii) AI-DIR-based dose mapping to compute dose metrics. AIDA dose metrics were compared to the planned dose and manual contour dose mapping (manual DA). Results AIDA required ∼2 min/patient. Esophagus and heart segmentations were generated with a mean Dice similarity coefficient (DSC) of 0.80±0.15 and 0.94±0.05, a Hausdorff distance at 95th percentile (HD95) of 3.9±3.4 mm and 14.1±8.3 mm, respectively. AIDA heart dose was significantly lower than the planned heart dose (p = 0.04). Larger dose deviations (>=1Gy) were more frequently observed between AIDA and the planned dose (N = 26) than with manual DA (N = 6). Conclusions Rapid estimation of RT dose to thoracic tissues from CBCT is feasible with AIDA. AIDA-derived metrics and segmentations were similar to manual DA, thus motivating the use of AIDA for RT applications.
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Affiliation(s)
- Jue Jiang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Chloe Min Seo Choi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Joseph O. Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Maria Thor
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
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He Y, Cazoulat G, Wu C, Svensson S, Almodovar-Abreu L, Rigaud B, McCollum E, Peterson C, Wooten Z, Rhee DJ, Balter P, Pollard-Larkin J, Cardenas C, Court L, Liao Z, Mohan R, Brock K. Quantifying the Effect of 4-Dimensional Computed Tomography-Based Deformable Dose Accumulation on Representing Radiation Damage for Patients with Locally Advanced Non-Small Cell Lung Cancer Treated with Standard-Fractionated Intensity-Modulated Radiation Therapy. Int J Radiat Oncol Biol Phys 2024; 118:231-241. [PMID: 37552151 DOI: 10.1016/j.ijrobp.2023.07.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 06/04/2023] [Accepted: 07/14/2023] [Indexed: 08/09/2023]
Abstract
PURPOSE The aim of this study was to investigate the dosimetric and clinical effects of 4-dimensional computed tomography (4DCT)-based longitudinal dose accumulation in patients with locally advanced non-small cell lung cancer treated with standard-fractionated intensity-modulated radiation therapy (IMRT). METHODS AND MATERIALS Sixty-seven patients were retrospectively selected from a randomized clinical trial. Their original IMRT plan, planning and verification 4DCTs, and ∼4-month posttreatment follow-up CTs were imported into a commercial treatment planning system. Two deformable image registration algorithms were implemented for dose accumulation, and their accuracies were assessed. The planned and accumulated doses computed using average-intensity images or phase images were compared. At the organ level, mean lung dose and normal-tissue complication probability (NTCP) for grade ≥2 radiation pneumonitis were compared. At the region level, mean dose in lung subsections and the volumetric overlap between isodose intervals were compared. At the voxel level, the accuracy in estimating the delivered dose was compared by evaluating the fit of a dose versus radiographic image density change (IDC) model. The dose-IDC model fit was also compared for subcohorts based on the magnitude of NTCP difference (|ΔNTCP|) between planned and accumulated doses. RESULTS Deformable image registration accuracy was quantified, and the uncertainty was considered for the voxel-level analysis. Compared with planned doses, accumulated doses on average resulted in <1-Gy lung dose increase and <2% NTCP increase (up to 8.2 Gy and 18.8% for a patient, respectively). Volumetric overlap of isodose intervals between the planned and accumulated dose distributions ranged from 0.01 to 0.93. Voxel-level dose-IDC models demonstrated a fit improvement from planned dose to accumulated dose (pseudo-R2 increased 0.0023) and a further improvement for patients with ≥2% |ΔNTCP| versus for patients with <2% |ΔNTCP|. CONCLUSIONS With a relatively large cohort, robust image registrations, multilevel metric comparisons, and radiographic image-based evidence, we demonstrated that dose accumulation more accurately represents the delivered dose and can be especially beneficial for patients with greater longitudinal response.
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Affiliation(s)
- Yulun He
- Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center UTHealth Houston, Houston, Texas; Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Guillaume Cazoulat
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Carol Wu
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | | | - Bastien Rigaud
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Emma McCollum
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Christine Peterson
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Zachary Wooten
- Department of Statistics, Rice University, Houston, Texas
| | - Dong Joo Rhee
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Peter Balter
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Julianne Pollard-Larkin
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Carlos Cardenas
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Laurence Court
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Zhongxing Liao
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Radhe Mohan
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kristy Brock
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
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Boekhoff MR, Lagendijk JJ, L.H.M.W. van Lier A, Mook S, Meijer GJ. Intrafraction motion analysis in online adaptive radiotherapy for esophageal cancer. Phys Imaging Radiat Oncol 2023; 26:100432. [PMID: 37020582 PMCID: PMC10068261 DOI: 10.1016/j.phro.2023.100432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 03/14/2023] [Accepted: 03/15/2023] [Indexed: 03/22/2023] Open
Abstract
Intrafraction motion during magnetic resonance (MR)-guided dose delivery of esophageal cancer tumors was retrospectively analyzed. Deformable image registration of cine-MR series resulted in gross tumor volume motion profiles in all directions, which were subsequently filtered to isolate respiratory and drift motion. A large variability in intrafraction motion patterns was observed between patients. Median 95% peak-to-peak motion was 7.7 (3.7 - 18.3) mm, 2.1 (0.7 - 5.7) mm and 2.4 (0.5 - 5.6) mm in cranio-caudal, left-right and anterior-posterior directions, relatively. Furthermore, intrafraction drift was generally modest (<5mm). A patient specific approach could lead to very small margins (<3mm) for most patients.
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Li Kuan Ong A, Knight K, Panettieri V, Dimmock M, Kit Loong Tuan J, Qi Tan H, Wright C. Predictors for late genitourinary toxicity in men receiving radiotherapy for high-risk prostate cancer using planned and accumulated dose. Phys Imaging Radiat Oncol 2023; 25:100421. [PMID: 36817981 PMCID: PMC9932727 DOI: 10.1016/j.phro.2023.100421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/24/2023] [Accepted: 01/25/2023] [Indexed: 02/05/2023] Open
Abstract
Background and purpose Significant deviations between bladder dose planned (DP) and dose accumulated (DA) have been reported in patients receiving radiotherapy for prostate cancer. This study aimed to construct multivariate analysis (MVA) models to predict the risk of late genitourinary (GU) toxicity with clinical and DP or DA as dose-volume (DV) variables. Materials and methods Bladder DA obtained from 150 patients were compared with DP. MVA models were built from significant clinical and DV variables (p < 0.05) at univariate analysis. Previously developed dose-based-region-of-interest (DB-ROI) metrics using expanded ring structures from the prostate were included. Goodness-of-fit test and calibration plots were generated to determine model performance. Internal validation was accomplished using Bootstrapping. Results Intermediate-high DA (V30-65 Gy and DB-ROI-20-50 mm) for bladder increased compared to DP. However, at the very high dose region, DA (D0.003 cc, V75 Gy, and DB-ROI-5-10 mm) were significantly lower. In MVA, single variable models were generated with odds ratio (OR) < 1. DB-ROI-50 mm was predictive of Grade ≥ 1 GU toxicity for DA and DP (DA and DP; OR: 0.96, p: 0.04) and achieved an area under the receiver operating curve (AUC) of > 0.6. Prostate volume (OR: 0.87, p: 0.01) was significant in predicting Grade 2 GU toxicity with a high AUC of 0.81. Conclusions Higher DA (V30-65 Gy) received by the bladder were not translated to higher late GU toxicity. DB-ROIs demonstrated higher predictive power than standard DV metrics in associating Grade ≥ 1 toxicity. Smaller prostate volumes have a minor protective effect on late Grade 2 GU toxicity.
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Affiliation(s)
- Ashley Li Kuan Ong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore,Department of Medical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia,Corresponding author at: Division of Radiation Oncology, National Cancer Centre Singapore, 11 Hospital Crescent, Singapore 169610, Singapore
| | - Kellie Knight
- Department of Medical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia
| | - Vanessa Panettieri
- Department of Medical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia,Central Clinical School, Monash University, Melbourne, VIC, Australia,Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Mathew Dimmock
- Department of Medical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia,School of Allied Health Professions, Keele University, Staffordshire, UK
| | | | - Hong Qi Tan
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore
| | - Caroline Wright
- Department of Medical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia
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Lee D, Alam S, Jiang J, Cervino L, Hu YC, Zhang P. Seq2Morph: A deep learning deformable image registration algorithm for longitudinal imaging studies and adaptive radiotherapy. Med Phys 2023; 50:970-979. [PMID: 36303270 PMCID: PMC10388694 DOI: 10.1002/mp.16026] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/25/2022] [Accepted: 10/02/2022] [Indexed: 11/08/2022] Open
Abstract
PURPOSE To simultaneously register all the longitudinal images acquired in a radiotherapy course for analyzing patients' anatomy changes for adaptive radiotherapy (ART). METHODS To address the unique needs of ART, we designed Seq2Morph, a novel deep learning-based deformable image registration (DIR) network. Seq2Morph was built upon VoxelMorph which is a general-purpose framework for learning-based image registration. The major upgrades are (1) expansion of inputs to all weekly cone-beam computed tomography (CBCTs) acquired for monitoring treatment responses throughout a radiotherapy course, for registration to their planning CT; (2) incorporation of 3D convolutional long short-term memory between the encoder and decoder of VoxelMorph, to parse the temporal patterns of anatomical changes; and (3) addition of bidirectional pathways to calculate and minimize inverse consistency errors (ICEs). Longitudinal image sets from 50 patients, including a planning CT and 6 weekly CBCTs per patient, were utilized for network training and cross-validation. The outputs were deformation vector fields for all the registration pairs. The loss function was composed of a normalized cross-correlation for image intensity similarity, a DICE for contour similarity, an ICE, and a deformation regularization term. For performance evaluation, DICE and Hausdorff distance (HD) for the manual versus predicted contours of tumor and esophagus on weekly basis were quantified and further compared with other state-of-the-art algorithms, including conventional VoxelMorph and large deformation diffeomorphic metric mapping (LDDMM). RESULTS Visualization of the hidden states of Seq2Morph revealed distinct spatiotemporal anatomy change patterns. Quantitatively, Seq2Morph performed similarly to LDDMM, but significantly outperformed VoxelMorph as measured by GTV DICE: (0.799±0.078, 0.798±0.081, and 0.773±0.078), and 50% HD (mm): (0.80±0.57, 0.88±0.66, and 0.95±0.60). The per-patient inference of Seq2Morph took 22 s, much less than LDDMM (∼30 min). CONCLUSIONS Seq2Morph can provide accurate and fast DIR for longitudinal image studies by exploiting spatial-temporal patterns. It closely matches the clinical workflow and has the potential to serve both online and offline ART.
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Affiliation(s)
- Donghoon Lee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Sadegh Alam
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Jue Jiang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Laura Cervino
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Yu-Chi Hu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA
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Lee D, Hu YC, Kuo L, Alam S, Yorke E, Li A, Rimner A, Zhang P. Deep learning driven predictive treatment planning for adaptive radiotherapy of lung cancer. Radiother Oncol 2022; 169:57-63. [PMID: 35189155 PMCID: PMC9018570 DOI: 10.1016/j.radonc.2022.02.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 01/24/2022] [Accepted: 02/14/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND PURPOSE To develop a novel deep learning algorithm of sequential analysis, Seq2Seq, for predicting weekly anatomical changes of lung tumor and esophagus during definitive radiotherapy, incorporate the potential tumor shrinkage into a predictive treatment planning paradigm, and improve the therapeutic ratio. METHODS AND MATERIALS Seq2Seq starts with the primary tumor and esophagus observed on the planning CT to predict their geometric evolution during radiotherapy on a weekly basis, and subsequently updates the predictions with new snapshots acquired via weekly CBCTs. Seq2Seq is equipped with convolutional long short term memory to analyze the spatial-temporal changes of longitudinal images, trained and validated using a dataset including sixty patients. Predictive plans were optimized according to each weekly prediction and made ready for weekly deployment to mitigate the clinical burden of online weekly replanning. RESULTS Seq2Seq tracks structural changes well: DICE between predicted and actual weekly tumor and esophagus were (0.83 ± 0.10, 0.79 ± 0.14, 0.78 ± 0.12, 0.77 ± 0.12, 0.75 ± 0.12, 0.71 ± 0.17), and (0.72 ± 0.16, 0.73 ± 0.11, 0.75 ± 0.08, 0.74 ± 0.09, 0.72 ± 0.14, 0.71 ± 0.14), respectively, while the average Hausdorff distances were within 2 mm. Evaluating dose to the actual weekly tumor and esophagus, a 4.2 Gy reduction in esophagus mean dose while maintaining 60 Gy tumor coverage was achieved with the predictive weekly plans, compared to the plan optimized using the initial tumor and esophagus alone, primarily due to noticeable tumor shrinkage during radiotherapy. CONCLUSION It is feasible to predict the longitudinal changes of tumor and esophagus with the Seq2Seq, which could lead to improving the efficiency and effectiveness of lung adaptive radiotherapy.
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Alam S, Veeraraghavan H, Tringale K, Amoateng E, Subashi E, Wu AJ, Crane CH, Tyagi N. Inter- and intrafraction motion assessment and accumulated dose quantification of upper gastrointestinal organs during magnetic resonance-guided ablative radiation therapy of pancreas patients. Phys Imaging Radiat Oncol 2022; 21:54-61. [PMID: 35243032 PMCID: PMC8861831 DOI: 10.1016/j.phro.2022.02.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 02/02/2022] [Accepted: 02/11/2022] [Indexed: 12/24/2022] Open
Abstract
Background and purpose Stereotactic body radiation therapy (SBRT) of locally advanced pancreatic cancer (LAPC) is challenging due to significant motion of gastrointestinal (GI) organs. The goal of our study was to quantify inter and intrafraction deformations and dose accumulation of upper GI organs in LAPC patients. Materials and methods Five LAPC patients undergoing five-fraction magnetic resonance-guided radiation therapy (MRgRT) using abdominal compression and daily online plan adaptation to 50 Gy were analyzed. A pre-treatment, verification, and post-treatment MR imaging (MRI) for each of the five fractions (75 total) were used to calculate intra and interfraction motion. The MRIs were registered using Large Deformation Diffeomorphic Metric Mapping (LDDMM) deformable image registration (DIR) method and total dose delivered to stomach_duodenum, small bowel (SB) and large bowel (LB) were accumulated. Deformations were quantified using gradient magnitude and Jacobian integral of the Deformation Vector Fields (DVF). Registration DVFs were geometrically assessed using Dice and 95th percentile Hausdorff distance (HD95) between the deformed and physician’s contours. Accumulated doses were then calculated from the DVFs. Results Median Dice and HD95 were: Stomach_duodenum (0.9, 1.0 mm), SB (0.9, 3.6 mm), and LB (0.9, 2.0 mm). Median (max) interfraction deformation for stomach_duodenum, SB and LB was 6.4 (25.8) mm, 7.9 (40.5) mm and 7.6 (35.9) mm. Median intrafraction deformation was 5.5 (22.6) mm, 8.2 (37.8) mm and 7.2 (26.5) mm. Accumulated doses for two patients exceeded institutional constraints for stomach_duodenum, one of whom experienced Grade1 acute and late abdominal toxicity. Conclusion LDDMM method indicates feasibility to measure large GI motion and accumulate dose. Further validation on larger cohort will allow quantitative dose accumulation to more reliably optimize online MRgRT.
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Affiliation(s)
- Sadegh Alam
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Kathryn Tringale
- Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Emmanuel Amoateng
- Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Ergys Subashi
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Abraham J. Wu
- Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Christopher H. Crane
- Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
- Corresponding author at: Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, 545 East 74th Street, New York, NY 10021, USA.
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Bojechko C, Nelson T, Simpson D, Moiseenko V. Assessment of predictive indicators of acute gastrointestinal toxicity using in vivo transmission images. Med Phys 2021; 48:8152-8162. [PMID: 34664718 DOI: 10.1002/mp.15304] [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: 03/29/2021] [Revised: 09/08/2021] [Accepted: 10/09/2021] [Indexed: 11/12/2022] Open
Abstract
PURPOSE For pelvic and abdominal treatments, excess dose to the bowel can result in acute toxicities. Current estimates of bowel toxicity are based on pre-treatment dose-volume histogram data. However, the actual dose the bowel receives depends on interfraction variations, such as patient anatomy changes. We propose a method to model bowel toxicities, incorporating in vivo patient information using transit electronic portal imaging device (EPID) images. METHODS AND MATERIALS For 63 patients treated to the lower thorax, abdomen, or pelvis on the Varian Halcyon, weekly chart review was performed to obtain incidences of grade 2 or higher toxicity, RTOG scale. Twenty patients presented with acute gastrointestinal (GI) toxicity. All patients were treated with conventional fractionation. For each treatment plan, the absolute volume dose-volume histogram of the bowel was exported and analyzed. Additionally, for each fraction of treatment, in vivo EPID images were collected and used to estimate the change in radiation transmission during the course of treatment. A logistic model was used to test correlations between acute GI toxicity and bowel dosimetric parameters as well as metrics obtained from in vivo image measurements. After performing the fit to the in vivo EPID data, the bootstrap resampling method was used to create confidence intervals. In vivo EPID image metrics from an additional 42 patients treated to the lower thorax, abdomen, or pelvis were used to validate the logistic model fit. RESULTS The incidence of toxicity versus the volume of 40 Gy to the bowel space was fitted with a logistic function, which was superior to an average model (p < 0.0001) and agrees with previously published models. For the initial in vivo EPID data, the incidence of toxicity versus the sum of in vivo transmission measurements showed marginal significance after 15 fractions (p = 0.10) of treatment and a significance of p = 0.038 is seen at the 20th fraction, when compared to an average model. For the validation data set, the logistic model of the in vivo transmission measurement after 20 fractions was superior to the average model (p = 0.043), with the model falling within the 68% confidence interval of the fit of the initial data set. CONCLUSIONS Dose-volume constraints to reduce the incidence of acute GI toxicity have been validated. The presented novel EPID transmission-based metric can be used to identify GI toxicity as patients progress through treatment.
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Affiliation(s)
- Casey Bojechko
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Tyler Nelson
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Daniel Simpson
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Vitali Moiseenko
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
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Lee D, Alam SR, Jiang J, Zhang P, Nadeem S, Hu YC. Deformation driven Seq2Seq longitudinal tumor and organs-at-risk prediction for radiotherapy. Med Phys 2021; 48:4784-4798. [PMID: 34245602 DOI: 10.1002/mp.15075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 05/21/2021] [Accepted: 06/07/2021] [Indexed: 11/06/2022] Open
Abstract
PURPOSE Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ-at-risk (OAR) prediction during treatment. The challenges include tumor inflammation/edema and radiation-induced changes in organ geometry, whereas the clinical requirements demand flexibility in input/output sequence timepoints to update the predictions on rolling basis and the grounding of all predictions in relationship to the pre-treatment imaging information for response and toxicity assessment in adaptive radiotherapy. METHODS To deal with the aforementioned challenges and to comply with the clinical requirements, we present a novel 3D sequence-to-sequence model based on Convolution Long Short-Term Memory (ConvLSTM) that makes use of series of deformation vector fields (DVFs) between individual timepoints and reference pre-treatment/planning CTs to predict future anatomical deformations and changes in gross tumor volume as well as critical OARs. High-quality DVF training data are created by employing hyper-parameter optimization on the subset of the training data with DICE coefficient and mutual information metric. We validated our model on two radiotherapy datasets: a publicly available head-and-neck dataset (28 patients with manually contoured pre-, mid-, and post-treatment CTs), and an internal non-small cell lung cancer dataset (63 patients with manually contoured planning CT and 6 weekly CBCTs). RESULTS The use of DVF representation and skip connections overcomes the blurring issue of ConvLSTM prediction with the traditional image representation. The mean and standard deviation of DICE for predictions of lung GTV at weeks 4, 5, and 6 were 0.83 ± 0.09, 0.82 ± 0.08, and 0.81 ± 0.10, respectively, and for post-treatment ipsilateral and contralateral parotids, were 0.81 ± 0.06 and 0.85 ± 0.02. CONCLUSION We presented a novel DVF-based Seq2Seq model for medical images, leveraging the complete 3D imaging information of a relatively large longitudinal clinical dataset, to carry out longitudinal GTV/OAR predictions for anatomical changes in HN and lung radiotherapy patients, which has potential to improve RT outcomes.
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Affiliation(s)
- Donghoon Lee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sadegh R Alam
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jue Jiang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Saad Nadeem
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yu-Chi Hu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Jiang J, Riyahi Alam S, Chen I, Zhang P, Rimner A, Deasy JO, Veeraraghavan H. Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation. Med Phys 2021; 48:3702-3713. [PMID: 33905558 DOI: 10.1002/mp.14902] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 03/27/2021] [Accepted: 04/06/2021] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Despite the widespread availability of in-treatment room cone beam computed tomography (CBCT) imaging, due to the lack of reliable segmentation methods, CBCT is only used for gross set up corrections in lung radiotherapies. Accurate and reliable auto-segmentation tools could potentiate volumetric response assessment and geometry-guided adaptive radiation therapies. Therefore, we developed a new deep learning CBCT lung tumor segmentation method. METHODS The key idea of our approach called cross-modality educed distillation (CMEDL) is to use magnetic resonance imaging (MRI) to guide a CBCT segmentation network training to extract more informative features during training. We accomplish this by training an end-to-end network comprised of unpaired domain adaptation (UDA) and cross-domain segmentation distillation networks (SDNs) using unpaired CBCT and MRI datasets. UDA approach uses CBCT and MRI that are not aligned and may arise from different sets of patients. The UDA network synthesizes pseudo MRI from CBCT images. The SDN consists of teacher MRI and student CBCT segmentation networks. Feature distillation regularizes the student network to extract CBCT features that match the statistical distribution of MRI features extracted by the teacher network and obtain better differentiation of tumor from background. The UDA network was implemented with a cycleGAN improved with contextual losses separately on Unet and dense fully convolutional segmentation networks (DenseFCN). Performance comparisons were done against CBCT only using 2D and 3D networks. We also compared against an alternative framework that used UDA with MR segmentation network, whereby segmentation was done on the synthesized pseudo MRI representation. All networks were trained with 216 weekly CBCTs and 82 T2-weighted turbo spin echo MRI acquired from different patient cohorts. Validation was done on 20 weekly CBCTs from patients not used in training. Independent testing was done on 38 weekly CBCTs from patients not used in training or validation. Segmentation accuracy was measured using surface Dice similarity coefficient (SDSC) and Hausdroff distance at 95th percentile (HD95) metrics. RESULTS The CMEDL approach significantly improved (p < 0.001) the accuracy of both Unet (SDSC of 0.83 ± 0.08; HD95 of 7.69 ± 7.86 mm) and DenseFCN (SDSC of 0.75 ± 0.13; HD95 of 11.42 ± 9.87 mm) over CBCT only 2DUnet (SDSC of 0.69 ± 0.11; HD95 of 21.70 ± 16.34 mm), 3D Unet (SDSC of 0.72 ± 0.20; HD95 15.01 ± 12.98 mm), and DenseFCN (SDSC of 0.66 ± 0.15; HD95 of 22.15 ± 17.19 mm) networks. The alternate framework using UDA with the MRI network was also more accurate than the CBCT only methods but less accurate the CMEDL approach. CONCLUSIONS Our results demonstrate feasibility of the introduced CMEDL approach to produce reasonably accurate lung cancer segmentation from CBCT images. Further validation on larger datasets is necessary for clinical translation.
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Affiliation(s)
- Jue Jiang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 1006, USA
| | - Sadegh Riyahi Alam
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 1006, USA
| | - Ishita Chen
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 1006, USA
| | - Perry Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 1006, USA
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 1006, USA
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 1006, USA
| | - Harini Veeraraghavan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 1006, USA
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Alam SR, Li T, Zhang P, Zhang SY, Nadeem S. Generalizable cone beam CT esophagus segmentation using physics-based data augmentation. Phys Med Biol 2021; 66:065008. [PMID: 33535199 DOI: 10.1088/1361-6560/abe2eb] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Automated segmentation of the esophagus is critical in image-guided/adaptive radiotherapy of lung cancer to minimize radiation-induced toxicities such as acute esophagitis. We have developed a semantic physics-based data augmentation method for segmenting the esophagus in both planning CT (pCT) and cone beam CT (CBCT) using 3D convolutional neural networks. One hundred and ninety-one cases with their pCTs and CBCTs from four independent datasets were used to train a modified 3D U-Net architecture and a multi-objective loss function specifically designed for soft-tissue organs such as the esophagus. Scatter artifacts and noises were extracted from week-1 CBCTs using a power-law adaptive histogram equalization method and induced to the corresponding pCT were reconstructed using CBCT reconstruction parameters. Moreover, we leveraged physics-based artifact induction in pCTs to drive the esophagus segmentation in real weekly CBCTs. Segmentations were evaluated using the geometric Dice coefficient and Hausdorff distance as well as dosimetrically using mean esophagus dose and D 5cc. Due to the physics-based data augmentation, our model trained just on the synthetic CBCTs was robust and generalizable enough to also produce state-of-the-art results on the pCTs and CBCTs, achieving Dice overlaps of 0.81 and 0.74, respectively. It is concluded that our physics-based data augmentation spans the realistic noise/artifact spectrum across patient CBCT/pCT data and can generalize well across modalities, eventually improving the accuracy of treatment setup and response analysis.
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Affiliation(s)
- Sadegh R Alam
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
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12
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Alam SR, Zhang P, Zhang SY, Chen I, Rimner A, Tyagi N, Hu YC, Lu W, Yorke ED, Deasy JO, Thor M. Early Prediction of Acute Esophagitis for Adaptive Radiation Therapy. Int J Radiat Oncol Biol Phys 2021; 110:883-892. [PMID: 33453309 DOI: 10.1016/j.ijrobp.2021.01.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 12/30/2020] [Accepted: 01/07/2021] [Indexed: 12/25/2022]
Abstract
PURPOSE Acute esophagitis (AE) is a common dose-limiting toxicity in radiation therapy of locally advanced non-small cell lung cancer (LA-NSCLC). We developed an early AE prediction model from weekly accumulated esophagus dose and its associated local volumetric change. METHODS AND MATERIALS Fifty-one patients with LA-NSCLC underwent treatment with intensity modulated radiation therapy to 60 Gy in 2-Gy fractions with concurrent chemotherapy and weekly cone beam computed tomography (CBCT). Twenty-eight patients (55%) developed grade ≥2 AE (≥AE2) at a median of 4 weeks after the start of radiation therapy. For early ≥AE2 prediction, the esophagus on CBCT of the first 2 weeks was deformably registered to the planning computed tomography images, and weekly esophagus dose was accumulated. Week 1-to-week 2 (w1→w2) esophagus volume changes including maximum esophagus expansion (MEex%) and volumes with ≥x% local expansions (VEx%; x = 5, 10, 15) were calculated from the Jacobian map of deformation vector field gradients. Logistic regression model with 5-fold cross-validation was built using combinations of the accumulated mean esophagus doses (MED) and the esophagus change parameters with the lowest P value in univariate analysis. The model was validated on an additional 18 and 11 patients with weekly CBCT and magnetic resonance imaging (MRI), respectively, and compared with models using only planned mean dose (MEDPlan). Performance was assessed using area under the curve (AUC) and Hosmer-Lemeshow test (PHL). RESULTS Univariately, w1→w2 VE10% (P = .004), VE5% (P = .01) and MEex% (P = .02) significantly predicted ≥AE2. A model combining MEDW2 and w1→w2 VE10% had the best performance (AUC = 0.80; PHL = 0.43), whereas the MEDPlan model had a lower accuracy (AUC = 0.67; PHL = 0.26). The combined model also showed high accuracy in the CBCT (AUC = 0.78) and MRI validations (AUC = 0.75). CONCLUSIONS A CBCT-based, cross-validated, and internally validated model on MRI with a combination of accumulated esophagus dose and local volume change from the first 2 weeks of chemotherapy significantly improved AE prediction compared with conventional models using only the planned dose. This model could inform plan adaptation early to lower the risk of esophagitis.
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Affiliation(s)
- Sadegh R Alam
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Pengpeng Zhang
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Si-Yuan Zhang
- Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Ishita Chen
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Neelam Tyagi
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yu-Chi Hu
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Wei Lu
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ellen D Yorke
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Joseph O Deasy
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
| | - Maria Thor
- Department of Medical Physics Memorial Sloan Kettering Cancer Center, New York, New York
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13
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Lee D, Zhang P, Nadeem S, Alam S, Jiang J, Caringi A, Allgood N, Aristophanous M, Mechalakos J, Hu YC. Predictive dose accumulation for HN adaptive radiotherapy. Phys Med Biol 2020; 65:235011. [PMID: 33007769 DOI: 10.1088/1361-6560/abbdb8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
During radiation therapy (RT) of head and neck (HN) cancer, the shape and volume of the parotid glands (PG) may change significantly, resulting in clinically relevant deviations of delivered dose from the planning dose. Early and accurate longitudinal prediction of PG anatomical changes during the RT can be valuable to inform decisions on plan adaptation. We developed a deep neural network for longitudinal predictions using the displacement fields (DFs) between the planning computed tomography (pCT) and weekly cone beam computed tomography (CBCT). Sixty-three HN patients treated with volumetric modulated arc were retrospectively studied. We calculated DFs between pCT and week 1-3 CBCT by B-spline and Demon deformable image registration (DIR). The resultant DFs were subsequently used as input to our novel network to predict the week 4 to 6 DFs for generating predicted weekly PG contours and weekly dose distributions. For evaluation, we measured dice similarity (DICE), and the uncertainty of accumulated dose. Moreover, we compared the detection accuracies of candidates for adaptive radiotherapy (ART) when the trigger criteria were mean dose difference more than 10%, 7.5%, and 5%, respectively. The DICE of ipsilateral/contralateral PG at week 4 to 6 using the prediction model trained with B-spline were 0.81 [Formula: see text] 0.07/0.81 [Formula: see text] 0.04 (week 4), 0.79 [Formula: see text] 0.06/0.81 [Formula: see text] 0.05 (week 5) and 0.78 [Formula: see text] 0.06/0.82 [Formula: see text] (week 6). The DICE with the Demons model were 0.78 [Formula: see text] 0.08/0.82 [Formula: see text] 0.03 (week 4), 0.77 [Formula: see text] 0.07/0.82 [Formula: see text] 0.04 (week 5) and 0.75 [Formula: see text] 0.07/0.82 [Formula: see text] 0.02 (week 6). The dose volume histogram (DVH) analysis with the predicted accumulated dose showed the feasibility of predicting dose uncertainty due to the PG anatomical changes. The AUC of ART candidate detection with our predictive model was over 0.90. In conclusion, the proposed network was able to predict future anatomical changes and dose uncertainty of PGs with clinically acceptable accuracy, and hence can be readily integrated into the ART workflow.
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Affiliation(s)
- Donghoon Lee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center New York, NY, United States of America
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14
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Wang C, R Alam S, Zhang S, Hu YC, Nadeem S, Tyagi N, Rimner A, Lu W, Thor M, Zhang P. Predicting spatial esophageal changes in a multimodal longitudinal imaging study via a convolutional recurrent neural network. Phys Med Biol 2020; 65:235027. [PMID: 33245052 DOI: 10.1088/1361-6560/abb1d9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Acute esophagitis (AE) occurs among a significant number of patients with locally advanced lung cancer treated with radiotherapy. Early prediction of AE, indicated by esophageal wall expansion, is critical, as it can facilitate the redesign of treatment plans to reduce radiation-induced esophageal toxicity in an adaptive radiotherapy (ART) workflow. We have developed a novel machine learning framework to predict the patient-specific spatial presentation of the esophagus in the weeks following treatment, using magnetic resonance imaging (MRI)/ cone-beam CT (CBCT) scans acquired earlier in the 6 week radiotherapy course. Our algorithm captures the response patterns of the esophagus to radiation on a patch level, using a convolutional neural network. A recurrence neural network then parses the evolutionary patterns of the selected features in the time series, and produces a predicted esophagus-or-not label for each individual patch over future weeks. Finally, the esophagus is reconstructed, using all the predicted labels. The algorithm is trained and validated by means of ∼ 250 000 patches taken from MRI scans acquired weekly from a variety of patients, and tested using both weekly MRI and CBCT scans under a leave-one-patient-out scheme. In addition, our approach is externally validated using a publicly available dataset (Hugo 2017). Using the first three weekly scans, the algorithm can predict the condition of the esophagus over the succeeding 3 weeks with a Dice coefficient of 0.83 ± 0.04, estimate esophagus volume highly (0.98), correlated with the actual volume, using our institutional MRI/CBCT data. When evaluated using the external weekly CBCT data, the averaged Dice coefficient is 0.89 ± 0.03. Our novel algorithm may prove useful in enabling radiation oncologists to monitor and detect AE in its early stages, and could potentially play an important role in the ART decision-making process.
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Affiliation(s)
- Chuang Wang
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, United States of America
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15
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Redalen KR, Thorwarth D. Future directions on the merge of quantitative imaging and artificial intelligence in radiation oncology. Phys Imaging Radiat Oncol 2020; 15:44-45. [PMID: 33458325 PMCID: PMC7807640 DOI: 10.1016/j.phro.2020.06.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
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16
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Casares-Magaz O, Moiseenko V, Witte M, Rancati T, Muren LP. Towards spatial representations of dose distributions to predict risk of normal tissue morbidity after radiotherapy. Phys Imaging Radiat Oncol 2020; 15:105-107. [PMID: 33458334 PMCID: PMC7807547 DOI: 10.1016/j.phro.2020.08.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
- Oscar Casares-Magaz
- Department of Medical Physics - Oncology, Aarhus University/Aarhus University Hospital, Aarhus, Denmark
| | - Vitali Moiseenko
- Department of Radiation Medicine and Applied Science, University of California San Diego, La Jolla, CA, United States
| | - Marnix Witte
- Cluster Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Tiziana Rancati
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Ludvig P Muren
- Department of Medical Physics - Oncology, Aarhus University/Aarhus University Hospital, Aarhus, Denmark
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