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Thummerer A, Seller Oria C, Zaffino P, Meijers A, Guterres Marmitt G, Wijsman R, Seco J, Langendijk JA, Knopf AC, Spadea MF, Both S. Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer. Med Phys 2021; 48:7673-7684. [PMID: 34725829 PMCID: PMC9299115 DOI: 10.1002/mp.15333] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/22/2021] [Accepted: 10/27/2021] [Indexed: 01/14/2023] Open
Abstract
Purpose Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone‐beam CT (CBCT) can provide these daily images, but x‐ray scattering limits CBCT‐image quality and hampers dose calculation accuracy. The purpose of this study was to generate CBCT‐based synthetic CTs using a deep convolutional neural network (DCNN) and investigate image quality and clinical suitability for proton dose calculations in lung cancer patients. Methods A dataset of 33 thoracic cancer patients, containing CBCTs, same‐day repeat CTs (rCT), planning‐CTs (pCTs), and clinical proton treatment plans, was used to train and evaluate a DCNN with and without a pCT‐based correction method. Mean absolute error (MAE), mean error (ME), peak signal‐to‐noise ratio, and structural similarity were used to quantify image quality. The evaluation of clinical suitability was based on recalculation of clinical proton treatment plans. Gamma pass ratios, mean dose to target volumes and organs at risk, and normal tissue complication probabilities (NTCP) were calculated. Furthermore, proton radiography simulations were performed to assess the HU‐accuracy of sCTs in terms of range errors. Results On average, sCTs without correction resulted in a MAE of 34 ± 6 HU and ME of 4 ± 8 HU. The correction reduced the MAE to 31 ± 4HU (ME to 2 ± 4HU). Average 3%/3 mm gamma pass ratios increased from 93.7% to 96.8%, when the correction was applied. The patient specific correction reduced mean proton range errors from 1.5 to 1.1 mm. Relative mean target dose differences between sCTs and rCT were below ± 0.5% for all patients and both synthetic CTs (with/without correction). NTCP values showed high agreement between sCTs and rCT (<2%). Conclusion CBCT‐based sCTs can enable accurate proton dose calculations for APT of lung cancer patients. The patient specific correction method increased the image quality and dosimetric accuracy but had only a limited influence on clinically relevant parameters.
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Affiliation(s)
- Adrian Thummerer
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Carmen Seller Oria
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Paolo Zaffino
- Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy
| | - Arturs Meijers
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Gabriel Guterres Marmitt
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Robin Wijsman
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Joao Seco
- Department of Biomedical Physics in Radiation Oncology, Deutsches Krebsfoschungszentrum (DKFZ), Heidelberg, Germany.,Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Johannes Albertus Langendijk
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Antje-Christin Knopf
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Department I of Internal Medicine, Center for Integrated Oncology Cologne, University Hospital of Cologne, Cologne, Germany
| | - Maria Francesca Spadea
- Department of Experimental and Clinical Medicine, Magna Graecia University, Catanzaro, Italy
| | - Stefan Both
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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Bäumer C, Bäcker CM, Conti M, Fragoso Costa P, Herrmann K, Kazek SL, Jentzen W, Panin V, Siegel S, Teimoorisichani M, Wulff J, Timmermann B. Can a ToF-PET photon attenuation reconstruction test stopping-power estimations in proton therapy? A phantom study. Phys Med Biol 2021; 66. [PMID: 34534971 DOI: 10.1088/1361-6560/ac27b5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/13/2021] [Indexed: 01/19/2023]
Abstract
Objective. The aim of the phantom study was to validate and to improve the computed tomography (CT) images used for the dose computation in proton therapy. It was tested, if the joint reconstruction of activity and attenuation images of time-of-flight PET (ToF-PET) scans could improve the estimation of the proton stopping-power.Approach. The attenuation images, i.e. CT images with 511 keV gamma-rays (γCTs), were jointly reconstructed with activity maps from ToF-PET scans. Theβ+activity was produced with FDG and in a separate experiment with proton-induced radioactivation. The phantoms contained slabs of tissue substitutes. The use of theγCTs for the prediction of the beam stopping in proton therapy was based on a linear relationship between theγ-ray attenuation, the electron density, and the stopping-power of fast protons.Main results. The FDG based experiment showed sufficient linearity to detect a bias of bony tissue in the heuristic look-up table, which maps between x-ray CT images and proton stopping-power.γCTs can be used for dose computation, if the electron density of one type of tissue is provided as a scaling factor. A possible limitation is imposed by the spatial resolution, which is inferior by a factor of 2.5 compared to the one of the x-ray CT.γCTs can also be derived from off-line, ToF-PET scans subsequent to the application of a proton field with a hypofractionated dose level.Significance. γCTs are a viable tool to support the estimation of proton stopping with radiotracer-based ToF-PET data from diagnosis or staging. This could be of higher potential relevance in MRI-guided proton therapy.γCTs could form an alternative approach to make use of in-beam or off-line PET scans of proton-inducedβ+activity with possible clinical limitations due to the low number of coincidence counts.
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Affiliation(s)
- C Bäumer
- West German Proton Therapy Centre Essen, Am Mühlenbach 1, Essen, Germany.,University Hospital Essen, Hufelandstr. 55, Essen, Germany.,West German Cancer Center (WTZ), Hufelandstr. 55, Essen, Germany.,German Cancer Consortium (DKTK), Heidelberg, Germany.,TU Dortmund University, Department of Physics, Otto-Hahn-Str. 4a, Dortmund, Germany
| | - C M Bäcker
- West German Proton Therapy Centre Essen, Am Mühlenbach 1, Essen, Germany.,University Hospital Essen, Hufelandstr. 55, Essen, Germany.,West German Cancer Center (WTZ), Hufelandstr. 55, Essen, Germany.,TU Dortmund University, Department of Physics, Otto-Hahn-Str. 4a, Dortmund, Germany
| | - M Conti
- Siemens Medical Solutions USA Inc., Knoxville, Tennessee, United States of America
| | - P Fragoso Costa
- University Hospital Essen, Hufelandstr. 55, Essen, Germany.,University Hospital Essen, Clinic for Nuclear Medicine, Hufelandstr. 55, Essen, Germany
| | - K Herrmann
- University Hospital Essen, Hufelandstr. 55, Essen, Germany.,German Cancer Consortium (DKTK), Heidelberg, Germany.,University Hospital Essen, Clinic for Nuclear Medicine, Hufelandstr. 55, Essen, Germany
| | - S L Kazek
- University Hospital Essen, Hufelandstr. 55, Essen, Germany.,University Hospital Essen, Clinic for Nuclear Medicine, Hufelandstr. 55, Essen, Germany
| | - W Jentzen
- University Hospital Essen, Hufelandstr. 55, Essen, Germany.,University Hospital Essen, Clinic for Nuclear Medicine, Hufelandstr. 55, Essen, Germany
| | - V Panin
- Siemens Medical Solutions USA Inc., Knoxville, Tennessee, United States of America
| | - S Siegel
- Siemens Medical Solutions USA Inc., Knoxville, Tennessee, United States of America
| | - M Teimoorisichani
- Siemens Medical Solutions USA Inc., Knoxville, Tennessee, United States of America
| | - J Wulff
- West German Proton Therapy Centre Essen, Am Mühlenbach 1, Essen, Germany.,University Hospital Essen, Hufelandstr. 55, Essen, Germany.,West German Cancer Center (WTZ), Hufelandstr. 55, Essen, Germany
| | - B Timmermann
- West German Proton Therapy Centre Essen, Am Mühlenbach 1, Essen, Germany.,University Hospital Essen, Hufelandstr. 55, Essen, Germany.,West German Cancer Center (WTZ), Hufelandstr. 55, Essen, Germany.,German Cancer Consortium (DKTK), Heidelberg, Germany.,University Hospital Essen, Department of Particle Therapy, Hufelandstr. 55, Essen, Germany
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53
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Borm KJ, Junker Y, Düsberg M, Devečka M, Münch S, Dapper H, Oechsner M, Combs SE. Impact of CBCT frequency on target coverage and dose to the organs at risk in adjuvant breast cancer radiotherapy. Sci Rep 2021; 11:17378. [PMID: 34462489 PMCID: PMC8405651 DOI: 10.1038/s41598-021-96836-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 08/17/2021] [Indexed: 11/16/2022] Open
Abstract
The current study aims to assess the effect of cone beam computed tomography (CBCT) frequency during adjuvant breast cancer radiotherapy with simultaneous integrated boost (SIB) on target volume coverage and dose to the organs at risk (OAR). 50 breast cancer patients receiving either non-hypofractionated or hypofractionated radiotherapy after lumpectomy including a SIB to the tumor bed were selected for this study. All patients were treated in volumetric modulated arc therapy (VMAT) technique and underwent daily CBCT imaging. In order to estimate the delivered dose during the treatment, the applied fraction doses were recalculated on daily CBCT scans and accumulated using deformable image registration. Based on a total of 2440 dose recalculations, dose coverage in the clinical target volumes (CTV) and OAR was compared depending on the CBCT frequency. The estimated delivered dose (V95%) for breast-CTV and SIB-CTV was significantly lower than the planned dose distribution, irrespective of the CBCT-frequency. Between daily CBCT and CBCT on alternate days, no significant dose differences were found regarding V95% for both, breast-CTV and SIB-CTV. Dose distribution in the OAR was similar for both imaging protocols. Weekly CBCT though led to a significant decrease in dose coverage compared to daily CBCT and a small but significant dose increase in most OAR. Daily CBCT imaging might not be necessary to ensure adequate dose coverage in the target volumes while efficiently sparing the OAR during adjuvant breast cancer radiotherapy with SIB.
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Affiliation(s)
- Kai J Borm
- Department of Radiation Oncology, Klinikum Rechts Der Isar, Medical School, Technical University Munich, Ismaningerstraße 22, 81675, Munich, Germany.
| | - Yannis Junker
- Department of Radiation Oncology, Klinikum Rechts Der Isar, Medical School, Technical University Munich, Ismaningerstraße 22, 81675, Munich, Germany
| | - Mathias Düsberg
- Department of Radiation Oncology, Klinikum Rechts Der Isar, Medical School, Technical University Munich, Ismaningerstraße 22, 81675, Munich, Germany
| | - Michal Devečka
- Department of Radiation Oncology, Klinikum Rechts Der Isar, Medical School, Technical University Munich, Ismaningerstraße 22, 81675, Munich, Germany
| | - Stefan Münch
- Department of Radiation Oncology, Klinikum Rechts Der Isar, Medical School, Technical University Munich, Ismaningerstraße 22, 81675, Munich, Germany
| | - Hendrik Dapper
- Department of Radiation Oncology, Klinikum Rechts Der Isar, Medical School, Technical University Munich, Ismaningerstraße 22, 81675, Munich, Germany
| | - Markus Oechsner
- Department of Radiation Oncology, Klinikum Rechts Der Isar, Medical School, Technical University Munich, Ismaningerstraße 22, 81675, Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum Rechts Der Isar, Medical School, Technical University Munich, Ismaningerstraße 22, 81675, Munich, Germany.,Deutsches Konsortium Für Translationale Krebsforschung (DKTK)-Partner Site Munich, Munich, Germany.,Institute of Radiation Medicine, Helmholtzzentrum München, Munich, Germany
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Yan J, Zhu J, Chen K, Yu L, Zhang F. Intra-fractional dosimetric analysis of image-guided intracavitary brachytherapy of cervical cancer. Radiat Oncol 2021; 16:144. [PMID: 34348758 PMCID: PMC8335895 DOI: 10.1186/s13014-021-01870-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 07/25/2021] [Indexed: 11/14/2022] Open
Abstract
Background To assess the intra-fractional dosimetric variations of image-guided brachytherapy of cervical cancer. Methods A total of 38 fractions (9 patients) undergoing brachytherapy for cervical cancer underwent a CT scanning for treatment planning (planning CT) and a Cone-beam CT (CBCT) scanning immediately prior to delivery (pre-delivery CBCT). The variations of volumes as well as the dosimetric impact from treatment planning to delivery (intra-application) were evaluated. The dose volume histogram parameters including volume, D90 of high-risk clinical target volume (HRCTV) and D2cc of organs at risk (OARs) were recorded. Results The relative differences (mean ± 1SD) of the volume and D90 HRCTV across the two scans were − 2.0 ± 3.3% and − 1.2 ± 4.5%, respectively. The variations of D2cc for bladder, rectum, sigmoid and small intestine are − 0.6 ± 17.1%, 9.3 ± 14.6%, 7.2% ± 20.5% and 1.5 ± 12.6%, respectively. Most of them are statistically nonsignificant except the D2cc for rectum, which showed a significant increase (P = 0.001). Using 5% and 10% uncertainty of physical dose for HRCTV at a 6 Gy × 5 high-dose-rate schedule, the possibility of total equivalent doses in 2 Gy fractions (EQD2) lower than 85 Gy is close to 0% and 3%, respectively. Performing similar simulation at 15% and 20% uncertainty of a 4 Gy physical dose for OARs, the possibility of total EQD2 dose exceeding 75 Gy is about 70%. Less than 1% of the total EQD2 of OARs would exceed 80 Gy. Conclusions Average intra-fractional dosimetric variation of HRCTV was small in an interval of less than 1 h, and the possibility of total EQD2 exceeding 85 Gy is higher than 97%. The intra-fractional dosimetric variations of OARs might result in an overdose for OARs in a single fraction or the whole treatment. It is necessary to detect unfavorable anatomical changes by re-imaging and take interventions to minimize applied doses and reduce the risk of complications. Supplementary Information The online version contains supplementary material available at 10.1186/s13014-021-01870-x.
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Affiliation(s)
- Junfang Yan
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academe of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Jiawei Zhu
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academe of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Kai Chen
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, Guangdong, China
| | - Lang Yu
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academe of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Fuquan Zhang
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academe of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
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Iliadou V, Economopoulos TL, Karaiskos P, Kouloulias V, Platoni K, Matsopoulos GK. Deformable image registration to assist clinical decision for radiotherapy treatment adaptation for head and neck cancer patients. Biomed Phys Eng Express 2021; 7. [PMID: 34265756 DOI: 10.1088/2057-1976/ac14d1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 07/15/2021] [Indexed: 11/12/2022]
Abstract
Head and neck (H&N) cancer patients often present anatomical and geometrical changes in tumors and organs at risk (OARs) during radiotherapy treatment. These changes may result in the need to adapt the existing treatment planning, using an expert's subjective opinion, for offline adaptive radiotherapy and a new treatment planning before each treatment, for online adaptive radiotherapy. In the present study, a fast methodology is proposed to assist in planning adaptation clinical decision using tumor and parotid glands percentage volume changes during treatment. The proposed approach was applied to 40 Η&Ν cases, with one planning Computed Tomography (pCT) image and CBCT scans for 6 weeks of treatment per case. Deformable registration was used for each patient's pCT image alignment to its weekly CBCT. The calculated transformations were used to align each patient's anatomical structures to the weekly anatomy. Clinical target volume (CTV) and parotid gland volume percentage changes were calculated in each case. The accuracy of the achieved image alignment was validated qualitatively and quantitatively. Furthermore, statistical analysis was performed to test if there is a statistically significant correlation between CTV and parotid glands volume percentage changes. Average MDA for CTV and parotid glands between corresponding structures defined by an expert in CBCTs and automatically calculated through registration was 1.4 ± 0.1 mm and 1.5 ± 0.1 mm, respectively. The mean registration time of the first CBCT image registration for 40 cases was lower than 3.4 min. Five patients show more than 20% tumor volume change. Six patients show more than 30% parotid glands volume change. Ten out of 40 patients proposed for planning adaptation. All the statistical tests performed showed no correlation between CTV/parotid glands percentage volume changes. The aim to assist in clinical decision making on a fast and automatic way was achieved using the proposed methodology, thereby reducing workload in clinical practice.
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Affiliation(s)
- Vasiliki Iliadou
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Theodore L Economopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Pantelis Karaiskos
- Medical Physics Laboratory, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Vasileios Kouloulias
- 2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, Athens, Greece
| | - Kalliopi Platoni
- 2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, Athens, Greece
| | - George K Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
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Jiang X, Fang C, Hu P, Cui H, Zhu L, Yang Y. Fast and effective single-scan dual-energy cone-beam CT reconstruction and decomposition denoising based on dual-energy vectorization. Med Phys 2021; 48:4843-4856. [PMID: 34289129 DOI: 10.1002/mp.15117] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/11/2021] [Accepted: 07/02/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Flat-panel detector (FPD) based dual-energy cone-beam computed tomography (DE-CBCT) is a promising imaging technique for dedicated clinical applications. In this paper, we proposed a fully analytical method for fast and effective single-scan DE-CBCT image reconstruction and decomposition. METHODS A rotatable Mo filter was inserted between an x-ray source and imaged object to alternately produce low and high-energy x-ray spectra. First, filtered-backprojection (FBP) method was applied on down-sampled projections to reconstruct low and high-energy images. Then, the two images were converted into a vectorized form represented with an amplitude and an argument image. Using amplitude image as a guide, a joint bilateral filter was applied to denoise the argument image. Then, high-quality dual-energy images were recovered from the amplitude image and the denoised argument image. Finally, the recovered dual-energy images were further used for low-noise material decomposition and electron density synthesis. Imaging was conducted on a Catphan® 600 phantom and an anthropomorphic head phantom. The proposed method was evaluated via comparison with the traditional two-scan method and a commonly used filtering method (HYPR-LR). RESULTS On the Catphan® 600 phantom, the proposed method successfully reduced streaking artifacts and preserved spatial resolution and noise-power-spectrum (NPS) pattern. In the electron density image, the proposed method increased contrast-to-noise ratio (CNR) by more than 2.5 times and achieved <1.2% error for electron density values. On the anthropomorphic head phantom, the proposed method greatly improved the soft-tissue contrast and the fine detail differentiation ability. In the selected ROIs on different human tissues, the differences between the CT number obtained by the proposed method and that by the two-scan method were less than 4 HU. In the material images, the proposed method suppressed noise by over 75.5% compared with two-scan results, and by over 40.4% compared with HYPR-LR results. Implementation of the whole algorithm took 44.5 s for volumetric imaging, including projection preprocessing, FBP reconstruction, joint bilateral filtering, and material decomposition. CONCLUSIONS Using down-sampled projections in single-scan DE-CBCT, the proposed method could effectively and efficiently produce high-quality DE-CBCT images and low-noise material decomposition images. This method demonstrated superior performance on spatial resolution enhancement, NPS preservation, noise reduction, and electron density accuracy, indicating better prospect in material differentiation and dose calculation.
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Affiliation(s)
- Xiao Jiang
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China
| | - Chengyijue Fang
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China
| | - Panpan Hu
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China.,Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Hehe Cui
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China
| | - Lei Zhu
- Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, Anhui, China
| | - Yidong Yang
- Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.,School of Physical Sciences & Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui, China
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57
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Dong G, Zhang C, Liang X, Deng L, Zhu Y, Zhu X, Zhou X, Song L, Zhao X, Xie Y. A Deep Unsupervised Learning Model for Artifact Correction of Pelvis Cone-Beam CT. Front Oncol 2021; 11:686875. [PMID: 34350115 PMCID: PMC8327750 DOI: 10.3389/fonc.2021.686875] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/25/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose In recent years, cone-beam computed tomography (CBCT) is increasingly used in adaptive radiation therapy (ART). However, compared with planning computed tomography (PCT), CBCT image has much more noise and imaging artifacts. Therefore, it is necessary to improve the image quality and HU accuracy of CBCT. In this study, we developed an unsupervised deep learning network (CycleGAN) model to calibrate CBCT images for the pelvis to extend potential clinical applications in CBCT-guided ART. Methods To train CycleGAN to generate synthetic PCT (sPCT), we used CBCT and PCT images as inputs from 49 patients with unpaired data. Additional deformed PCT (dPCT) images attained as CBCT after deformable registration are utilized as the ground truth before evaluation. The trained uncorrected CBCT images are converted into sPCT images, and the obtained sPCT images have the characteristics of PCT images while keeping the anatomical structure of CBCT images unchanged. To demonstrate the effectiveness of the proposed CycleGAN, we use additional nine independent patients for testing. Results We compared the sPCT with dPCT images as the ground truth. The average mean absolute error (MAE) of the whole image on testing data decreased from 49.96 ± 7.21HU to 14.6 ± 2.39HU, the average MAE of fat and muscle ROIs decreased from 60.23 ± 7.3HU to 16.94 ± 7.5HU, and from 53.16 ± 9.1HU to 13.03 ± 2.63HU respectively. Conclusion We developed an unsupervised learning method to generate high-quality corrected CBCT images (sPCT). Through further evaluation and clinical implementation, it can replace CBCT in ART.
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Affiliation(s)
- Guoya Dong
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China.,Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin, China
| | - Chenglong Zhang
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China.,Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin, China.,Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xiaokun Liang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Lei Deng
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yulin Zhu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xuanyu Zhu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Xuanru Zhou
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Liming Song
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xiang Zhao
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yaoqin Xie
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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Nikolov S, Blackwell S, Zverovitch A, Mendes R, Livne M, De Fauw J, Patel Y, Meyer C, Askham H, Romera-Paredes B, Kelly C, Karthikesalingam A, Chu C, Carnell D, Boon C, D'Souza D, Moinuddin SA, Garie B, McQuinlan Y, Ireland S, Hampton K, Fuller K, Montgomery H, Rees G, Suleyman M, Back T, Hughes CO, Ledsam JR, Ronneberger O. Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study. J Med Internet Res 2021; 23:e26151. [PMID: 34255661 PMCID: PMC8314151 DOI: 10.2196/26151] [Citation(s) in RCA: 118] [Impact Index Per Article: 39.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/10/2021] [Accepted: 04/30/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. OBJECTIVE Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. METHODS The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. RESULTS We demonstrated the model's clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model's generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. CONCLUSIONS Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.
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Affiliation(s)
| | | | | | - Ruheena Mendes
- University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | | | | | | | | | | | | | | | | | | | - Dawn Carnell
- University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Cheng Boon
- Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Derek D'Souza
- University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Syed Ali Moinuddin
- University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | | | | | | | | | | | | | - Geraint Rees
- University College London, London, United Kingdom
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McKenzie EM, Tong N, Ruan D, Cao M, Chin RK, Sheng K. Using neural networks to extend cropped medical images for deformable registration among images with differing scan extents. Med Phys 2021; 48:4459-4471. [PMID: 34101198 DOI: 10.1002/mp.15039] [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: 03/24/2021] [Revised: 05/07/2021] [Accepted: 05/27/2021] [Indexed: 11/11/2022] Open
Abstract
PURPOSE Missing or discrepant imaging volume is a common challenge in deformable image registration (DIR). To minimize the adverse impact, we train a neural network to synthesize cropped portions of head and neck CT's and then test its use in DIR. METHODS Using a training dataset of 409 head and neck CT's, we trained a generative adversarial network to take in a cropped 3D image and output an image with synthesized anatomy in the cropped region. The network used a 3D U-Net generator along with Visual Geometry Group (VGG) deep feature losses. To test our technique, for each of the 53 test volumes, we used Elastix to deformably register combinations of a randomly cropped, full, and synthetically full volume to a single cropped, full, and synthetically full target volume. We additionally tested our method's robustness to crop extent by progressively increasing the amount of cropping, synthesizing the missing anatomy using our network, and then performing the same registration combinations. Registration performance was measured using 95% Hausdorff distance across 16 contours. RESULTS We successfully trained a network to synthesize missing anatomy in superiorly and inferiorly cropped images. The network can estimate large regions in an incomplete image, far from the cropping boundary. Registration using our estimated full images was not significantly different from registration using the original full images. The average contour matching error for full image registration was 9.9 mm, whereas our method was 11.6, 12.1, and 13.6 mm for synthesized-to-full, full-to-synthesized, and synthesized-to-synthesized registrations, respectively. In comparison, registration using the cropped images had errors of 31.7 mm and higher. Plotting the registered image contour error as a function of initial preregistered error shows that our method is robust to registration difficulty. Synthesized-to-full registration was statistically independent of cropping extent up to 18.7 cm superiorly cropped. Synthesized-to-synthesized registration was nearly independent, with a -0.04 mm of change in average contour error for every additional millimeter of cropping. CONCLUSIONS Different or inadequate in scan extent is a major cause of DIR inaccuracies. We address this challenge by training a neural network to complete cropped 3D images. We show that with image completion, the source of DIR inaccuracy is eliminated, and the method is robust to varying crop extent.
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Affiliation(s)
- Elizabeth M McKenzie
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Nuo Tong
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Dan Ruan
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Minsong Cao
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Robert K Chin
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Ke Sheng
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
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Washio H, Ohira S, Funama Y, Ueda Y, Isono M, Inui S, Miyazaki M, Teshima T. Accuracy of dose calculation on iterative CBCT for head and neck radiotherapy. Phys Med 2021; 86:106-112. [PMID: 34102546 DOI: 10.1016/j.ejmp.2021.05.027] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 05/15/2021] [Accepted: 05/19/2021] [Indexed: 10/21/2022] Open
Abstract
PURPOSE To evaluate the feasibility of the use of iterative cone-beam computed tomography (CBCT) for dose calculation in the head and neck region. METHODS This study includes phantom and clinical studies. All acquired CBCT images were reconstructed with Feldkamp-Davis-Kress algorithm-based CBCT (FDK-CBCT) and iterative CBCT (iCBCT) algorithm. The Hounsfield unit (HU) consistency between the head and body phantoms was determined in both reconstruction techniques. Volumetric modulated arc therapy (VMAT) plans were generated for 16 head and neck patients on a planning CT scan, and the doses were recalculated on FDK-CBCT and iCBCT with Anisotropic Analytical Algorithm (AAA) and Acuros XB (AXB). As a comparison of the accuracy of dose calculations, the absolute dosimetric difference and 1%/1 mm gamma passing rate analysis were analyzed. RESULTS The difference in the mean HU values between the head and body phantoms was larger for FDK-CBCT (max value: 449.1 HU) than iCBCT (260.0 HU). The median dosimetric difference from the planning CT were <1.0% for both FDK-CBCT and iCBCT but smaller differences were found with iCBCT (planning target volume D50%: 0.38% (0.15-0.59%) for FDK-CBCT, 0.28% (0.13-0.49%) for iCBCT, AAA; 0.14% (0.04-0.19%) for FDK-CBCT, 0.07% (0.02-0.20%) for iCBCT). The mean gamma passing rate was significantly better in iCBCT than FDK-CBCT (AAA: 98.7% for FDK-CBCT, 99.4% for iCBCT; AXB: 96.8% for FDK_CBCT, 97.5% for iCBCT). CONCLUSION The iCBCT-based dose calculation in VMAT for head and neck cancer was accurate compared to FDK-CBCT.
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Affiliation(s)
- Hayate Washio
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan; Graduate School of Health Sciences, Kumamoto University, Kumamoto, Japan
| | - Shingo Ohira
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan.
| | - Yoshinori Funama
- Department of Medical Radiation Sciences, Faculty of Life Science, Kumamoto University, Kumamoto, Japan
| | - Yoshihiro Ueda
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Masaru Isono
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Shoki Inui
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan; Department of Medical Physics and Engineering, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Masayoshi Miyazaki
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Teruki Teshima
- Department of Radiation Oncology, Osaka International Cancer Institute, Osaka, Japan
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Hussein M, Akintonde A, McClelland J, Speight R, Clark CH. Clinical use, challenges, and barriers to implementation of deformable image registration in radiotherapy - the need for guidance and QA tools. Br J Radiol 2021; 94:20210001. [PMID: 33882253 PMCID: PMC8173691 DOI: 10.1259/bjr.20210001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 04/06/2021] [Accepted: 04/12/2021] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE The aim of this study was to evaluate the current status of the clinical use of deformable image registration (DIR) in radiotherapy and to gain an understanding of the challenges faced by centres in clinical implementation of DIR, including commissioning and quality assurance (QA), and to determine the barriers faced. The goal was to inform whether additional guidance and QA tools were needed. METHODS A survey focussed on clinical use, metrics used, how centres would like to use DIR in the future and challenges faced, was designed and sent to 71 radiotherapy centres in the UK. Data were gathered specifically on which centres we using DIR clinically, which applications were being used, what commissioning and QA tests were performed, and what barriers were preventing the integration of DIR into the clinical workflow. Centres that did not use DIR clinically were encouraged to fill in the survey and were asked if they have any future plans and in what timescale. RESULTS 51 out of 71 (70%) radiotherapy centres responded. 47 centres reported access to a commercial software that could perform DIR. 20 centres already used DIR clinically, and 22 centres had plans to implement an application of DIR within 3 years of the survey. The most common clinical application of DIR was to propagate contours from one scan to another (19 centres). In each of the applications, the types of commissioning and QA tests performed varied depending on the type of application and between centres. Some of the key barriers were determining when a DIR was satisfactory including which metrics to use, and lack of resources. CONCLUSION The survey results highlighted that there is a need for additional guidelines, training, better tools for commissioning DIR software and for the QA of registration results, which should include developing or recommending which quantitative metrics to use. ADVANCES IN KNOWLEDGE This survey has given a useful picture of the clinical use and lack of use of DIR in UK radiotherapy centres. The survey provided useful insight into how centres commission and QA DIR applications, especially the variability among centres. It was also possible to highlight key barriers to implementation and determine factors that may help overcome this which include the need for additional guidance specific to different applications, better tools and metrics.
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Affiliation(s)
- Mohammad Hussein
- Metrology for Medical Physics Centre, National Physical Laboratory, Teddington, UK
| | - Adeyemi Akintonde
- Centre for Medical Image Computing, University College London, London, UK
| | - Jamie McClelland
- Centre for Medical Image Computing, University College London, London, UK
| | - Richard Speight
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
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Chen W, Li Y, Yuan N, Qi J, Dyer BA, Sensoy L, Benedict SH, Shang L, Rao S, Rong Y. Clinical Enhancement in AI-Based Post-processed Fast-Scan Low-Dose CBCT for Head and Neck Adaptive Radiotherapy. Front Artif Intell 2021; 3:614384. [PMID: 33733226 PMCID: PMC7904899 DOI: 10.3389/frai.2020.614384] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 12/28/2020] [Indexed: 11/13/2022] Open
Abstract
Purpose: To assess image quality and uncertainty in organ-at-risk segmentation on cone beam computed tomography (CBCT) enhanced by deep-learning convolutional neural network (DCNN) for head and neck cancer. Methods: An in-house DCNN was trained using forty post-operative head and neck cancer patients with their planning CT and first-fraction CBCT images. Additional fifteen patients with repeat simulation CT (rCT) and CBCT scan taken on the same day (oCBCT) were used for validation and clinical utility assessment. Enhanced CBCT (eCBCT) images were generated from the oCBCT using the in-house DCNN. Quantitative imaging quality improvement was evaluated using HU accuracy, signal-to-noise-ratio (SNR), and structural similarity index measure (SSIM). Organs-at-risk (OARs) were delineated on o/eCBCT and compared with manual structures on the same day rCT. Contour accuracy was assessed using dice similarity coefficient (DSC), Hausdorff distance (HD), and center of mass (COM) displacement. Qualitative assessment of users’ confidence in manual segmenting OARs was performed on both eCBCT and oCBCT by visual scoring. Results: eCBCT organs-at-risk had significant improvement on mean pixel values, SNR (p < 0.05), and SSIM (p < 0.05) compared to oCBCT images. Mean DSC of eCBCT-to-rCT (0.83 ± 0.06) was higher than oCBCT-to-rCT (0.70 ± 0.13). Improvement was observed for mean HD of eCBCT-to-rCT (0.42 ± 0.13 cm) vs. oCBCT-to-rCT (0.72 ± 0.25 cm). Mean COM was less for eCBCT-to-rCT (0.28 ± 0.19 cm) comparing to oCBCT-to-rCT (0.44 ± 0.22 cm). Visual scores showed OAR segmentation was more accessible on eCBCT than oCBCT images. Conclusion: DCNN improved fast-scan low-dose CBCT in terms of the HU accuracy, image contrast, and OAR delineation accuracy, presenting potential of eCBCT for adaptive radiotherapy.
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Affiliation(s)
- Wen Chen
- Department of Radiation Oncology, Xiangya Hospital, Central South University, Changsha, China.,Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States
| | - Yimin Li
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States.,Department of Radiation Oncology, Xiamen Cancer Center, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Nimu Yuan
- Department of Biomedical Engineering, University of California, Davis, CA, United States
| | - Jinyi Qi
- Department of Biomedical Engineering, University of California, Davis, CA, United States
| | - Brandon A Dyer
- Department of Radiation Oncology, University of Washington, Seattle, WA, United States
| | - Levent Sensoy
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States
| | - Lu Shang
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States
| | - Shyam Rao
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States
| | - Yi Rong
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States.,Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, United States
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Liang X, Bibault JE, Leroy T, Escande A, Zhao W, Chen Y, Buyyounouski MK, Hancock SL, Bagshaw H, Xing L. Automated contour propagation of the prostate from pCT to CBCT images via deep unsupervised learning. Med Phys 2021; 48:1764-1770. [PMID: 33544390 DOI: 10.1002/mp.14755] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Revised: 01/13/2021] [Accepted: 01/23/2021] [Indexed: 12/28/2022] Open
Abstract
PURPOSE To develop and evaluate a deep unsupervised learning (DUL) framework based on a regional deformable model for automated prostate contour propagation from planning computed tomography (pCT) to cone-beam CT (CBCT). METHODS We introduce a DUL model to map the prostate contour from pCT to on-treatment CBCT. The DUL framework used a regional deformable model via narrow-band mapping to augment the conventional strategy. Two hundred and fifty-one anonymized CBCT images from prostate cancer patients were retrospectively selected and divided into three sets: 180 were used for training, 12 for validation, and 59 for testing. The testing dataset was divided into two groups. Group 1 contained 50 CBCT volumes, with one physician-generated prostate contour on CBCT image. Group 2 contained nine CBCT images, each including prostate contours delineated by four independent physicians and a consensus contour generated using the STAPLE method. Results were compared between the proposed DUL and physician-generated contours through the Dice similarity coefficients (DSCs), the Hausdorff distances, and the distances of the center-of-mass. RESULTS The average DSCs between DUL-based prostate contours and reference contours for test data in group 1 and group 2 consensus were 0.83 ± 0.04, and 0.85 ± 0.04, respectively. Correspondingly, the mean center-of-mass distances were 3.52 mm ± 1.15 mm, and 2.98 mm ± 1.42 mm, respectively. CONCLUSIONS This novel DUL technique can automatically propagate the contour of the prostate from pCT to CBCT. The proposed method shows that highly accurate contour propagation for CBCT-guided adaptive radiotherapy is achievable via the deep learning technique.
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Affiliation(s)
- Xiaokun Liang
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA
| | | | - Thomas Leroy
- Department of Radiation Oncology, Clinique des Dentellières, Valenciennes, France
| | - Alexandre Escande
- Department of Radiation Oncology, Oscar Lambret Cancer Center, Lille, France
| | - Wei Zhao
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA
| | - Yizheng Chen
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA
| | - Mark K Buyyounouski
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA
| | - Steven L Hancock
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA
| | - Hilary Bagshaw
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA
| | - Lei Xing
- Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA
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van der Horst A, Kok HP, Crezee J. Effect of gastrointestinal gas on the temperature distribution in pancreatic cancer hyperthermia treatment planning. Int J Hyperthermia 2021; 38:229-240. [PMID: 33602033 DOI: 10.1080/02656736.2021.1882709] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
PURPOSE In pancreatic cancer treatment, hyperthermia can be added to increase efficacy of chemo- and/or radiotherapy. Gas in stomach, intestines and colon is often in close proximity to the target volume. We investigated the impact of variations in gastrointestinal gas (GG) on temperature distributions during simulated hyperthermia treatment (HT). METHODS We used sets of one CT and eight cone-beam CT (CBCT) scans obtained prior to/during fractionated image-guided radiotherapy in four pancreatic cancer patients. In Plan2Heat, we simulated locoregional heating by an ALBA-4D phased array radiofrequency system and calculated temperature distributions for (i) the segmented CT (sCT), (ii) sCT with GG replaced by muscle (sCT0), (iii) sCT0 with eight different GG distributions as visible on CBCT inserted (sCTCBCT). We calculated cumulative temperature-volume histograms for the clinical target volume (CTV) for all ten temperature distributions for each patient and investigated the relationship between GG volume and change in ΔT50 (temperature increase at 50% of CTV volume). We determined location and volume of normal tissue receiving a high thermal dose. RESULTS GG volume on CBCT varied greatly (9-991 cm3). ΔT50 increased for increasing GG volume; maximum ΔT50 difference per patient was 0.4-0.6 °C. The risk for GG-associated treatment-limiting hot spots appeared low. Normal tissue high-temperature regions mostly occurred anteriorly; their volume and maximum temperature showed moderate positive correlations with GG volume, while fat-muscle interfaces were associated with higher risks for hot spots. CONCLUSIONS Considerable changes in volume and position of gastrointestinal gas can occur and are associated with clinically relevant tumor temperature differences.
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Affiliation(s)
- Astrid van der Horst
- Department of Radiation Oncology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - H Petra Kok
- Department of Radiation Oncology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Johannes Crezee
- Department of Radiation Oncology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
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Dai X, Lei Y, Wang T, Dhabaan AH, McDonald M, Beitler JJ, Curran WJ, Zhou J, Liu T, Yang X. Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy. Phys Med Biol 2021; 66:045021. [PMID: 33412527 DOI: 10.1088/1361-6560/abd953] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Organ-at-risk (OAR) delineation is a key step for cone-beam CT (CBCT) based adaptive radiotherapy planning that can be a time-consuming, labor-intensive, and subject-to-variability process. We aim to develop a fully automated approach aided by synthetic MRI for rapid and accurate CBCT multi-organ contouring in head-and-neck (HN) cancer patients. MRI has superb soft-tissue contrasts, while CBCT offers bony-structure contrasts. Using the complementary information provided by MRI and CBCT is expected to enable accurate multi-organ segmentation in HN cancer patients. In our proposed method, MR images are firstly synthesized using a pre-trained cycle-consistent generative adversarial network given CBCT. The features of CBCT and synthetic MRI (sMRI) are then extracted using dual pyramid networks for final delineation of organs. CBCT images and their corresponding manual contours were used as pairs to train and test the proposed model. Quantitative metrics including Dice similarity coefficient (DSC), Hausdorff distance 95% (HD95), mean surface distance, and residual mean square distance (RMS) were used to evaluate the proposed method. The proposed method was evaluated on a cohort of 65 HN cancer patients. CBCT images were collected from those patients who received proton therapy. Overall, DSC values of 0.87 ± 0.03, 0.79 ± 0.10/0.79 ± 0.11, 0.89 ± 0.08/0.89 ± 0.07, 0.90 ± 0.08, 0.75 ± 0.06/0.77 ± 0.06, 0.86 ± 0.13, 0.66 ± 0.14, 0.78 ± 0.05/0.77 ± 0.04, 0.96 ± 0.04, 0.89 ± 0.04/0.89 ± 0.04, 0.83 ± 0.02, and 0.84 ± 0.07 for commonly used OARs for treatment planning including brain stem, left/right cochlea, left/right eye, larynx, left/right lens, mandible, optic chiasm, left/right optic nerve, oral cavity, left/right parotid, pharynx, and spinal cord, respectively, were achieved. This study provides a rapid and accurate OAR auto-delineation approach, which can be used for adaptive radiation therapy.
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Affiliation(s)
- Xianjin Dai
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
| | - Yang Lei
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
| | - Tonghe Wang
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Anees H Dhabaan
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Mark McDonald
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Jonathan J Beitler
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Walter J Curran
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Jun Zhou
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Tian Liu
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
| | - Xiaofeng Yang
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America
- Winship Cancer Institute, Emory University, Atlanta, GA, United States of America
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Utena Y, Takatsu J, Sugimoto S, Sasai K. Trajectory log analysis and cone-beam CT-based daily dose calculation to investigate the dosimetric accuracy of intensity-modulated radiotherapy for gynecologic cancer. J Appl Clin Med Phys 2021; 22:108-117. [PMID: 33426810 PMCID: PMC7882102 DOI: 10.1002/acm2.13163] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 11/13/2020] [Accepted: 12/15/2020] [Indexed: 11/21/2022] Open
Abstract
This study evaluated unexpected dosimetric errors caused by machine control accuracy, patient setup errors, and patient weight changes/internal organ deformations. Trajectory log files for 13 gynecologic plans with seven‐ or nine‐beam dynamic multileaf collimator (MLC) intensity‐modulated radiation therapy (IMRT), and differences between expected and actual MLC positions and MUs were evaluated. Effects of patient setup errors on dosimetry were estimated by in‐house software. To simulate residual patient setup errors after image‐guided patient repositioning, planned dose distributions were recalculated (blurred dose) after the positions were randomly moved in three dimensions 0–2 mm (translation) and 0°–2° (rotation) 28 times per patient. Differences between planned and blurred doses in the clinical target volume (CTV) D98% and D2% were evaluated. Daily delivered doses were calculated from cone‐beam computed tomography by the Hounsfield unit‐to‐density conversion method. Fractional and accumulated dose differences between original plans and actual delivery were evaluated by CTV D98% and D2%. The significance of accumulated doses was tested by the paired t test. Trajectory log file analysis showed that MLC positional errors were −0.01 ± 0.02 mm and MU delivery errors were 0.10 ± 0.10 MU. Differences in CTV D98% and D2% were <0.5% for simulated patient setup errors. Differences in CTV D98% and D2% were 2.4% or less between the fractional planned and delivered doses, but were 1.7% or less for the accumulated dose. Dosimetric errors were primarily caused by patient weight changes and internal organ deformation in gynecologic radiation therapy.
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Affiliation(s)
- Yohei Utena
- Department of Radiation Oncology, Graduate School of Medicine, Juntendo University, Tokyo, Japan.,Department of Radiology, Toranomon Hospital, Tokyo, Japan
| | - Jun Takatsu
- Department of Radiation Oncology, Faculty of Medicine, Juntendo University, Tokyo, Japan
| | - Satoru Sugimoto
- Department of Radiation Oncology, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Keisuke Sasai
- Department of Radiation Oncology, Graduate School of Medicine, Juntendo University, Tokyo, Japan
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Synthetic CT in assessment of anatomical and dosimetric variations in radiotherapy - procedure validation. POLISH JOURNAL OF MEDICAL PHYSICS AND ENGINEERING 2020. [DOI: 10.2478/pjmpe-2020-0022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Abstract
Introduction: One of many procedures to control the quality of radiotherapy is daily imaging of the patient’s anatomy. The CBCT (Cone Beam Computed Tomography) plays an important role in patient positioning, and dose delivery monitoring. Nowadays, CBCT is a baseline for the calculation of fraction and total dose. Thus, it provides the potential for more comprehensive monitoring of the delivered dose and adaptive radiotherapy. However, due to the poor quality and the presence of numerous artifacts, the replacement of the CBCT image with the corrected one is desired for dose calculation. The aim of the study was to validate a method for generating a synthetic CT image based on deformable image registration.
Material and methods: A Head & Torso Freepoint phantom, model 002H9K (Computerized Imaging Reference Systems, Norfolk, USA) with inserts was imaged with CT (Computed Tomography). Then, contouring and treatment plan were created in Eclipse (Varian Medical Systems, Palo Alto, CA, USA) treatment planning system. The phantom was scanned again with the CBCT. The planning CT was registered and deformed to the CBCT, resulting in a synthetic CT in Velocity software (Varian Medical Systems, Palo Alto, CA, USA). The dose distribution was recalculated based on the created CT image.
Results: Differences in structure volumes and dose statistics calculated both on CT and synthetic CT were evaluated. Discrepancies between the original and delivered plan from 0.0 to 2.5% were obtained. Dose comparison was performed on the DVH (Dose-Volume Histogram) for all delineated inserts.
Conclusions: Our findings suggest the potential utility of deformable registration and synthetic CT for providing dose reconstruction. This study reports on the limitation of the procedure related to the limited length of the CBCT volume and deformable fusion inaccuracies.
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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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Anthropomorphic lung phantom based validation of in-room proton therapy 4D-CBCT image correction for dose calculation. Z Med Phys 2020; 32:74-84. [PMID: 33248812 PMCID: PMC9948846 DOI: 10.1016/j.zemedi.2020.09.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 09/18/2020] [Accepted: 09/23/2020] [Indexed: 12/27/2022]
Abstract
PURPOSE Ventilation-induced tumour motion remains a challenge for the accuracy of proton therapy treatments in lung patients. We investigated the feasibility of using a 4D virtual CT (4D-vCT) approach based on deformable image registration (DIR) and motion-aware 4D CBCT reconstruction (MA-ROOSTER) to enable accurate daily proton dose calculation using a gantry-mounted CBCT scanner tailored to proton therapy. METHODS Ventilation correlated data of 10 breathing phases were acquired from a porcine ex-vivo functional lung phantom using CT and CBCT. 4D-vCTs were generated by (1) DIR of the mid-position 4D-CT to the mid-position 4D-CBCT (reconstructed with the MA-ROOSTER) using a diffeomorphic Morphons algorithm and (2) subsequent propagation of the obtained mid-position vCT to the individual 4D-CBCT phases. Proton therapy treatment planning was performed to evaluate dose calculation accuracy of the 4D-vCTs. A robust treatment plan delivering a nominal dose of 60Gy was generated on the average intensity image of the 4D-CT for an approximated internal target volume (ITV). Dose distributions were then recalculated on individual phases of the 4D-CT and the 4D-vCT based on the optimized plan. Dose accumulation was performed for 4D-vCT and 4D-CT using DIR of each phase to the mid position, which was chosen as reference. Dose based on the 4D-vCT was then evaluated against the dose calculated on 4D-CT both, phase-by-phase as well as accumulated, by comparing dose volume histogram (DVH) values (Dmean, D2%, D98%, D95%) for the ITV, and by a 3D-gamma index analysis (global, 3%/3mm, 5Gy, 20Gy and 30Gy dose thresholds). RESULTS Good agreement was found between the 4D-CT and 4D-vCT-based ITV-DVH curves. The relative differences ((CT-vCT)/CT) between accumulated values of ITV Dmean, D2%, D95% and D98% for the 4D-CT and 4D-vCT-based dose distributions were -0.2%, 0.0%, -0.1% and -0.1%, respectively. Phase specific values varied between -0.5% and 0.2%, -0.2% and 0.5%, -3.5% and 1.5%, and -5.7% and 2.3%. The relative difference of accumulated Dmean over the lungs was 2.3% and Dmean for the phases varied between -5.4% and 5.8%. The gamma pass-rates with 5Gy, 20Gy and 30Gy thresholds for the accumulated doses were 96.7%, 99.6% and 99.9%, respectively. Phase-by-phase comparison yielded pass-rates between 86% and 97%, 88% and 98%, and 94% and 100%. CONCLUSIONS Feasibility of the suggested 4D-vCT workflow using proton therapy specific imaging equipment was shown. Results indicate the potential of the method to be applied for daily 4D proton dose estimation.
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Irmak S, Georg D, Lechner W. Comparison of CBCT conversion methods for dose calculation in the head and neck region. Z Med Phys 2020; 30:289-299. [PMID: 32620322 DOI: 10.1016/j.zemedi.2020.05.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 04/28/2020] [Accepted: 05/26/2020] [Indexed: 01/21/2023]
Abstract
The purpose of this study was to compare different methods of CBCT conversion respect to dose calculation accuracy. Twelve head and neck cancer patients treated with VMAT using simultaneous integrated boost technique were selected for the study. For each patient a planning CT (pCT), a control. CT acquired in the fourth week of treatment and a CBCT scan acquired on the closest day with the control CT were used. In order to re-calculate dose directly on CBCT image sets, a population based approach (CBCTPop) and a Histogram Matching (HM) approach based on rigid (CBCTHM-R) and deformable registration (CBCTHM-D) were used. Additionally, virtual CTs (vCTs) were generated using two deformable image registration algorithms (CTELX and CTANC) of the planning CT to the CBCT by using two different deformable image registration (DIR) algorithms. The corresponding control CTs were selected as ground truth and dose distributions on CBCT were analyzed using 3D global gamma index analysis applying a threshold of 10% with respect to the prescribed dose. Using the 2%/2mm gamma criterion, the results were 89.9%(±8.3%), 94.1%(±5.0%), 94.3%(±5.7%), 96.1%(±3.9%), 93.4%(±6.3%) for the CBCTPop, CBCTHM-R, CBCTHM-D, CTELX and CTANC, respectively. On average, the HM and DIR techniques showed a higher accuracy compared to the population based approach, but Kruskal-Wallis test did not show significant difference among the investigated dose calculation techniques assuming p<0.05. More sophisticated CBCT dose calculation methods seem to improve the dose calculation accuracy, but statistical significance remains to be demonstrated.
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Affiliation(s)
- Sinan Irmak
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Dietmar Georg
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Lechner
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria.
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Glide-Hurst CK, Lee P, Yock AD, Olsen JR, Cao M, Siddiqui F, Parker W, Doemer A, Rong Y, Kishan AU, Benedict SH, Li XA, Erickson BA, Sohn JW, Xiao Y, Wuthrick E. Adaptive Radiation Therapy (ART) Strategies and Technical Considerations: A State of the ART Review From NRG Oncology. Int J Radiat Oncol Biol Phys 2020; 109:1054-1075. [PMID: 33470210 DOI: 10.1016/j.ijrobp.2020.10.021] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 10/08/2020] [Accepted: 10/19/2020] [Indexed: 12/21/2022]
Abstract
The integration of adaptive radiation therapy (ART), or modifying the treatment plan during the treatment course, is becoming more widely available in clinical practice. ART offers strong potential for minimizing treatment-related toxicity while escalating or de-escalating target doses based on the dose to organs at risk. Yet, ART workflows add complexity into the radiation therapy planning and delivery process that may introduce additional uncertainties. This work sought to review presently available ART workflows and technological considerations such as image quality, deformable image registration, and dose accumulation. Quality assurance considerations for ART components and minimum recommendations are described. Personnel and workflow efficiency recommendations are provided, as is a summary of currently available clinical evidence supporting the implementation of ART. Finally, to guide future clinical trial protocols, an example ART physician directive and a physics template following standard NRG Oncology protocol is provided.
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Affiliation(s)
- Carri K Glide-Hurst
- Department of Human Oncology, University of Wisconsin-Madison, Madison, Wisconsin.
| | - Percy Lee
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Adam D Yock
- Department of Radiation Oncology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jeffrey R Olsen
- Department of Radiation Oncology, University of Colorado- Denver, Denver, Colorado
| | - Minsong Cao
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, California
| | - Farzan Siddiqui
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, Michigan
| | - William Parker
- Department of Radiation Oncology, McGill University, Montreal, Quebec, Canada
| | - Anthony Doemer
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, Michigan
| | - Yi Rong
- Department of Radiation Oncology, University of California-Davis, Sacramento, California
| | - Amar U Kishan
- Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, California
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California-Davis, Sacramento, California
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Beth A Erickson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jason W Sohn
- Department of Radiation Oncology, Allegheny Health Network, Pittsburgh, Pennsylvania
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Evan Wuthrick
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida
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Huang Y, Wang H, Li C, Hu Q, Liu H, Deng J, Li W, Wang R, Wu H, Zhang Y. A Preliminary Simulation Study of Dose-Guided Adaptive Radiotherapy Based on Halcyon MV Cone-Beam CT Images With Retrospective Data From a Phase II Clinical Trial. Front Oncol 2020; 10:574889. [PMID: 33134173 PMCID: PMC7550711 DOI: 10.3389/fonc.2020.574889] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 08/17/2020] [Indexed: 01/21/2023] Open
Abstract
Background and purpose: To evaluate the feasibility of dose-guided adaptive radiotherapy (ART) based on deformable image registration (DIR) using fractional megavoltage cone-beam CT (MVCBCT) images from Halcyon system that uses identical beams for treatment and imaging and to retrospectively investigate the influence of anatomic changes on target coverage and organ-at-risk (OAR) sparing across various tumor sites. Materials and Methods: Four hundred twenty-two MVCBCT images from 16 patients (three head and neck, seven thoracic, three abdominal, and three pelvic cases) treated in a phase II clinical trial for Halcyon were selected. DIR between the planning CT and daily MVCBCT image was implemented by Velocity software to create pseudo CT. To investigate the accuracy of dose calculation on pseudo CT, three evaluation patients with rescanned CT and adaptive plans were selected. Dose distribution of adaptive plans calculated on pseudo CT was compared with that calculated on the rescanned planning CT on the three evaluation patients. To investigate the impact of inter-fractional anatomic changes on target dose coverage and dose to OARs of the 16 patients, fractional dose was calculated and accumulated incrementally based on deformable registration between planning CT and daily MVCBCT images. Results: Passing rates using 3 mm/3%/10% threshold local gamma analysis were 93.04, 96.00, and 91.68%, respectively, for the three evaluation patients between the reconstructed dose on pseudo CT (MVCBCT) and rescanned CT, where accumulated dose deviations of over 97% voxels were smaller than 0.5 Gy. Planning target volume (PTV) D95% and D90% (the minimum dose received by at least 95/90% of the volume) of the accumulated dose could be as low as 93.8 and 94.5% of the planned dose, respectively. OAR overdose of various degrees were observed in the 16 patients relative to the planned dose. In most cases, OARs' dose volume histogram (DVH) lines of accumulated and planned dose were very close to each other if not overlapping. Among cases with visible deviations, the differences were bilateral without apparent patterns specific to tumor sites or organs. Conclusion: As a confidence building measure, this simulation study suggested the possibility of ART for Halcyon based on DIR between planning CT and MVCBCT. Preliminary clinical data suggested the benefit of patient-specific dose reconstruction and ART to avoid unacceptable target underdosage and OAR overdosage.
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Affiliation(s)
- Yuliang Huang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Haiyang Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Chenguang Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Qiaoqiao Hu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Hongjia Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Jun Deng
- Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT, United States
| | - Weibo Li
- Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), Institute of Radiation Medicine, Neuherberg, Germany
| | - Ruoxi Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Hao Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China.,Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Yibao Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China.,Institute of Medical Technology, Peking University Health Science Center, Beijing, China
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Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance. Radiother Oncol 2020; 153:55-66. [PMID: 32920005 DOI: 10.1016/j.radonc.2020.09.008] [Citation(s) in RCA: 136] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 09/02/2020] [Accepted: 09/03/2020] [Indexed: 02/06/2023]
Abstract
Artificial Intelligence (AI) is currently being introduced into different domains, including medicine. Specifically in radiation oncology, machine learning models allow automation and optimization of the workflow. A lack of knowledge and interpretation of these AI models can hold back wide-spread and full deployment into clinical practice. To facilitate the integration of AI models in the radiotherapy workflow, generally applicable recommendations on implementation and quality assurance (QA) of AI models are presented. For commonly used applications in radiotherapy such as auto-segmentation, automated treatment planning and synthetic computed tomography (sCT) the basic concepts are discussed in depth. Emphasis is put on the commissioning, implementation and case-specific and routine QA of AI models needed for a methodical introduction in clinical practice.
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Yoon SW, Lin H, Alonso-Basanta M, Anderson N, Apinorasethkul O, Cooper K, Dong L, Kempsey B, Marcel J, Metz J, Scheuermann R, Li T. Initial Evaluation of a Novel Cone-Beam CT-Based Semi-Automated Online Adaptive Radiotherapy System for Head and Neck Cancer Treatment - A Timing and Automation Quality Study. Cureus 2020; 12:e9660. [PMID: 32923257 PMCID: PMC7482986 DOI: 10.7759/cureus.9660] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Introduction A novel on-line adaptive radiotherapy (ART) system based on O-ring linear accelerator (LINAC) and cone-beam CT (CBCT) was evaluated for treatment and management of head & neck (H&N) cancer in an emulated environment accessed via remote desktop connection. In this on-line ART system, organs-at-risk (OARs) and target contours and radiotherapy (RT) plans are semi-automatically generated based on the patient CBCT, expediting a typically hours-long RT planning session to under half an hour. In this paper, we describe our initial experiences with the system and explore optimization strategies to expedite the process further. Methods We retroactively studied five patients with head and neck cancers, treated 16-35 fractions to 50-70 Gys. For each patient, on-line ART was simulated with one planning CT and three daily CBCT images taken beginning, middle, and end of treatment (tx). Key OAR (mandible, parotids, and spinal cord) and target (planning target volume (PTV) = clinical target volume (CTV) + 3 mm margin) contours were auto-generated and adjusted as needed by therapist/dosimetrist and attending physician, respectively. Duration of OAR contouring, target contouring, and plan review was recorded. Key OAR auto-contours were qualitatively rated from 1 (unacceptable) - 5 (perfect OAR delineation), and then quantitatively compared to human-adjusted “ground truth” contours via dice similarity coefficient (DSC) and 95-percentile Hausdorff distance (HD95%). Once contours were approved, adapted RT plans were auto-generated for physician review. Simulated doses to OARs and targets from the adapted plan were compared to that from the original (un-adapted) plan. Results Median on-line ART planning duration in the remote emulated environment was 19 min 34 sec (range: 13 min 10 sec - 31 min 20 sec). Automated key OAR quality was satisfactory overall (98% scored ≥3; 82% ≥4), though mandible was rated lower than others (p < 0.05). Most key OARs and all targets were within 2 mm margin of human-adjusted contours, but a few parotid and spinal cord contours deviated up to 5 mm. Anatomical changes over tx course further increased auto-contour error (p < 0.05, ΔHD95% = 0.77 mm comparing start and end of tx). Further optimizing auto-contoured OAR and target quality could reduce the on-line treatment planning duration by ~5 min and ~4.5 min, respectively. Dosimetrically, adapted plan spared OARs at a rate much greater than random chance compared to the original plan (χ2 = 22.3, p << 0.001), while maintaining similar therapeutic dose to treatment target CTV (χ2 = 1.14, p > 0.05). In addition, a general decrease in accumulated OAR dose was observed with adaptation. Unsupervised adapted plans where contours were auto-generated without human review still spared OAR at a greater rate than the original plans, suggesting benefits of adaptation can be maintained even with some leniency in contour accuracy. Conclusion Feasibility of a novel, semi-automated on-line ART system for various head and neck (H&N) cancer sites was demonstrated in terms of treatment duration, dosimetric benefits, and automated contour accuracy in a remote emulator environment. Adaptive planning duration was clinically viable at 19 min and 34 sec, but further improvements in automated contour accuracy and performance improvements of plan auto-generation may reduce adaptive planning duration by up to 10 minutes.
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Affiliation(s)
- Suk Whan Yoon
- Radiation Oncology, University of Pennsylvania, Philadelphia, USA
| | - Hui Lin
- Radiation Oncology, University of Pennsylvania, Philadelphia, USA
| | | | - Nate Anderson
- Radiation Oncology, University of Pennsylvania, Philadelphia, USA
| | | | - Karima Cooper
- Radiation Oncology, University of Pennsylvania, Philadelphia, USA
| | - Lei Dong
- Radiation Oncology, University of Pennsylvania, Philadelphia, USA
| | - Brian Kempsey
- Radiation Oncology, University of Pennsylvania, Philadelphia, USA
| | - Jaclyn Marcel
- Radiation Oncology, University of Pennsylvania, Philadelphia, USA
| | - James Metz
- Radiation Oncology, University of Pennsylvania, Philadelphia, USA
| | - Ryan Scheuermann
- Radiation Oncology, University of Pennsylvania, Philadelphia, USA
| | - Taoran Li
- Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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Barateau A, De Crevoisier R, Largent A, Mylona E, Perichon N, Castelli J, Chajon E, Acosta O, Simon A, Nunes JC, Lafond C. Comparison of CBCT-based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning. Med Phys 2020; 47:4683-4693. [PMID: 32654160 DOI: 10.1002/mp.14387] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 06/16/2020] [Accepted: 06/23/2020] [Indexed: 01/26/2023] Open
Abstract
PURPOSE Anatomical variations occur during head and neck (H&N) radiotherapy treatment. kV cone-beam computed tomography (CBCT) images can be used for daily dose monitoring to assess dose variations owing to anatomic changes. Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) from CBCT to perform dose calculation. This study aims to evaluate the accuracy of a DLM and to compare this method with three existing methods of dose calculation from CBCT in H&N cancer radiotherapy. METHODS Forty-four patients received VMAT for H&N cancer (70-63-56 Gy). For each patient, reference CT (Bigbore, Philips) and CBCT images (XVI, Elekta) were acquired. The DLM was based on a generative adversarial network. The three compared methods were: (a) a method using a density to Hounsfield Unit (HU) relation from phantom CBCT image (HU-D curve method), (b) a water-air-bone density assignment method (DAM), and iii) a method using deformable image registration (DIR). The imaging endpoints were the mean absolute error (MAE) and mean error (ME) of HU from pCT and reference CT (CTref ). The dosimetric endpoints were dose discrepancies and 3D gamma analyses (local, 2%/2 mm, 30% dose threshold). Dose discrepancies were defined as the mean absolute differences between DVHs calculated from the CTref and pCT of each method. RESULTS In the entire body, the MAEs and MEs of the DLM, HU-D curve method, DAM, and DIR method were 82.4 and 17.1 HU, 266.6 and 208.9 HU, 113.2 and 14.2 HU, and 95.5 and -36.6 HU, respectively. The MAE obtained using the DLM differed significantly from those of other methods (Wilcoxon, P ≤ 0.05). The DLM dose discrepancies were 7 ± 8 cGy (maximum = 44 cGy) for the ipsilateral parotid gland Dmean and 5 ± 6 cGy (max = 26 cGy) for the contralateral parotid gland mean dose (Dmean ). For the parotid gland Dmean , no significant dose difference was observed between the DLM and other methods. The mean 3D gamma pass rate ± standard deviation was 98.1 ± 1.2%, 91.0 ± 5.3%, 97.9 ± 1.6%, and 98.8 ± 0.7% for the DLM, HU-D method, DAM, and DIR method, respectively. The gamma pass rates and mean gamma results of the HU-D curve method, DAM, and DIR method differed significantly from those of the DLM. CONCLUSIONS For H&N radiotherapy, DIR method and DLM appears as the most appealing CBCT-based dose calculation methods among the four methods in terms of dose accuracy as well as calculation time. Using the DIR method or DLM with CBCT images enables dose monitoring in the parotid glands during the treatment course and may be used to trigger replanning.
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Affiliation(s)
- Anaïs Barateau
- Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, F-35000, France
| | - Renaud De Crevoisier
- Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, F-35000, France
| | - Axel Largent
- Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, F-35000, France
| | - Eugenia Mylona
- Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, F-35000, France
| | - Nicolas Perichon
- Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, F-35000, France
| | - Joël Castelli
- Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, F-35000, France
| | - Enrique Chajon
- Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, F-35000, France
| | - Oscar Acosta
- Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, F-35000, France
| | - Antoine Simon
- Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, F-35000, France
| | - Jean-Claude Nunes
- Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, F-35000, France
| | - Caroline Lafond
- Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, F-35000, France
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Giacometti V, Hounsell AR, McGarry CK. A review of dose calculation approaches with cone beam CT in photon and proton therapy. Phys Med 2020; 76:243-276. [DOI: 10.1016/j.ejmp.2020.06.017] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 06/04/2020] [Accepted: 06/22/2020] [Indexed: 01/12/2023] Open
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Talbot A, Devos L, Dubus F, Vermandel M. Multimodal imaging in radiotherapy: Focus on adaptive therapy and quality control. Cancer Radiother 2020; 24:411-417. [PMID: 32517893 DOI: 10.1016/j.canrad.2020.04.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 04/23/2020] [Accepted: 04/24/2020] [Indexed: 12/16/2022]
Abstract
Improved computer resources in radiation oncology department have greatly facilitated the integration of multimodal imaging into the workflow of radiation therapy. Nowadays, physicians have highly informative imaging modalities of the anatomical region to be treated. These images contribute to the targeting accuracy with the current treatment device, impacting both segmentation or patient's positioning. Additionally, in a constant effort to deliver personalized care, many teams seek to confirm the benefits of adaptive radiotherapy. The published works highlight the importance of registration algorithms, particularly those of elastic or deformable registration necessary to take into account the anatomical evolutions of the patients during the course of their therapy. These algorithms, often considered as "black boxes", tend to be better controlled and understood by physicists and physicians thanks to the generalization of evaluation and validation methods. Given the still significant development of medical imaging techniques, it is foreseeable that multimodal registration needs require more efficient algorithms well integrated within the flow of data.
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Affiliation(s)
- A Talbot
- Medical Physics Department, CHU de Lille, 59037 Lille, France; Neurosurgery Department, hôpital Roger-Salengro, CHU de Lille, 59037 Lille, France
| | - L Devos
- Neurosurgery Department, hôpital Roger-Salengro, CHU de Lille, 59037 Lille, France; Nuclear Medicine Department, hôpital Roger-Salengro, CHU de Lille, 59037 Lille, France
| | - F Dubus
- Medical Physics Department, CHU de Lille, 59037 Lille, France; Neurosurgery Department, hôpital Roger-Salengro, CHU de Lille, 59037 Lille, France
| | - M Vermandel
- Medical Physics Department, CHU de Lille, 59037 Lille, France; Neurosurgery Department, hôpital Roger-Salengro, CHU de Lille, 59037 Lille, France; Nuclear Medicine Department, hôpital Roger-Salengro, CHU de Lille, 59037 Lille, France; Université de Lille, 59000 Lille, France; Inserm, U1189, 59000 Lille, France; ONCO-THAI-Image-Assisted Laser Therapy for Oncology, CHU de Lille, 59000 Lille, France.
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78
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Kanehira T, Svensson S, van Kranen S, Sonke JJ. Accurate estimation of daily delivered radiotherapy dose with an external treatment planning system. Phys Imaging Radiat Oncol 2020; 14:39-42. [PMID: 33458312 PMCID: PMC7807587 DOI: 10.1016/j.phro.2020.05.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 05/16/2020] [Accepted: 05/18/2020] [Indexed: 11/28/2022] Open
Abstract
Accurate estimation of the daily radiotherapy dose is challenging in a multi-institutional collaboration when the institution specific treatment planning system (TPS) is not available. We developed and evaluated a method to tackle this problem. Residual errors in daily estimations were minimized with single correction based on the planned dose. For nine patients, medians of the absolute estimation errors for targets and OARs were less than 0.2 Gy (Dmean), 0.3 Gy (D1), and 0.1 Gy (D99). In general, mimicking errors were significantly smaller than dose differences caused by anatomical changes. The demonstrated accuracy may facilitate dose accumulation in a multi-institutional/multi-vendor setting.
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Affiliation(s)
- Takahiro Kanehira
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | | | - Simon van Kranen
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
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79
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Huang Y, Li C, Wang H, Hu Q, Wang R, Chang C, Ma W, Li W, Wu H, Zhang Y. A quantitative evaluation of deformable image registration based on MV cone beam CT images: Impact of deformation magnitudes and image modalities. Phys Med 2020; 71:82-87. [PMID: 32097874 DOI: 10.1016/j.ejmp.2020.02.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 02/15/2020] [Accepted: 02/19/2020] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND AND PURPOSE To evaluate the impact of deformation magnitude and image modality on deformable-image-registration (DIR) accuracy using Halcyon megavoltage cone beam CT images (MVCBCT). MATERIALS AND METHODS Planning CT images of an anthropomorphic Head phantom were aligned rigidly with MVCBCT and re-sampled to achieve the same resolution, denoted as pCT. MVCBCT was warped with twenty simulated pre-known virtual deformation fields (Ti, i = 1-20) with increasing deformation magnitudes, yielding warped CBCT (wCBCT). The pCT and MVCBCT were registered to wCBCT respectively (Multi-modality and Uni-modality DIR), generating deformation vector fields Vi and Vi' (i = 1-20). Vi and Vi' were compared with Ti respectively to assess the DIR accuracy geometrically. In addition, Vi, Ti, and Vi' were applied to pCT, generating deformed CT (dCTi), ground-truth CT (Gi) and deformed CT' (dCTi') respectively. The Hounsfield Unit (HU) on these virtual CT images were also compared. RESULTS The mean errors of vector displacement increased with the deformation magnitude. For deformation magnitudes between 2.82 mm and 7.71 mm, the errors of uni-modality DIR were 1.16 mm ~ 1.73 mm smaller than that of multi-modality (p = 0.0001, Wilcoxon signed rank test). DIR could reduce the maximum signed and absolute HU deviations from 70.8 HU to 11.4 HU and 208 HU to 46.2 HU respectively. CONCLUSIONS As deformation magnitude increases, DIR accuracy continues to deteriorate and uni-modality DIR consistently outperformed multi-modality DIR. DIR-based adaptive radiotherapy utilizing the noisy MVCBCT images is only conditionally applicable with caution.
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Affiliation(s)
- Yuliang Huang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Chenguang Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Haiyang Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Qiaoqiao Hu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Ruoxi Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Cheng Chang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Wenjun Ma
- State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing 100871, China
| | - Weibo Li
- Institute of Radiation Medicine, Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Ingolstädter Landstr, 85764 Neuherberg, Germany
| | - Hao Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China; Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China.
| | - Yibao Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China; Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China.
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80
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Hammers JE, Pirozzi S, Lindsay D, Kaidar-Person O, Tan X, Chen RC, Das SK, Mavroidis P. Evaluation of a commercial DIR platform for contour propagation in prostate cancer patients treated with IMRT/VMAT. J Appl Clin Med Phys 2020; 21:14-25. [PMID: 32058663 PMCID: PMC7020979 DOI: 10.1002/acm2.12787] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 02/08/2019] [Accepted: 08/06/2019] [Indexed: 11/12/2022] Open
Abstract
Purpose To assess the performance and limitations of contour propagation with three commercial deformable image registration (DIR) algorithms using fractional scans of CT‐on‐rails (CTOR) and Cone Beam CT (CBCT) in image guided prostate therapy patients treated with IMRT/VMAT. Methods Twenty prostate cancer patients treated with IMRT/VMAT were selected for analysis. A total of 453 fractions across those patients were analyzed. Image data were imported into MIM (MIM Software, Inc., Cleveland, OH) and three DIR algorithms (DIR Profile, normalized intensity‐based (NIB) and shadowed NIB DIR algorithms) were applied to deformably register each fraction with the planning CT. Manually drawn contours of bladder and rectum were utilized for comparison against the DIR propagated contours in each fraction. Four metrics were utilized in the evaluation of contour similarity, the Hausdorff Distance (HD), Mean Distance to Agreement (MDA), Dice Similarity Coefficient (DSC), and Jaccard indices. A subfactor analysis was performed per modality (CTOR vs. CBCT) and time (fraction). Point estimates and 95% confidence intervals were assessed via a Linear Mixed Effect model for the contour similarity metrics. Results No statistically significant differences were observed between the DIR Profile and NIB algorithms. However, statistically significant differences were observed between the shadowed NIB and NIB algorithms for some of the DIR evaluation metrics. The Hausdorff Distance calculation showed the NIB propagated contours vs. shadowed NIB propagated contours against the manual contours were 14.82 mm vs. 8.34 mm for bladder and 15.87 mm vs. 11 mm for rectum, respectively. Similarly, the Mean Distance to Agreement calculation comparing the NIB propagated contours vs. shadowed NIB propagated contours against the manual contours were 2.43 mm vs. 0.98 mm for bladder and 2.57 mm vs. 1.00 mm for rectum, respectively. The Dice Similarity Coefficients comparing the NIB propagated contours and shadowed NIB propagated contours against the manual contours were 0.844 against 0.936 for bladder and 0.772 against 0.907 for rectum, respectively. The Jaccard indices comparing the NIB propagated contours and shadowed NIB propagated contours against the manual contours were 0.749 against 0.884 for bladder and 0.637 against 0.831 for rectum, respectively. The shadowed NIB DIR, which showed the closest agreement with the manual contours performed significantly better than the DIR Profile in all the comparisons. The OAR with the greatest agreement varied substantially across patients and image guided radiation therapy (IGRT) modality. Intra‐patient variability of contour metric evaluation was insignificant across all the DIR algorithms. Statistical significance at α = 0.05 was observed for manual vs. deformably propagated contours for bladder for all the metrics except Hausdorff Distance (P = 0.01 for MDA, P = 0.02 for DSC, P = 0.01 for Jaccard), whereas the corresponding values for rectum were: P = 0.03 for HD, P = 0.01 for MDA, P < 0.01 for DSC, P < 0.01 for Jaccard. The performance of the different metrics varied slightly across the fractions of each patient, which indicates that weekly contour propagation models provide a reasonable approximation of the daily contour propagation models. Conclusion The high variance of Hausdorff Distance across all automated methods for bladder indicates widely variable agreement across fractions for all patients. Lower variance across all modalities, methods, and metrics were observed for rectum. The shadowed NIB propagated contours were substantially more similar to the manual contours than the DIR Profile or NIB contours for both the CTOR and CBCT imaging modalities. The relationship of each algorithm to similarity with manual contours is consistent across all observed metrics and organs. Screening of image guidance for substantial differences in bladder and rectal filling compared with the planning CT reference could aid in identifying fractions for which automated DIR would prove insufficient.
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Affiliation(s)
- Jacob E Hammers
- Department of Radiation Oncology, University of North Carolina at Chapel Hill, NC
| | | | - Daniel Lindsay
- Department of Radiation Oncology, University of North Carolina at Chapel Hill, NC
| | - Orit Kaidar-Person
- Department of Radiation Oncology, University of North Carolina at Chapel Hill, NC
| | - Xianming Tan
- Lineberger Comprehensive Cancer Center, University of North Carolina Hospitals, Chapel Hill, NC
| | - Ronald C Chen
- Department of Radiation Oncology, University of North Carolina at Chapel Hill, NC
| | - Shiva K Das
- Department of Radiation Oncology, University of North Carolina at Chapel Hill, NC
| | - Panayiotis Mavroidis
- Department of Radiation Oncology, University of North Carolina at Chapel Hill, NC
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81
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Lowther NJ, Marsh SH, Louwe RJW. Quantifying the dose accumulation uncertainty after deformable image registration in head-and-neck radiotherapy. Radiother Oncol 2020; 143:117-125. [PMID: 32063377 DOI: 10.1016/j.radonc.2019.12.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 12/12/2019] [Accepted: 12/15/2019] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND PURPOSE Deformable image registration (DIR) facilitated dose reconstruction and accumulation can be applied to assess delivered dose and verify the validity of the treatment plan during treatment. This retrospective study used in silico deformations based on clinically observed anatomical changes as ground truth to investigate the uncertainty of reconstructed and accumulated dose in head-and-neck radiotherapy (HNRT). MATERIALS AND METHODS A planning CT (pCT), cone beam CT (CBCT) from week one of treatment and three later CBCTs were selected for 12 HNRT patients. These images were used to generate in silico reference CBCTs and deformation vector fields (DVFs) as ground truth with B-spline DIR. Inverse consistency (IC) of voxels was assessed by determining their net displacement after successive application of the forward and backward DVF. The reconstructed dose based on demons DIR was compared to the ground truth to assess the structure-specific uncertainties of this DIR algorithm for inverse consistent and inverse inconsistent voxels. RESULTS Overall, 98.5% of voxels were inverse consistent with the 95% level of confidence range for dose reconstruction of a single fraction equal to [-2.3%; +2.1%], [-10.2%; +15.2%] and [-9.5%; +12.5%] relative to their planned dose for target structures, critical organs at risk (OARs) and non-critical OARs, respectively. Inverse inconsistent voxels generally showed a higher level of uncertainty. CONCLUSION The uncertainty in accumulated dose using DIR can be accurately quantified and incorporated in dose-volume histograms (DVHs). This method can be used to prospectively assess the adequacy of target coverage during treatment in an objective manner.
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Affiliation(s)
- Nicholas J Lowther
- Wellington Blood and Cancer Centre, Department of Radiation Oncology, Wellington, New Zealand; University of Canterbury, School of Physical and Chemical Sciences, Christchurch, New Zealand
| | - Steven H Marsh
- University of Canterbury, School of Physical and Chemical Sciences, Christchurch, New Zealand
| | - Robert J W Louwe
- Wellington Blood and Cancer Centre, Department of Radiation Oncology, Wellington, New Zealand.
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82
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Yuan Z, Rong Y, Benedict SH, Daly ME, Qiu J, Yamamoto T. "Dose of the day" based on cone beam computed tomography and deformable image registration for lung cancer radiotherapy. J Appl Clin Med Phys 2019; 21:88-94. [PMID: 31816170 PMCID: PMC6964750 DOI: 10.1002/acm2.12793] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 02/04/2019] [Accepted: 11/17/2019] [Indexed: 12/25/2022] Open
Abstract
Purpose Adaptive radiotherapy (ART) has potential to reduce toxicity and facilitate safe dose escalation. Dose calculations with the planning CT deformed to cone beam CT (CBCT) have shown promise for estimating the “dose of the day”. The purpose of this study is to investigate the “dose of the day” calculation accuracy based on CBCT and deformable image registration (DIR) for lung cancer radiotherapy. Methods A total of 12 lung cancer patients were identified, for which daily CBCT imaging was performed for treatment positioning. A re‐planning CT (rCT) was acquired after 20 Gy for all patients. A virtual CT (vCT) was created by deforming initial planning CT (pCT) to the simulated CBCT that was generated from deforming CBCT to rCT acquired on the same day. Treatment beams from the initial plan were copied to the vCT and rCT for dose calculation. Dosimetric agreement between vCT‐based and rCT‐based accumulated doses was evaluated using the Bland‐Altman analysis. Results Mean differences in dose‐volume metrics between vCT and rCT were smaller than 1.5%, and most discrepancies fell within the range of ± 5% for the target volume, lung, esophagus, and heart. For spinal cord Dmax, a large mean difference of −5.55% was observed, which was largely attributed to very limited CBCT image quality (e.g., truncation artifacts). Conclusion This study demonstrated a reasonable agreement in dose‐volume metrics between dose accumulation based on vCT and rCT, with the exception for cases with poor CBCT image quality. These findings suggest potential utility of vCT for providing a reasonable estimate of the “dose of the day”, and thus facilitating the process of ART for lung cancer.
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Affiliation(s)
- Zilong Yuan
- Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA.,Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Rong
- Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA
| | - Megan E Daly
- Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA
| | - Jianfeng Qiu
- Medical Engineering and Technology Research Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China
| | - Tokihiro Yamamoto
- Department of Radiation Oncology, University of California Davis Comprehensive Cancer Center, Sacramento, CA, USA
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83
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Kurz C, Maspero M, Savenije MHF, Landry G, Kamp F, Pinto M, Li M, Parodi K, Belka C, van den Berg CAT. CBCT correction using a cycle-consistent generative adversarial network and unpaired training to enable photon and proton dose calculation. Phys Med Biol 2019; 64:225004. [PMID: 31610527 DOI: 10.1088/1361-6560/ab4d8c] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
In presence of inter-fractional anatomical changes, clinical benefits are anticipated from image-guided adaptive radiotherapy. Nowadays, cone-beam CT (CBCT) imaging is mostly utilized during pre-treatment imaging for position verification. Due to various artifacts, image quality is typically not sufficient for photon or proton dose calculation, thus demanding accurate CBCT correction, as potentially provided by deep learning techniques. This work aimed at investigating the feasibility of utilizing a cycle-consistent generative adversarial network (cycleGAN) for prostate CBCT correction using unpaired training. Thirty-three patients were included. The network was trained to translate uncorrected, original CBCT images (CBCTorg) into planning CT equivalent images (CBCTcycleGAN). HU accuracy was determined by comparison to a previously validated CBCT correction technique (CBCTcor). Dosimetric accuracy was inferred for volumetric-modulated arc photon therapy (VMAT) and opposing single-field uniform dose (OSFUD) proton plans, optimized on CBCTcor and recalculated on CBCTcycleGAN. Single-sided SFUD proton plans were utilized to assess proton range accuracy. The mean HU error of CBCTcycleGAN with respect to CBCTcor decreased from 24 HU for CBCTorg to -6 HU. Dose calculation accuracy was high for VMAT, with average pass-rates of 100%/89% for a 2%/1% dose difference criterion. For proton OSFUD plans, the average pass-rate for a 2% dose difference criterion was 80%. Using a (2%, 2 mm) gamma criterion, the pass-rate was 96%. 93% of all analyzed SFUD profiles had a range agreement better than 3 mm. CBCT correction time was reduced from 6-10 min for CBCTcor to 10 s for CBCTcycleGAN. Our study demonstrated the feasibility of utilizing a cycleGAN for CBCT correction, achieving high dose calculation accuracy for VMAT. For proton therapy, further improvements may be required. Due to unpaired training, the approach does not rely on anatomically consistent training data or potentially inaccurate deformable image registration. The substantial speed-up for CBCT correction renders the method particularly interesting for adaptive radiotherapy.
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Affiliation(s)
- Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany. Department of Radiotherapy, Center for Image Sciences, Universitair Medisch Centrum Utrecht, Utrecht, the Netherlands. Department of Medical Physics, Fakultät für Physik, Ludwig-Maximilians-Universität München (LMU Munich), Garching, Germany. Author to whom correspondence should be addressed
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84
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Frederick A, Roumeliotis M, Grendarova P, Craighead P, Abedin T, Watt E, Olivotto IA, Meyer T, Quirk S. A Framework for Clinical Validation of Automatic Contour Propagation: Standardizing Geometric and Dosimetric Evaluation. Pract Radiat Oncol 2019; 9:448-455. [DOI: 10.1016/j.prro.2019.06.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 06/11/2019] [Accepted: 06/25/2019] [Indexed: 10/26/2022]
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85
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Belshaw L, Agnew CE, Irvine DM, Rooney KP, McGarry CK. Adaptive radiotherapy for head and neck cancer reduces the requirement for rescans during treatment due to spinal cord dose. Radiat Oncol 2019; 14:189. [PMID: 31675962 PMCID: PMC6825357 DOI: 10.1186/s13014-019-1400-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 10/16/2019] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Patients treated with radiotherapy for head and neck (H&N) cancer often experience anatomical changes. The potential compromises to Planning Target Volume (PTV) coverage or Organ at Risk (OAR) sparing has prompted the use of adaptive radiotherapy (ART) for these patients. However, implementation of ART is time and resource intensive. This study seeks to define a clinical trigger for H&N re-plans based on spinal cord safety using kV Cone-Beam Computed Tomography (CBCT) verification imaging, in order to best balance clinical benefit with additional workload. METHODS Thirty-one H&N patients treated with Volumetric Modulated Arc Therapy (VMAT) who had a rescan CT (rCT) during treatment were included in this study. Contour volume changes between the planning CT (pCT) and rCT were determined. The original treatment plan was calculated on the pCT, CBCT prior to the rCT, pCT deformed to the anatomy of the CBCT (dCT), and rCT (considered the gold standard). The dose to 0.1 cc (D0.1cc) spinal cord was evaluated from the Dose Volume Histograms (DVHs). RESULTS The median dose increase to D0.1cc between the pCT and rCT was 0.7 Gy (inter-quartile range 0.2-1.9 Gy, p < 0.05). No correlation was found between contour volume changes and the spinal cord dose increase. Three patients exhibited an increase of 7.0-7.2 Gy to D0.1cc, resulting in a re-plan; these patients were correctly identified using calculations on the CBCT/dCT. CONCLUSIONS An adaptive re-plan can be triggered using spinal cord doses calculated on the CBCT/dCT. Implementing this trigger can reduce patient appointments and radiation dose by eliminating up to 90% of additional un-necessary CT scans, reducing the workload for radiographers, physicists, dosimetrists, and clinicians.
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Affiliation(s)
- Louise Belshaw
- Radiotherapy Physics, Northern Ireland Cancer Centre, Belfast City Hospital, Belfast, Northern Ireland
| | - Christina E Agnew
- Radiotherapy Physics, Northern Ireland Cancer Centre, Belfast City Hospital, Belfast, Northern Ireland
| | - Denise M Irvine
- Radiotherapy Physics, Northern Ireland Cancer Centre, Belfast City Hospital, Belfast, Northern Ireland
| | - Keith P Rooney
- Clinical Oncology, Northern Ireland Cancer Centre, Belfast City Hospital, Belfast, Northern Ireland
| | - Conor K McGarry
- Radiotherapy Physics, Northern Ireland Cancer Centre, Belfast City Hospital, Belfast, Northern Ireland. .,Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland.
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86
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Mori M, Dell'Oca I, Branchini M, Foti S, Broggi S, Perna L, Cattaneo GM, Calandrino R, Di Muzio NG, Fiorino C. Monitoring skin dose changes during image-guided helical tomotherapy for head and neck cancer patients. Strahlenther Onkol 2019; 196:243-251. [PMID: 31586231 DOI: 10.1007/s00066-019-01520-y] [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: 05/15/2019] [Accepted: 09/10/2019] [Indexed: 11/26/2022]
Abstract
PURPOSE An increase of skin dose during head and neck cancer (HNC) radiotherapy is potentially dangerous. Aim of this study was to quantify skin dose variation and to assess the need of planning adaptation (ART) to counteract it. METHODS Planning CTs of 32 patients treated with helical tomotherapy (HT) according to a Simultaneous Integrated Boost (SIB) technique delivering 54/66 Gy in 30 fractions were deformably co-registered to MVCTs taken at fractions 15 and 30; in addition, the first fraction was also considered. The delivered dose-of-the-day was calculated on the corresponding deformed images. Superficial body layers (SL) were considered as a surrogate for skin, considering a layer thickness of 2 mm. Variations of SL DVH (∆SL) during therapy were quantified, focusing on ∆SL95% (i.e., 62.7 Gy). RESULTS Small changes (within ± 1 cc for ∆SL95%) were seen in 15/32 patients. Only 2 patients experienced ∆SL95% > 1 cc in at least one of the two monitored fractions. Negative ∆SL95% > 1 cc (up to 17 cc) were much more common (15/32 patients). The trend of skin dose changes was mostly detected at the first fraction. Negative changes were correlated with the presence of any overlap between PTV and SL at planning and were explained in terms of how the planning system optimizes the PTV dose coverage near the skin. Acute toxicity was associated with planning DVH and this association was not improved if considering DVHs referring to fractions 15/30. CONCLUSION About half of the patients treated with SIB with HT for HNC experienced a skin-sparing effect during therapy; only 6% experienced an increase. Our findings do not support skin-sparing ART, while suggesting the introduction of improved skin-sparing planning techniques.
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Affiliation(s)
- Martina Mori
- Medical Physics, San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milano, Italy.
| | - Italo Dell'Oca
- Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - Marco Branchini
- Medical Physics, San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milano, Italy
| | - Silvia Foti
- Radiotherapy, San Raffaele Scientific Institute, Milano, Italy
| | - Sara Broggi
- Medical Physics, San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milano, Italy
| | - Lucia Perna
- Medical Physics, San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milano, Italy
| | | | - Riccardo Calandrino
- Medical Physics, San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milano, Italy
| | | | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milano, Italy
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87
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État des lieux de la radiothérapie adaptative en 2019 : de la mise en place à l’utilisation clinique. Cancer Radiother 2019; 23:581-591. [DOI: 10.1016/j.canrad.2019.07.142] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 07/12/2019] [Indexed: 12/20/2022]
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88
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Guerreiro F, Zachiu C, Seravalli E, Ribeiro CO, Janssens GO, Ries M, de Senneville BD, Maduro JH, Brouwer CL, Korevaar EW, Knopf AC, Raaymakers BW. Evaluating the benefit of PBS vs. VMAT dose distributions in terms of dosimetric sparing and robustness against inter-fraction anatomical changes for pediatric abdominal tumors. Radiother Oncol 2019; 138:158-165. [DOI: 10.1016/j.radonc.2019.06.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 05/27/2019] [Accepted: 06/17/2019] [Indexed: 11/16/2022]
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89
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Li Y, Zhu J, Liu Z, Teng J, Xie Q, Zhang L, Liu X, Shi J, Chen L. A preliminary study of using a deep convolution neural network to generate synthesized CT images based on CBCT for adaptive radiotherapy of nasopharyngeal carcinoma. Phys Med Biol 2019; 64:145010. [PMID: 31170699 DOI: 10.1088/1361-6560/ab2770] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
This study aims to utilize a deep convolutional neural network (DCNN) for synthesized CT image generation based on cone-beam CT (CBCT) and to apply the images to dose calculations for nasopharyngeal carcinoma (NPC). An encoder-decoder 2D U-Net neural network was produced. A total of 70 CBCT/CT paired images of NPC cancer patients were used for training (50), validation (10) and testing (10) datasets. The testing datasets were treated with the same prescription dose (70 Gy to PTVnx70, 68 Gy to PTVnd68, 62 Gy to the PTV62 and 54 Gy to the PTV54). The mean error (ME) and mean absolute error (MAE) for the true CT images were calculated for image quality evaluation of the synthesized CT. The dose-volume histogram (DVH) dose metric difference and 3D gamma pass rate for the true CT images were calculated for dose analysis, and the results were compared with those for the CBCT images (original CBCT images without any correction) and a patient-specific calibration (PSC) method. Compared with CBCT, the range of the MAE for synthesized CT images improved from (60, 120) to (6, 27) Hounsfield units (HU), and the ME improved from (-74, 51) to (-26, 4) HU. Compared with the true CT method, the average DVH dose metric differences for the CBCT, PSC and synthesized CT methods were 0.8% ± 1.9%, 0.4% ± 0.7% and 0.2% ± 0.6%, respectively. The 1%/1 mm gamma pass rates within the body for the CBCT, PSC and synthesized CT methods were 90.8% ± 6.2%, 94.1% ± 4.4% and 95.5% ± 1.6%, respectively, and the rates within the PTVnx70 were 80.3% ± 16.6%, 87.9% ± 19.7%, 98.6% ± 2.9%, respectively. The DCNN model can generate high-quality synthesized CT images from CBCT images and be used for accurate dose calculations for NPC patients. This finding has great significance for the clinical application of adaptive radiotherapy for NPC.
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Affiliation(s)
- Yinghui Li
- School of Physics, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China. Physics Department of the Radiotherapy Department, The First People's Hospital of FoShan (Affiliated FoShan Hospital of Sun Yat-sen University), Foshan, Guangdong, People's Republic of China. State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Sun Yat-Sen University of Medical Sciences, Guangzhou, Guangdong, People's Republic of China
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90
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Rigaud B, Simon A, Castelli J, Lafond C, Acosta O, Haigron P, Cazoulat G, de Crevoisier R. Deformable image registration for radiation therapy: principle, methods, applications and evaluation. Acta Oncol 2019; 58:1225-1237. [PMID: 31155990 DOI: 10.1080/0284186x.2019.1620331] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background: Deformable image registration (DIR) is increasingly used in the field of radiation therapy (RT) to account for anatomical deformations. The aims of this paper are to describe the main applications of DIR in RT and discuss current DIR evaluation methods. Methods: Articles on DIR published from January 2000 to October 2018 were extracted from PubMed and Science Direct. Our search was restricted to articles that report data obtained from humans, were written in English, and address DIR methods for RT. A total of 207 articles were selected from among 2506 identified in the search process. Results: At planning, DIR is used for organ delineation using atlas-based segmentation, deformation-based planning target volume definition, functional planning and magnetic resonance imaging-based dose calculation. In image-guided RT, DIR is used for contour propagation and dose calculation on per-treatment imaging. DIR is also used to determine the accumulated dose from fraction to fraction in external beam RT and brachytherapy, both for dose reporting and adaptive RT. In the case of re-irradiation, DIR can be used to estimate the cumulated dose of the two irradiations. Finally, DIR can be used to predict toxicity in voxel-wise population analysis. However, the evaluation of DIR remains an open issue, especially when dealing with complex cases such as the disappearance of matter. To quantify DIR uncertainties, most evaluation methods are limited to geometry-based metrics. Software companies have now integrated DIR tools into treatment planning systems for clinical use, such as contour propagation and fraction dose accumulation. Conclusions: DIR is increasingly important in RT applications, from planning to toxicity prediction. DIR is routinely used to reduce the workload of contour propagation. However, its use for complex dosimetric applications must be carefully evaluated by combining quantitative and qualitative analyses.
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Affiliation(s)
- Bastien Rigaud
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Antoine Simon
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Joël Castelli
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Caroline Lafond
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Oscar Acosta
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Pascal Haigron
- CLCC Eugène Marquis, University of Rennes, Inserm , Rennes , France
| | - Guillaume Cazoulat
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center , Houston , TX , USA
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91
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Rafic KM, Timothy Peace SB, Manu M, Arvind S, Ravindran BP. A rationale for cone beam CT with extended longitudinal field-of-view in image guided adaptive radiotherapy. Phys Med 2019; 62:129-139. [PMID: 31153392 DOI: 10.1016/j.ejmp.2019.03.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 03/06/2019] [Accepted: 03/09/2019] [Indexed: 11/29/2022] Open
Abstract
PURPOSE To investigate the efficacy of using cone beam CT with extended longitudinal field-of-view (CBCTeLFOV) for image guided adaptive radiotherapy (IGART). METHODS The protocol acquires two CBCT scans with a linear translation of treatment couch in the patient plane, allowing a 1 cm penumbral overlap (i.e. cone beam abutment) and fused as a single DICOM set (CBCTeLFOV) using a custom-developed software script (coded in MatLab®) for extended localization. Systemic validation was performed to evaluate the geometric and Hounsfield Units accuracy at the overlapping regions of the CBCTeLFOV using a Catphan®-504 phantom. Two case studies were used to illustrate the CBCTeLFOV-based IGART workflow in terms of dosimetric and clinical perspectives. Segmentation accuracy/association between repeat CT (re-CT) and CBCTeLFOV was evaluated. Moreover, the efficacy of the CBCTeLFOV image data in deformable registration was also described. RESULTS Slice geometry, spatial resolution, line profiles and HU accuracy in the overlapping regions of the CBCTeLFOV yielded identical results when compared with reference CBCT. In patient studies, the dice-similarity-coefficient evaluation showed a good association (>0.9) between re-CT and CBCTeLFOV. Dosimetric analysis of the CBCTeLFOV-based adaptive re-plans showed excellent agreement with re-CT based re-plans. Moreover, a similar and consistent pattern of results was also observed using deformed image data (initial planning CT deformed to CBCTeLFOV) with extended longitudinal projection and the same frame-of-reference as that of the CBCTeLFOV. CONCLUSION Utilization of CBCTeLFOV proves to be clinically appropriate and enables accurate prediction of geometric and dosimetric consequences within the planned course of treatment. The ability to compute CBCTeLFOV-based treatment plans equivalent to re-CT promises a potential improvement in IGART practice.
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Affiliation(s)
- K Mohamathu Rafic
- Department of Radiation Oncology, Christian Medical College, Vellore 632004, Tamil Nadu, India.
| | | | - Mathew Manu
- Department of Radiation Oncology, Christian Medical College, Vellore 632004, Tamil Nadu, India
| | - Sathyamurthy Arvind
- Department of Radiation Oncology, Christian Medical College, Vellore 632004, Tamil Nadu, India
| | - B Paul Ravindran
- Department of Radiation Oncology, Christian Medical College, Vellore 632004, Tamil Nadu, India.
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92
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Schröder L, Stankovic U, Remeijer P, Sonke JJ. Evaluating the impact of cone-beam computed tomography scatter mitigation strategies on radiotherapy dose calculation accuracy. Phys Imaging Radiat Oncol 2019; 10:35-40. [PMID: 33458266 PMCID: PMC7807872 DOI: 10.1016/j.phro.2019.04.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 03/27/2019] [Accepted: 04/03/2019] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND AND PURPOSE The scatter induced image quality degradation of cone-beam computed tomography (CBCT) prevents more advanced applications in radiotherapy. We evaluated the dose calculation accuracy on CBCT of various disease sites using different scatter mitigation strategies. MATERIALS AND METHODS CBCT scans of two patient cohorts (C1, C2) were reconstructed using a uniform (USC) and an iterative scatter correction (ISC) method, combined with an anti-scatter grid (ASG). Head and neck (H&N), lung, pelvic region, and prostate patients were included. To achieve a high accuracy Hounsfield unit and physical density calibrations were performed. The dose distributions of the original treatment plans were analyzed with the γ evaluation method using criteria of 1%/2 mm using the planning CT as the reference. The investigated parameters were the mean γ (γmean), the points in agreement (Pγ≤1) and the 99th percentile (γ1%). RESULTS Significant differences between USC and ISC in C1 were found for the lung and prostate, where the latter using the ISC produced the best results with medians of 0.38, 98%, and 1.1 for γmean, Pγ≤1 and γ1%, respectively. For C2 the ISC with ASG showed an improvement for all imaging sites. The lung demonstrated the largest relative increase in accuracy with improvements between 48% and 54% for the medians of γmean, Pγ≤1 and γ1%. CONCLUSIONS The introduced method demonstrated high dosimetric accuracy for H&N, prostate and pelvic region if an ASG is applied. A significantly lower accuracy was seen for lung. The ISC yielded a higher robustness against scatter variations than the USC.
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Affiliation(s)
- Lukas Schröder
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Uros Stankovic
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Peter Remeijer
- 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
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93
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Giraud P, Giraud P, Gasnier A, El Ayachy R, Kreps S, Foy JP, Durdux C, Huguet F, Burgun A, Bibault JE. Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers. Front Oncol 2019; 9:174. [PMID: 30972291 PMCID: PMC6445892 DOI: 10.3389/fonc.2019.00174] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 02/28/2019] [Indexed: 12/13/2022] Open
Abstract
Introduction: An increasing number of parameters can be considered when making decisions in oncology. Tumor characteristics can also be extracted from imaging through the use of radiomics and add to this wealth of clinical data. Machine learning can encompass these parameters and thus enhance clinical decision as well as radiotherapy workflow. Methods: We performed a description of machine learning applications at each step of treatment by radiotherapy in head and neck cancers. We then performed a systematic review on radiomics and machine learning outcome prediction models in head and neck cancers. Results: Machine Learning has several promising applications in treatment planning with automatic organ at risk delineation improvements and adaptative radiotherapy workflow automation. It may also provide new approaches for Normal Tissue Complication Probability models. Radiomics may provide additional data on tumors for improved machine learning powered predictive models, not only on survival, but also on risk of distant metastasis, in field recurrence, HPV status and extra nodal spread. However, most studies provide preliminary data requiring further validation. Conclusion: Promising perspectives arise from machine learning applications and radiomics based models, yet further data are necessary for their implementation in daily care.
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Affiliation(s)
- Paul Giraud
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France
| | - Philippe Giraud
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France
| | - Anne Gasnier
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France
| | - Radouane El Ayachy
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France
| | - Sarah Kreps
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France
| | - Jean-Philippe Foy
- Department of Oral and Maxillo-Facial Surgery, Sorbonne University, Pitié-Salpêtriére Hospital, Paris, France.,Univ Lyon, Université Claude Bernard Lyon 1, INSERM 1052, CNRS 5286, Centre Léon Bérard, Centre de Recherche en Cancérologie de Lyon, Lyon, France
| | - Catherine Durdux
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France
| | - Florence Huguet
- Department of Radiation Oncology, Tenon University Hospital, Hôpitaux Universitaires Est Parisien, Sorbonne University Medical Faculty, Paris, France
| | - Anita Burgun
- Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France.,INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Paris Descartes University, Sorbonne Paris Cité, Paris, France
| | - Jean-Emmanuel Bibault
- Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique-Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France.,Cancer Research and Personalized Medicine-Integrated Cancer Research Center (SIRIC), Georges Pompidou European Hospital, Assistance Publique-Hôitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France.,INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Paris Descartes University, Sorbonne Paris Cité, Paris, France
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94
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Giacometti V, King RB, Agnew CE, Irvine DM, Jain S, Hounsell AR, McGarry CK. An evaluation of techniques for dose calculation on cone beam computed tomography. Br J Radiol 2019; 92:20180383. [PMID: 30433821 DOI: 10.1259/bjr.20180383] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
OBJECTIVE: To assess the accuracy and efficiency of four different techniques, thus determining the optimum method for recalculating dose on cone beam CT (CBCT) images acquired during radiotherapy treatments. METHODS: Four established techniques were investigated and their accuracy assessed via dose calculations: (1) applying a standard planning CT (pCT) calibration curve, (2) applying a CBCT site-specific calibration curve, (3) performing a density override and (4) using deformable registration. Each technique was applied to 15 patients receiving volumetric modulated arc therapy to one of three treatment sites, head and neck, lung and prostate. Differences between pCT and CBCT recalculations were determined with dose volume histogram metrics and 2.0%/0.1 mm gamma analysis using the pCT dose distribution as a reference. RESULTS: Dose volume histogram analysis indicated that all techniques yielded differences from expected results between 0.0 and 2.3% for both target volumes and organs at risk. With volumetric gamma analysis, the dose recalculation on deformed images yielded the highest pass-rates. The median pass-rate ranges at 50% threshold were 99.6-99.9%, 94.6-96.0%, and 94.8.0-96.0% for prostate, head and neck and lung patients, respectively. CONCLUSION: Deformable registration, HU override and site-specific calibration curves were all identified as dosimetrically accurate and efficient methods for dose calculation on CBCT images. ADVANCES IN KNOWLEDGE: With the increasing adoption of CBCT, this study provides clinical radiotherapy departments with invaluable information regarding the comparison of dose reconstruction methods, enabling a more accurate representation of a patient's treatment. It can also integrate studies in which CBCT is used in image-guided radiation therapy and for adaptive radiotherapy planning processes.
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Affiliation(s)
- Valentina Giacometti
- 1 Centre for Cancer Research and Cell Biology, Queen's University Belfast , Belfast , UK
| | - Raymond B King
- 1 Centre for Cancer Research and Cell Biology, Queen's University Belfast , Belfast , UK.,2 Radiotherapy Physics, Northern Ireland Cancer Centre , Belfast , UK
| | - Christina E Agnew
- 2 Radiotherapy Physics, Northern Ireland Cancer Centre , Belfast , UK
| | - Denise M Irvine
- 2 Radiotherapy Physics, Northern Ireland Cancer Centre , Belfast , UK
| | - Suneil Jain
- 1 Centre for Cancer Research and Cell Biology, Queen's University Belfast , Belfast , UK.,2 Radiotherapy Physics, Northern Ireland Cancer Centre , Belfast , UK
| | - Alan R Hounsell
- 1 Centre for Cancer Research and Cell Biology, Queen's University Belfast , Belfast , UK.,2 Radiotherapy Physics, Northern Ireland Cancer Centre , Belfast , UK
| | - Conor K McGarry
- 1 Centre for Cancer Research and Cell Biology, Queen's University Belfast , Belfast , UK.,2 Radiotherapy Physics, Northern Ireland Cancer Centre , Belfast , UK
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95
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Sealy MJ, Dechaphunkul T, van der Schans CP, Krijnen WP, Roodenburg JLN, Walker J, Jager-Wittenaar H, Baracos VE. Low muscle mass is associated with early termination of chemotherapy related to toxicity in patients with head and neck cancer. Clin Nutr 2019; 39:501-509. [PMID: 30846324 DOI: 10.1016/j.clnu.2019.02.029] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Revised: 02/11/2019] [Accepted: 02/16/2019] [Indexed: 01/06/2023]
Abstract
BACKGROUND & AIMS We studied whether low pre-treatment muscle mass, measured with CT at thoracic (T4) or lumbar level (L3) associates with early termination of chemotherapy related to toxicity in head and neck cancer (HNC) patients. METHODS This was a retrospective chart and image review. Adult HNC patients treated with (surgery and) platinum-based chemo-radiotherapy were included if a pre-treatment CT scan at T4 or L3 level was available. Muscle mass was evaluated by assessment of skeletal muscle index (SMI; cm2/m2). T4 and L3 SMI measurements were corrected for deviation from their respective means and were merged into one score for SMI difference (cm2/m2). All cases were assessed for presence of toxicity-related unplanned early termination of chemotherapy ('early termination'). Univariate and multivariate logistic regression models were used to investigate associations between pooled SMI and early termination. RESULTS 213 patients (age: 57.9 ± 10.3 y, male: 77%, T4 image: 45%) were included. A significant association between SMI as a continuous variable and early termination was found, both in the univariate analysis (p = 0.007, OR = 0.96 [0.94-0.99]) and the multivariate analysis (p = 0.021, OR 0.96 [0.92-0.99]). The multivariate models identified potential associations with type of chemotherapy, presence of co-morbidity, a combination of (former) smoking and alcohol consumption, and sex. CONCLUSION Lower muscle mass was robustly associated with higher odds of early termination of chemotherapy in HNC patients. Further prospective studies are required to tailor the care for patients with low muscle mass and to avoid early termination of chemotherapy.
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Affiliation(s)
- Martine J Sealy
- Research Group Healthy Ageing, Allied Health Care and Nursing, Hanze University of Applied Sciences, Petrus Driessenstraat 3, 9714 CA, Groningen, the Netherlands; Department of Maxillofacial Surgery, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, the Netherlands.
| | - Tanadech Dechaphunkul
- Department of Oncology, University of Alberta, Edmonton, AB, Canada; Department of Otorhinolaryngology Head and Neck Surgery, Faculty of Medicine, Prince of Songkla University, Hatyai, Songkhla, 90110, Thailand.
| | - Cees P van der Schans
- Research Group Healthy Ageing, Allied Health Care and Nursing, Hanze University of Applied Sciences, Petrus Driessenstraat 3, 9714 CA, Groningen, the Netherlands; Department of Rehabilitation Medicine, University of Groningen, University Medical Center, Groningen, the Netherlands; Department of Health Psychology Research, University of Groningen, University Medical Center, Groningen, the Netherlands.
| | - Wim P Krijnen
- Research Group Healthy Ageing, Allied Health Care and Nursing, Hanze University of Applied Sciences, Petrus Driessenstraat 3, 9714 CA, Groningen, the Netherlands; Johan Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Groningen, the Netherlands.
| | - Jan L N Roodenburg
- Department of Maxillofacial Surgery, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, the Netherlands.
| | - John Walker
- Department of Oncology, University of Alberta, Edmonton, AB, Canada.
| | - Harriët Jager-Wittenaar
- Research Group Healthy Ageing, Allied Health Care and Nursing, Hanze University of Applied Sciences, Petrus Driessenstraat 3, 9714 CA, Groningen, the Netherlands; Department of Maxillofacial Surgery, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, the Netherlands.
| | - Vickie E Baracos
- Department of Oncology, University of Alberta, Edmonton, AB, Canada.
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96
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Marin Anaya V, Fairfoul J. Assessing the feasibility of adaptive planning for prostate radiotherapy using Smartadapt deformable image registration. Med Eng Phys 2019; 64:65-73. [DOI: 10.1016/j.medengphy.2019.01.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 01/07/2019] [Accepted: 01/08/2019] [Indexed: 10/27/2022]
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97
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Landry G, Hansen D, Kamp F, Li M, Hoyle B, Weller J, Parodi K, Belka C, Kurz C. Comparing Unet training with three different datasets to correct CBCT images for prostate radiotherapy dose calculations. Phys Med Biol 2019; 64:035011. [PMID: 30523998 DOI: 10.1088/1361-6560/aaf496] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Image intensity correction is crucial to enable cone beam computed tomography (CBCT) based radiotherapy dose calculations. This study evaluated three different deep learning based correction methods using a U-shaped convolutional neural network architecture (Unet) in terms of their photon and proton dose calculation accuracy. CT and CBCT imaging data of 42 prostate cancer patients were included. For target ground truth data generation, a CBCT correction method based on CT to CBCT deformable image registration (DIR) was used. The method yields a deformed CT called (i) virtual CT (vCT) which is used to generate (ii) corrected CBCT projections allowing the reconstruction of (iii) a final corrected CBCT image. The single Unet architecture was trained using these three different datasets: (Unet1) raw and corrected CBCT projections, (Unet2) raw CBCT and vCT image slices and (Unet3) raw and reference corrected CBCT image slices. Volumetric arc therapy (VMAT) and proton pencil beam scanning (PBS) single field uniform dose (SFUD) plans were optimized on the reference corrected image and recalculated on the obtained Unet-corrected CBCT images. The mean error (ME) and mean absolute error (MAE) for Unet1/2/3 were [Formula: see text] Hounsfield units (HU) and [Formula: see text] HU. The 1% dose difference pass rates were better than 98.4% for VMAT for 8 test patients not seen during training, with little difference between Unets. Gamma evaluation results were even better. For protons a gamma evaluation was employed to account for small range shifts, and [Formula: see text] mm pass rates for Unet1/2/3 were better than [Formula: see text] and 91%. A 3 mm range difference threshold was established. Only for Unet3 the 5th and 95th percentiles of the range difference distributions over all fields, test patients and dose profiles were within this threshold. A single Unet architecture was successfully trained using both CBCT projections and CBCT image slices. Since the results of the other Unets were poorer than Unet3, we conclude that training using corrected CBCT image slices as target data is optimal for PBS SFUD proton dose calculations, while for VMAT all Unets provided sufficient accuracy.
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Affiliation(s)
- Guillaume Landry
- Department of Medical Physics, Fakultät für Physik, Ludwig-Maximilians-Universität München (LMU Munich), Garching, Germany
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98
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Nobnop W, Chitapanarux I, Wanwilairat S, Tharavichitkul E, Lorvidhaya V, Sripan P. Effect of Deformation Methods on the Accuracy of Deformable Image Registration From Kilovoltage CT to Tomotherapy Megavoltage CT. Technol Cancer Res Treat 2019; 18:1533033818821186. [PMID: 30803375 PMCID: PMC6373993 DOI: 10.1177/1533033818821186] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
INTRODUCTION The registration accuracy of megavoltage computed tomography images is limited by low image contrast when compared to that of kilovoltage computed tomography images. Such issues may degrade the deformable image registration accuracy. This study evaluates the deformable image registration from kilovoltage to megavoltage images when using different deformation methods and assessing nasopharyngeal carcinoma patient images. METHODS The kilovoltage and the megavoltage images from the first day and the 20th fractions of the treatment day of 12 patients with nasopharyngeal carcinoma were used to evaluate the deformable image registration application. The deformable image registration image procedures were classified into 3 groups, including kilovoltage to kilovoltage, megavoltage to megavoltage, and kilovoltage to megavoltage. Three deformable image registration methods were employed using the deformable image registration and adaptive radiotherapy software. The validation was compared by volume-based, intensity-based, and deformation field analyses. RESULTS The use of different deformation methods greatly affected the deformable image registration accuracy from kilovoltage to megavoltage. The asymmetric transformation with the demon method was significantly better than other methods and illustrated satisfactory value for adaptive applications. The deformable image registration accuracy from kilovoltage to megavoltage showed no significant difference from the kilovoltage to kilovoltage images when using the appropriate method of registration. CONCLUSIONS The choice of deformation method should be considered when applying the deformable image registration from kilovoltage to megavoltage images. The deformable image registration accuracy from kilovoltage to megavoltage revealed a good agreement in terms of intensity-based, volume-based, and deformation field analyses and showed clinically useful methods for nasopharyngeal carcinoma adaptive radiotherapy in tomotherapy applications.
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Affiliation(s)
- Wannapha Nobnop
- 1 Division of Radiation Oncology, Department of Radiology, Chiang Mai University, Chiang Mai, Thailand
| | - Imjai Chitapanarux
- 1 Division of Radiation Oncology, Department of Radiology, Chiang Mai University, Chiang Mai, Thailand
| | - Somsak Wanwilairat
- 1 Division of Radiation Oncology, Department of Radiology, Chiang Mai University, Chiang Mai, Thailand
| | - Ekkasit Tharavichitkul
- 1 Division of Radiation Oncology, Department of Radiology, Chiang Mai University, Chiang Mai, Thailand
| | - Vicharn Lorvidhaya
- 1 Division of Radiation Oncology, Department of Radiology, Chiang Mai University, Chiang Mai, Thailand
| | - Patumrat Sripan
- 1 Division of Radiation Oncology, Department of Radiology, Chiang Mai University, Chiang Mai, Thailand
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Role of deformable image registration for delivered dose accumulation of adaptive external beam radiation therapy and brachytherapy in cervical cancer. J Contemp Brachytherapy 2018; 10:542-550. [PMID: 30662477 PMCID: PMC6335550 DOI: 10.5114/jcb.2018.79840] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2018] [Accepted: 11/03/2018] [Indexed: 12/01/2022] Open
Abstract
Purpose Deformable image registration (DIR) can be used to accumulate the absorbed dose distribution of daily image-guided adaptive external beam radiation treatment (EBRT) and brachytherapy (BT). Since dose-volume parameter addition assumes a uniform delivered EBRT dose around the planned BT boost, the added value of DIR over direct addition was investigated for dose accumulation in bladder and rectum. Material and methods For 10 patients (EBRT 46/46.2 GyEQD2, EBRT + BT: D90 85-90 GyEQD2, in equivalent dose in 2 Gy fractions), the actually delivered dose from adaptive volumetric-modulated arc therapy (VMAT)/intensity-modulated radiotherapy (IMRT) EBRT was calculated using the daily anatomy from the cone-beam computed tomography (CBCT) scans acquired prior to irradiation. The CBCT of the first EBRT fraction and the BT planning MRI were registered using DIR. The cumulative dose to the 2 cm3 with the highest dose (D2cm3) from EBRT and BT to the bladder and rectum was calculated and compared to direct addition assuming a uniform EBRT dose (UD). Results Differences (DIR-UD) in the total EBRT + BT dose ranged between –0.2-3.9 GyEQD2 (bladder) and –1.0-3.7 GyEQD2 (rectum). The total EBRT + BT dose calculated with DIR was at most 104% of the dose calculated with the UD method. Conclusions Differences between UD and DIR were small (< 3.9 GyEQD2). The dose delivered with adaptive VMAT/IMRT EBRT to bladder and rectum near the planned BT boost can be considered uniform for the evaluation of bladder/rectum D2cm3.
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Botas P, Kim J, Winey B, Paganetti H. Online adaption approaches for intensity modulated proton therapy for head and neck patients based on cone beam CTs and Monte Carlo simulations. ACTA ACUST UNITED AC 2018; 64:015004. [DOI: 10.1088/1361-6560/aaf30b] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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