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Midroni J, Salunkhe R, Liu Z, Chow R, Boldt G, Palma D, Hoover D, Vinogradskiy Y, Raman S. Incorporation of Functional Lung Imaging Into Radiation Therapy Planning in Patients With Lung Cancer: A Systematic Review and Meta-Analysis. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00481-4. [PMID: 38631538 DOI: 10.1016/j.ijrobp.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/27/2024] [Accepted: 04/02/2024] [Indexed: 04/19/2024]
Abstract
Our purpose was to provide an understanding of current functional lung imaging (FLI) techniques and their potential to improve dosimetry and outcomes for patients with lung cancer receiving radiation therapy (RT). Excerpta Medica dataBASE (EMBASE), PubMed, and Cochrane Library were searched from 1990 until April 2023. Articles were included if they reported on FLI in one of: techniques, incorporation into RT planning for lung cancer, or quantification of RT-related outcomes for patients with lung cancer. Studies involving all RT modalities, including stereotactic body RT and particle therapy, were included. Meta-analyses were conducted to investigate differences in dose-function parameters between anatomic and functional RT planning techniques, as well as to investigate correlations of dose-function parameters with grade 2+ radiation pneumonitis (RP). One hundred seventy-eight studies were included in the narrative synthesis. We report on FLI modalities, dose-response quantification, functional lung (FL) definitions, FL avoidance techniques, and correlations between FL irradiation and toxicity. Meta-analysis results show that FL avoidance planning gives statistically significant absolute reductions of 3.22% to the fraction of well-ventilated lung receiving 20 Gy or more, 3.52% to the fraction of well-perfused lung receiving 20 Gy or more, 1.3 Gy to the mean dose to the well-ventilated lung, and 2.41 Gy to the mean dose to the well-perfused lung. Increases in the threshold value for defining FL are associated with decreases in functional parameters. For intensity modulated RT and volumetric modulated arc therapy, avoidance planning results in a 13% rate of grade 2+ RP, which is reduced compared with results from conventional planning cohorts. A trend of increased predictive ability for grade 2+ RP was seen in models using FL information but was not statistically significant. FLI shows promise as a method to spare FL during thoracic RT, but interventional trials related to FL avoidance planning are sparse. Such trials are critical to understanding the effect of FL avoidance planning on toxicity reduction and patient outcomes.
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Affiliation(s)
- Julie Midroni
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada
| | - Rohan Salunkhe
- Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada
| | - Zhihui Liu
- Biostatistics, Princess Margaret Cancer Center, Toronto, Canada
| | - Ronald Chow
- Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
| | - Gabriel Boldt
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
| | - David Palma
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada; Ontario Institute for Cancer Research, Toronto, Canada
| | - Douglas Hoover
- London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, United States of America; Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, United States of America
| | - Srinivas Raman
- Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada.
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Gu J, Qiu Q, Zhu J, Cao Q, Hou Z, Li B, Shu H. Deep learning-based combination of [18F]-FDG PET and CT images for producing pulmonary perfusion image. Med Phys 2023; 50:7779-7790. [PMID: 37387645 DOI: 10.1002/mp.16566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 06/07/2023] [Indexed: 07/01/2023] Open
Abstract
BACKGROUND The main application of [18F] FDG-PET (18 FDG-PET) and CT images in oncology is tumor identification and quantification. Combining PET and CT images to mine pulmonary perfusion information for functional lung avoidance radiation therapy (FLART) is desirable but remains challenging. PURPOSE To develop a deep-learning-based (DL) method to combine 18 FDG-PET and CT images for producing pulmonary perfusion images (PPI). METHODS Pulmonary technetium-99 m-labeled macroaggregated albumin SPECT (PPISPECT ), 18 FDG-PET, and CT images obtained from 53 patients were enrolled. CT and PPISPECT images were rigidly registered, and registration displacement was subsequently used to align 18 FDG-PET and PPISPECT images. The left/right lung was separated and rigidly registered again to improve the registration accuracy. A DL model based on 3D Unet architecture was constructed to directly combine multi-modality 18 FDG-PET and CT images for producing PPI (PPIDLM ). 3D Unet architecture was used as the basic architecture, and the input was expanded from a single-channel to a dual-channel to combine multi-modality images. For comparative evaluation, 18 FDG-PET images were also used alone to generate PPIDLPET . Sixty-seven samples were randomly selected for training and cross-validation, and 36 were used for testing. The Spearman correlation coefficient (rs ) and multi-scale structural similarity index measure (MS-SSIM) between PPIDLM /PPIDLPET and PPISPECT were computed to assess the statistical and perceptual image similarities. The Dice similarity coefficient (DSC) was calculated to determine the similarity between high-/low- functional lung (HFL/LFL) volumes. RESULTS The voxel-wise rs and MS-SSIM of PPIDLM /PPIDLPET were 0.78 ± 0.04/0.57 ± 0.03, 0.93 ± 0.01/0.89 ± 0.01 for cross-validation and 0.78 ± 0.11/0.55 ± 0.18, 0.93 ± 0.03/0.90 ± 0.04 for testing. PPIDLM /PPIDLPET achieved averaged DSC values of 0.78 ± 0.03/0.64 ± 0.02 for HFL and 0.83 ± 0.01/0.72 ± 0.03 for LFL in the training dataset and 0.77 ± 0.11/0.64 ± 0.12, 0.82 ± 0.05/0.72 ± 0.06 in the testing dataset. PPIDLM yielded a stronger correlation and higher MS-SSIM with PPISPECT than PPIDLPET (p < 0.001). CONCLUSIONS The DL-based method integrates lung metabolic and anatomy information for producing PPI and significantly improved the accuracy over methods based on metabolic information alone. The generated PPIDLM can be applied for pulmonary perfusion volume segmentation, which is potentially beneficial for FLART treatment plan optimization.
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Affiliation(s)
- Jiabing Gu
- Laboratory of Image Science and Technology, School of Computer Science and Engineering Southeast University, Nanjing, Jiangsu, P.R. China
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, P.R. China
| | - Qingtao Qiu
- Laboratory of Image Science and Technology, School of Computer Science and Engineering Southeast University, Nanjing, Jiangsu, P.R. China
| | - Jian Zhu
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, P.R. China
- Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University, Qingdao, P.R. China
| | - Qiang Cao
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, P.R. China
| | - Zhen Hou
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, P.R. China
| | - Baosheng Li
- Laboratory of Image Science and Technology, School of Computer Science and Engineering Southeast University, Nanjing, Jiangsu, P.R. China
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, P.R. China
| | - Huazhong Shu
- Laboratory of Image Science and Technology, School of Computer Science and Engineering Southeast University, Nanjing, Jiangsu, P.R. China
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Shiinoki T, Fujimoto K, Kawazoe Y, Yuasa Y, Kajima M, Manabe Y, Hirano T, Matsunaga K, Tanaka H. Assessing four-dimensional CT stress maps derived from patient-specific biomechanical models of the lung with pulmonary function test data in lung cancer patients. Br J Radiol 2023; 96:20221149. [PMID: 37393529 PMCID: PMC10461275 DOI: 10.1259/bjr.20221149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 05/23/2023] [Accepted: 06/12/2023] [Indexed: 07/03/2023] Open
Abstract
OBJECTIVE This study aims to retrospectively compare the stress map of the lung with pulmonary function test (PFT) results in lung cancer patients and to evaluate the potential of the stress map as an imaging biomarker for chronic obstructive pulmonary disease (COPD). METHODS 25 lung cancer patients with pre-treatment four-dimensional CT (4DCT) and PFT data were retrospectively analysed. PFT metrics were used to diagnose obstructive lung disease. For each patient, forced expiratory volume in 1 s (FEV1 % predicted) and the ratio of FEV1 and forced vital capacity (FEV1/FVC) were recorded. 4DCT and biomechanical model-deformable image registration (BM-DIR) were used to obtain the lung stress map. The relationship between the mean of the total lung stress and PFT data was evaluated, and the COPD classification grade was also evaluated. RESULTS The mean values of the total lung stress and FEV1 % predicted showed a significant strong correlation [R = 0.833, (p < 0.001)]. The mean values and FEV1/FVC showed a significant strong correlation [R = 0.805, (p < 0.001)]. For the total lung stress, the area under the curve and the optimal cut-off value were 0.94 and 510.8 Pa for the classification of normal or abnormal lung function, respectively. CONCLUSION This study has demonstrated the potential of lung stress maps based on BM-DIR to accurately assess lung function by comparing them with PFT data. ADVANCES IN KNOWLEDGE The derivation of stress map directly from 4DCT is novel method. The BM-DIR-based lung stress map can provide an accurate assessment of lung function.
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Affiliation(s)
- Takehiro Shiinoki
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Koya Fujimoto
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Yusuke Kawazoe
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Yuki Yuasa
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Miki Kajima
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Yuki Manabe
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Tsunahiko Hirano
- Department of Respiratory Medicine and Infectious Disease, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Kazuto Matsunaga
- Department of Respiratory Medicine and Infectious Disease, Graduate School of Medicine, Yamaguchi University, Ube, Japan
| | - Hidekazu Tanaka
- Department of Radiation Oncology, Graduate School of Medicine, Yamaguchi University, Ube, Japan
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Huang YH, Teng X, Zhang J, Chen Z, Ma Z, Ren G, Kong FMS, Ge H, Cai J. Respiratory Invariant Textures From Static Computed Tomography Scans for Explainable Lung Function Characterization. J Thorac Imaging 2023; 38:286-296. [PMID: 37265243 DOI: 10.1097/rti.0000000000000717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
PURPOSE The inherent characteristics of lung tissue independent of breathing maneuvers may provide fundamental information for function assessment. This paper attempted to correlate textural signatures from computed tomography (CT) with pulmonary function measurements. MATERIALS AND METHODS Twenty-one lung cancer patients with thoracic 4-dimensional CT, DTPA-single-photon emission CT ventilation ( VNM ) scans, and available spirometry measurements (forced expiratory volume in 1 s, FEV 1 ; forced vital capacity, FVC; and FEV 1 /FVC) were collected. In subregional feature discovery, function-correlated candidates were identified from 79 radiomic features based on the statistical strength to differentiate defected/nondefected lung regions. Feature maps (FMs) of selected candidates were generated on 4-dimensional CT phases for a voxel-wise feature distribution study. Quantitative metrics were applied for validations, including the Spearman correlation coefficient (SCC) and the Dice similarity coefficient for FM- VNM spatial agreement assessments, intraclass correlation coefficient for FM interphase robustness evaluations, and FM-spirometry comparisons. RESULTS At the subregion level, 8 function-correlated features were identified (effect size>0.330). The FMs of candidates yielded moderate-to-strong voxel-wise correlations with the reference VNM . The FMs of gray level dependence matrix dependence nonuniformity showed the highest robust (intraclass correlation coefficient=0.96 and P <0.0001) spatial correlation, with median SCCs ranging from 0.54 to 0.59 throughout the 10 breathing phases. Its phase-averaged FM achieved a median SCC of 0.60, a median Dice similarity coefficient of 0.60 (0.65) for high (low) functional lung volumes, and a correlation of 0.565 (0.646) between the spatially averaged feature values and FEV 1 (FEV 1 /FVC). CONCLUSIONS The results provide further insight into the underlying association of specific pulmonary textures with both local ( VNM ) and global (FEV 1 /FVC, FEV 1 ) functions. Further validations of the FM generalizability and the standardization of implementation protocols are warranted before clinically relevant investigations.
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Affiliation(s)
- Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Zhi Chen
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Zongrui Ma
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
| | - Feng-Ming Spring Kong
- Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR
- Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital, Shenzhen
| | - Hong Ge
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University
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Astley JR, Biancardi AM, Marshall H, Hughes PJC, Collier GJ, Hatton MQ, Wild JM, Tahir BA. A hybrid model- and deep learning-based framework for functional lung image synthesis from multi-inflation CT and hyperpolarized gas MRI. Med Phys 2023; 50:5657-5670. [PMID: 36932692 PMCID: PMC10946819 DOI: 10.1002/mp.16369] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 02/25/2023] [Accepted: 03/04/2023] [Indexed: 03/19/2023] Open
Abstract
BACKGROUND Hyperpolarized gas MRI is a functional lung imaging modality capable of visualizing regional lung ventilation with exceptional detail within a single breath. However, this modality requires specialized equipment and exogenous contrast, which limits widespread clinical adoption. CT ventilation imaging employs various metrics to model regional ventilation from non-contrast CT scans acquired at multiple inflation levels and has demonstrated moderate spatial correlation with hyperpolarized gas MRI. Recently, deep learning (DL)-based methods, utilizing convolutional neural networks (CNNs), have been leveraged for image synthesis applications. Hybrid approaches integrating computational modeling and data-driven methods have been utilized in cases where datasets are limited with the added benefit of maintaining physiological plausibility. PURPOSE To develop and evaluate a multi-channel DL-based method that combines modeling and data-driven approaches to synthesize hyperpolarized gas MRI lung ventilation scans from multi-inflation, non-contrast CT and quantitatively compare these synthetic ventilation scans to conventional CT ventilation modeling. METHODS In this study, we propose a hybrid DL configuration that integrates model- and data-driven methods to synthesize hyperpolarized gas MRI lung ventilation scans from a combination of non-contrast, multi-inflation CT and CT ventilation modeling. We used a diverse dataset comprising paired inspiratory and expiratory CT and helium-3 hyperpolarized gas MRI for 47 participants with a range of pulmonary pathologies. We performed six-fold cross-validation on the dataset and evaluated the spatial correlation between the synthetic ventilation and real hyperpolarized gas MRI scans; the proposed hybrid framework was compared to conventional CT ventilation modeling and other non-hybrid DL configurations. Synthetic ventilation scans were evaluated using voxel-wise evaluation metrics such as Spearman's correlation and mean square error (MSE), in addition to clinical biomarkers of lung function such as the ventilated lung percentage (VLP). Furthermore, regional localization of ventilated and defect lung regions was assessed via the Dice similarity coefficient (DSC). RESULTS We showed that the proposed hybrid framework is capable of accurately replicating ventilation defects seen in the real hyperpolarized gas MRI scans, achieving a voxel-wise Spearman's correlation of 0.57 ± 0.17 and an MSE of 0.017 ± 0.01. The hybrid framework significantly outperformed CT ventilation modeling alone and all other DL configurations using Spearman's correlation. The proposed framework was capable of generating clinically relevant metrics such as the VLP without manual intervention, resulting in a Bland-Altman bias of 3.04%, significantly outperforming CT ventilation modeling. Relative to CT ventilation modeling, the hybrid framework yielded significantly more accurate delineations of ventilated and defect lung regions, achieving a DSC of 0.95 and 0.48 for ventilated and defect regions, respectively. CONCLUSION The ability to generate realistic synthetic ventilation scans from CT has implications for several clinical applications, including functional lung avoidance radiotherapy and treatment response mapping. CT is an integral part of almost every clinical lung imaging workflow and hence is readily available for most patients; therefore, synthetic ventilation from non-contrast CT can provide patients with wider access to ventilation imaging worldwide.
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Affiliation(s)
- Joshua R Astley
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, UK
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Alberto M Biancardi
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Helen Marshall
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Paul J C Hughes
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Guilhem J Collier
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Matthew Q Hatton
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, UK
| | - Jim M Wild
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
- Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, UK
| | - Bilal A Tahir
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, UK
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
- Insigneo Institute for In Silico Medicine, The University of Sheffield, Sheffield, UK
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Iqbal GMD, Zhang H, D'Souza W, Ha L, Rosenberger JM. Four-dimensional computed tomography-based ventilation imaging in intensity-modulated radiation therapy treatment planning for pulmonary functional avoidance. J Appl Clin Med Phys 2023:e13920. [PMID: 36727606 DOI: 10.1002/acm2.13920] [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: 03/31/2022] [Revised: 10/30/2022] [Accepted: 01/12/2023] [Indexed: 02/03/2023] Open
Abstract
PURPOSE To incorporate four-dimensional computed tomography (4DCT)-based ventilation imaging into intensity-modulated radiation therapy (IMRT) treatment planning for pulmonary functional avoidance. METHODS AND MATERIALS Nineteen locally advanced lung cancer patients are retrospectively studied. 4DCT images are employed to create ventilation maps for each patient via a density-change-based algorithm with mass correction. The regional ventilation is directly incorporated into the mathematical formulation of a direct aperture optimization model in IMRT treatment planning to achieve functional avoidance and a voxel-based treatment plan. The proposed functional avoidance planning and voxel-based planning are compared to the conventional treatment planning approach purely based on the anatomy of patients. Paired sample t-tests are conducted to see whether dosimetric differences among the three approaches are significant. RESULTS Similar planning target volume (PTV) coverage is achieved by anatomical, functional avoidance, and voxel-based approaches. The voxel-based treatment planning performs better than both functional avoidance and anatomical planning to the lung. For a total lung, the average volume reductions in a functional avoidance plan from an anatomical plan, a voxel-based plan from an anatomical plan, and a voxel-based plan from a functional avoidance plan are 7.0% , 16.8%, and 10.6%, respectively for V40 ; and 0.4%, 6.4%, and 6.0%, respectively for mean Lung Dose (MLD). For a functional lung, the reductions are 8.8% , 17.2%, and 9.2%, respectively, for fV40 ; and 1.1%, 6.2%, and 5.2%, respectively, for functional mean lung dose (fMLD). These reductions are obtained without significantly increasing doses to other organs-at-risk. All the pairwise treatment planning comparisons for both total lung and functional lung are statistically significant (p-value < α = 0.05 $< \alpha =0.05$ ) except for the functional avoidance plan with the anatomical plan pair in which the p-value > α = 0.05 $> \alpha =0.05$ . From these results, we can conclude that voxel-based treatment planning outperforms both anatomical and functional-avoidance planning. CONCLUSIONS We propose a treatment planning framework that directly utilizes functional images and compares voxel-based treatment planning with functional avoidance and anatomical treatment planning.
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Affiliation(s)
| | - Hao Zhang
- University of Maryland Medical Systems, Linthicum, Maryland, USA
| | - Wareen D'Souza
- University of Maryland Medical Systems, Linthicum, Maryland, USA
| | - Lidan Ha
- College of Business, Coppin State University, Baltimore, Maryland, USA
| | - Jay M Rosenberger
- Industrial, Manufacturing, & Systems Engineering, University of Texas at Arlington, Arlington, Texas, USA
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Katsuta Y, Kadoya N, Kajikawa T, Mouri S, Kimura T, Takeda K, Yamamoto T, Imano N, Tanaka S, Ito K, Kanai T, Nakajima Y, Jingu K. Radiation pneumonitis prediction model with integrating multiple dose-function features on 4DCT ventilation images. Phys Med 2023; 105:102505. [PMID: 36535238 DOI: 10.1016/j.ejmp.2022.11.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 11/18/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022] Open
Abstract
PURPOSE Radiation pneumonitis (RP) is dose-limiting toxicity for non-small-cell cancer (NSCLC). This study developed an RP prediction model by integrating dose-function features from computed four-dimensional computed tomography (4DCT) ventilation using the least absolute shrinkage and selection operator (LASSO). METHODS Between 2013 and 2020, 126 NSCLC patients were included in this study who underwent a 4DCT scan to calculate ventilation images. We computed two sets of candidate dose-function features from (1) the percentage volume receiving > 20 Gy or the mean dose on the functioning zones determined with the lower cutoff percentile ventilation value, (2) the functioning zones determined with lower and upper cutoff percentile ventilation value using 4DCT ventilation images. An RP prediction model was developed by LASSO while simultaneously determining the regression coefficient and feature selection through fivefold cross-validation. RESULTS We found 39.3 % of our patients had a ≥ grade 2 RP. The mean area under the curve (AUC) values for the developed models using clinical, dose-volume, and dose-function features with a lower cutoff were 0.791, and the mean AUC values with lower and upper cutoffs were 0.814. The relative regression coefficient (RRC) on dose-function features with upper and lower cutoffs revealed a relative impact of dose to each functioning zone to RP. RRCs were 0.52 for the mean dose on the functioning zone, with top 20 % of all functioning zone was two times greater than that of 0.19 for these with 60 %-80 % and 0.17 with 40 %-60 % (P < 0.01). CONCLUSIONS The introduction of dose-function features computed from functioning zones with lower and upper cutoffs in a machine learning framework can improve RP prediction. The RRC given by LASSO using dose-function features allows for the quantification of the RP impact of dose on each functioning zones and having the potential to support treatment planning on functional image-guided radiotherapy.
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Affiliation(s)
- Yoshiyuki Katsuta
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan.
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Tomohiro Kajikawa
- Department of Radiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Shina Mouri
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Tomoki Kimura
- Department of Radiation Oncology, Kochi Medical School, Kochi University, Nangoku, Japan
| | - Kazuya Takeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Takaya Yamamoto
- Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Nobuki Imano
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Shohei Tanaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kengo Ito
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Takayuki Kanai
- Department of Radiation Oncology, Yamagata University, Yamagata, Japan
| | - Yujiro Nakajima
- Department of Radiological Sciences, Komazawa University, Tokyo, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
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Xue P, Fu Y, Zhang J, Ma L, Ren M, Zhang Z, Dong E. Effective lung ventilation estimation based on 4D CT image registration and supervoxels. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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9
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Chen Z, Huang YH, Kong FM, Ho WY, Ren G, Cai J. A super-voxel-based method for generating surrogate lung ventilation images from CT. Front Physiol 2023; 14:1085158. [PMID: 37179833 PMCID: PMC10171197 DOI: 10.3389/fphys.2023.1085158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 04/06/2023] [Indexed: 05/15/2023] Open
Abstract
Purpose: This study aimed to develop and evaluate CTVISVD , a super-voxel-based method for surrogate computed tomography ventilation imaging (CTVI). Methods and Materials: The study used four-dimensional CT (4DCT) and single-photon emission computed tomography (SPECT) images and corresponding lung masks from 21 patients with lung cancer obtained from the Ventilation And Medical Pulmonary Image Registration Evaluation dataset. The lung volume of the exhale CT for each patient was segmented into hundreds of super-voxels using the Simple Linear Iterative Clustering (SLIC) method. These super-voxel segments were applied to the CT and SPECT images to calculate the mean density values (D mean) and mean ventilation values (Vent mean), respectively. The final CT-derived ventilation images were generated by interpolation from the D mean values to yield CTVISVD. For the performance evaluation, the voxel- and region-wise differences between CTVISVD and SPECT were compared using Spearman's correlation and the Dice similarity coefficient index. Additionally, images were generated using two deformable image registration (DIR)-based methods, CTVIHU and CTVIJac, and compared with the SPECT images. Results: The correlation between the D mean and Vent mean of the super-voxel was 0.59 ± 0.09, representing a moderate-to-high correlation at the super-voxel level. In the voxel-wise evaluation, the CTVISVD method achieved a stronger average correlation (0.62 ± 0.10) with SPECT, which was significantly better than the correlations achieved with the CTVIHU (0.33 ± 0.14, p < 0.05) and CTVIJac (0.23 ± 0.11, p < 0.05) methods. For the region-wise evaluation, the Dice similarity coefficient of the high functional region for CTVISVD (0.63 ± 0.07) was significantly higher than the corresponding values for the CTVIHU (0.43 ± 0.08, p < 0.05) and CTVIJac (0.42 ± 0.05, p < 0.05) methods. Conclusion: The strong correlation between CTVISVD and SPECT demonstrates the potential usefulness of this novel method of ventilation estimation for surrogate ventilation imaging.
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Affiliation(s)
- Zhi Chen
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Feng-Ming Kong
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, China
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China
| | - Wai Yin Ho
- Department of Nuclear Medicine, Queen Mary Hospital, Hong Kong, China
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- *Correspondence: Ge Ren, ; Jing Cai,
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- *Correspondence: Ge Ren, ; Jing Cai,
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10
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Yamamoto T, Katsuta Y, Sato K, Tsukita Y, Umezawa R, Takahashi N, Suzuki Y, Takeda K, Kishida K, Omata S, Miyauchi E, Saito R, Kadoya N, Jingu K. Longitudinal analyses and predictive factors of radiation-induced lung toxicity-related parameters after stereotactic radiotherapy for lung cancer. PLoS One 2022; 17:e0278707. [PMID: 36459528 PMCID: PMC9718403 DOI: 10.1371/journal.pone.0278707] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/21/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND AND PURPOSE The purpose of this prospective study was to investigate changes in longitudinal parameters after stereotactic radiotherapy for lung cancer and to identify possible pretreatment factors related to radiation-induced lung toxicity and the decline in pulmonary function after radiotherapy. MATERIALS AND METHODS Protocol-specified examinations, including 4-D CT, laboratory tests, pulmonary function tests (PFTs) and body composition measurements, were performed before SRT and at 1 month, 4 months and 12 months after stereotactic radiotherapy. Longitudinal differences were tested by using repeated-measures analysis of variance. Correlations were examined by using the Pearson product-moment correlation coefficient (r). RESULTS Sixteen patients were analyzed in this study. During a median follow-up period of 26.6 months, grade 1 and 2 lung toxicity occurred in 11 patients and 1 patient, respectively. The mean Hounsfield units (HU) and standard deviation (SD) of the whole lung, as well as sialylated carbohydrate antigen KL-6 (KL-6) and surfactant protein-D (SP-D), peaked at 4 months after radiotherapy (p = 0.11, p<0.01, p = 0.04 and p<0.01, respectively). At 4 months, lung V20 Gy (%) and V40 Gy (%) were correlated with changes in SP-D, whereas changes in the mean HU of the lung were related to body mass index and lean body mass index (r = 0.54, p = 0.02; r = 0.57, p = 0.01; r = 0.69, p<0.01; and r = 0.69, p<0.01, respectively). The parameters of PFTs gradually declined over time. When regarding the change in PFTs from pretreatment to 12 months, lung V5 Gy (cc) showed significant correlations with diffusion capacity for carbon monoxide (DLCO), DLCO/alveolar volume and the relative change in DLCO (r = -0.72, p<0.01; r = -0.73, p<0.01; and r = -0.63, p = 0.01, respectively). CONCLUSIONS The results indicated that some parameters peaked at 4 months, but PFTs were the lowest at 12 months. Significant correlations between lung V5 Gy (cc) and changes in DLCO and DLCO/alveolar volume were observed.
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Affiliation(s)
- Takaya Yamamoto
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
- * E-mail:
| | - Yoshiyuki Katsuta
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kiyokazu Sato
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yoko Tsukita
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Rei Umezawa
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Noriyoshi Takahashi
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yu Suzuki
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kazuya Takeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Keita Kishida
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - So Omata
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Eisaku Miyauchi
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Ryota Saito
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
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11
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Zhou P, Wang R, Yu H, Liao Z, Zhang Y, Huang Z, Zhang S. 4DCT ventilation function image-based functional lung protection for esophageal cancer radiotherapy. Strahlenther Onkol 2022; 199:445-455. [PMID: 36331584 DOI: 10.1007/s00066-022-02012-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 09/25/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND 4DCT (four-dimensional computed tomography) can effectively obtain functional lung ventilation images for patients and integrate them into radiotherapy treatment planning. Studies have not been performed on esophageal cancer, and there is no clear consensus on the optimal functional lung threshold for functional lung. METHODS Functional lung images were generated for 11 patients with esophageal cancer. The correlation between the dose-volume parameters of functional lung (FL) as defined by different thresholds and the change of PFT/PDFT (pulmonary [diffusion] function test) metrics before and after radiotherapy were evaluated. FL-sparing planning was generated for each patient to preserve the functional lung and compared to conventional anatomical CT (non-sparing) planning. RESULTS There was a significant positive correlation between the FL0.8 (defined Jacobian value ≤ 0.8), FL0.84, and FL0.9 dose-volume parameters and ΔFEV1/FVC (reduction before and after radiotherapy), and the FL0.8‑V30 correlation was the strongest (r = 0.819, P < 0.01). The FL-sparing planning had a target area conformity index and homogeneity index comparable to the non-sparing planning (P > 0.05). For FL, the FL-sparing planning achieved lower FL-MLD (6.30 ± 2.14 Gy vs. 7.83 ± 2.70 Gy), V10 (17.13 ± 7.70% vs. 27.40 ± 9.48%), and V20 (6.96 ± 3.85% vs. 11.63 ± 7.19%) compared to the non-sparing planning (P < 0.05), while heart and spinal cord doses were not significantly different between the two planning groups. CONCLUSION The 4DCT-based FL irradiation dose for esophageal cancer was significantly associated with a decrease in FEV1/FVC. The optimal FL defined as a Jacobian value ≤ 0.8 or about 21% of the whole lung volume may be a good choice. FL-sparing planning significantly reduced the FL dose without compromising target area coverage.
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Affiliation(s)
- Pixiao Zhou
- Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Ruihao Wang
- Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Hui Yu
- Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Zhiwei Liao
- Department of Radiotherapy, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Ying Zhang
- Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Zouqin Huang
- Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Shuxu Zhang
- Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
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12
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Yang Z, Lafata KJ, Chen X, Bowsher J, Chang Y, Wang C, Yin FF. Quantification of lung function on CT images based on pulmonary radiomic filtering. Med Phys 2022; 49:7278-7286. [PMID: 35770964 DOI: 10.1002/mp.15837] [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: 06/14/2021] [Revised: 06/06/2022] [Accepted: 06/08/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE To develop a radiomics filtering technique for characterizing spatial-encoded regional pulmonary ventilation information on lung computed tomography (CT). METHODS The lung volume was segmented on 46 CT images, and a 3D sliding window kernel was implemented across the lung volume to capture the spatial-encoded image information. Fifty-three radiomic features were extracted within the kernel, resulting in a fourth-order tensor object. As such, each voxel coordinate of the original lung was represented as a 53-dimensional feature vector, such that radiomic features could be viewed as feature maps within the lungs. To test the technique as a potential pulmonary ventilation biomarker, the radiomic feature maps were compared to paired functional images (Galligas PET or DTPA-SPECT) based on the Spearman correlation (ρ) analysis. RESULTS The radiomic feature maps GLRLM-based Run-Length Non-Uniformity and GLCOM-based Sum Average are found to be highly correlated with the functional imaging. The achieved ρ (median [range]) for the two features are 0.46 [0.05, 0.67] and 0.45 [0.21, 0.65] across 46 patients and 2 functional imaging modalities, respectively. CONCLUSIONS The results provide evidence that local regions of sparsely encoded heterogeneous lung parenchyma on CT are associated with diminished radiotracer uptake and measured lung ventilation defects on PET/SPECT imaging. These findings demonstrate the potential of radiomics to serve as a complementary tool to the current lung quantification techniques and provide hypothesis-generating data for future studies.
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Affiliation(s)
- Zhenyu Yang
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
| | - Kyle J Lafata
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA
- Department of Radiology, Duke University, Durham, North Carolina, USA
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA
| | - Xinru Chen
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
| | - James Bowsher
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
| | - Yushi Chang
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA
| | - Chunhao Wang
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA
- Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
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13
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Zhou PX, Zhang SX. Functional lung imaging in thoracic tumor radiotherapy: Application and progress. Front Oncol 2022; 12:908345. [PMID: 36212454 PMCID: PMC9544588 DOI: 10.3389/fonc.2022.908345] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 08/17/2022] [Indexed: 12/12/2022] Open
Abstract
Radiotherapy plays an irreplaceable and unique role in treating thoracic tumors, but the occurrence of radiation-induced lung injury has limited the increase in tumor target doses and has influenced patients’ quality of life. However, the introduction of functional lung imaging has been incorporating functional lungs into radiotherapy planning. The design of the functional lung protection plan, while meeting the target dose requirements and dose limitations of the organs at risk (OARs), minimizes the radiation dose to the functional lung, thus reducing the occurrence of radiation-induced lung injury. In this manuscript, we mainly reviewed the lung ventilation or/and perfusion functional imaging modalities, application, and progress, as well as the results based on the functional lung protection planning in thoracic tumors. In addition, we also discussed the problems that should be explored and further studied in the practical application based on functional lung radiotherapy planning.
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Affiliation(s)
- Pi-Xiao Zhou
- Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
- Department of Oncology, The First People's Hospital of Changde City, Changde, China
| | - Shu-Xu Zhang
- Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
- *Correspondence: Shu-Xu Zhang,
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14
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Zhou PX, Wang RH, Yu H, Zhang Y, Zhang GQ, Zhang SX. Different functional lung-sparing strategies and radiotherapy techniques for patients with esophageal cancer. Front Oncol 2022; 12:898141. [PMID: 36091164 PMCID: PMC9459335 DOI: 10.3389/fonc.2022.898141] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 07/29/2022] [Indexed: 12/24/2022] Open
Abstract
BackgroundIntegration of 4D-CT ventilation function images into esophageal cancer radiation treatment planning aimed to assess dosimetric differences between different functional lung (FL) protection strategies and radiotherapy techniques.MethodsA total of 15 patients with esophageal cancer who had 4D-CT scans were included. Lung ventilation function images based on Jacobian values were obtained by deformation image registration and ventilation imaging algorithm. Several different plans were designed for each patient: clinical treatment planning (non-sparing planning), the same beam distribution to FL-sparing planning, three fixed-beams FL-sparing intensity-modulated radiation therapy (IMRT) planning (5F-IMRT, 7F-IMRT, 9F-IMRT), and two FL-sparing volumetric modulated arc therapy (VMAT) planning [1F-VMAT (1-Arc), 2F-VMAT (2-Arc)]. The dosimetric parameters of the planning target volume (PTV) and organs at risk (OARs) were compared and focused on dosimetric differences in FL.ResultsThe FL-sparing planning compared with the non-sparing planning significantly decreased the FL-Dmean, V5-30 and Lungs-Dmean, V10-30 (Vx: volume of receiving ≥X Gy), although it slightly compromised PTV conformability and increased Heart-V40 (P< 0.05). The 5F-IMRT had the lowest PTV-conformability index (CI) but had a lower Lungs and Heart irradiation dose compared with those of the 7F-IMRT and 9F-IMRT (P< 0.05). The 2F-VMAT had higher PTV-homogeneity index (HI) and reduced irradiation dose to FL, Lungs, and Heart compared to those of the 1F-VMAT planning (P< 0.05). The 2F-VMAT had higher PTV conformability and homogeneity and decreased FL-Dmean, V5-20 and Lungs-Dmean, V5-10 but correspondingly increased spinal cord-Dmean compared with those of the 5F-IMRT planning (P< 0.05).ConclusionIn this study, 4D-CT ventilation function image-based FL-sparing planning for esophageal cancer can effectively reduce the dose of the FL. The 2F-VMAT planning is better than the 5F-IMRT planning in reducing the dose of FL.
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15
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Vliegenthart R, Fouras A, Jacobs C, Papanikolaou N. Innovations in thoracic imaging: CT, radiomics, AI and x-ray velocimetry. Respirology 2022; 27:818-833. [PMID: 35965430 PMCID: PMC9546393 DOI: 10.1111/resp.14344] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 07/08/2022] [Indexed: 12/11/2022]
Abstract
In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of ‘non visual’ markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID‐19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x‐ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra‐low‐dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon‐counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X‐ray velocimetry integrates x‐ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation. See relatedEditorial
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Affiliation(s)
- Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.,Data Science in Health (DASH), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Colin Jacobs
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Nickolas Papanikolaou
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.,AI Hub, The Royal Marsden NHS Foundation Trust, London, UK.,The Institute of Cancer Research, London, UK
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16
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Yaremko BP, Capaldi DP, Sheikh K, Palma DA, Warner A, Dar AR, Yu E, Rodrigues GB, Louie AV, Landis M, Sanatani M, Vincent MD, Younus J, Kuruvilla S, Chen JZ, Erickson A, Gaede S, Parraga G, Hoover DA. Functional Lung Avoidance for Individualized Radiotherapy (FLAIR): Results of a Double-Blind, Randomized Controlled Trial. Int J Radiat Oncol Biol Phys 2022; 113:1072-1084. [DOI: 10.1016/j.ijrobp.2022.04.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/29/2022] [Accepted: 04/30/2022] [Indexed: 10/18/2022]
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17
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Quantitative analysis of changes in lung density by dynamic chest radiography in association with CT values: a virtual imaging study and initial clinical corroboration. Radiol Phys Technol 2022; 15:45-53. [PMID: 35091991 DOI: 10.1007/s12194-021-00648-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 12/20/2021] [Accepted: 12/22/2021] [Indexed: 10/19/2022]
Abstract
Dynamic chest radiography (DCR) identifies pulmonary impairments as decreased changes in radiographic lung density during respiration (Δpixel values), but not as scaled/standardized computed tomography (CT) values. Quantitative analysis correlated with CT values is beneficial for a better understanding of Δpixel values in DCR-based assessment of pulmonary function. The present study aimed to correlate Δpixel values from DCR with changes in CT values during respiration (ΔCT values) through a computer-based phantom study. A total of 20 four-dimensional computational phantoms during forced breathing were created to simulate both CT and projection images of the same virtual patients. The Δpixel and ΔCT values of the lung fields were correlated on a regression line, and the inclination was statistically evaluated to determine whether there were significant differences among physical types, sex, and breathing methods. The resulting conversion expression was also assessed in the DCR images of 37 patients. The resulting Δpixel values for 30/37 (81%) real patients, 6/7 (86%) normal controls, and 24/30 (80%) chronic obstructive pulmonary disorder patients were within the range of ΔCT values ± standard deviation (SD) reported in a previous study. In addition, no significant differences were detected for each condition of thoracic breathing, suggesting that the same regression line inclination values measured across the entire lung can be used for the conversion of Δpixel values, providing a quantitative analysis that can be correlated with ΔCT values. The developed conversion expression may be helpful for improving the understanding of respiratory changes using radiographic lung densities from DCR-based assessments of pulmonary function.
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18
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Katsuta Y, Kadoya N, Mouri S, Tanaka S, Kanai T, Takeda K, Yamamoto T, Ito K, Kajikawa T, Nakajima Y, Jingu K. Prediction of radiation pneumonitis with machine learning using 4D-CT based dose-function features. JOURNAL OF RADIATION RESEARCH 2022; 63:71-79. [PMID: 34718683 PMCID: PMC8776701 DOI: 10.1093/jrr/rrab097] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 08/20/2021] [Indexed: 06/13/2023]
Abstract
In this article, we highlight the fundamental importance of the simultaneous use of dose-volume histogram (DVH) and dose-function histogram (DFH) features based on functional images calculated from 4-dimensional computed tomography (4D-CT) and deformable image registration (DIR) in developing a multivariate radiation pneumonitis (RP) prediction model. The patient characteristics, DVH features and DFH features were calculated from functional images by Hounsfield unit (HU) and Jacobian metrics, for an RP grade ≥ 2 multivariate prediction models were computed from 85 non-small cell lung cancer patients. The prediction model is developed using machine learning via a kernel-based support vector machine (SVM) machine. In the patient cohort, 21 of the 85 patients (24.7%) presented with RP grade ≥ 2. The median area under curve (AUC) was 0.58 for the generated 50 prediction models with patient clinical features and DVH features. When HU metric and Jacobian metric DFH features were added, the AUC improved to 0.73 and 0.68, respectively. We conclude that predictive RP models that incorporate DFH features were successfully developed via kernel-based SVM. These results demonstrate that effectiveness of the simultaneous use of DVH features and DFH features calculated from 4D-CT and DIR on functional image-guided radiotherapy.
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Affiliation(s)
- Yoshiyuki Katsuta
- Corresponding author. Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan, Tel: +81-22-717-7312, Fax: +81-22-717-7316, E-mail:
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19
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Feng A, Shao Y, Wang H, Chen H, Gu H, Duan Y, Gan W, Xu Z. A novel lung-avoidance planning strategy based on 4DCT ventilation imaging and CT density characteristics for stage III non-small-cell lung cancer patients. Strahlenther Onkol 2021; 197:1084-1092. [PMID: 34351454 PMCID: PMC8604857 DOI: 10.1007/s00066-021-01821-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 07/02/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Functional planning based merely on 4DCT ventilation imaging has limitations. In this study, we proposed a radiotherapy planning strategy based on 4DCT ventilation imaging and CT density characteristics. MATERIALS AND METHODS For 20 stage III non-small-cell lung cancer (NSCLC) patients, clinical plans and lung-avoidance plans were generated. Through deformable image registration (DIR) and quantitative image analysis, a 4DCT ventilation map was calculated. High-, medium-, and low-ventilation regions of the lung were defined based on the ventilation value. In addition, the total lung was also divided into high-, medium-, and low-density areas according to the HU threshold. The lung-avoidance plan aimed to reduce the dose to functional and high-density lungs while meeting standard target and critical structure constraints. Standard and dose-function metrics were compared between the clinical and lung-avoidance plans. RESULTS Lung avoidance plans led to significant reductions in high-function and high-density lung doses, without significantly increasing other organ at risk (OAR) doses, but at the expense of a significantly degraded homogeneity index (HI) and conformity index (CI; p < 0.05) of the planning target volume (PTV) and a slight increase in monitor units (MU) as well as in the number of segments (p > 0.05). Compared with the clinical plan, the mean lung dose (MLD) in the high-function and high-density areas was reduced by 0.59 Gy and 0.57 Gy, respectively. CONCLUSION A lung-avoidance plan based on 4DCT ventilation imaging and CT density characteristics is feasible and implementable, with potential clinical benefits. Clinical trials will be crucial to show the clinical relevance of this lung-avoidance planning strategy.
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Affiliation(s)
- AiHui Feng
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, NO.241 West Huaihai Road, Xuhui District, 20030, Shanghai, China
| | - Yan Shao
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, NO.241 West Huaihai Road, Xuhui District, 20030, Shanghai, China
| | - Hao Wang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, NO.241 West Huaihai Road, Xuhui District, 20030, Shanghai, China
| | - Hua Chen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, NO.241 West Huaihai Road, Xuhui District, 20030, Shanghai, China
| | - HengLe Gu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, NO.241 West Huaihai Road, Xuhui District, 20030, Shanghai, China
| | - YanHua Duan
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, NO.241 West Huaihai Road, Xuhui District, 20030, Shanghai, China
| | - WuTian Gan
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, NO.241 West Huaihai Road, Xuhui District, 20030, Shanghai, China
| | - ZhiYong Xu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, NO.241 West Huaihai Road, Xuhui District, 20030, Shanghai, China.
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20
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Tachibana H. [11. Deformable Image Registration and Dose Warping]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2021; 77:733-738. [PMID: 34305060 DOI: 10.6009/jjrt.2021_jsrt_77.7.733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Hidenobu Tachibana
- Radiation Safety and Quality Assurance Division, National Cancer Center East Hospital
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21
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Forghani F, Patton T, Kwak J, Thomas D, Diot Q, Rusthoven C, Castillo R, Castillo E, Grills I, Guerrero T, Miften M, Vinogradskiy Y. Characterizing spatial differences between SPECT-ventilation and SPECT-perfusion in patients with lung cancer undergoing radiotherapy. Radiother Oncol 2021; 160:120-124. [PMID: 33964328 PMCID: PMC8489737 DOI: 10.1016/j.radonc.2021.04.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/24/2021] [Accepted: 04/28/2021] [Indexed: 12/25/2022]
Abstract
This study investigates agreement between ventilation and perfusion for lung cancer patients undergoing radiotherapy. Ventilation-perfusion scans of nineteen patients with stage III lung cancer from a prospective protocol were compared using voxel-wise Spearman correlation-coefficients. The presented results show in about 25% of patients, ventilation and perfusion exhibit lower agreement.
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Affiliation(s)
- Farnoush Forghani
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Taylor Patton
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States, United States(1); Department of Radiation Oncology, Mayo Clinic, Rochester, MN, United States(2)
| | - Jennifer Kwak
- Department of Radiology, University of Colorado School of Medicine, Aurora, CO, United States
| | - David Thomas
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Quentin Diot
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Chad Rusthoven
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States
| | - Edward Castillo
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, United States
| | - Inga Grills
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, United States
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, United States
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States, United States(1); Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, United States(2)
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22
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Wang C, Huang L, Xiao S, Li Z, Ye C, Xia L, Zhou X. Early prediction of lung lesion progression in COVID-19 patients with extended CT ventilation imaging. Eur J Nucl Med Mol Imaging 2021; 48:4339-4349. [PMID: 34137946 PMCID: PMC8210511 DOI: 10.1007/s00259-021-05435-8] [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: 04/20/2021] [Accepted: 05/25/2021] [Indexed: 12/25/2022]
Abstract
PURPOSE In the prediction of COVID-19 disease progression, a clear illustration and early determination of an area that will be affected by pneumonia remain great challenges. In this study, we aimed to predict and visualize the progression of lung lesions in COVID-19 patients in the early stage of illness by using chest CT. METHODS COVID-19 patients who underwent three chest CT scans in the progressive phase were retrospectively enrolled. An extended CT ventilation imaging (CTVI) method was proposed in this work that was adapted to use two chest CT scans acquired on different days, and then lung ventilation maps were generated. The prediction maps were obtained according to the fractional ventilation values, which were related to pulmonary regional function and tissue property changes. The third CT scan was used to validate whether the prediction maps could be used to distinguish healthy regions and potential lesions. RESULTS A total of 30 patients (mean age ± SD, 43 ± 10 years, 19 females, and 2-12 days between the second and third CT scans) were included in this study. The predicted lesion locations and sizes were almost the same as the true ones visualized in third CT scan. Quantitatively, the predicted lesion volumes and true lesion volumes showed both a good Pearson correlation (R2 = 0.80; P < 0.001) and good consistency in the Bland-Altman plot (mean bias = 0.04 cm3). Regarding the enlargements of the existing lesions, prediction results also exhibited a good Pearson correlation (R2 = 0.76; P < 0.001) with true lesion enlargements. CONCLUSION The present findings demonstrated that the extended CTVI method could accurately predict and visualize the progression of lung lesions in COVID-19 patients in the early stage of illness, which is helpful for physicians to predetermine the severity of COVID-19 pneumonia and make effective treatment plans in advance.
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Affiliation(s)
- Cheng Wang
- School of Physics, Huazhong University of Science and Technology, Wuhan, 430074, China.,State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, 430071, China
| | - Lu Huang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Sa Xiao
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, 430071, China
| | - Zimeng Li
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, 430071, China
| | - Chaohui Ye
- School of Physics, Huazhong University of Science and Technology, Wuhan, 430074, China.,State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, 430071, China
| | - Liming Xia
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Xin Zhou
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences - Wuhan National Laboratory for Optoelectronics, Wuhan, 430071, China.
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23
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Fujita Y, Kent M, Wisner E, Johnson L, Stern J, Qi L, Boone J, Yamamoto T. Combined Assessment of Pulmonary Ventilation and Perfusion with Single-Energy Computed Tomography and Image Processing. Acad Radiol 2021; 28:636-646. [PMID: 32534966 DOI: 10.1016/j.acra.2020.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 04/03/2020] [Accepted: 04/04/2020] [Indexed: 10/24/2022]
Abstract
RATIONALE AND OBJECTIVES To establish a proof-of-principle for combined assessment of pulmonary ventilation and perfusion using single-energy computed tomography (CT) and image processing/analysis (denoted as single-energy CT ventilation/perfusion imaging). MATERIALS AND METHODS Breath-hold CT scans were acquired at end-expiration and end-inspiration before injection of iodinated contrast agents, and repeated at end-inspiration after contrast injection for 17 canines (8 normal and 9 diseased lung subjects). Ventilation images were calculated with deformable image registration to map the end-expiratory and end-inspiratory CT images and quantitative analysis for regional volume changes as surrogates for ventilation. Perfusion images were calculated by subtracting the end-inspiratory precontrast CT from the deformably registered end-inspiratory postcontrast CT, yielding a map of regional Hounsfield unit enhancement as a surrogate for perfusion. Ventilation-perfusion matching, spatial heterogeneity, and gravitationally directed gradients were compared between two groups using a Wilcoxon rank-sum test. RESULTS The normal group had significantly higher Dice similarity coefficients for spatial overlap of segmented functional volumes between ventilation and perfusion (median 0.40 vs. 0.33, p = 0.05), suggesting stronger ventilation-perfusion matching. The normal group also had greater Spearman's correlation coefficients based on 16 regions of interest (median 0.58 vs. 0.40, p = 0.09). The coefficients of variation were comparable (median, ventilation 0.71 vs. 0.91, p = 0.60; perfusion 0.63 vs. 0.75, p = 0.27). The linear regression slopes of gravitationally directed gradient were also comparable for ventilation (median, ventilation -0.26 vs. -0.18, p = 0.19; perfusion -0.17 vs. -0.06, p = 0.11). CONCLUSION These findings provide proof-of-principle for single-energy CT ventilation/perfusion imaging.
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24
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Ren G, Lam SK, Zhang J, Xiao H, Cheung ALY, Ho WY, Qin J, Cai J. Investigation of a Novel Deep Learning-Based Computed Tomography Perfusion Mapping Framework for Functional Lung Avoidance Radiotherapy. Front Oncol 2021; 11:644703. [PMID: 33842356 PMCID: PMC8024641 DOI: 10.3389/fonc.2021.644703] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/02/2021] [Indexed: 11/25/2022] Open
Abstract
Functional lung avoidance radiation therapy aims to minimize dose delivery to the normal lung tissue while favoring dose deposition in the defective lung tissue based on the regional function information. However, the clinical acquisition of pulmonary functional images is resource-demanding, inconvenient, and technically challenging. This study aims to investigate the deep learning-based lung functional image synthesis from the CT domain. Forty-two pulmonary macro-aggregated albumin SPECT/CT perfusion scans were retrospectively collected from the hospital. A deep learning-based framework (including image preparation, image processing, and proposed convolutional neural network) was adopted to extract features from 3D CT images and synthesize perfusion as estimations of regional lung function. Ablation experiments were performed to assess the effects of each framework component by removing each element of the framework and analyzing the testing performances. Major results showed that the removal of the CT contrast enhancement component in the image processing resulted in the largest drop in framework performance, compared to the optimal performance (~12%). In the CNN part, all the three components (residual module, ROI attention, and skip attention) were approximately equally important to the framework performance; removing one of them resulted in a 3–5% decline in performance. The proposed CNN improved ~4% overall performance and ~350% computational efficiency, compared to the U-Net model. The deep convolutional neural network, in conjunction with image processing for feature enhancement, is capable of feature extraction from CT images for pulmonary perfusion synthesis. In the proposed framework, image processing, especially CT contrast enhancement, plays a crucial role in the perfusion synthesis. This CTPM framework provides insights for relevant research studies in the future and enables other researchers to leverage for the development of optimized CNN models for functional lung avoidance radiation therapy.
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Affiliation(s)
- Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Sai-Kit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Andy Lai-Yin Cheung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Wai-Yin Ho
- Department of Nuclear Medicine, Queen Mary Hospital, Hong Kong, Hong Kong
| | - Jing Qin
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
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25
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Cazoulat G, Balter JM, Matuszak MM, Jolly S, Owen D, Brock KK. Mapping lung ventilation through stress maps derived from biomechanical models of the lung. Med Phys 2020; 48:715-723. [PMID: 33617034 DOI: 10.1002/mp.14643] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 09/16/2020] [Accepted: 11/25/2020] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Most existing computed tomography (CT)-ventilation imaging techniques are based on deformable image registration (DIR) of different respiratory phases of a four-dimensonal CT (4DCT) scan of the lung, followed by the quantification of local breathing-induced changes in Hounsfield Units (HU) or volume. To date, only moderate correlations have been reported between these CT-ventilation metrics and standard ventilation imaging modalities for adaptive lung radiation therapy. This study evaluates the use of stress maps derived from biomechanical model-based DIR as an alternative CT-ventilation metric. MATERIALS AND METHODS Six patients treated for lung cancer with conventional radiation therapy were retrospectively analyzed. For each patient, a 4DCT scan and Tc-99m SPECT-V image acquired for treatment planning were collected. Biomechanical model-based DIR was applied between the inhale and exhale phase of the 4DCT scans and stress maps were calculated. The voxel-wise correlation between the reference SPECT-V image and map of the maximum principal stress was measured with a Spearman correlation coefficient. The overlap between high (above the 75th percentile) and low (below the 25th percentile) functioning volumes extracted from the reference SPECT-V and the stress maps was measured with Dice similarity coefficients (DSC). The results were compared to those obtained when using two classical CT-ventilation metrics: the change in HU and Jacobian determinant. RESULTS The mean Spearman correlation coefficients were: 0.37 ± 18 and 0.39 ± 13 and 0.59 ± 0.13 considering the changes in HU, Jacobian and maximum principal stress, respectively. The corresponding mean DSC coefficients were 0.38 ± 0.09, 0.37 ± 0.07 and 0.52 ± 0.07 for the high ventilation function volumes and 0.48 ± 0.13, 0.44 ± 0.09 and 0.52 ± 0.07 for the low ventilation function volumes. CONCLUSION For presenting a significantly stronger and more consistent correlation with standard SPECT-V images than previously proposed CT-ventilation metrics, stress maps derived with the proposed method appear to be a promising tool for incorporation into functional lung avoidance strategies.
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Affiliation(s)
- Guillaume Cazoulat
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - James M Balter
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Martha M Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Dawn Owen
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Kristy K Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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26
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Lee SJ, Park HJ. Single photon emission computed tomography (SPECT) or positron emission tomography (PET) imaging for radiotherapy planning in patients with lung cancer: a meta-analysis. Sci Rep 2020; 10:14864. [PMID: 32913277 PMCID: PMC7483712 DOI: 10.1038/s41598-020-71445-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 08/13/2020] [Indexed: 12/16/2022] Open
Abstract
Functional imaging modalities enable practitioners to identify functional lung regions. This analysis evaluated the feasibility of nuclear medicine imaging to avoid doses to the functional lung in radiotherapy (RT) planning for patients with lung cancer. This systematic review and meta-analysis was carried out according to PRISMA-P guidelines. A search of EMBASE and PubMed for studies published throughout the last 20 years was performed using the following search criteria: (a) ‘lung cancer’ or ‘lung malignancy’ and (b) ‘radiotherapy’ or ‘radiation therapy’ or ‘RT planning’ and (c) ‘SPECT’ or ‘single positron emission computed tomography’ or ‘functional image.’ The analyzed planning parameters were the volumes of the normal lung that have received ≥ 10 Gy and ≥ 20 Gy of radiation (V10 and V20, respectively) and the mean lung dose (MLD). We compared the planning parameters obtained from anatomical RT planning and functional RT planning using perfusion or ventilation imaging (‘V10, V20 or MLD’ in anatomical plan vs. ‘fV10, fV20 or fMLD’ in functional plan). A total of 309 patients with 344 RT plan sets from 15 publications (11 perfusion SPECT, 2 ventilation SPECT, and 1 SPECT and 1 PET with both perfusion and ventilation) were included in the meta-analysis. The standard mean differences in planning parameters in functional plans using nuclear imaging were significantly reduced compared to those of anatomical plans (P < 0.01 for all): − 0.42 (95% confidence interval (CI) − 0.78 to − 0.07) for ‘V10 vs. fV10′, − 0.41 (95% CI − 0.64 to − 0.17) for ‘V20 vs. fV20′, and − 0.24 (95% CI − 0.45 to − 0.03) for ‘MLD vs. fMLD’. In subgroup analysis, the functional plan using perfusion was significantly lower than the anatomical plan in all planning parameters, but there was no significant difference for ventilation. RT planning with nuclear functional lung imaging has potential to reduce radiation-induced lung injury. Perfusion imaging seems to be more promising than ventilation imaging for all planning parameters. There were not enough studies using ventilation imaging to determine what the effect is on the lung plan parameters.
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Affiliation(s)
- Soo Jin Lee
- Department of Nuclear Medicine, Hanyang University Medical Center, 222-1 Wangsimni-ro, Seongdong-gu, Seoul, 04763, South Korea
| | - Hae Jin Park
- Department of Radiation Oncology, Hanyang University College of Medicine, 222-1 Wangsimni-ro, Seongdong-gu, Seoul, 04763, South Korea.
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27
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Kadoya N, Nemoto H, Kajikawa T, Nakajima Y, Kanai T, Ieko Y, Ikeda R, Sato K, Dobashi S, Takeda K, Jingu K. Evaluation of four-dimensional cone beam computed tomography ventilation images acquired with two different linear accelerators at various gantry speeds using a deformable lung phantom. Phys Med 2020; 77:75-83. [PMID: 32795891 DOI: 10.1016/j.ejmp.2020.07.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 06/24/2020] [Accepted: 07/26/2020] [Indexed: 10/23/2022] Open
Abstract
We evaluated four-dimensional cone beam computed tomography (4D-CBCT) ventilation images (VICBCT) acquired with two different linear accelerator systems at various gantry speeds using a deformable lung phantom. The 4D-CT and 4D-CBCT scans were performed using a computed tomography (CT) scanner, an X-ray volume imaging system (Elekta XVI) mounted in Versa HD, and an On-Board Imager (OBI) system mounted in TrueBeam. Intensity-based deformable image registration (DIR) was performed between peak-exhale and peak-inhale images. VICBCT- and 4D-CT-based ventilation images (VICT) were derived by DIR using two metrics: one based on the Jacobian determinant and one on changes in the Hounsfield unit (HU). Three different DIR regularization values (λ) were used for VICBCT. Correlations between the VICBCT and VICT values were evaluated using voxel-wise Spearman's rank correlation coefficient (r). In case of both metrics, the Jacobian-based VICBCT with a gantry speed of 0.6 deg/sec in Versa HD showed the highest correlation for all the gantry speeds (e.g., λ = 0.05 and r = 0.68). Thus, the r value of the Jacobian-based VICBCT was greater or equal to that of the HU-based VICBCT. In addition, the ventilation accuracy of VICBCT increased at low gantry speeds. Thus, the image quality of VICBCT was affected by the change in gantry speed in both the imaging systems. Additionally, DIR regularization considerably influenced VICBCT in both the imaging systems. Our results have the potential to assist in designing CBCT protocols, incorporating VICBCT imaging into the functional avoidance planning process.
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Affiliation(s)
- Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan.
| | - Hikaru Nemoto
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Radiotherapy, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan
| | - Tomohiro Kajikawa
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Radiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yujiro Nakajima
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Radiotherapy, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan
| | - Takayuki Kanai
- Department of Radiation Oncology, Yamagata University Faculty of Medicine, Yamagata, Japan
| | - Yoshiro Ieko
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Radiation Oncology, Iwate Medical University School of Medicine, Iwate, Japan
| | - Ryutaro Ikeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan; Department of Radiology, Japanese Red Cross Ishinomaki Hospital, Ishinomaki, Japan
| | - Kiyokazu Sato
- Radiation Technology, Tohoku University Hospital, Sendai, Japan
| | - Suguru Dobashi
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Ken Takeda
- Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
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28
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Ieko Y, Kadoya N, Kanai T, Nakajima Y, Arai K, Kato T, Ito K, Miyasaka Y, Takeda K, Iwai T, Nemoto K, Jingu K. The impact of 4DCT-ventilation imaging-guided proton therapy on stereotactic body radiotherapy for lung cancer. Radiol Phys Technol 2020; 13:230-237. [PMID: 32537735 DOI: 10.1007/s12194-020-00572-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 06/05/2020] [Accepted: 06/08/2020] [Indexed: 01/01/2023]
Abstract
Functional lung avoidance during radiotherapy can help reduce pulmonary toxicity. This study assessed the potential impact of four-dimensional computed tomography (4DCT)-ventilation imaging-guided proton radiotherapy (PT) on stereotactic body radiotherapy (SBRT) by comparing it with three-dimensional conformal radiotherapy (3D-CRT) and volumetric modulated arc therapy (VMAT), which employ photon beams. Thirteen lung cancer patients who received SBRT with 3D-CRT were included in the study. 4DCT ventilation was calculated using the patients' 4DCT data, deformable image registration, and a density-change-based algorithm. Three functional treatment plans sparing the functional lung regions were developed for each patient using 3D-CRT, VMAT, and PT. The prescribed doses and dose constraints were based on the Radiation Therapy Oncology Group 0618 protocol. We evaluated the region of interest (ROI) and functional map-based dose-function metrics for 4DCT ventilation and the irradiated dose. Using 3D-CRT, VMAT, and PT, the percentages of the functional lung regions that received ≥ 5 Gy (fV5) were 26.0%, 21.9%, and 10.7%, respectively; the fV10 were 14.4%, 11.4%, and 9.0%, respectively; and fV20 were 6.5%, 6.4%, and 6.6%, respectively, and the functional mean lung doses (fMLD) were 5.6 Gy, 5.2 Gy, and 3.8 Gy, respectively. These results indicated that PT resulted in a significant reduction in fMLD, fV5, and fV10, but not fV20. The use of PT reduced the radiation to highly functional lung regions compared with those for 3D-CRT and VMAT while meeting all dose constraints.
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Affiliation(s)
- Yoshiro Ieko
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.,Department of Heavy Particle Medical Science, Yamagata University Graduate School of Medical Science, Yamagata, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.
| | - Takayuki Kanai
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.,Department of Radiation Oncology, Faculty of Medicine, Yamagata University, Yamagata, Japan
| | - Yujiro Nakajima
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.,Department of Radiation Oncology, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan
| | - Kazuhiro Arai
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.,Department of Radiation Physics and Technology, Southern Tohoku Proton Therapy Center, Koriyama, Japan
| | - Takahiro Kato
- Department of Radiation Physics and Technology, Southern Tohoku Proton Therapy Center, Koriyama, Japan.,Preparing Section for New Facility of Medical Science, Fukushima Medical University, Fukushima, Japan
| | - Kengo Ito
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
| | - Yuya Miyasaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.,Department of Heavy Particle Medical Science, Yamagata University Graduate School of Medical Science, Yamagata, Japan
| | - Ken Takeda
- Department of Radiological Technology, Graduate School of Health Sciences, Faculty of Medicine, Tohoku University, Sendai, Japan
| | - Takeo Iwai
- Department of Heavy Particle Medical Science, Yamagata University Graduate School of Medical Science, Yamagata, Japan
| | - Kenji Nemoto
- Department of Heavy Particle Medical Science, Yamagata University Graduate School of Medical Science, Yamagata, Japan.,Department of Radiation Oncology, Faculty of Medicine, Yamagata University, Yamagata, Japan
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan
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29
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O’Reilly S, Jain V, Huang Q, Cheng C, Teo BKK, Yin L, Zhang M, Diffenderfer E, Li T, Levin W, Xiao Y, Dong L, Feigenberg S, Berman AT, Zou W. Dose to Highly Functional Ventilation Zones Improves Prediction of Radiation Pneumonitis for Proton and Photon Lung Cancer Radiation Therapy. Int J Radiat Oncol Biol Phys 2020; 107:79-87. [DOI: 10.1016/j.ijrobp.2020.01.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 12/08/2019] [Accepted: 01/10/2020] [Indexed: 12/14/2022]
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30
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Defraene G, van Elmpt W, De Ruysscher D. Regional lung avoidance by CT numbers to reduce radiation-induced lung damage risk in non-small-cell lung cancer: a simulation study. Acta Oncol 2020; 59:201-207. [PMID: 31549562 DOI: 10.1080/0284186x.2019.1669814] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background: Selective avoidance aims at sparing functional lung regions. Here, we preferentially direct radiation to irreversibly nonfunctional lung areas based on planning CT imaging to reduce functional lung damage.Materials and methods: For 12 stage I-IV NSCLC patients, 5 lung substructures were segmented on the planning CT, combining voxels <-900HU, -900HU to -801HU, -800HU to -701HU, -700HU to -601HU and ≥-600HU (Level 1 to 5). Two VMAT plans were optimized: a reference plan blinded from substructures and a selective avoidance plan (AV) imposing gradually stricter constraints on Level 1-5, based on previously validated associations between lung subvolume baseline density and density increase (ΔHU) after treatment. Characteristics of treatment plans were evaluated, including subvolumes, dose, and predicted ΔHU (with reported 95% CI reflecting prediction model uncertainty).Results: Segmented substructures were on average 477 cc, 1157 cc, 484 cc, 69 cc, and 123 cc (Level 1-5). AV plans could spare Level 3-5, e.g., mean dose decrease of 3.5 Gy (range 0.6 Gy; 6.0 Gy) for Level 5, p<.001. This significantly reduced the average lung mass with predicted ΔHU>20HU by 12.5 g (95% CI: 5.4-16.9) and 27.1 g (95% CI: 10.2-32.9) for a median and upper 10th percentile patient susceptibility for damage simulation, respectively.Conclusions: Lung damage avoidance based on CT density is feasible and easy to implement. A biomarker providing a reliable selection of patients with high susceptibility for lung damage will be crucial to show the clinical relevance of this avoidance planning strategy.
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Affiliation(s)
- Gilles Defraene
- Department of Oncology, Experimental Radiation Oncology, KU Leuven—University of Leuven, Leuven, Belgium
| | - Wouter van Elmpt
- Department of Radiation Oncology (Maastro Clinic), Maastricht University Medical Center, GROW School for Developmental Biology and Oncology, Maastricht, The Netherlands
| | - Dirk De Ruysscher
- Department of Oncology, Experimental Radiation Oncology, KU Leuven—University of Leuven, Leuven, Belgium
- Department of Radiation Oncology (Maastro Clinic), Maastricht University Medical Center, GROW School for Developmental Biology and Oncology, Maastricht, The Netherlands
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31
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Tian Y, Miao J, Liu Z, Huang P, Wang W, Wang X, Zhai Y, Wang J, Li M, Ma P, Zhang K, Yan H, Dai J. Availability of a simplified lung ventilation imaging algorithm based on four-dimensional computed tomography. Phys Med 2019; 65:53-58. [PMID: 31430587 DOI: 10.1016/j.ejmp.2019.08.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 07/08/2019] [Accepted: 08/05/2019] [Indexed: 01/05/2023] Open
Abstract
PURPOSE It is still not conclusive which four-dimensional computed tomography (4DCT)-based ventilation imaging algorithm is most accurate and efficient. In this study, we proposed a simplified algorithm (VIAAVG) which only requires the average computed tomography (AVG CT) as input, and quantitatively compared its accuracy and efficiency with three other popular algorithms. MATERIAL AND METHODS Fifty patients with lung or esophageal cancer who underwent radiotherapy were enrolled. Single photon emission computed tomography (SPECT) ventilation images (VI-SPECT) and 4DCT were acquired 1-3 days before the first treatment session. The end of exhalation and the end of inhalation CT were registered to derive deformable vector field (DVF) using MIMvista. 4DCT-based ventilation images (CTVI) were first calculated respectively by means of four algorithms (VIAJAC, VIAHU, VIAPRO and VIAAVG). The computation times were compared using paired t-test. The corresponding CTVIs (CTVIJAC, CTVIHU, CTVIPRO and CTVIAVG) and VI-SPECT were segmented into three equal sub-volumes (high, medium and low function lung, respectively) after smoothing and normalization. The Dice Similarity Coefficients (DSCs) were calculated for each sub-volume between each CTVI and VI-SPECT. The average DSCs for high, medium and low function lung in different CTVIs for each patient were compared using paired t-test. RESULTS The mean DSCs for CTVIJAC, CTVIHU, CTVIPRO and CTVIAVG were 0.3255, 0.4465, 0.5865 and 0.5958, respectively. The average computation times for CTVIJAC, CTVIHU, CTVIPRO and CTVIAVG were 18.3 s, 24.2 s, 144.8 s and 15.0 s. CONCLUSION VIAAVG is available for clinical use because of its high accuracy, improved efficiency and less input requirement compared to the other algorithms.
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Affiliation(s)
- Yuan Tian
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Junjie Miao
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Zhiqiang Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Peng Huang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Wenqin Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Xin Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Yirui Zhai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Jingbo Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Minghui Li
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Pan Ma
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Ke Zhang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Hui Yan
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China
| | - Jianrong Dai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100021, China.
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Hegi-Johnson F, de Ruysscher D, Keall P, Hendriks L, Vinogradskiy Y, Yamamoto T, Tahir B, Kipritidis J. Imaging of regional ventilation: Is CT ventilation imaging the answer? A systematic review of the validation data. Radiother Oncol 2019; 137:175-185. [DOI: 10.1016/j.radonc.2019.03.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 03/08/2019] [Accepted: 03/10/2019] [Indexed: 01/08/2023]
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Vinogradskiy Y. CT-based ventilation imaging in radiation oncology. BJR Open 2019; 1:20180035. [PMID: 33178925 PMCID: PMC7592480 DOI: 10.1259/bjro.20180035] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 01/28/2019] [Accepted: 03/11/2019] [Indexed: 11/06/2022] Open
Abstract
A form of lung function imaging is emerging that uses phase-resolved four-dimensional CT (4DCT or breath-hold CT) images along with image processing techniques to generate lung function maps that provide a surrogate of lung ventilation. CT-based ventilation (referred to as CT-ventilation) research has gained momentum in Radiation Oncology because many lung cancer patients undergo four-dimensional CT simulation as part of the standard treatment planning process. Therefore, generating CT-ventilation images provides functional information without burdening the patient with an extra imaging procedure. CT-ventilation has progressed from an image processing calculation methodology, to validation efforts, to retrospective demonstration of clinical utility in Radiation Oncology. In particular, CT-ventilation has been proposed for two main clinical applications: functional avoidance radiation therapy and thoracic dose-response assessment. The idea of functional avoidance radiation therapy is to preferentially spare functional portions of the lung (as measured by CT-ventilation) during radiation therapy with the hypothesis that reducing dose to functional portions of the lung will lead to reduced rates of radiation-related thoracic toxicity. The idea of imaging-based dose-response assessment is to evaluate pre- to post-treatment CT-ventilation-based imaging changes. The hypothesis is that early, imaging-change-based response can be an early predictor of subsequent thoracic toxicity. Based on the retrospective evidence, the clinical applications of CT-ventilation have progressed from the retrospective setting to on-going prospective clinical trials. This review will cover basic CT-ventilation calculation methodologies, validation efforts, presentation of clinical applications, summarize on-going clinical trials, review potential uncertainties and shortcomings of CT-ventilation, and discuss future directions of CT-ventilation research.
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Affiliation(s)
- Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO
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Tahir BA, Marshall H, Hughes PJC, Brightling CE, Collier G, Ireland RH, Wild JM. Comparison of CT ventilation imaging and hyperpolarised gas MRI: effects of breathing manoeuvre. Phys Med Biol 2019; 64:055013. [PMID: 30673634 DOI: 10.1088/1361-6560/ab0145] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Image registration of lung CT images acquired at different inflation levels has been proposed as a surrogate method to map lung 'ventilation'. Prior to clinical use, it is important to understand how this technique compares with direct ventilation imaging modalities such as hyperpolarised gas MRI. However, variations in lung inflation level have been shown to affect regional ventilation distributions. Therefore, the aim of this study was to evaluate the impact of lung inflation levels when comparing CT ventilation imaging to ventilation from 3He-MRI. Seven asthma patients underwent breath-hold CT at total lung capacity (TLC) and functional residual capacity (FRC). 3He-MRI and a same-breath 1H-MRI were acquired at FRC+1L and TLC. Percentage ventilated volumes (%VVs) were calculated for FRC+1L and TLC 3He-MRI. TLC-CT and registered FRC-CT were used to compute a surrogate ventilation map from voxel-wise intensity differences in Hounsfield unit values, which was thresholded at the 10th and 20th percentiles. For direct comparison of CT and 3He-MRI ventilation, FRC+1L and TLC 3He-MRI were registered to TLC-CT indirectly via the corresponding same-breath 1H-MRI data. For 3He-MRI and CT ventilation comparison, Dice similarity coefficients (DSCs) between the binary segmentations were computed. The median (range) of %VVs for FRC+1L and TLC 3He-MRI were 90.5 (54.9-93.6) and 91.8 (67.8-96.2), respectively (p = 0.018). For MRI versus CT ventilation comparison, statistically significant improvements in DSCs were observed for TLC 3He MRI when compared with FRC+1L, with median (range) values of 0.93 (0.86-0.93) and 0.86 (0.68-0.92), respectively (p = 0.017), for the 10-100th percentile and 0.87 (0.83-0.88) and 0.81 (0.66-0.87), respectively (p = 0.027), for the 20-100th percentile. Correlation of CT ventilation imaging and hyperpolarised gas MRI is sensitive to lung inflation level. For ventilation maps derived from CT acquired at FRC and TLC, a higher correlation with gas ventilation MRI can be achieved if the MRI is acquired at TLC.
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Affiliation(s)
- Bilal A Tahir
- POLARIS, Academic Radiology, University of Sheffield, Sheffield, United Kingdom. Academic Unit of Clinical Oncology, University of Sheffield, Sheffield, United Kingdom. Author to whom any correspondence should be addressed
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Kipritidis J, Tahir BA, Cazoulat G, Hofman MS, Siva S, Callahan J, Hardcastle N, Yamamoto T, Christensen GE, Reinhardt JM, Kadoya N, Patton TJ, Gerard SE, Duarte I, Archibald-Heeren B, Byrne M, Sims R, Ramsay S, Booth JT, Eslick E, Hegi-Johnson F, Woodruff HC, Ireland RH, Wild JM, Cai J, Bayouth JE, Brock K, Keall PJ. The VAMPIRE challenge: A multi-institutional validation study of CT ventilation imaging. Med Phys 2019; 46:1198-1217. [PMID: 30575051 DOI: 10.1002/mp.13346] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 11/18/2018] [Accepted: 11/23/2018] [Indexed: 01/31/2023] Open
Abstract
PURPOSE CT ventilation imaging (CTVI) is being used to achieve functional avoidance lung cancer radiation therapy in three clinical trials (NCT02528942, NCT02308709, NCT02843568). To address the need for common CTVI validation tools, we have built the Ventilation And Medical Pulmonary Image Registration Evaluation (VAMPIRE) Dataset, and present the results of the first VAMPIRE Challenge to compare relative ventilation distributions between different CTVI algorithms and other established ventilation imaging modalities. METHODS The VAMPIRE Dataset includes 50 pairs of 4DCT scans and corresponding clinical or experimental ventilation scans, referred to as reference ventilation images (RefVIs). The dataset includes 25 humans imaged with Galligas 4DPET/CT, 21 humans imaged with DTPA-SPECT, and 4 sheep imaged with Xenon-CT. For the VAMPIRE Challenge, 16 subjects were allocated to a training group (with RefVI provided) and 34 subjects were allocated to a validation group (with RefVI blinded). Seven research groups downloaded the Challenge dataset and uploaded CTVIs based on deformable image registration (DIR) between the 4DCT inhale/exhale phases. Participants used DIR methods broadly classified into B-splines, Free-form, Diffeomorphisms, or Biomechanical modeling, with CT ventilation metrics based on the DIR evaluation of volume change, Hounsfield Unit change, or various hybrid approaches. All CTVIs were evaluated against the corresponding RefVI using the voxel-wise Spearman coefficient r S , and Dice similarity coefficients evaluated for low function lung ( DSC low ) and high function lung ( DSC high ). RESULTS A total of 37 unique combinations of DIR method and CT ventilation metric were either submitted by participants directly or derived from participant-submitted DIR motion fields using the in-house software, VESPIR. The r S and DSC results reveal a high degree of inter-algorithm and intersubject variability among the validation subjects, with algorithm rankings changing by up to ten positions depending on the choice of evaluation metric. The algorithm with the highest overall cross-modality correlations used a biomechanical model-based DIR with a hybrid ventilation metric, achieving a median (range) of 0.49 (0.27-0.73) for r S , 0.52 (0.36-0.67) for DSC low , and 0.45 (0.28-0.62) for DSC high . All other algorithms exhibited at least one negative r S value, and/or one DSC value less than 0.5. CONCLUSIONS The VAMPIRE Challenge results demonstrate that the cross-modality correlation between CTVIs and the RefVIs varies not only with the choice of CTVI algorithm but also with the choice of RefVI modality, imaging subject, and the evaluation metric used to compare relative ventilation distributions. This variability may arise from the fact that each of the different CTVI algorithms and RefVI modalities provides a distinct physiologic measurement. Ultimately this variability, coupled with the lack of a "gold standard," highlights the ongoing importance of further validation studies before CTVI can be widely translated from academic centers to the clinic. It is hoped that the information gleaned from the VAMPIRE Challenge can help inform future validation efforts.
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Affiliation(s)
- John Kipritidis
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, NSW, Australia.,Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Bilal A Tahir
- Academic Unit of Clinical Oncology, University of Sheffield, Sheffield, UK.,Academic Radiology, POLARIS, University of Sheffield, Sheffield, UK
| | - Guillaume Cazoulat
- The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, Houston, TX, USA
| | | | - Shankar Siva
- Peter MacCallum Cancer Centre, Melbourne, Vic., Australia
| | - Jason Callahan
- Peter MacCallum Cancer Centre, Melbourne, Vic., Australia
| | | | - Tokihiro Yamamoto
- University of California Davis School of Medicine, Sacramento, CA, USA
| | | | | | - Noriyuki Kadoya
- Tohoku University Graduate School of Medicine, Sendai, Japan
| | | | | | | | - Ben Archibald-Heeren
- Radiation Oncology Centres, Sydney Adventist Hospital, Sydney, NSW, Australia.,University of Wollongong, Wollongong, NSW, Australia
| | - Mikel Byrne
- Radiation Oncology Centres, Sydney Adventist Hospital, Sydney, NSW, Australia
| | - Rick Sims
- Auckland Radiation Oncology, Auckland, New Zealand
| | - Scott Ramsay
- Auckland Radiation Oncology, Auckland, New Zealand
| | - Jeremy T Booth
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, NSW, Australia.,School of Physics, University of Sydney, Sydney, NSW, Australia
| | - Enid Eslick
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, NSW, Australia.,Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Fiona Hegi-Johnson
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia.,Peter MacCallum Cancer Centre, Melbourne, Vic., Australia
| | - Henry C Woodruff
- The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Rob H Ireland
- Academic Unit of Clinical Oncology, University of Sheffield, Sheffield, UK
| | - Jim M Wild
- Academic Radiology, POLARIS, University of Sheffield, Sheffield, UK
| | - Jing Cai
- Duke University Medical Center, Durham, NC, USA.,Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong
| | | | - Kristy Brock
- The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, Houston, TX, USA
| | - Paul J Keall
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
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Vinogradskiy Y, Rusthoven CG, Schubert L, Jones B, Faught A, Castillo R, Castillo E, Gaspar LE, Kwak J, Waxweiler T, Dougherty M, Gao D, Stevens C, Miften M, Kavanagh B, Guerrero T, Grills I. Interim Analysis of a Two-Institution, Prospective Clinical Trial of 4DCT-Ventilation-based Functional Avoidance Radiation Therapy. Int J Radiat Oncol Biol Phys 2018; 102:1357-1365. [PMID: 30353873 PMCID: PMC6919556 DOI: 10.1016/j.ijrobp.2018.07.186] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2017] [Revised: 06/13/2018] [Accepted: 07/17/2018] [Indexed: 12/13/2022]
Abstract
PURPOSE Functional imaging has been proposed that uses 4DCT images to calculate 4DCT-based lung ventilation (4DCT-ventilation). We have started a 2-institution, phase 2 prospective trial evaluating the feasibility, safety, and preliminary efficacy of 4DCT-ventilation functional avoidance. The trial hypothesis is that the rate of grade ≥2 radiation pneumonitis could be reduced to 12% with functional avoidance, compared with a 25% rate of pneumonitis with a historical control. The trial employed a Simon 2-stage design with a planned futility analysis after 17 evaluable patients. The purpose of this work is to present the trial design and implementation, dosimetric data, and clinical results for the planned futility analysis. METHODS AND MATERIALS Eligible patients were patients with lung cancer who were prescribed doses of 45 to 75 Gy. For each patient, the 4DCT data were used to generate a 4DCT-ventilation image using the Hounsfield unit technique along with a compressible flow-based image registration algorithm. Two intensity modulated radiation therapy treatment plans were generated: (1) a standard lung plan and (2) a functional avoidance treatment plan that aimed to reduce dose to functional lung while meeting target and normal tissue constraints. Patients were treated with the functional avoidance plan and evaluated for thoracic toxicity (presented as rate and 95% confidence intervals [CI]) with a 1-year follow-up. RESULTS The V20 to functional lung was 21.6% ± 9.5% (mean ± standard deviation) with functional avoidance, representing a decrease of 3.2% (P < .01) relative to standard, nonfunctional treatment plans. The rates of grade ≥2 and grade ≥3 radiation pneumonitis were 17.6% (95% CI, 3.8%-43.4%) and 5.9% (95% CI, 0.1%-28.7%), respectively. CONCLUSIONS Dosimetrically, functional avoidance achieved reduction in doses to functional lung while meeting target and organ at risk constraints. On the basis of Simon's 2-stage design and the 17.6% grade ≥2 pneumonitis rate, the trial met its futility criteria and has continued accrual.
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Affiliation(s)
- Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado.
| | - Chad G Rusthoven
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Leah Schubert
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Bernard Jones
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Austin Faught
- Department of Radiation Oncology, St Jude Children's Research Hospital, Memphis, Tennessee
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, Georgia
| | - Edward Castillo
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Laurie E Gaspar
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Jennifer Kwak
- Department of Radiology, University of Colorado School of Medicine, Aurora, Colorado
| | - Timothy Waxweiler
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | | | - Dexiang Gao
- Department of Pediatrics and Department of Biostatistics and Informatics, University of Colorado School of Medicine, Aurora, Colorado
| | - Craig Stevens
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Brian Kavanagh
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Inga Grills
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
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Yamamoto T, Kabus S, Bal M, Bzdusek K, Keall PJ, Wright C, Benedict SH, Daly ME. Changes in Regional Ventilation During Treatment and Dosimetric Advantages of CT Ventilation Image Guided Radiation Therapy for Locally Advanced Lung Cancer. Int J Radiat Oncol Biol Phys 2018; 102:1366-1373. [PMID: 29891207 PMCID: PMC6443402 DOI: 10.1016/j.ijrobp.2018.04.063] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Accepted: 04/23/2018] [Indexed: 12/25/2022]
Abstract
PURPOSE Lung functional image guided radiation therapy (RT) that avoids irradiating highly functional regions has potential to reduce pulmonary toxicity following RT. Tumor regression during RT is common, leading to recovery of lung function. We hypothesized that computed tomography (CT) ventilation image-guided treatment planning reduces the functional lung dose compared to standard anatomic image-guided planning in 2 different scenarios with or without plan adaptation. METHODS AND MATERIALS CT scans were acquired before RT and during RT at 2 time points (16-20 Gy and 30-34 Gy) for 14 patients with locally advanced lung cancer. Ventilation images were calculated by deformable image registration of four-dimensional CT image data sets and image analysis. We created 4 treatment plans at each time point for each patient: functional adapted, anatomic adapted, functional unadapted, and anatomic unadapted plans. Adaptation was performed at 2 time points. Deformable image registration was used for accumulating dose and calculating a composite of dose-weighted ventilation used to quantify the lung accumulated dose-function metrics. The functional plans were compared with the anatomic plans for each scenario separately to investigate the hypothesis at a significance level of 0.05. RESULTS Tumor volume was significantly reduced by 20% after 16 to 20 Gy (P = .02) and by 32% after 30 to 34 Gy (P < .01) on average. In both scenarios, the lung accumulated dose-function metrics were significantly lower in the functional plans than in the anatomic plans without compromising target volume coverage and adherence to constraints to critical structures. For example, functional planning significantly reduced the functional mean lung dose by 5.0% (P < .01) compared to anatomic planning in the adapted scenario and by 3.6% (P = .03) in the unadapted scenario. CONCLUSIONS This study demonstrated significant reductions in the accumulated dose to the functional lung with CT ventilation image-guided planning compared to anatomic image-guided planning for patients showing tumor regression and changes in regional ventilation during RT.
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Affiliation(s)
- Tokihiro Yamamoto
- Department of Radiation Oncology, University of California Davis, Sacramento, California.
| | - Sven Kabus
- Department of Digital Imaging, Philips Research, Hamburg, Germany
| | | | | | - Paul J Keall
- Radiation Physics Laboratory, Sydney Medical School, University of Sydney, New South Wales, Australia
| | - Cari Wright
- Department of Radiation Oncology, University of California Davis, Sacramento, California
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California Davis, Sacramento, California
| | - Megan E Daly
- Department of Radiation Oncology, University of California Davis, Sacramento, California
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Tahir BA, Hughes PJ, Robinson SD, Marshall H, Stewart NJ, Norquay G, Biancardi A, Chan HF, Collier GJ, Hart KA, Swinscoe JA, Hatton MQ, Wild JM, Ireland RH. Spatial Comparison of CT-Based Surrogates of Lung Ventilation With Hyperpolarized Helium-3 and Xenon-129 Gas MRI in Patients Undergoing Radiation Therapy. Int J Radiat Oncol Biol Phys 2018; 102:1276-1286. [DOI: 10.1016/j.ijrobp.2018.04.077] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 03/21/2018] [Accepted: 04/26/2018] [Indexed: 11/30/2022]
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Characterizing Spatial Lung Function for Esophageal Cancer Patients Undergoing Radiation Therapy. Int J Radiat Oncol Biol Phys 2018; 103:738-746. [PMID: 30612962 DOI: 10.1016/j.ijrobp.2018.10.024] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 10/11/2018] [Accepted: 10/19/2018] [Indexed: 12/25/2022]
Abstract
PURPOSE Patients with esophageal cancer treated with chemoradiation and surgery can develop pulmonary complications. Four-dimensional computed tomography-ventilation (4DCT-ventilation) is a developing imaging modality that uses 4DCT data to calculate lung ventilation. 4DCT-ventilation has been studied in the lung-cancer population but has yet to be extended to patients with esophageal cancer. The purpose of this study was to characterize 4DCT-ventilation-based spatial lung function for patients with esophageal cancer. METHODS AND MATERIALS Thirty-five patients with esophageal cancer who underwent 4DCT scans participated in the study. A 4DCT-ventilation map was calculated using the patient's 4DCT imaging and a density change-based algorithm. To assess each patient's ventilation profile, radiologist interpretations and quantitative metrics were used. A radiologist interpreted the 4DCT-ventilation images for lobar-based defects and gravity-dependent atelectasis. The 4DCT-ventilation maps were reduced to single metrics intended to reflect the degree of ventilation heterogeneity. The quantitative metrics included the coefficient of variation and metrics based on the ventilation in each lung and each lung third (superior-inferior ventilation [Vent-SI] and anteroposterior ventilation). The functional profile of patients with esophageal cancer was characterized and compared (using the Mann-Whitney test) for cohorts based on thoracic comorbidities and radiologist-identified defects. RESULTS Radiologist observations revealed that 26% of patients with esophageal cancer had lobar-based defects and 46% had gravity-dependent atelectasis. The baseline values were 0.52 ± 0.20 (mean ± SD), 11.2 ± 12.5, and 72.5 ± 14.6 for the coefficient of variation, the ventilation ratio of right to left lung, and Vent-SI metrics, respectively. The Vent-SI values were significantly different between patients with and without thoracic comorbidities (P = .05), and the anteroposterior ventilation metric was able to delineate patients with and without gravity-dependent atelectasis (P < .01). CONCLUSIONS Our data demonstrate that approximately 30% of patients with esophageal cancer have significant ventilation heterogeneities. The current work uses radiologist observations and quantitative metrics to characterize 4DCT ventilation-based lung function for patients with esophageal cancer and presents data that can be used for future applications of 4DCT-ventilation to reduce thoracic toxicity for patients with esophageal cancer.
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Functional lung imaging in radiation therapy for lung cancer: A systematic review and meta-analysis. Radiother Oncol 2018; 129:196-208. [PMID: 30082143 DOI: 10.1016/j.radonc.2018.07.014] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 06/14/2018] [Accepted: 07/18/2018] [Indexed: 12/25/2022]
Abstract
RATIONALE Advanced imaging techniques allow functional information to be derived and integrated into treatment planning. METHODS A systematic review was conducted with the primary objective to evaluate the ability of functional lung imaging to predict risk of radiation pneumonitis. Secondary objectives were to evaluate dose-response relationships on post treatment functional imaging and assess the utility in including functional lung information into treatment planning. A structured search for publications was performed following PRISMA guidelines and registered on PROSPERO. RESULTS 814 articles were screened against review criteria and 114 publications met criteria. Methods of identifying functional lung included using CT, MRI, SPECT and PET to image ventilation or perfusion. Six studies compared differences between functional and anatomical lung imaging at predicting radiation pneumonitis. These found higher predictive values using functional lung imaging. Twenty-one studies identified a dose-response relationship on post-treatment functional lung imaging. Nineteen planning studies demonstrated the ability of functional lung optimised planning techniques to spare regions of functional lung. Meta-analysis of these studies found that mean (95% CI) functional volume receiving 20 Gy was reduced by 4.2% [95% CI: 2.3: 6.0] and mean lung dose by 2.2 Gy [95% CI: 1.2: 3.3] when plans were optimised to spare functional lung. There was significant variation between publications in the definition of functional lung. CONCLUSION Functional lung imaging may have potential utility in radiation therapy planning and delivery, although significant heterogeneity was identified in approaches and reporting. Recommendations have been made based on the available evidence for future functional lung trials.
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Rankine LJ, Wang Z, Driehuys B, Marks LB, Kelsey CR, Das SK. Correlation of Regional Lung Ventilation and Gas Transfer to Red Blood Cells: Implications for Functional-Avoidance Radiation Therapy Planning. Int J Radiat Oncol Biol Phys 2018; 101:1113-1122. [PMID: 29907488 PMCID: PMC6689416 DOI: 10.1016/j.ijrobp.2018.04.017] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 03/02/2018] [Accepted: 04/05/2018] [Indexed: 02/08/2023]
Abstract
PURPOSE To investigate the degree to which lung ventilation and gas exchange are regionally correlated, using the emerging technology of hyperpolarized (HP)-129Xe magnetic resonance imaging (MRI). METHODS AND MATERIALS Hyperpolarized-129Xe MRI studies were performed on 17 institutional review board-approved human subjects, including 13 healthy volunteers, 1 emphysema patient, and 3 non-small cell lung cancer patients imaged before and approximately 11 weeks after radiation therapy (RT). Subjects inhaled 1 L of HP-129Xe mixture, followed by the acquisition of interleaved ventilation and gas exchange images, from which maps were obtained of the relative HP-129Xe distribution in three states: (1) gaseous, in lung airspaces; (2) dissolved interstitially, in alveolar barrier tissue; and (3) transferred to red blood cells (RBCs), in the capillary vasculature. The relative spatial distributions of HP-129Xe in airspaces (regional ventilation) and RBCs (regional gas transfer) were compared. Further, we investigated the degree to which ventilation and RBC transfer images identified similar functional regions of interest (ROIs) suitable for functionally guided RT. For the RT patients, both ventilation and RBC functional images were used to calculate differences in the lung dose-function histogram and functional effective uniform dose. RESULTS The correlation of ventilation and RBC transfer was ρ = 0.39 ± 0.15 in healthy volunteers. For the RT patients, this correlation was ρ = 0.53 ± 0.02 before treatment and ρ = 0.39 ± 0.07 after treatment; for the emphysema patient it was ρ = 0.24. Comparing functional ROIs, ventilation and RBC transfer demonstrated poor spatial agreement: Dice similarity coefficient = 0.50 ± 0.07 and 0.26 ± 0.12 for the highest-33%- and highest-10%-function ROIs in healthy volunteers, and in RT patients (before treatment) these were 0.58 ± 0.04 and 0.40 ± 0.04. The average magnitude of the differences between RBC- and ventilation-derived functional effective uniform dose, fV20Gy, fV10Gy, and fV5Gy were 1.5 ± 1.4 Gy, 4.1% ± 3.8%, 5.0% ± 3.8%, and 5.3% ± 3.9%, respectively. CONCLUSION Ventilation may not be an effective surrogate for true regional lung function for all patients.
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Affiliation(s)
- Leith J Rankine
- Department of Radiation Oncology, University of North Carolina School of Medicine, Chapel Hill, North Carolina; Medical Physics Graduate Program, Duke University, Durham, North Carolina.
| | - Ziyi Wang
- Department of Biomedical Engineering, Duke University, Durham, North Carolina
| | - Bastiaan Driehuys
- Medical Physics Graduate Program, Duke University, Durham, North Carolina; Department of Biomedical Engineering, Duke University, Durham, North Carolina; Radiology, Duke University, Durham, North Carolina
| | - Lawrence B Marks
- Department of Radiation Oncology, University of North Carolina School of Medicine, Chapel Hill, North Carolina
| | - Chris R Kelsey
- Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina
| | - Shiva K Das
- Department of Radiation Oncology, University of North Carolina School of Medicine, Chapel Hill, North Carolina
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Evaluation of deformation parameters for deformable image registration-based ventilation imaging using an air-ventilating non-rigid phantom. Phys Med 2018; 50:20-25. [DOI: 10.1016/j.ejmp.2018.05.016] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 04/11/2018] [Accepted: 05/16/2018] [Indexed: 11/21/2022] Open
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Lee HJ, Zeng J, Vesselle HJ, Patel SA, Rengan R, Bowen SR. Correlation of Functional Lung Heterogeneity and Dosimetry to Radiation Pneumonitis using Perfusion SPECT/CT and FDG PET/CT Imaging. Int J Radiat Oncol Biol Phys 2018; 102:1255-1264. [PMID: 30108002 DOI: 10.1016/j.ijrobp.2018.05.051] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Revised: 04/18/2018] [Accepted: 05/22/2018] [Indexed: 12/25/2022]
Abstract
PURPOSE To apply a previously designed framework for predicting radiation pneumonitis by using pretreatment lung function heterogeneity metrics, anatomic dosimetry, and functional lung dosimetry derived from 2 imaging modalities within the same cohort. METHODS AND MATERIALS Treatment planning computed tomography (CT) scans were co-registered with pretreatment [99mTc] macro-aggregated albumin perfusion single-photon positron emission tomography (SPECT)/CT scans and [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT scans of 28 patients who underwent definitive thoracic radiation. Clinical radiation pneumonitis was defined as grade ≥2 (Common Terminology Criteria for Adverse Events, v. 4). Anatomic dosimetric parameters (mean lung dose [MLD], volume receiving ≥20 Gy [V20]) were collected from treatment planning scans. Baseline functional lung heterogeneity parameters and functional lung dose-volume parameters were calculated from pretreatment SPECT/CT and FDG PET/CT scans. Functional heterogeneity parameters calculated over the tumor-subtracted lung included skewness, kurtosis, and coefficient of variation from perfusion SPECT and FDG PET and the global lung parenchymal glycolysis and mean standardized uptake value from FDG PET. Functional dose-volume parameters calculated in regions of highly functional lung, defined on perfusion (p) or SUV (s) images, included mean lung dose (pMLD, sMLD) and V20 (pV20, sV20). Fraction of integral lung function receiving ≥20 Gy (pF20, sF20) was also calculated. Equivalent doses in 2 Gy per fraction (EQD2) were calculated to account for differences in treatment regimens and dose fractionation (EQD2Lung). RESULTS Two anatomic dosimetric parameters (MLD, V20) and 4 functional dosimetric parameters (pMLD, pV20, pF20, sF20) were significant predictors of grade ≥2 pneumonitis (area under the curve >0.84; P < .05). Dose-independent functional lung heterogeneity metrics were not associated with pneumonitis incidence. At thresholds of 100% sensitivity and 65% to 91% specificity, corresponding to maximum prediction accuracy for pneumonitis, these parameters had the following cutoff values: MLD = 13.6 Gy EQD2Lung, V20 = 25%, pMLD = 13.2 Gy EQD2Lung, pV20 = 15%, pF20 = 17%, and sF20 = 25%. Significant parameters MLD, V20, pF20, and sF20 were not cross-correlated to significant parameters pMLD and pV20, indicating that they may offer independently predictive information (Spearman ρ < 0.7). CONCLUSIONS We reported differences in anatomic and functional lung dosimetry between patients with and without pneumonitis in this limited patient cohort. Adding selected independent functional lung parameters may risk stratify patients for pneumonitis. Validation studies are ongoing in a prospective functional lung avoidance trial at our institution.
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Affiliation(s)
- Howard J Lee
- Duke University School of Medicine, Durham, North Carolina
| | - Jing Zeng
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington
| | - Hubert J Vesselle
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Shilpen A Patel
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington
| | - Ramesh Rengan
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington
| | - Stephen R Bowen
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington; Department of Radiology, University of Washington School of Medicine, Seattle, Washington.
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Miyakawa S, Tachibana H, Moriya S, Kurosawa T, Nishio T, Sato M. Design and development of a nonrigid phantom for the quantitative evaluation of DIR-based mapping of simulated pulmonary ventilation. Med Phys 2018; 45:3496-3505. [PMID: 29807393 DOI: 10.1002/mp.13017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 05/16/2018] [Accepted: 05/16/2018] [Indexed: 12/25/2022] Open
Abstract
PURPOSE The validation of deformable image registration (DIR)-based pulmonary ventilation mapping is time consuming and prone to inaccuracies and is also affected by deformation parameters. In this study, we developed a nonrigid phantom as a quality assurance (QA) tool that simulates ventilation to evaluate DIR-based images quantitatively. METHODS The phantom consists of an acrylic cylinder filled with polyurethane foam designed to simulate pulmonic alveoli. A polyurethane membrane is attached to the inferior end of the phantom to simulate the diaphragm. In addition, tracheobronchial-tree-shaped polyurethane tubes are inserted through the foam and converge outside the phantom to simulate the trachea. Solid polyurethane is also used to model arteries, which closely follow the model airways. Two three-dimensional (3D) CT scans were performed during exhalation and inhalation phases using xenon (Xe) gas as the inhaled contrast agent. The exhalation 3D-CT image is deformed to an inhalation 3D-CT image using our in-house program based on the NiftyReg open-source package. The target registration error (TRE) between the two images was calculated for 16 landmarks located in the simulated lung volume. The DIR-based ventilation image was generated using Jacobian determinant (JD) metrics. Subsequently, differences in the Hounsfield unit (HU) values between the two images were measured. The correlation coefficient between the JD and HU differences was calculated. In addition, three 4D-CT scans are performed to evaluate the reproducibility of the phantom motion and Xe gas distribution. RESULTS The phantom exhibited a variety of displacements for each landmark (range: 1-20 mm). The reproducibility analysis indicated that the location differences were <1 mm for all landmarks, and the HU variation in the Xe gas distribution was close to zero. The mean TRE in the evaluation of spatial accuracy according to the DIR software was 1.47 ± 0.71 mm (maximum: 2.6 mm). The relationship between the JD and HU differences had a large correlation (R = -0.71) for the DIR software. CONCLUSION The phantom implemented new features, namely, deformation and simulated ventilation. To assess the accuracy of the DIR-based mapping of the simulated pulmonary ventilation, the phantom allows for simulation of Xe gas wash-in and wash-out. The phantom may be an effective QA tool, because the DIR algorithm can be quickly changed and its accuracy evaluated with a high degree of precision.
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Affiliation(s)
- Shin Miyakawa
- Department of Medical Physics, Graduate School of Medicine, Tokyo Women's Medical University, Tokyo, 162-8666, Japan
- Radiological Sciences, Graduate Division of Health Sciences, Komazawa University, Tokyo, 154-8525, Japan
| | - Hidenobu Tachibana
- Particle Therapy Division, Research Center for Innovative Oncology, National Cancer Center, Chiba, 277-8577, Japan
| | - Shunsuke Moriya
- Doctoral Program in Biomedical Sciences, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Chiba, 305-8577, Japan
| | - Tomoyuki Kurosawa
- Department of Medical Physics, Graduate School of Medicine, Tokyo Women's Medical University, Tokyo, 162-8666, Japan
- Radiological Sciences, Graduate Division of Health Sciences, Komazawa University, Tokyo, 154-8525, Japan
| | - Teiji Nishio
- Department of Medical Physics, Graduate School of Medicine, Tokyo Women's Medical University, Tokyo, 162-8666, Japan
| | - Masanori Sato
- Radiological Sciences, Graduate Division of Health Sciences, Komazawa University, Tokyo, 154-8525, Japan
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Evaluation of functionally weighted dose-volume parameters for thoracic stereotactic ablative radiotherapy (SABR) using CT ventilation. Phys Med 2018; 49:47-51. [PMID: 29866342 DOI: 10.1016/j.ejmp.2018.05.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 04/27/2018] [Accepted: 05/01/2018] [Indexed: 01/04/2023] Open
Abstract
For the purpose of reducing radiation pneumontisis (RP), four-dimensional CT (4DCT)-based ventilation can be used to reduce functionally weighted lung dose. This study aimed to evaluate the functionally weighted dose-volume parameters and to investigate an optimal weighting method to realize effective planning optimization in thoracic stereotactic ablative radiotherapy (SABR). Forty patients treated with SABR were analyzed. Ventilation images were obtained from 4DCT using deformable registration and Hounsfield unit-based calculation. Functionally-weighted mean lung dose (fMLD) and functional lung fraction receiving at least x Gy (fVx) were calculated by two weighting methods: thresholding and linear weighting. Various ventilation thresholds (5th-95th, every 5th percentile) were tested. The predictive accuracy for CTCAE grade ≧ 2 pneumonitis was evaluated by area under the curve (AUC) of receiver operating characteristic analysis. AUC values varied from 0.459 to 0.570 in accordance with threshold and dose-volume metrics. A combination of 25th percentile threshold and fV30 showed the best result (AUC: 0.570). AUC values with fMLD, fV10, fV20, and fV40 were 0.541, 0.487, 0.548 and 0.563 using a 25th percentile threshold. Although conventional MLD, V10, V20, V30 and V40 showed lower AUC values (0.516, 0.477, 0.534, 0.552 and 0.527), the differences were not statistically significant. fV30 with 25th percentile threshold was the best predictor of RP. Our results suggested that the appropriate weighting should be used for better treatment outcomes in thoracic SABR.
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CT ventilation imaging derived from breath hold CT exhibits good regional accuracy with Galligas PET. Radiother Oncol 2018; 127:267-273. [DOI: 10.1016/j.radonc.2017.12.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Revised: 12/16/2017] [Accepted: 12/16/2017] [Indexed: 11/23/2022]
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Faught AM, Olsen L, Schubert L, Rusthoven C, Castillo E, Castillo R, Zhang J, Guerrero T, Miften M, Vinogradskiy Y. Functional-guided radiotherapy using knowledge-based planning. Radiother Oncol 2018; 129:494-498. [PMID: 29628292 DOI: 10.1016/j.radonc.2018.03.025] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 03/12/2018] [Accepted: 03/23/2018] [Indexed: 10/17/2022]
Abstract
BACKGROUND AND PURPOSE There are two significant challenges when implementing functional-guided radiotherapy using 4DCT-ventilation imaging: (1) lack of knowledge of realistic patient specific dosimetric goals for functional lung and (2) ensuring consistent plan quality across multiple planners. Knowledge-based planning (KBP) is positioned to address both concerns. MATERIAL AND METHODS A KBP model was created from 30 previously planned functional-guided lung patients. Standard organs at risk (OAR) in lung radiotherapy and a ventilation contour delineating areas of high ventilation were included. Model validation compared dose-metrics to standard OARs and functional dose-metrics from 20 independent cases that were planned with and without KBP. RESULTS A significant improvement was observed for KBP optimized plans in V20Gy and mean dose to functional lung (p = 0.005 and 0.001, respectively), V20Gy and mean dose to total lung minus GTV (p = 0.002 and 0.01, respectively), and mean doses to esophagus (p = 0.005). CONCLUSION The current work developed a KBP model for functional-guided radiotherapy. Modest, but statistically significant, improvements were observed in functional lung and total lung doses.
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Affiliation(s)
- Austin M Faught
- University of Colorado School of Medicine, Department of Radiation Oncology, Aurora, United States; St. Jude Children's Research Hospital, Department of Radiation Oncology, Memphis, United States.
| | - Lindsey Olsen
- Memorial Hospital, Department of Radiation Oncology, Colorado Springs, United States
| | - Leah Schubert
- University of Colorado School of Medicine, Department of Radiation Oncology, Aurora, United States
| | - Chad Rusthoven
- University of Colorado School of Medicine, Department of Radiation Oncology, Aurora, United States
| | - Edward Castillo
- Beaumont Health System, Department of Radiation Oncology, Royal Oak, United States
| | - Richard Castillo
- Emory University, Department of Radiation Oncology, Atlanta, United States
| | - Jingjing Zhang
- Beaumont Health System, Department of Radiation Oncology, Royal Oak, United States
| | - Thomas Guerrero
- Beaumont Health System, Department of Radiation Oncology, Royal Oak, United States
| | - Moyed Miften
- University of Colorado School of Medicine, Department of Radiation Oncology, Aurora, United States
| | - Yevgeniy Vinogradskiy
- University of Colorado School of Medicine, Department of Radiation Oncology, Aurora, United States
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Hasse K, O'Connell D, Min Y, Neylon J, Low DA, Santhanam A. Estimation and validation of patient‐specific high‐resolution lung elasticity derived from 4DCT. Med Phys 2017; 45:666-677. [DOI: 10.1002/mp.12697] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 11/16/2017] [Accepted: 11/16/2017] [Indexed: 11/08/2022] Open
Affiliation(s)
- Katelyn Hasse
- Department of Radiation Oncology University of California Los Angeles CA USA
| | - Dylan O'Connell
- Department of Radiation Oncology University of California Los Angeles CA USA
| | - Yugang Min
- Department of Radiation Oncology University of California Los Angeles CA USA
| | - John Neylon
- Department of Radiation Oncology University of California Los Angeles CA USA
| | - Daniel A. Low
- Department of Radiation Oncology University of California Los Angeles CA USA
| | - Anand Santhanam
- Department of Radiation Oncology University of California Los Angeles CA USA
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Yamamoto T, Kabus S. Technical Note: Correction for the effect of breathing variations in CT pulmonary ventilation imaging. Med Phys 2017; 45:322-327. [PMID: 29072320 DOI: 10.1002/mp.12634] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 10/16/2017] [Accepted: 10/17/2017] [Indexed: 11/12/2022] Open
Abstract
PURPOSE The accuracy and precision of computed tomography (CT) pulmonary ventilation imaging with conventional CT scanners are limited by breathing variations. We propose a method to correct for the effect of breathing variations in CT ventilation imaging based on external respiratory signals acquired throughout a scan. METHODS The proposed method is based on: (a) calculating voxel-by-voxel abdominal surface motion ranges using four-dimensional (4D) CT image datasets spatiotemporally correlated with external respiratory monitor data, and (b) applying the correction factor, which is defined as the ratio of the overall mean of the abdominal surface motion range in the lungs to that of each voxel, to the CT ventilation value. The performance of the proposed method was investigated by comparing voxel-wise correlations of the uncorrected and corrected CT ventilation images with single-photon emission CT (SPECT) ventilation images as a ground truth for nine patients. CT ventilation images were calculated by deformable image registration of the 4D-CT image datasets, followed by calculation of regional volume changes. A Steiger's Z-test was used to determine the statistical significance of the difference between the correlations for the uncorrected and corrected CT ventilation images. RESULTS The proposed correction method resulted in significant increases (P < 0.05) in the correlation between CT and SPECT ventilation in three patients, trends toward significant increase (P: 0.13-0.18) in two patients, no significant differences in three patients, and a significantly decreased correlation in one patient. The average standard deviation of the abdominal surface motion range in three patients showing significant increases was 0.27 (range 0.10-0.37), which was greater than 0.17 (range 0.07-0.38) in the other six patients. CONCLUSIONS The proposed method to correct for the effect of breathing variations could be readily implemented and has the potential to improve the accuracy of CT ventilation imaging as demonstrated in a nine-patient study.
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Affiliation(s)
- Tokihiro Yamamoto
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, CA, 95817, USA
| | - Sven Kabus
- Department of Digital Imaging, Philips Research, 22335, Hamburg, Germany
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Bahig H, Campeau MP, Lapointe A, Bedwani S, Roberge D, de Guise J, Blais D, Vu T, Lambert L, Chartrand-Lefebvre C, Lord M, Filion E. Phase 1-2 Study of Dual-Energy Computed Tomography for Assessment of Pulmonary Function in Radiation Therapy Planning. Int J Radiat Oncol Biol Phys 2017; 99:334-343. [DOI: 10.1016/j.ijrobp.2017.05.051] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Revised: 04/24/2017] [Accepted: 05/30/2017] [Indexed: 12/25/2022]
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