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Vicente EM, Grande Gutierrez N, Oakes JM, Cammin J, Gopal A, Kipritidis J, Modiri A, Mossahebi S, Mohindra P, Citron WK, Matuszak MM, Timmerman R, Sawant A. Integrating local and distant radiation-induced lung injury: Development and validation of a predictive model for ventilation loss. Med Phys 2024. [PMID: 38820385 DOI: 10.1002/mp.17187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/04/2024] [Accepted: 05/11/2024] [Indexed: 06/02/2024] Open
Abstract
BACKGROUND Investigations on radiation-induced lung injury (RILI) have predominantly focused on local effects, primarily those associated with radiation damage to lung parenchyma. However, recent studies from our group and others have revealed that radiation-induced damage to branching serial structures such as airways and vessels may also have a substantial impact on post-radiotherapy (RT) lung function. Furthermore, recent results from multiple functional lung avoidance RT trials, although promising, have demonstrated only modest toxicity reduction, likely because they were primarily focused on dose avoidance to lung parenchyma. These observations emphasize the critical need for predictive dose-response models that effectively incorporate both local and distant RILI effects. PURPOSE We develop and validate a predictive model for ventilation loss after lung RT. This model, referred to as P+A, integrates local (parenchyma [P]) and distant (central and peripheral airways [A]) radiation-induced damage, modeling partial (narrowing) and complete (collapse) obstruction of airways. METHODS In an IRB-approved prospective study, pre-RT breath-hold CTs (BHCTs) and pre- and one-year post-RT 4DCTs were acquired from lung cancer patients treated with definitive RT. Up to 13 generations of airways were automatically segmented on the BHCTs using a research virtual bronchoscopy software. Ventilation maps derived from the 4DCT scans were utilized to quantify pre- and post-RT ventilation, serving, respectively, as input data and reference standard (RS) in model validation. To predict ventilation loss solely due to parenchymal damage (referred to as P model), we used a normal tissue complication probability (NTCP) model. Our model used this NTCP-based estimate and predicted additional loss due radiation-induced partial or complete occlusion of individual airways, applying fluid dynamics principles and a refined version of our previously developed airway radiosensitivity model. Predictions of post-RT ventilation were estimated in the sublobar volumes (SLVs) connected to the terminal airways. To validate the model, we conducted a k-fold cross-validation. Model parameters were optimized as the values that provided the lowest root mean square error (RMSE) between predicted post-RT ventilation and the RS for all SLVs in the training data. The performance of the P+A and the P models was evaluated by comparing their respective post-RT ventilation values with the RS predictions. Additional evaluation using various receiver operating characteristic (ROC) metrics was also performed. RESULTS We extracted a dataset of 560 SLVs from four enrolled patients. Our results demonstrated that the P+A model consistently outperformed the P model, exhibiting RMSEs that were nearly half as low across all patients (13 ± 3 percentile for the P+A model vs. 24 ± 3 percentile for the P model on average). Notably, the P+A model aligned closely with the RS in ventilation loss distributions per lobe, particularly in regions exposed to doses ≥13.5 Gy. The ROC analysis further supported the superior performance of the P+A model compared to the P model in sensitivity (0.98 vs. 0.07), accuracy (0.87 vs. 0.25), and balanced predictions. CONCLUSIONS These early findings indicate that airway damage is a crucial factor in RILI that should be included in dose-response modeling to enhance predictions of post-RT lung function.
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Affiliation(s)
- Esther M Vicente
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Noelia Grande Gutierrez
- Mechanical Engineering Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Jessica M Oakes
- Department of Bioengineering, Northeastern University, Boston, Massachusetts, USA
| | - Jochen Cammin
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Arun Gopal
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - John Kipritidis
- Department of Radiotherapy, Northern Sydney Cancer Centre, Sydney, Australia
| | - Arezoo Modiri
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Sina Mossahebi
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Pranshu Mohindra
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Wendla K Citron
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Martha M Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Robert Timmerman
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Amit Sawant
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland, USA
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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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Flakus MJ, Wuschner AE, Wallat EM, Shao W, Meudt J, Shanmuganayagam D, Christensen GE, Reinhardt JM, Bayouth JE. Robust quantification of CT-ventilation biomarker techniques and repeatability in a porcine model. Med Phys 2023; 50:6366-6378. [PMID: 36999913 PMCID: PMC10544701 DOI: 10.1002/mp.16400] [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: 11/24/2022] [Accepted: 03/13/2023] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND Biomarkers estimating local lung ventilation have been derived from computed tomography (CT) imaging using various image acquisition and post-processing techniques. CT-ventilation biomarkers have potential clinical use in functional avoidance radiation therapy (RT), in which RT treatment plans are optimized to reduce dose delivered to highly ventilated lung. Widespread clinical implementation of CT-ventilation biomarkers necessitates understanding of biomarker repeatability. Performing imaging within a highly controlled experimental design enables quantification of error associated with remaining variables. PURPOSE To characterize CT-ventilation biomarker repeatability and dependence on image acquisition and post-processing methodology in anesthetized and mechanically ventilated pigs. METHODS Five mechanically ventilated Wisconsin Miniature Swine (WMS) received multiple consecutive four-dimensional CT (4DCT) and maximum inhale and exhale breath-hold CT (BH-CT) scans on five dates to generate CT-ventilation biomarkers. Breathing maneuvers were controlled with an average tidal volume difference <200 cc. As surrogates for ventilation, multiple local expansion ratios (LERs) were calculated from the acquired CT scans using Jacobian-based post-processing techniques.L E R 2 $LER_2$ measured local expansion between an image pair using either inhale and exhale BH-CT images or two 4DCT breathing phase images.L E R N $LER_N$ measured the maximum local expansion across the 4DCT breathing phase images. Breathing maneuver consistency, intra- and interday biomarker repeatability, image acquisition and post-processing technique dependence were quantitatively analyzed. RESULTS Biomarkers showed strong agreement with voxel-wise Spearman correlationρ > 0.9 $\rho > 0.9$ for intraday repeatability andρ > 0.8 $\rho > 0.8$ for all other comparisons, including between image acquisition techniques. Intra- and interday repeatability were significantly different (p < 0.01). LER2 and LERN post-processing did not significantly affect intraday repeatability. CONCLUSIONS 4DCT and BH-CT ventilation biomarkers derived from consecutive scans show strong agreement in controlled experiments with nonhuman subjects.
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Affiliation(s)
- Mattison J Flakus
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Antonia E Wuschner
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Eric M Wallat
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Wei Shao
- Department of Medicine, University of Florida, Gainesville, Florida, USA
| | - Jen Meudt
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Dhanansayan Shanmuganayagam
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Surgery, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Gary E Christensen
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa, USA
| | - Joseph M Reinhardt
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
| | - John E Bayouth
- Department of Radiation Medicine, Oregon Health Sciences University, Portland, Oregon, USA
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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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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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7
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Astley JR, Biancardi AM, Marshall H, Smith LJ, Hughes PJC, Collier GJ, Saunders LC, Norquay G, Tofan MM, Hatton MQ, Hughes R, Wild JM, Tahir BA. PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation. Sci Rep 2023; 13:11273. [PMID: 37438406 DOI: 10.1038/s41598-023-38105-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 07/03/2023] [Indexed: 07/14/2023] Open
Abstract
Functional lung imaging modalities such as hyperpolarized gas MRI ventilation enable visualization and quantification of regional lung ventilation; however, these techniques require specialized equipment and exogenous contrast, limiting clinical adoption. Physiologically-informed techniques to map proton (1H)-MRI ventilation have been proposed. These approaches have demonstrated moderate correlation with hyperpolarized gas MRI. Recently, deep learning (DL) has been used for image synthesis applications, including functional lung image synthesis. Here, we propose a 3D multi-channel convolutional neural network that employs physiologically-informed ventilation mapping and multi-inflation structural 1H-MRI to synthesize 3D ventilation surrogates (PhysVENeT). The dataset comprised paired inspiratory and expiratory 1H-MRI scans and corresponding hyperpolarized gas MRI scans from 170 participants with various pulmonary pathologies. We performed fivefold cross-validation on 150 of these participants and used 20 participants with a previously unseen pathology (post COVID-19) for external validation. Synthetic ventilation surrogates were evaluated using voxel-wise correlation and structural similarity metrics; the proposed PhysVENeT framework significantly outperformed conventional 1H-MRI ventilation mapping and other DL approaches which did not utilize structural imaging and ventilation mapping. PhysVENeT can accurately reflect ventilation defects and exhibits minimal overfitting on external validation data compared to DL approaches that do not integrate physiologically-informed mapping.
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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
| | - Laurie J Smith
- 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
| | - Laura C Saunders
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Graham Norquay
- POLARIS, Department of Infection, Immunity & Cardiovascular Disease, The University of Sheffield, Sheffield, UK
| | - Malina-Maria Tofan
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, UK
| | - Matthew Q Hatton
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, UK
| | - Rod Hughes
- Early Development Respiratory Medicine, AstraZeneca, Cambridge, 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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Mid-treatment adaptive planning during thoracic radiation using 68 Ventilation-Perfusion Positron emission tomography. Clin Transl Radiat Oncol 2023; 40:100599. [PMID: 36879654 PMCID: PMC9984948 DOI: 10.1016/j.ctro.2023.100599] [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: 08/21/2022] [Revised: 02/11/2023] [Accepted: 02/12/2023] [Indexed: 02/17/2023] Open
Abstract
Four-Dimensional Gallium 68 Ventilation-Perfusion Positron Emission Tomography (68Ga-4D-V/Q PET/CT) allows for dynamic imaging of lung function. To date there has been no assessment of the feasibility of adapting radiation therapy plans to changes in lung function imaged at mid-treatment function using 68Ga-4D-V/Q PET/CT. This study assessed the potential reductions of dose to the functional lung when radiation therapy plans were adapted to avoid functional lung at the mid-treatment timepoint using volumetric arc radiotherapy (VMAT). Methods A prospective clinical trial (U1111-1138-4421) was performed in patients undergoing conventionally fractionated radiation therapy for non-small cell lung cancer (NSCLC). A 68Ga-4D-V/Q PET/CT was acquired at baseline and in the 4th week of treatment. Functional lung target volumes using the ventilated and perfused lung were created. Baseline functional volumes were compared to the week 4 V/Q functional volumes to describe the change in function over time. For each patient, 3 VMAT plans were created and optimised to spare ventilated, perfused or anatomical lung. All key dosimetry metrics were then compared including dose to target volumes, dose to organs at risk and dose to the anatomical and functional sub-units of lung. Results 25 patients had both baseline and 4 week mid treatment 68Ga-4D-V/Q PET/CT imaging. This resulted in a total of 75 adapted VMAT plans. The HPLung volume decreased in 16/25 patients with a mean of the change in volume (cc) -28 ± 515 cc [±SD, range -996 cc to 1496 cc]. The HVLung volume increased in 13/25 patients with mean of the change in volume (cc) + 112 ± 590 cc. [±SD, range -1424 cc to 950 cc]. The functional lung sparing technique was found to be feasible with no significant differences in dose to anatomically defined organs at risk. Most patients did derive a benefit with a reduction in functional volume receiving 20 Gy (fV20) and/or functional mean lung dose (fMLD) in either perfusion and/or ventilation. Patients with the most reduction in fV20 and fMLD were those with stage III NSCLC. Conclusion Functional lung volumes change during treatment. Some patients benefit from using 68Ga-4D-V/Q PET/CT in the 4th week of radiation therapy to adapt radiation plans. In these patients, the role of mid-treatment adaptation requires further prospective investigation.
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Flakus MJ, Kent SP, Wallat EM, Wuschner AE, Tennant E, Yadav P, Burr A, Yu M, Christensen GE, Reinhardt JM, Bayouth JE, Baschnagel AM. Metrics of dose to highly ventilated lung are predictive of radiation-induced pneumonitis in lung cancer patients. Radiother Oncol 2023; 182:109553. [PMID: 36813178 PMCID: PMC10283046 DOI: 10.1016/j.radonc.2023.109553] [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: 11/29/2022] [Revised: 02/07/2023] [Accepted: 02/12/2023] [Indexed: 02/22/2023]
Abstract
PURPOSE To identify metrics of radiation dose delivered to highly ventilated lung that are predictive of radiation-induced pneumonitis. METHODS AND MATERIALS A cohort of 90 patients with locally advanced non-small cell lung cancer treated with standard fractionated radiation therapy (RT) (60-66 Gy in 30-33 fractions) were evaluated. Regional lung ventilation was determined from pre-RT 4-dimensional computed tomography (4DCT) using the Jacobian determinant of a B-spline deformable image registration to estimate lung tissue expansion during respiration. Multiple voxel-wise population- and individual-based thresholds for defining high functioning lung were considered. Mean dose and volumes receiving dose ≥ 5-60 Gy were analyzed for both total lung-ITV (MLD,V5-V60) and highly ventilated functional lung-ITV (fMLD,fV5-fV60). The primary endpoint was symptomatic grade 2+ (G2+) pneumonitis. Receiver operator curve (ROC) analyses were used to identify predictors of pneumonitis. RESULTS G2+ pneumonitis occurred in 22.2% of patients, with no differences between stage, smoking status, COPD, or chemo/immunotherapy use between G<2 and G2+ patients (P≥ 0.18). Highly ventilated lung was defined as voxels exceeding the population-wide median of 18% voxel-level expansion. All total and functional metrics were significantly different between patients with and without pneumonitis (P≤ 0.039). Optimal ROC points predicting pneumonitis from functional lung dose were fMLD ≤ 12.3 Gy, fV5 ≤ 54% and fV20 ≤ 19 %. Patients with fMLD ≤ 12.3 Gy had a 14% risk of developing G2+ pneumonitis whereas risk significantly increased to 35% for those with fMLD > 12.3 Gy (P = 0.035). CONCLUSIONS Dose to highly ventilated lung is associated with symptomatic pneumonitis and treatment planning strategies should focus on limiting dose to functional regions. These findings provide important metrics to be used in functional lung avoidance RT planning and designing clinical trials.
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Affiliation(s)
- Mattison J. Flakus
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Sean P. Kent
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Eric M. Wallat
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Antonia E. Wuschner
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Erica Tennant
- University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Poonam Yadav
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago Illinois
| | - Adam Burr
- Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, Wisconsin
| | - Menggang Yu
- Department of Biostatistics and Medical Informatics, University of Wisconsin Hospital and Clinics, Madison, Wisconsin
| | - Gary E Christensen
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa
| | - Joseph M. Reinhardt
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa
| | - John E. Bayouth
- Department of Radiation Medicine, Oregon Health & Science University, Portland, Oregon
| | - Andrew M. Baschnagel
- Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, Wisconsin
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Flakus MJ, Wuschner AE, Wallat EM, Shao W, Shanmuganayagam D, Christensen GE, Reinhardt JM, Li K, Bayouth JE. Quantifying robustness of CT-ventilation biomarkers to image noise. Front Physiol 2023; 14:1040028. [PMID: 36866176 PMCID: PMC9971492 DOI: 10.3389/fphys.2023.1040028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 01/31/2023] [Indexed: 02/16/2023] Open
Abstract
Purpose: To quantify the impact of image noise on CT-based lung ventilation biomarkers calculated using Jacobian determinant techniques. Methods: Five mechanically ventilated swine were imaged on a multi-row CT scanner with acquisition parameters of 120 kVp and 0.6 mm slice thickness in static and 4-dimensional CT (4DCT) modes with respective pitches of 1 and 0.09. A range of tube current time product (mAs) values were used to vary image dose. On two dates, subjects received two 4DCTs: one with 10 mAs/rotation (low-dose, high-noise) and one with CT simulation standard of care 100 mAs/rotation (high-dose, low-noise). Additionally, 10 intermediate noise level breath-hold (BHCT) scans were acquired with inspiratory and expiratory lung volumes. Images were reconstructed with and without iterative reconstruction (IR) using 1 mm slice thickness. The Jacobian determinant of an estimated transformation from a B-spline deformable image registration was used to create CT-ventilation biomarkers estimating lung tissue expansion. 24 CT-ventilation maps were generated per subject per scan date: four 4DCT ventilation maps (two noise levels each with and without IR) and 20 BHCT ventilation maps (10 noise levels each with and without IR). Biomarkers derived from reduced dose scans were registered to the reference full dose scan for comparison. Evaluation metrics were gamma pass rate (Γ) with 2 mm distance-to-agreement and 6% intensity criterion, voxel-wise Spearman correlation (ρ) and Jacobian ratio coefficient of variation (CoV JR ). Results: Comparing biomarkers derived from low (CTDI vol = 6.07 mGy) and high (CTDI vol = 60.7 mGy) dose 4DCT scans, mean Γ, ρ and CoV JR values were 93% ± 3%, 0.88 ± 0.03 and 0.04 ± 0.009, respectively. With IR applied, those values were 93% ± 4%, 0.90 ± 0.04 and 0.03 ± 0.003. Similarly, comparisons between BHCT-based biomarkers with variable dose (CTDI vol = 1.35-7.95 mGy) had mean Γ, ρ and CoV JR of 93% ± 4%, 0.97 ± 0.02 and 0.03 ± 0.006 without IR and 93% ± 4%, 0.97 ± 0.03 and 0.03 ± 0.007 with IR. Applying IR did not significantly change any metrics (p > 0.05). Discussion: This work demonstrated that CT-ventilation, calculated using the Jacobian determinant of an estimated transformation from a B-spline deformable image registration, is invariant to Hounsfield Unit (HU) variation caused by image noise. This advantageous finding may be leveraged clinically with potential applications including dose reduction and/or acquiring repeated low-dose acquisitions for improved ventilation characterization.
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Affiliation(s)
- Mattison J. Flakus
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States,*Correspondence: Mattison J. Flakus,
| | - Antonia E. Wuschner
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States
| | - Eric M. Wallat
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States
| | - Wei Shao
- Department of Medicine, University of Florida, Gainesville, FL, United States
| | | | - Gary E. Christensen
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, United States
| | - Joseph M. Reinhardt
- Roy J Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States
| | - Ke Li
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States
| | - John E. Bayouth
- Department of Human Oncology, University of Wisconsin-Madison, Madison, WI, United States
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11
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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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12
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Ren G, Li B, Lam SK, Xiao H, Huang YH, Cheung ALY, Lu Y, Mao R, Ge H, Kong FM(S, Ho WY, Cai J. A Transfer Learning Framework for Deep Learning-Based CT-to-Perfusion Mapping on Lung Cancer Patients. Front Oncol 2022; 12:883516. [PMID: 35847874 PMCID: PMC9283770 DOI: 10.3389/fonc.2022.883516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 06/02/2022] [Indexed: 11/18/2022] Open
Abstract
Purpose Deep learning model has shown the feasibility of providing spatial lung perfusion information based on CT images. However, the performance of this method on lung cancer patients is yet to be investigated. This study aims to develop a transfer learning framework to evaluate the deep learning based CT-to-perfusion mapping method specifically on lung cancer patients. Methods SPECT/CT perfusion scans of 33 lung cancer patients and 137 non-cancer patients were retrospectively collected from two hospitals. To adapt the deep learning model on lung cancer patients, a transfer learning framework was developed to utilize the features learned from the non-cancer patients. These images were processed to extract features from three-dimensional CT images and synthesize the corresponding CT-based perfusion images. A pre-trained model was first developed using a dataset of patients with lung diseases other than lung cancer, and subsequently fine-tuned specifically on lung cancer patients under three-fold cross-validation. A multi-level evaluation was performed between the CT-based perfusion images and ground-truth SPECT perfusion images in aspects of voxel-wise correlation using Spearman’s correlation coefficient (R), function-wise similarity using Dice Similarity Coefficient (DSC), and lobe-wise agreement using mean perfusion value for each lobe of the lungs. Results The fine-tuned model yielded a high voxel-wise correlation (0.8142 ± 0.0669) and outperformed the pre-trained model by approximately 8%. Evaluation of function-wise similarity indicated an average DSC value of 0.8112 ± 0.0484 (range: 0.6460-0.8984) for high-functional lungs and 0.8137 ± 0.0414 (range: 0.6743-0.8902) for low-functional lungs. Among the 33 lung cancer patients, high DSC values of greater than 0.7 were achieved for high functional volumes in 32 patients and low functional volumes in all patients. The correlations of the mean perfusion value on the left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe were 0.7314, 0.7134, 0.5108, 0.4765, and 0.7618, respectively. Conclusion For lung cancer patients, the CT-based perfusion images synthesized by the transfer learning framework indicated a strong voxel-wise correlation and function-wise similarity with the SPECT perfusion images. This suggests the great potential of the deep learning method in providing regional-based functional information 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 SAR, China
| | - Bing Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China
| | - Sai-kit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Andy Lai-yin Cheung
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
| | - Yufei Lu
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China
| | - Ronghu Mao
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China
| | - Hong Ge
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China
| | - Feng-Ming (Spring) Kong
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Wai-yin Ho
- Department of Nuclear Medicine, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
- *Correspondence: Jing Cai,
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Kajikawa T, Kadoya N, Maehara Y, Miura H, Katsuta Y, Nagasawa S, Suzuki G, Yamazaki H, Tamaki N, Yamada K. A deep learning method for translating 3D-CT to SPECT ventilation imaging: First comparison with 81m Kr-gas SPECT ventilation imaging. Med Phys 2022; 49:4353-4364. [PMID: 35510535 PMCID: PMC9545310 DOI: 10.1002/mp.15697] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 03/29/2022] [Accepted: 04/18/2022] [Indexed: 11/10/2022] Open
Abstract
PURPOSE This study aimed to evaluate the accuracy of deep learning (DL)-based computed tomography (CT) ventilation imaging (CTVI). METHODS A total of 71 cases who underwent single-photon emission CT 81m Kr-gas ventilation (SPECT V) and CT imaging were included. Sixty cases were assigned to the training and validation sets and the remaining 11 cases were assigned to the test set. To directly transform 3DCT (free-breathing CT) images to SPECT V images, a DL-based model was implemented based on the U-Net architecture. The input and output data were 3DCT- and SPECT V-masked, respectively, except for whole-lung volumes. These data were rearranged in voxel size, registered rigidly, cropped, and normalized in pre-processing. In addition to a standard estimation method (i.e., without dropout during the estimation process), a Monte-Carlo Dropout (MCD) method (i.e., with dropout during the estimation process) was used to calculate prediction uncertainty. To evaluate the two models' (CTVIMCD U-Net , CTVIU-Net ) performance, we used five-fold cross-validation for the training and validation sets. To test the final model performances for both approaches, we applied the test set to each trained model and averaged the test prediction results from the five trained models to acquire the mean test result (bagging) for each approach. For the MCD method, the models were predicted repeatedly (sample size = 200), and the average and standard deviation maps were calculated in each voxel from the predicted results: the average maps were defined as test prediction results in each fold. As an evaluation index, the voxel-wise Spearman rank correlation coefficient (Spearman rs ) and dice similarity coefficient (DSC) were calculated. The DSC was calculated for three functional regions (high, moderate, and low) separated by an almost equal volume. The coefficient of variation was defined as prediction uncertainty, and these average values were calculated within three functional regions. The Wilcoxon signed-rank test was used to test for a significant difference between the two DL-based approaches RESULTS: The average indexes with one standard deviation (1SD) between CTVIMCD U-Net and SPECT V were 0.76 ± 0.06, 0.69 ± 0.07, 0.51 ± 0.06, and 0.75 ± 0.04 for Spearman rs , DSChigh , DSCmoderate , and DSClow , respectively. The average indexes with 1SD between CTVIU-Net and SPECT V were 0.72 ± 0.05, 0.66 ± 0.04, 0.48 ± 0.04, and 0.74 ± 0.06 for Spearman rs , DSChigh , DSCmoderate , and DSClow , respectively. These indexes between CTVIMCD U-Net and CTVIU-Net showed no significance difference (Spearman rs , p = 0.175; DSChigh , p = 0.123; DSCmoderate , p = 0.278; DSClow , p = 0.520). The average coefficient of variations with 1SD were 0.27 ± 0.00, 0.27 ± 0.01, and 0.36 ± 0.03 for the high-, moderate-, and low-functional regions, respectively, and the low-functional region showed a tendency to exhibit larger uncertainties than the others. CONCLUSION We evaluated DL-based framework for estimating lung-functional ventilation images only from CT images. The results indicated that the DL-based approach could potentially be used for lung-ventilation estimation. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Tomohiro Kajikawa
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.,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
| | - Yosuke Maehara
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Hiroshi Miura
- Department of Radiology, Japanese Red Cross Kyoto Daiichi Hospital, Kyoto, Japan
| | - Yoshiyuki Katsuta
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Shinsuke Nagasawa
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Gen Suzuki
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Hideya Yamazaki
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Nagara Tamaki
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Kei Yamada
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
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Grover J, Byrne HL, Sun Y, Kipritidis J, Keall P. Investigating the use of machine learning to generate ventilation images from CT scans. Med Phys 2022; 49:5258-5267. [PMID: 35502763 PMCID: PMC9545612 DOI: 10.1002/mp.15688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 03/15/2022] [Accepted: 04/25/2022] [Indexed: 11/16/2022] Open
Abstract
Background Radiotherapy treatment planning incorporating ventilation imaging can reduce the incidence of radiation‐induced lung injury. The gold‐standard of ventilation imaging, using nuclear medicine, has limitations with respect to availability and cost. Purpose An alternative type of ventilation imaging to nuclear medicine uses 4DCT (or breath‐hold CT [BHCT] pair) with deformable image registration (DIR) and a ventilation metric to produce a CT ventilation image (CTVI). The purpose of this study is to investigate the application of machine learning as an alternative to DIR‐based methods when producing CTVIs. Methods A patient dataset of 15 inhale and exhale BHCTs and Galligas PET ventilation images were used to train and test a 2D U‐Net style convolutional neural network. The neural network established relationships between axial input BHCT image pairs and axial labeled Galligas PET images and was evaluated using eightfold cross‐validation. Once trained, the neural network could produce a CTVI from an input BHCT image pair. The CTVIs produced by the neural network were qualitatively assessed visually and quantitatively compared to a Galligas PET ventilation image using a Spearman correlation and Dice similarity coefficient (DSC). The DSC measured the spatial overlap between three segmented equal lung volumes by ventilation (high, medium, and low functioning lung [LFL]). Results The mean Spearman correlation between the CTVIs and the Galligas PET ventilation images was 0.58 ± 0.14. The mean DSC over high, medium, and LFL between the CTVIs and Galligas PET ventilation images was 0.55 ± 0.06. Visually, a systematic overprediction of ventilation within the lung was observed in the CTVIs with respect to the Galligas PET ventilation images, with jagged regions of ventilation in the sagittal and coronal planes. Conclusions A convolutional neural network was developed that could produce a CTVI from a BHCT image pair, which was then compared with a Galligas PET ventilation image. The performance of this machine learning method was comparable to previous benchmark studies investigating a DIR‐based CTVI, warranting future development, and investigation of applying machine learning to a CTVI.
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Affiliation(s)
- James Grover
- ACRF Image X Institute, Faculty of Medicine and Health, The University of Sydney, Australia.,School of Physics, The University of Sydney, Australia
| | - Hilary L Byrne
- ACRF Image X Institute, Faculty of Medicine and Health, The University of Sydney, Australia
| | - Yu Sun
- School of Physics, The University of Sydney, Australia
| | - John Kipritidis
- ACRF Image X Institute, Faculty of Medicine and Health, The University of Sydney, Australia.,Northern Sydney Cancer Centre, Royal North Shore Hospital, Australia
| | - Paul Keall
- ACRF Image X Institute, Faculty of Medicine and Health, The University of Sydney, Australia
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Ding Y, Yang L, Zhou Q, Bi J, Li Y, Pi G, Wei W, Hu D, Rao Q, Li H, Zhao L, Liu A, Du D, Wang X, Zhou X, Han G, Qing K. A pilot study of function-based radiation therapy planning for lung cancer using hyperpolarized xenon-129 ventilation MRI. J Appl Clin Med Phys 2022; 23:e13502. [PMID: 35045204 PMCID: PMC8906214 DOI: 10.1002/acm2.13502] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 11/01/2021] [Accepted: 11/29/2021] [Indexed: 11/05/2022] Open
Abstract
PURPOSE Radiation-induced lung injury (RILI) is a common side effect in patients with non-small cell lung cancer (NSCLC) treated with radiotherapy. Minimizing irradiation into highly functional areas of the lung may reduce the occurrence of RILI. The aim of this study is to evaluate the feasibility and utility of hyperpolarized xenon-129 magnetic resonance imaging (MRI), an imaging tool for evaluation of the pulmonary function, to guide radiotherapy planning. METHODS Ten locally advanced NSCLC patients were recruited. Each patient underwent a simulation computed tomography (CT) scan and hyperpolarized xenon-129 MRI, then received 64 Gyin 32 fractions for radiotherapy. Clinical contours were drawn on CT. Lung regions with good ventilation were contoured based on the MRI. Two intensity-modulated radiation therapy plans were made for each patient: an anatomic plan (Plan-A) based on CT alone and a function-based plan (Plan-F) based on CT and MRI results. Compared to Plan-A, Plan-F was generated with two additional steps: (1) beam angles were carefully chosen to minimize direct radiation entering well-ventilated areas, and (2) additional optimization criteria were applied to well-ventilated areas to minimize dose exposure. V20Gy , V10Gy , V5Gy , and the mean dose in the lung were compared between the two plans. RESULTS Plan-A and Plan-F were both clinically acceptable and met similar target coverage and organ-at-risk constraints (p > 0.05) except for the ventilated lungs. Compared with Plan-A, V5Gy (Plan-A: 30.7 ± 11.0%, Plan-F: 27.2 ± 9.3%), V10Gy (Plan-A: 22.0 ± 8.6%, Plan-F: 19.3 ± 7.0%), and V20Gy (Plan-A: 12.5 ± 5.6%, Plan-F: 11.0 ± 4.1%) for well-ventilated lung areas were significantly reduced in Plan-F (p < 0.05). CONCLUSION In this pilot study, function-based radiotherapy planning using hyperpolarized xenon-129 MRI is demonstrated to be feasible in 10 patients with NSCLC with the potential to reduce radiation exposure in well-ventilated areas of the lung defined by hyperpolarized xenon-129 MRI.
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Affiliation(s)
- Yi Ding
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lu Yang
- Department of Radiation Oncology, Renmin Hospital, Wuhan University, Wuhan, China
| | - Qian Zhou
- Key Laboratory of Magnetic Resonance in Biological Systems, 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, Wuhan National Laboratory for Optoelectronics, Chinese Academy of Sciences, Wuhan, China
| | - Jianping Bi
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Li
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guoliang Pi
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Wei
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Desheng Hu
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qiuchen Rao
- Key Laboratory of Magnetic Resonance in Biological Systems, 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, Wuhan National Laboratory for Optoelectronics, Chinese Academy of Sciences, Wuhan, China
| | - Haidong Li
- Key Laboratory of Magnetic Resonance in Biological Systems, 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, Wuhan National Laboratory for Optoelectronics, Chinese Academy of Sciences, Wuhan, China
| | - Li Zhao
- Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
| | - An Liu
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, California, USA
| | - Dongsu Du
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, California, USA
| | - Xiao Wang
- Department of Radiation Oncology, Cancer Institute of New Jersey, Rutgers University, New Brunswick, New Jersey, USA
| | - Xin Zhou
- Key Laboratory of Magnetic Resonance in Biological Systems, 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, Wuhan National Laboratory for Optoelectronics, Chinese Academy of Sciences, Wuhan, China
| | - Guang Han
- Department of Radiation Oncology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kun Qing
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, California, USA
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16
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Huang P, Yan H, Hu Z, Liu Z, Tian Y, Dai J. Predicting radiation pneumonitis with fuzzy clustering neural network using 4DCT ventilation image based dosimetric parameters. Quant Imaging Med Surg 2021; 11:4731-4741. [PMID: 34888185 DOI: 10.21037/qims-20-1095] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 03/05/2021] [Indexed: 12/25/2022]
Abstract
Background To develop a fuzzy clustering neural network to predict radiation-induced pneumonitis (RP) using four-dimensional computed tomography (4DCT) ventilation image (VI) based dosimetric parameters for thoracic cancer patients. Methods The VI were retrospectively calculated from pre-treatment 4DCT data using a deformable image registration (DIR) and an improved VI algorithm. Similar to dose-volume histogram (DVH) of intensity modulated radiotherapy (IMRT), dose-function histogram (DFH) was derived from dose distribution and VI. Then, the dose-function metrics were calculated from DFH. For comparison, the dose-volume metrics were calculated from DVH. Correspondingly, two sets of feature vectors were formed from the dose-volume metrics and the dose-function metrics, respectively. For the feature vectors of each set, they were first pre-processed by principal component analysis (PCA) to reduce feature dimensions. Then, they were grouped to few clusters determined by fuzzy c-means (FCM) algorithm. Next, the neural network was trained to correlate the dosimetric parameters with RP based on the feature vectors of each cluster. Finally, the occurrence of RP was predicted by the neural network on the test data. Results Through PCA analysis, the top 5 principal components were selected. Their contribution is more than 98%, which is adequate to represent the original feature space of input data. Based on the clustering validity indexes, the optimal number of clusters is 4 and used for subsequent fuzzy clustering of the input data. After network training, the areas under the curve (AUC) of the prediction model is 0.77 using VI-based dosimetric parameters and 0.67 using structure-based dosimetric parameters. Conclusions Compared to the structure-based dosimetric features, the VI-based dosimetric features are more relevant to lung function and presented higher prediction accuracy of RP. The fuzzy clustering neural network improved the prediction accuracy of RP compared to the conventional neural network. The combination of VI-based dose-function metrics and fuzzy clustering neural network provides an effective predictive model for assessing lung toxicity risk after radiotherapy.
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Affiliation(s)
- Peng Huang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hui Yan
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhihui Hu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhiqiang Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuan Tian
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianrong Dai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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17
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Tanaka R, Matsumoto I, Tamura M, Takata M, Yoshida S, Saito D, Tanaka Y, Inoue D, Ohkura N, Kasahara K. Dynamic chest radiography: clinical validation of ventilation and perfusion metrics derived from changes in radiographic lung density compared to nuclear medicine imaging. Quant Imaging Med Surg 2021; 11:4016-4027. [PMID: 34476186 DOI: 10.21037/qims-20-1217] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 04/08/2021] [Indexed: 01/04/2023]
Abstract
Background Dynamic chest radiography (DCR) is a type of non-contrast-enhanced functional lung imaging with a dynamic flat-panel detector (FPD). This study aimed to assess the clinical significance of ventilation and perfusion metrics derived from changes in radiographic lung density on DCR in comparison to nuclear medicine imaging-derived metrics. Methods DCR images of 42 lung cancer patients were sequentially obtained during respiration using a dynamic FPD imaging system. For each subdivided lung region, the maximum change in the averaged pixel value (Δmax), i.e., lung density, due to respiration and cardiac function was calculated, and the percentage of Δmax relative to the total of all lung regions (Δmax%) was computed for ventilation and perfusion, respectively. The Δmax% was compared to the accumulation of radioactive agents such as Tc-99m gas and Tc-99m macro-aggregated albumin (radioactive agents%) on ventilation and perfusion scans in the subdivided lung regions, by Spearman's correlation coefficient (r) and the Dice similarity coefficients (DSC). To facilitate visual evaluation, Δmax% was visualized as a color scaling, where larger Δmax values were indicated by higher color intensities. Results We found a moderate correlation between Δmax% and radioactive agents% on ventilation and perfusion scans, with perfusion metrics (r=0.57, P<0.001) showing a higher correlation than ventilation metrics (r=0.53, P<0.001). We also found a good or strong correlation (r≥0.5) in 80.9% (34/42) of patients for perfusion metrics (r=0.60±0.16) and in 52.4% (22/42) of patients for ventilation metrics (r=0.53±0.16). DSC indicated a moderate correlation for both metrics. Decreased pulmonary function was observed in the form of reduced color intensities on color-mapping images. Conclusions DCR-derived ventilation and perfusion metrics correlated reasonably well with nuclear medicine imaging findings in lung subdivisions, suggesting that DCR could provide useful information on pulmonary function without the use of radioactive contrast agents.
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Affiliation(s)
- Rie Tanaka
- College of Medical, Pharmaceutical & Health Sciences, Kanazawa University, Kanazawa, Ishikawa, Japan
| | - Isao Matsumoto
- Department of Thoracic Surgery, Kanazawa University, Kanazawa, Ishikawa, Japan
| | - Masaya Tamura
- Department of Thoracic Surgery, Kanazawa University, Kanazawa, Ishikawa, Japan
| | - Munehisa Takata
- Department of Thoracic Surgery, Kanazawa University, Kanazawa, Ishikawa, Japan
| | - Shuhei Yoshida
- Department of Thoracic Surgery, Kanazawa University, Kanazawa, Ishikawa, Japan
| | - Daisuke Saito
- Department of Thoracic Surgery, Kanazawa University, Kanazawa, Ishikawa, Japan
| | - Yusuke Tanaka
- Department of Thoracic Surgery, Kanazawa University, Kanazawa, Ishikawa, Japan
| | - Dai Inoue
- Department of Radiology, Kanazawa University Hospital, Kanazawa, Ishikawa, Japan
| | - Noriyuki Ohkura
- Department of Respiratory Medicine, Kanazawa University Hospital, Kanazawa, Ishikawa, Japan
| | - Kazuo Kasahara
- Department of Respiratory Medicine, Kanazawa University Hospital, Kanazawa, Ishikawa, Japan
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18
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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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19
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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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20
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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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21
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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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22
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McIntosh L, Jackson P, Hardcastle N, Bressel M, Kron T, Callahan JW, Steinfort D, Bucknell N, Hofman MS, Siva S. Automated assessment of functional lung imaging with 68Ga-ventilation/perfusion PET/CT using iterative histogram analysis. EJNMMI Phys 2021; 8:23. [PMID: 33677692 PMCID: PMC7937580 DOI: 10.1186/s40658-021-00375-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 02/25/2021] [Indexed: 12/28/2022] Open
Abstract
PURPOSE Functional lung mapping from Ga68-ventilation/perfusion (V/Q) PET/CT, which has been shown to correlate with pulmonary function tests (PFTs), may be beneficial in a number of clinical applications where sparing regions of high lung function is of interest. Regions of clumping in the proximal airways in patients with airways disease can result in areas of focal intense activity and artefact in ventilation imaging. These artefacts may even shine through to subsequent perfusion images and create a challenge for quantitative analysis of PET imaging. We aimed to develop an automated algorithm that interprets the uptake histogram of PET images to calculate a peak uptake value more representative of the global lung volume. METHODS Sixty-six patients recruited from a prospective clinical trial underwent both V/Q PET/CT imaging and PFT analysis before treatment. PET images were normalised using an iterative histogram analysis technique to account for tracer hotspots prior to the threshold-based delineation of varying values. Pearson's correlation between fractional lung function and PFT score was calculated for ventilation, perfusion, and matched imaging volumes at varying threshold values. RESULTS For all functional imaging thresholds, only FEV1/FVC PFT yielded reasonable correlations to image-based functional volume. For ventilation, a range of 10-30% of adapted peak uptake value provided a reasonable threshold to define a volume that correlated with FEV1/FVC (r = 0.54-0.61). For perfusion imaging, a similar correlation was observed (r = 0.51-0.56) in the range of 20-60% adapted peak threshold. Matched volumes were closely linked to ventilation with a threshold range of 15-35% yielding a similar correlation (r = 0.55-0.58). CONCLUSIONS Histogram normalisation may be implemented to determine the presence of tracer clumping hotspots in Ga-68 V/Q PET imaging allowing for automated delineation of functional lung and standardisation of functional volume reporting.
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Affiliation(s)
- Lachlan McIntosh
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, 3000, Australia.
| | - Price Jackson
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, 3000, Australia.,Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, 3000, Australia
| | - Nicholas Hardcastle
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, 3000, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, 3010, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, New South Wales, 2522, Australia
| | - Mathias Bressel
- Centre for Biostatistics and Clinical Trials, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Tomas Kron
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, 3000, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, 3010, Australia
| | - Jason W Callahan
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, 3000, Australia
| | - Daniel Steinfort
- Respiratory Medicine, Peter MacCallum Cancer Centre and Royal Melbourne Hospital, Melbourne, Australia
| | - Nicholas Bucknell
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, 3010, Australia.,Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, 3000, Australia
| | - Michael S Hofman
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, 3000, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, 3010, Australia
| | - Shankar Siva
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, 3010, Australia.,Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, 3000, Australia
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23
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Nyeng TB, Møller DS, Farr K, Kramer S, Khalil AA, Grau C, Hoffmann L. A comparison of two methods for segmentation of functional volumes in radiotherapy planning of lung cancer patients. Acta Oncol 2021; 60:353-360. [PMID: 33522851 DOI: 10.1080/0284186x.2021.1877811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
BACKGROUND In radiotherapy (RT) of lung cancer, dose to functional lung (FL) volumes segmented with two different methods (perfusion SPECT (Q-SPECT) and 4D-CT (4D) ventilation (V)) have been shown to correlate with the incidence of radiation pneumonitis (RP). This study aims to compare the FL volumes identified by both methods. MATERIAL AND METHODS Thirty lung cancer patients had a 4D and Q-SPECT prior to treatment. Seventeen of these patients also had a ventilation SPECT (V-SPECT). FL sub-volumes were segmented automatically, using cut-off values. The volumes were compared in terms of overlap fraction (OF) relative to the minimal volume, and intersection fraction (IF) of the FL volume relative to the total lung volume (VLung). RESULTS Cut-off values suggested in literature for Q-SPECT and 4D-V resulted in volumes differing in size by a median 18% [6%;31%], and a median OF and IF of 0.48 [0.23;0.70] and 0.09 [0.02;0.25], respectively. Segmenting volumes of comparable size of about 1/3 of VLung (FL-m(1/3), m = method) resulted in a median OF and IF of 0.43 [0.23;0.58] and 0.12 [0.06;0.19], respectively. Twenty-five patients (83%) had a reasonable overlap between FL-Q(1/3) and FL-4D-V(1/3) volumes, with OF values above 0.33. IF increased significantly (p = .036) compared to using fixed cut-off values. Similarly, volumes of comparable size of about 1/3 VLung were produced for V-SPECT, and FL-Q(1/3), FL-V(1/3), and FL-4D-V(1/3) were compared. The overlaps and intersections of FL-V(1/3) with FL-Q(1/3) volumes were significantly (p<.001) larger than the corresponding overlaps and intersections of FL-Q(1/3) with FL-4D(1/3) and FL-V(1/3) with FL-4D(1/3). CONCLUSION The Q-SPECT and 4D-V methods do not segment entirely the same FL volumes. A reasonable overlap of the volumes along with the findings of other studies that both correlate to RP incidence, suggests that a combination of both volumes, e.g. using the IF, may be useful in RT treatment planning.
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Affiliation(s)
- T. B. Nyeng
- Department of Oncology, Section for Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - D. S. Møller
- Department of Oncology, Section for Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - K. Farr
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - S. Kramer
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Aarhus, Denmark
| | - A. A. Khalil
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - C. Grau
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - L. Hoffmann
- Department of Oncology, Section for Medical Physics, Aarhus University Hospital, Aarhus, Denmark
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24
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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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Bucknell N, Hardcastle N, Jackson P, Hofman M, Callahan J, Eu P, Iravani A, Lawrence R, Martin O, Bressel M, Woon B, Blyth B, MacManus M, Byrne K, Steinfort D, Kron T, Hanna G, Ball D, Siva S. Single-arm prospective interventional study assessing feasibility of using gallium-68 ventilation and perfusion PET/CT to avoid functional lung in patients with stage III non-small cell lung cancer. BMJ Open 2020; 10:e042465. [PMID: 33303468 PMCID: PMC7733178 DOI: 10.1136/bmjopen-2020-042465] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND In the curative-intent treatment of locally advanced lung cancer, significant morbidity and mortality can result from thoracic radiation therapy. Symptomatic radiation pneumonitis occurs in one in three patients and can lead to radiation-induced fibrosis. Local failure occurs in one in three patients due to the lungs being a dose-limiting organ, conventionally restricting tumour doses to around 60 Gy. Functional lung imaging using positron emission tomography (PET)/CT provides a geographic map of regional lung function and preclinical studies suggest this enables personalised lung radiotherapy. This map of lung function can be integrated into Volumetric Modulated Arc Therapy (VMAT) radiotherapy planning systems, enabling conformal avoidance of highly functioning regions of lung, thereby facilitating increased doses to tumour while reducing normal tissue doses. METHODS AND ANALYSIS This prospective interventional study will investigate the use of ventilation and perfusion PET/CT to identify highly functioning lung volumes and avoidance of these using VMAT planning. This single-arm trial will be conducted across two large public teaching hospitals in Australia. Twenty patients with stage III non-small cell lung cancer will be recruited. All patients enrolled will receive dose-escalated (69 Gy) functional avoidance radiation therapy. The primary endpoint is feasibility with this achieved if ≥15 out of 20 patients meet pre-defined feasibility criteria. Patients will be followed for 12 months post-treatment with serial imaging, biomarkers, toxicity assessment and quality of life assessment. DISCUSSION Using advanced techniques such as VMAT functionally adapted radiation therapy may enable safe moderate dose escalation with an aim of improving local control and concurrently decreasing treatment related toxicity. If this technique is proven feasible, it will inform the design of a prospective randomised trial to assess the clinical benefits of functional lung avoidance radiation therapy. ETHICS AND DISSEMINATION This study was approved by the Peter MacCallum Human Research Ethics Committee. All participants will provide written informed consent. Results will be disseminated via publications. TRIALS REGISTRATION NUMBER NCT03569072; Pre-results.
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Affiliation(s)
- Nicholas Bucknell
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Nicholas Hardcastle
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Centre for Medical Radiation Physics, University of Wollongong, Wollongong, New South Wales, Australia
| | - Price Jackson
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Michael Hofman
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
- Molecular Imaging and Therapeutic Nuclear Medicine, Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Jason Callahan
- Molecular Imaging and Therapeutic Nuclear Medicine, Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Peter Eu
- Molecular Imaging and Therapeutic Nuclear Medicine, Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- School of Medicine, Deakin University, Geelong, Victoria, Australia
| | - Amir Iravani
- Molecular Imaging and Therapeutic Nuclear Medicine, Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Department of Nuclear Medicine, Washington University School of Medicine, St Louis, Missouri, USA
| | - Rhonda Lawrence
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Olga Martin
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Mathias Bressel
- Centre for Biostatistics and Clinical Trials, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Beverley Woon
- Department of Radiology, Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Benjamin Blyth
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Michael MacManus
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Keelan Byrne
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Daniel Steinfort
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
- Department of Respiratory Medicine, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Tomas Kron
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Gerard Hanna
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - David Ball
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Shankar Siva
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
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Vicente E, Modiri A, Kipritidis J, Hagan A, Yu K, Wibowo H, Yan Y, Owen DR, Matuszak MM, Mohindra P, Timmerman R, Sawant A. Functionally weighted airway sparing (FWAS): a functional avoidance method for preserving post-treatment ventilation in lung radiotherapy. Phys Med Biol 2020; 65:165010. [PMID: 32575096 DOI: 10.1088/1361-6560/ab9f5d] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Recent changes to the guidelines for screening and early diagnosis of lung cancer have increased the interest in preserving post-radiotherapy lung function. Current investigational approaches are based on spatially mapping functional regions and generating regional avoidance plans that preferentially spare highly ventilated/perfused lung. A potentially critical, yet overlooked, aspect of functional avoidance is radiation injury to peripheral airways, which serve as gas conduits to and from functional lung regions. Dose redistribution based solely on regional function may cause irreparable damage to the 'supply chain'. To address this deficiency, we propose the functionally weighted airway sparing (FWAS) method. FWAS (i) maps the bronchial pathways to each functional sub-lobar lung volume; (ii) assigns a weighting factor to each airway based on the relative contribution of the sub-volume to overall lung function; and (iii) creates a treatment plan that aims to preserve these functional pathways. To evaluate it, we used four cases from a retrospective cohort of SAbR patients treated for lung cancer. Each patient's airways were auto-segmented from a diagnostic-quality breath-hold CT using a research virtual bronchoscopy software. A ventilation map was generated from the planning 4DCT to map regional lung function. For each terminal airway, as resolved by the segmentation software, the total ventilation within the sub-lobar volume supported by that airway was estimated and used as a function-based weighting factor. Upstream airways were weighted based on the cumulative volumetric ventilation supported by corresponding downstream airways. Using a previously developed model for airway radiosensitivity, dose constraints were determined for each airway corresponding to a <5% probability of airway collapse. Airway dose constraints, ventilation scores, and clinical dose constraints were input to a swarm optimization-based inverse planning engine to create a 3D conformal SAbR plan (CRT). The FWAS plans were compared to the patients' prescribed CRT clinical plans and the inverse-optimized clinical plans. Depending on the size and location of the tumour, the FWAS plan showed superior preservation of ventilation due to airflow preservation through open pathways (i.e. cumulative ventilation score from the sub-lobar volumes of open pathways). Improvements ranged between 3% and 23%, when comparing to the prescribed clinical plans, and between 3% and 35%, when comparing to the inverse-optimized clinical plans. The three plans satisfied clinical requirements for PTV coverage and OAR dose constraints. These initial results suggest that by sparing pathways to high-functioning lung subregions it is possible to reduce post-SAbR loss of respiratory function.
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Affiliation(s)
- E Vicente
- University of Maryland School of Medicine, Baltimore, MD, United States of America
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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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Vicente E, Modiri A, Yu KC, Wibowo H, Yan Y, Timmerman R, Sawant A. Accounting for respiratory motion in small serial structures during radiotherapy planning: proof of concept in virtual bronchoscopy-guided lung functional avoidance radiotherapy. Phys Med Biol 2019; 64:225011. [PMID: 31665703 DOI: 10.1088/1361-6560/ab52a1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Respiratory motion management techniques in radiotherapy (RT) planning are primarily focused on maintaining tumor target coverage. An inadequately addressed need is accounting for motion in dosimetric estimations in smaller serial structures. Accurate dose estimations in such structures are more sensitive to motion because respiration can cause them to move completely in or out of a high dose-gradient field. In this work, we study three motion management strategies (m1-m3) to find an accurate method to estimate the dosimetry in airways. To validate these methods, we generated a 'ground truth' digital breathing model based on a 4DCT scan from a lung stereotactic ablative radiotherapy (SAbR) patient. We simulated 225 breathing cycles with ±10% perturbations in amplitude, respiratory period, and time per respiratory phase. A high-resolution breath-hold CT (BHCT) was also acquired and used with a research virtual bronchoscopy software to autosegment 239 airways. Contours for planning target volume (PTV) and organs at risk (OARs) were defined on the maximum intensity projection of the 4DCT (CTMIP) and transferred to the average of the 10 4DCT phases (CTAVG). To design the motion management methods, the RT plan was recreated using different images and structure definitions. Methods m1 and m2 recreated the plan using the CTAVG image. In method m1, airways were deformed to the CTAVG. In m2, airways were deformed to each of the 4DCT phases, and union structures were transferred onto the CTAVG. In m3, the RT plan was recreated on each of the 10 phases, and the dose distribution from each phase was deformed to the BHCT and summed. Dose errors (mean [min, max]) in airways were: m1: 21% (0.001%, 93%); m2: 45% (0.1%, 179%); and m3: 4% (0.006%, 14%). Our work suggests that accurate dose estimation in moving small serial structures requires customized motion management techniques (like m3 in this work) rather than current clinical and investigational approaches.
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Affiliation(s)
- Esther Vicente
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201, United States of America. Author to whom correspondence should be addressed
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29
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Castillo E, Vinogradskiy Y, Castillo R. Robust HU-based CT ventilation from an integrated mass conservation formulation. Med Phys 2019; 46:5036-5046. [PMID: 31514235 PMCID: PMC6842051 DOI: 10.1002/mp.13817] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 06/26/2019] [Accepted: 08/20/2019] [Indexed: 11/07/2022] Open
Abstract
Computed tomography (CT) ventilation algorithms estimate volume changes induced by respiratory motion. Existing Hounsfield Unit (HU) methods approximate volume change from the measured HU variations between spatially corresponding voxel locations within a temporally resolved CT image pair, assuming that volume changes are caused solely by changes in air content. Numerical implementations require a deformable image registration to determine the inhale/exhale spatial correspondence, a preprocessing lung volume segmentation, a preprocessing high-intensity vessel segmentation, and a post-processing smoothing applied to the raw volume change estimates obtained for each lung tissue voxel. PURPOSE We introduce the novel mass-conserving volume change (MCVC) method for estimating voxel volume changes from the HU values within an inhale/exhale CT image pair. MCVC is based on subregional volume change estimates that possess quantitatively characterized and controllable levels of uncertainty. MCVC is therefore robust to small variations in DIR solutions and the resulting ventilation images are overall more reproducible. In contrast to existing HU methods, MCVC does not require a preprocessing lung vessel segmentation or pre/post-processing Gaussian smoothing. METHODS Subregional volume change estimates are defined in terms of mean density ratios. As such, the corresponding uncertainty is characterized using Gaussian statistics and standard error analysis of the sample density means. A numerical solution is obtained from the MCVC formulation by solving a constrained linear least squares problem defined by a series of subregional volume change estimates. Reproducibility of the MCVC method with respect to DIR solution was assessed using expert-determined landmark point pairs and inhale/exhale phases from 10 four-dimensional CTs (4DCTs) available on www.dir-lab.com. MCVC was also compared to the robust Integrated Jacobian Formulation (IJF), a transformation-based ventilation method. RESULTS The ten Dir-Lab 4DCT cases were registered twice with the same DIR algorithm, but using different degrees of freedom (DIR 1 and DIR 2). Standard HU ventilation (HUV) and MCVC ventilation images were computed for both solutions. The average spatial errors (300 landmarks per case) for DIR 1 ranged between 0.74 and 1.50 mm, whereas for DIR 2 they ranged between 0.68 and 1.18 mm. Despite the differences in spatial errors between the two DIR solutions, the average Pearson correlation between the corresponding HUV images was 0.94 (0.03), while for the MCVC images it was 1.00 (0.00). The average correlation between MCVC and IJF ventilation over the ten test cases was 0.81 (0.14), whereas for HUV and IJF it was 0.56 (1.11). CONCLUSION While HUV is robust to DIR solution, its implementation depends on heuristic Gaussian smoothing and vessel segmentation. MCVC is based on subregional volume change measurements with quantifiable and controllable levels of uncertainty. The subregional approach eliminates the need for Gaussian smoothing and lung vasculature segmentation. Numerical experiments are consistent with the underlying mathematical theory and indicate that MCVC ventilation is more reproducible with respect to DIR algorithm than standard HU methods. MCVC results are also more consistent with the robust IJF method, which suggests that incorporating robustness leads to more consistent results across both DIRs and ventilation algorithms.
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Affiliation(s)
- Edward Castillo
- Department of Radiation OncologyBeaumont Health SystemsRoyal OakMIUSA
- Department of Computational and Applied MathematicsRice UniversityHoustonTXUSA
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30
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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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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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Castillo E, Castillo R, Vinogradskiy Y, Dougherty M, Solis D, Myziuk N, Thompson A, Guerra R, Nair G, Guerrero T. Robust CT ventilation from the integral formulation of the Jacobian. Med Phys 2019; 46:2115-2125. [PMID: 30779353 PMCID: PMC6510605 DOI: 10.1002/mp.13453] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 01/03/2019] [Accepted: 02/12/2019] [Indexed: 11/18/2022] Open
Abstract
Computed tomography (CT) derived ventilation algorithms estimate the apparent voxel volume changes within an inhale/exhale CT image pair. Transformation‐based methods compute these estimates solely from the spatial transformation acquired by applying a deformable image registration (DIR) algorithm to the image pair. However, approaches based on finite difference approximations of the transformation's Jacobian have been shown to be numerically unstable. As a result, transformation‐based CT ventilation is poorly reproducible with respect to both DIR algorithm and CT acquisition method. Purpose We introduce a novel Integrated Jacobian Formulation (IJF) method for estimating voxel volume changes under a DIR‐recovered spatial transformation. The method is based on computing volume estimates of DIR mapped subregions using the hit‐or‐miss sampling algorithm for integral approximation. The novel approach allows for regional volume change estimates that (a) respect the resolution of the digital grid and (b) are based on approximations with quantitatively characterized and controllable levels of uncertainty. As such, the IJF method is designed to be robust to variations in DIR solutions and thus overall more reproducible. Methods Numerically, Jacobian estimates are recovered by solving a simple constrained linear least squares problem that guarantees the recovered global volume change is equal to the global volume change obtained from the inhale and exhale lung segmentation masks. Reproducibility of the IJF method with respect to DIR solution was assessed using the expert‐determined landmark point pairs and inhale/exhale phases from 10 four‐dimensional computed tomographies (4DCTs) available on http://www.dir-lab.com. Reproducibility with respect to CT acquisition was assessed on the 4DCT and 4D cone beam CT (4DCBCT) images acquired for five lung cancer patients prior to radiotherapy. Results The ten Dir‐Lab 4DCT cases were registered twice with the same DIR algorithm, but with different smoothing parameter. Finite difference Jacobian (FDJ) and IFJ images were computed for both solutions. The average spatial errors (300 landmarks per case) for the two DIR solution methods were 0.98 (1.10) and 1.02 (1.11). The average Pearson correlation between the FDJ images computed from the two DIR solutions was 0.83 (0.03), while for the IJF images it was 1.00 (0.00). For intermodality assessment, the IJF and FDJ images were computed from the 4DCT and 4DCBCT of five patients. The average Pearson correlation of the spatially aligned FDJ images was 0.27 (0.11), while it was 0.77 (0.13) for the IFJ method. Conclusion The mathematical theory underpinning the IJF method allows for the generation of ventilation images that are (a) computed with respect to DIR spatial accuracy on the digital voxel grid and (b) based on DIR‐measured subregional volume change estimates acquired with quantifiable and controllable levels of uncertainty. Analyses of the experiments are consistent with the mathematical theory and indicate that IJF ventilation imaging has a higher reproducibility with respect to both DIR algorithm and CT acquisition method, in comparison to the standard finite difference approach.
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Affiliation(s)
- Edward Castillo
- Department of Radiation Oncology, Beaumont Health Systems, Royal Oak, MI, USA.,Department of Computational and Applied Mathematics, Rice University, Houston, TX, USA
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | | | | | - David Solis
- Department of Radiation Oncology, Beaumont Health Systems, Royal Oak, MI, USA
| | - Nicholas Myziuk
- Department of Radiation Oncology, Beaumont Health Systems, Royal Oak, MI, USA
| | - Andrew Thompson
- Department of Radiation Oncology, Beaumont Health Systems, Royal Oak, MI, USA
| | - Rudy Guerra
- Department of Statistics, Rice University, Houston, TX, USA
| | - Girish Nair
- Department of Internal Medicine, Beaumont Health Systems, Royal Oak, MI, USA
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health Systems, Royal Oak, MI, USA
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Zhong Y, Vinogradskiy Y, Chen L, Myziuk N, Castillo R, Castillo E, Guerrero T, Jiang S, Wang J. Technical Note: Deriving ventilation imaging from 4DCT by deep convolutional neural network. Med Phys 2019; 46:2323-2329. [PMID: 30714159 DOI: 10.1002/mp.13421] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 12/20/2018] [Accepted: 01/22/2019] [Indexed: 01/10/2023] Open
Abstract
PURPOSE Ventilation images can be derived from four-dimensional computed tomography (4DCT) by analyzing the change in HU values and deformable vector fields between different respiration phases of computed tomography (CT). As deformable image registration (DIR) is involved, accuracy of 4DCT-derived ventilation image is sensitive to the choice of DIR algorithms. To overcome the uncertainty associated with DIR, we develop a method based on deep convolutional neural network (CNN) to derive ventilation images directly from the 4DCT without explicit image registration. METHODS A total of 82 sets of 4DCT and ventilation images from patients with lung cancer were used in this study. In the proposed CNN architecture, the CT two-channel input data consist of CT at the end of exhale and the end of inhale phases. The first convolutional layer has 32 different kernels of size 5 × 5 × 5, followed by another eight convolutional layers each of which is equipped with an activation layer (ReLU). The loss function is the mean-squared-error (MSE) to measure the intensity difference between the predicted and reference ventilation images. RESULTS The predicted images were comparable to the label images of the test data. The similarity index, correlation coefficient, and Gamma index passing rate averaged over the tenfold cross validation were 0.880 ± 0.035, 0.874 ± 0.024, and 0.806 ± 0.014, respectively. CONCLUSIONS The results demonstrate that deep CNN can generate ventilation imaging from 4DCT without explicit deformable image registration, reducing the associated uncertainty.
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Affiliation(s)
- Yuncheng Zhong
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Liyuan Chen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Nick Myziuk
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - Edward Castillo
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Steve Jiang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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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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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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Huang Q, Jabbour SK, Xiao Z, Yue N, Wang X, Cao H, Kuang Y, Zhang Y, Nie K. Dosimetric feasibility of 4DCT-ventilation imaging guided proton therapy for locally advanced non-small-cell lung cancer. Radiat Oncol 2018; 13:78. [PMID: 29695284 PMCID: PMC5918906 DOI: 10.1186/s13014-018-1018-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 04/05/2018] [Indexed: 12/18/2022] Open
Abstract
Background The principle aim of this study is to incorporate 4DCT ventilation imaging into functional treatment planning that preserves high-functioning lung with both double scattering and scanning beam techniques in proton therapy. Methods Eight patients with locally advanced non-small-cell lung cancer were included in this study. Deformable image registration was performed for each patient on their planning 4DCTs and the resultant displacement vector field with Jacobian analysis was used to identify the high-, medium- and low-functional lung regions. Five plans were designed for each patient: a regular photon IMRT vs. anatomic proton plans without consideration of functional ventilation information using double scattering proton therapy (DSPT) and intensity modulated proton therapy (IMPT) vs. functional proton plans with avoidance of high-functional lung using both DSPT and IMPT. Dosimetric parameters were compared in terms of tumor coverage, plan heterogeneity, and avoidance of normal tissues. Results Our results showed that both DSPT and IMPT plans gave superior dose advantage to photon IMRTs in sparing low dose regions of the total lung in terms of V5 (volume receiving 5Gy). The functional DSPT only showed marginal benefit in sparing high-functioning lung in terms of V5 or V20 (volume receiving 20Gy) compared to anatomical plans. Yet, the functional planning in IMPT delivery, can further reduce the low dose in high-functioning lung without degrading the PTV dosimetric coverages, compared to anatomical proton planning. Although the doses to some critical organs might increase during functional planning, the necessary constraints were all met. Conclusions Incorporating 4DCT ventilation imaging into functional proton therapy is feasible. The functional proton plans, in intensity modulated proton delivery, are effective to further preserve high-functioning lung regions without degrading the PTV coverage.
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Affiliation(s)
- Qijie Huang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Salma K Jabbour
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Zhiyan Xiao
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA.,Proton Therapy Center, University of Cincinnati Medical Center, Cincinnati, OH, 45044, USA
| | - Ning Yue
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Xiao Wang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Hongbin Cao
- Department of Radiation Oncology, Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, Shanghai, China
| | - Yu Kuang
- Department of Medical Physics, University of Nevada, Las Vegas, NV, USA
| | - Yin Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Ke Nie
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
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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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Sawant A, Yamamoto T, Cai J. Treatment planning based on lung functional avoidance is not ready for clinical deployment. Med Phys 2018; 45:2353-2356. [PMID: 29570812 DOI: 10.1002/mp.12881] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Accepted: 03/15/2018] [Indexed: 12/25/2022] Open
Affiliation(s)
- Amit Sawant
- Department of Radiation Oncology, University of Maryland, Baltimore, Maryland, 21201
| | - Tokihiro Yamamoto
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, California, 95817
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Jensen KR, Brink C, Hansen O, Bernchou U. Ventilation measured on clinical 4D-CBCT: Increased ventilation accuracy through improved image quality. Radiother Oncol 2017; 125:459-463. [DOI: 10.1016/j.radonc.2017.10.024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 09/05/2017] [Accepted: 10/17/2017] [Indexed: 12/25/2022]
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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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46
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Vinogradskiy Y, Schubert L, Diot Q, Waxweiller T, Koo P, Castillo R, Castillo E, Guerrero T, Rusthoven C, Gaspar L, Kavanagh B, Miften M. Regional Lung Function Profiles of Stage I and III Lung Cancer Patients: An Evaluation for Functional Avoidance Radiation Therapy. Int J Radiat Oncol Biol Phys 2017; 95:1273-80. [PMID: 27354134 DOI: 10.1016/j.ijrobp.2016.02.058] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Revised: 02/17/2016] [Accepted: 02/25/2016] [Indexed: 02/01/2023]
Abstract
PURPOSE The development of clinical trials is underway to use 4-dimensional computed tomography (4DCT) ventilation imaging to preferentially spare functional lung in patients undergoing radiation therapy. The purpose of this work was to generate data to aide with clinical trial design by retrospectively characterizing dosimetric and functional profiles for patients with different stages of lung cancer. METHODS AND MATERIALS A total of 118 lung cancer patients (36% stage I and 64% stage III) from 2 institutions were used for the study. A 4DCT-ventilation map was calculated using the patient's 4DCT imaging, deformable image registration, and a density-change-based algorithm. To assess each patient's spatial ventilation profile both quantitative and qualitative metrics were developed, including an observer-based defect observation and metrics based on the ventilation in each lung third. For each patient we used the clinical doses to calculate functionally weighted mean lung doses and metrics that assessed the interplay between the spatial location of the dose and high-functioning lung. RESULTS Both qualitative and quantitative metrics revealed a significant difference in functional profiles between the 2 stage groups (P<.01). We determined that 65% of stage III and 28% of stage I patients had ventilation defects. Average functionally weighted mean lung dose was 19.6 Gy and 5.4 Gy for stage III and I patients, respectively, with both groups containing patients with large spatial overlap between dose and high-function regions. CONCLUSION Our 118-patient retrospective study found that 65% of stage III patients have regionally variant ventilation profiles that are suitable for functional avoidance. Our results suggest that regardless of disease stage, it is possible to have unique spatial interplay between dose and high-functional lung, highlighting the importance of evaluating the function of each patient and developing a personalized functional avoidance treatment approach.
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Affiliation(s)
- Yevgeniy Vinogradskiy
- 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
| | - Quentin Diot
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Timothy Waxweiller
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Phillip Koo
- Department of Radiology, University of Colorado School of Medicine, Aurora, Colorado
| | - Richard Castillo
- Department of Radiation Oncology, University of Texas Medical Branch, Galveston, Texas
| | - Edward Castillo
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Chad Rusthoven
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Laurie Gaspar
- 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
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
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Hegi-Johnson F, Keall P, Barber J, Bui C, Kipritidis J. Evaluating the accuracy of 4D-CT ventilation imaging: First comparison with Technegas SPECT ventilation. Med Phys 2017; 44:4045-4055. [PMID: 28477378 DOI: 10.1002/mp.12317] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Revised: 03/21/2017] [Accepted: 04/05/2017] [Indexed: 02/03/2023] Open
Abstract
PURPOSE Computed tomography ventilation imaging (CTVI) is a highly accessible functional lung imaging modality that can unlock the potential for functional avoidance in lung cancer radiation therapy. Previous attempts to validate CTVI against clinical ventilation single-photon emission computed tomography (V-SPECT) have been hindered by radioaerosol clumping artifacts. This work builds on those studies by performing the first comparison of CTVI with 99m Tc-carbon ('Technegas'), a clinical V-SPECT modality featuring smaller radioaerosol particles with less clumping. METHODS Eleven lung cancer radiotherapy patients with early stage (T1/T2N0) disease received treatment planning four-dimensional CT (4DCT) scans paired with Technegas V/Q-SPECT/CT. For each patient, we applied three different CTVI methods. Two of these used deformable image registration (DIR) to quantify breathing-induced lung density changes (CTVIDIR-HU ), or breathing-induced lung volume changes (CTVIDIR-Jac ) between the 4DCT exhale/inhale phases. A third method calculated the regional product of air-tissue densities (CTVIHU ) and did not involve DIR. Corresponding CTVI and V-SPECT scans were compared using the Dice similarity coefficient (DSC) for functional defect and nondefect regions, as well as the Spearman's correlation r computed over the whole lung. The DIR target registration error (TRE) was quantified using both manual and computer-selected anatomic landmarks. RESULTS Interestingly, the overall best performing method (CTVIHU ) did not involve DIR. For nondefect regions, the CTVIHU , CTVIDIR-HU , and CTVIDIR-Jac methods achieved mean DSC values of 0.69, 0.68, and 0.54, respectively. For defect regions, the respective DSC values were moderate: 0.39, 0.33, and 0.44. The Spearman r-values were generally weak: 0.26 for CTVIHU , 0.18 for CTVIDIR-HU , and -0.02 for CTVIDIR-Jac . The spatial accuracy of CTVI was not significantly correlated with TRE, however the DIR accuracy itself was poor with TRE > 3.6 mm on average, potentially indicative of poor quality 4DCT. Q-SPECT scans achieved good correlations with V-SPECT (mean r > 0.6), suggesting that the image quality of Technegas V-SPECT was not a limiting factor in this study. CONCLUSIONS We performed a validation of CTVI using clinically available 4DCT and Technegas V/Q-SPECT for 11 lung cancer patients. The results reinforce earlier findings that the spatial accuracy of CTVI exhibits significant interpatient and intermethod variability. We propose that the most likely factor affecting CTVI accuracy was poor image quality of clinical 4DCT.
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Affiliation(s)
- Fiona Hegi-Johnson
- Radiation Physics Laboratory, Faculty of Medicine, Sydney University, Camperdown, NSW, 2006, Australia.,Department of Medical Physics, School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, NSW, 2300, Australia.,Radiation Oncology Centre, Seventh Day Adventist Hospital, Wahroonga, NSW 2076, Australia.,Department of Radiation Oncology, Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Vic., 3000, Australia
| | - Paul Keall
- Radiation Physics Laboratory, Faculty of Medicine, Sydney University, Camperdown, NSW, 2006, Australia
| | - Jeff Barber
- Crown Princess Mary Cancer Care Centre, Blacktown Hospital, Blacktown, NSW, 2148, Australia
| | - Chuong Bui
- Department of Nuclear Medicine, Nepean Hospital, Nepean, NSW, 2750, Australia
| | - John Kipritidis
- Radiation Physics Laboratory, Faculty of Medicine, Sydney University, Camperdown, NSW, 2006, Australia.,Department of Radiotherapy, Royal North Shore Hospital, St Leonards, NSW, 2065, Australia
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Bailey DL, Eslick EM, Schembri GP, Roach PJ. (68)Ga PET Ventilation and Perfusion Lung Imaging-Current Status and Future Challenges. Semin Nucl Med 2017; 46:428-35. [PMID: 27553468 DOI: 10.1053/j.semnuclmed.2016.04.007] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Gallium-68 ((68)Ga) is a positron-emitting radionuclide suitable for positron emission tomography (PET) imaging that has a number of convenient features-it has a physical half life of 68 minutes, it is generator produced at the PET facility and needs no local cyclotron, and being a radiometal is able to be chelated to a number of useful molecules for diagnostic imaging with PET. (68)Ga has recently been investigated as a radiotracer for ventilation and perfusion (V/Q) lung imaging. It is relatively easy to produce both V/Q radiopharmaceuticals labeled with (68)Ga for PET studies, it offers higher spatial resolution than equivalent SPECT studies, the short half life allows for multiple (repeated) scans on the same day, and low amounts of radiotracer can be used thus limiting the radiation dose to the subject. In the usual clinical setting requiring a V/Q scan, that of suspected pulmonary embolism, the role of (68)Ga V/Q PET may be limited from a logistical perspective, however, in nonacute applications such as lung function evaluation, radiotherapy treatment planning, and respiratory physiology investigations it would appear to be an ideal modality to employ.
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Affiliation(s)
- Dale L Bailey
- Department of Nuclear Medicine, Royal North Shore Hospital, Sydney, Australia; Faculty of Health Sciences, University of Sydney, Sydney, Australia.
| | - Enid M Eslick
- Sydney Medical School, University of Sydney, Sydney, Australia
| | - Geoffrey P Schembri
- Department of Nuclear Medicine, Royal North Shore Hospital, Sydney, Australia; Sydney Medical School, University of Sydney, Sydney, Australia
| | - Paul J Roach
- Department of Nuclear Medicine, Royal North Shore Hospital, Sydney, Australia; Sydney Medical School, University of Sydney, Sydney, Australia
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49
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Faught AM, Miyasaka Y, Kadoya N, Castillo R, Castillo E, Vinogradskiy Y, Yamamoto T. Evaluating the Toxicity Reduction With Computed Tomographic Ventilation Functional Avoidance Radiation Therapy. Int J Radiat Oncol Biol Phys 2017; 99:325-333. [PMID: 28871982 DOI: 10.1016/j.ijrobp.2017.04.024] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Revised: 03/02/2017] [Accepted: 04/12/2017] [Indexed: 02/02/2023]
Abstract
PURPOSE Computed tomographic (CT) ventilation imaging is a new modality that uses 4-dimensional (4D) CT information to calculate lung ventilation. Although retrospective studies have reported on the reduction in dose to functional lung, no work to our knowledge has been published in which the dosimetric improvements have been translated to a reduction in the probability of pulmonary toxicity. Our work estimates the reduction in toxicity for CT ventilation-based functional avoidance planning. METHODS AND MATERIALS Seventy previously treated lung cancer patients who underwent 4DCT imaging were used for the study. CT ventilation maps were calculated with 4DCT deformable image registration and a density change-based algorithm. Pneumonitis was graded on the basis of imaging and clinical presentation. Maximum likelihood methods were used to generate normal tissue complication probability (NTCP) models predicting grade 2 or higher (2+) and grade 3+ pneumonitis as a function of dose (V5 Gy, V10 Gy, V20 Gy, V30 Gy, and mean dose) to functional lung. For 30 patients a functional plan was generated with the goal of reducing dose to the functional lung while meeting Radiation Therapy Oncology Group 0617 constraints. The NTCP models were applied to the functional plans and the clinically used plans to calculate toxicity reduction. RESULTS By the use of functional avoidance planning, absolute reductions in grade 2+ NTCP of 6.3%, 7.8%, and 4.8% were achieved based on the mean fV20 Gy, fV30 Gy, and mean dose to functional lung metrics, respectively. Absolute grade 3+ NTCP reductions of 3.6%, 4.8%, and 2.4% were achieved with fV20 Gy, fV30 Gy, and mean dose to functional lung. Maximum absolute reductions of 52.3% and 16.4% were seen for grade 2+ and grade 3+ pneumonitis for individual patients. CONCLUSION Our study quantifies the possible toxicity reduction from CT ventilation-based functional avoidance planning. Reductions in grades 2+ and 3+ pneumonitis were 7.1% and 4.7% based on mean dose-function metrics, with reductions as high as 52.3% for individual patients. Our work provides seminal data for determining the potential toxicity benefit from incorporating CT ventilation into thoracic treatment planning.
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Affiliation(s)
- Austin M Faught
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado.
| | - Yuya Miyasaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Richard Castillo
- Department of Radiation Oncology, University of Texas Medical Branch of Galveston, League City, Texas
| | - Edward Castillo
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Tokihiro Yamamoto
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, California
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50
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Waxweiler T, Schubert L, Diot Q, Faught A, Stuhr K, Castillo R, Castillo E, Guerrero T, Rusthoven C, Gaspar L, Kavanagh B, Miften M, Vinogradskiy Y. A complete 4DCT-ventilation functional avoidance virtual trial: Developing strategies for prospective clinical trials. J Appl Clin Med Phys 2017; 18:144-152. [PMID: 28436107 PMCID: PMC5689844 DOI: 10.1002/acm2.12086] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 01/30/2017] [Accepted: 03/08/2017] [Indexed: 12/25/2022] Open
Abstract
Introduction 4DCT‐ventilation is an exciting new imaging modality that uses 4DCT data to calculate lung‐function maps. Because 4DCTs are acquired as standard of care for lung cancer patients undergoing radiotherapy, 4DCT‐ventiltation provides functional information at no extra dosimetric or monetary cost to the patient. The development of clinical trials is underway to use 4DCT‐ventilation imaging to spare functional lung in patients undergoing radiotherapy. The purpose of this work was to perform a virtual trial using retrospective data to develop the practical aspects of a 4DCT‐ventilation functional avoidance clinical trial. Methods The study included 96 stage III lung cancer patients. A 4DCT‐ventilation map was calculated using the patient's 4DCT‐imaging, deformable registration, and a density‐change‐based algorithm. Clinical trial inclusion assessment used quantitative and qualitative metrics based on the patient's spatial ventilation profile. Clinical and functional plans were generated for 25 patients. The functional plan aimed to reduce dose to functional lung while meeting standard target and critical structure constraints. Standard and dose‐function metrics were compared between the clinical and functional plans. Results Our data showed that 69% and 59% of stage III patients have regional variability in function based on qualitative and quantitative metrics, respectively. Functional planning demonstrated an average reduction of 2.8 Gy (maximum 8.2 Gy) in the mean dose to functional lung. Conclusions Our work demonstrated that 60–70% of stage III patients would be eligible for functional planning and that a typical functional lung mean dose reduction of 2.8 Gy can be expected relative to standard clinical plans. These findings provide salient data for the development of functional clinical trials.
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Affiliation(s)
- Timothy Waxweiler
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Leah Schubert
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Quentin Diot
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Austin Faught
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Kelly Stuhr
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Richard Castillo
- Department of Radiation Oncology, University of Texas Medical Branch, Galveston, TX, USA
| | - Edward Castillo
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Chad Rusthoven
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Laurie Gaspar
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Brian Kavanagh
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
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