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Baschnagel AM, Flakus MJ, Wallat EM, Wuschner AE, Chappell RJ, Bayliss RA, Kimple RJ, Christensen GE, Reinhardt JM, Bassetti MF, Bayouth JE. A Phase 2 Randomized Clinical Trial Evaluating 4-Dimensional Computed Tomography Ventilation-Based Functional Lung Avoidance Radiation Therapy for Non-Small Cell Lung Cancer. Int J Radiat Oncol Biol Phys 2024; 119:1393-1402. [PMID: 38387810 DOI: 10.1016/j.ijrobp.2024.02.019] [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: 11/16/2023] [Revised: 01/10/2024] [Accepted: 02/08/2024] [Indexed: 02/24/2024]
Abstract
PURPOSE To determine whether 4-dimensional computed tomography (4DCT) ventilation-based functional lung avoidance radiation therapy preserves pulmonary function compared with standard radiation therapy for non-small cell lung cancer (NSCLC). METHODS AND MATERIALS This single center, randomized, phase 2 trial enrolled patients with NSCLC receiving curative intent radiation therapy with either stereotactic body radiation therapy or conventionally fractionated radiation therapy between 2016 and 2022. Patients were randomized 1:1 to standard of care radiation therapy or functional lung avoidance radiation therapy. The primary endpoint was the change in Jacobian-based ventilation as measured on 4DCT from baseline to 3 months postradiation. Secondary endpoints included changes in volume of high- and low-ventilating lung, pulmonary toxicity, and changes in pulmonary function tests (PFTs). RESULTS A total of 122 patients were randomized and 116 were available for analysis. Median follow up was 29.9 months. Functional avoidance plans significantly (P < .05) reduced dose to high-functioning lung without compromising target coverage or organs at risk constraints. When analyzing all patients, there was no difference in the amount of lung showing a reduction in ventilation from baseline to 3 months between the 2 arms (1.91% vs 1.87%; P = .90). Overall grade ≥2 and grade ≥3 pulmonary toxicities for all patients were 24.1% and 8.6%, respectively. There was no significant difference in pulmonary toxicity or changes in PFTs between the 2 study arms. In the conventionally fractionated cohort, there was a lower rate of grade ≥2 pneumonitis (8.2% vs 32.3%; P = .049) and less of a decline in change in forced expiratory volume in 1 second (-3 vs -5; P = .042) and forced vital capacity (1.5 vs -6; P = .005) at 3 months, favoring the functional avoidance arm. CONCLUSIONS There was no difference in posttreatment ventilation as measured by 4DCT between the arms. In the cohort of patients treated with conventionally fractionated radiation therapy with functional lung avoidance, there was reduced pulmonary toxicity, and less decline in PFTs suggesting a clinical benefit in patients with locally advanced NSCLC.
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Affiliation(s)
- Andrew M Baschnagel
- Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, Wisconsin.
| | - Mattison J Flakus
- Department of Medical Physics, 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
| | - Richard J Chappell
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin
| | - R Adam Bayliss
- Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, Wisconsin
| | - Randall J Kimple
- Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, Wisconsin
| | - Gary E Christensen
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa; Department of Radiation Oncology, University of Iowa, Iowa City, Iowa
| | - Joseph M Reinhardt
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa
| | - Michael F Bassetti
- Department of Human Oncology, University of Wisconsin Hospital and Clinics, Madison, Wisconsin
| | - John E Bayouth
- Department of Radiation Medicine, Oregon Health & Science University, Portland, Oregon.
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Xiong T, Zeng G, Chen Z, Huang YH, Li B, Zhou D, Liu X, Sheng Y, Ren G, Wu QJ, Ge H, Cai J. Automatic planning for functional lung avoidance radiotherapy based on function-guided beam angle selection and plan optimization. Phys Med Biol 2024; 69:155007. [PMID: 38959907 DOI: 10.1088/1361-6560/ad5ef5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 07/03/2024] [Indexed: 07/05/2024]
Abstract
Objective.This study aims to develop a fully automatic planning framework for functional lung avoidance radiotherapy (AP-FLART).Approach.The AP-FLART integrates a dosimetric score-based beam angle selection method and a meta-optimization-based plan optimization method, both of which incorporate lung function information to guide dose redirection from high functional lung (HFL) to low functional lung (LFL). It is applicable to both contour-based FLART (cFLART) and voxel-based FLART (vFLART) optimization options. A cohort of 18 lung cancer patient cases underwent planning-CT and SPECT perfusion scans were collected. AP-FLART was applied to generate conventional RT (ConvRT), cFLART, and vFLART plans for all cases. We compared automatic against manual ConvRT plans as well as automatic ConvRT against FLART plans, to evaluate the effectiveness of AP-FLART. Ablation studies were performed to evaluate the contribution of function-guided beam angle selection and plan optimization to dose redirection.Main results.Automatic ConvRT plans generated by AP-FLART exhibited similar quality compared to manual counterparts. Furthermore, compared to automatic ConvRT plans, HFL mean dose,V20, andV5were significantly reduced by 1.13 Gy (p< .001), 2.01% (p< .001), and 6.66% (p< .001) respectively for cFLART plans. Besides, vFLART plans showed a decrease in lung functionally weighted mean dose by 0.64 Gy (p< .01),fV20by 0.90% (p= 0.099), andfV5by 5.07% (p< .01) respectively. Though inferior conformity was observed, all dose constraints were well satisfied. The ablation study results indicated that both function-guided beam angle selection and plan optimization significantly contributed to dose redirection.Significance.AP-FLART can effectively redirect doses from HFL to LFL without severely degrading conventional dose metrics, producing high-quality FLART plans. It has the potential to advance the research and clinical application of FLART by providing labor-free, consistent, and high-quality plans.
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Affiliation(s)
- Tianyu Xiong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China, People's Republic of China
| | - Guangping Zeng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China, People's Republic of China
| | - Zhi Chen
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China, People's Republic of China
| | - Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China, People's Republic of China
| | - Bing Li
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, People's Republic of China
| | - Dejun Zhou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China, People's Republic of China
| | - Xi Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China, People's Republic of China
| | - Yang Sheng
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States of America
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China, People's Republic of China
| | - Qingrong Jackie Wu
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States of America
| | - Hong Ge
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital, Zhengzhou, People's Republic of China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China, People's Republic of China
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Hou Z, Kong Y, Wu J, Gu J, Liu J, Gao S, Yin Y, Zhang L, Han Y, Zhu J, Li S. A deep learning model for translating CT to ventilation imaging: analysis of accuracy and impact on functional avoidance radiotherapy planning. Jpn J Radiol 2024; 42:765-776. [PMID: 38536558 DOI: 10.1007/s11604-024-01550-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 02/19/2024] [Indexed: 07/03/2024]
Abstract
PURPOSE Radiotherapy planning incorporating functional lung images has the potential to reduce pulmonary toxicity. Free-breathing 4DCT-derived ventilation image (CTVI) may help quantify lung function. This study introduces a novel deep-learning model directly translating planning CT images into CTVI. We investigated the accuracy of generated images and the impact on functional avoidance planning. MATERIALS AND METHODS Paired planning CT and 4DCT scans from 48 patients with NSCLC were randomized to training (n = 41) and testing (n = 7) data sets. The ventilation maps were generated from 4DCT using a Jacobian-based algorithm to provide ground truth labels (CTVI4DCT). A 3D U-Net-based model was trained to map CT to synthetic CTVI (CTVISyn) and validated using fivefold cross-validation. The highest-performing model was applied to the testing set. Spearman's correlation (rs) and Dice similarity coefficient (DSC) determined voxel-wise and functional-wise concordance between CTVI4DCT and CTVISyn. Three plans were designed per patient in the testing set: one clinical plan without CTVI and two functional avoidance plans combined with CTVI4DCT or CTVISyn, aimed at sparing high-functional lungs defined as the top 50% of the percentile ventilation ranges. Dose-volume (DVH) parameters regarding the planning target volume (PTV) and organs at risk (OARs) were recorded. Radiation pneumonitis (RP) risk was estimated using a dose-function (DFH)-based normal tissue complication probability (NTCP) model. RESULTS CTVISyn showed a mean rs value of 0.65 ± 0.04 compared to CTVI4DCT. Mean DSC values over the top 50% and 60% of ventilation ranges were 0.41 ± 0.07 and 0.52 ± 0.10, respectively. In the test set (n = 7), all patients' RP-risk benefited from CTVI4DCT-guided plans (Riskmean_4DCT_vs_Clinical: 29.24% vs. 49.12%, P = 0.016), and six patients benefited from CTVISyn-guided plans (Riskmean_Syn_vs_Clinical: 31.13% vs. 49.12%, P = 0.022). There were no significant differences in DVH and DFH metrics between CTVISyn and CTVI4DCT-guided plan (P > 0.05). CONCLUSION Using deep-learning techniques, CTVISyn generated from planning CT exhibited a moderate-to-high correlation with CTVI4DCT. The CTVISyn-guided plans were comparable to the CTVI4DCT-guided plans, effectively reducing pulmonary toxicity in patients while maintaining acceptable plan quality. Further prospective trials are needed to validate these findings.
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Affiliation(s)
- Zhen Hou
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China
| | - Youyong Kong
- School of Computer Science and Engineering, Southeast University, Nanjing, 210000, Jiangsu, China
- Centre de Recherche en Information, BioMdicale Sino-Franais, Nanjing, China
- Centre de Recherche en Information, BioMdicale Sino-Franais, 35000, Rennes, France
| | - Junxian Wu
- School of Computer Science and Engineering, Southeast University, Nanjing, 210000, Jiangsu, China
| | - Jiabing Gu
- School of Computer Science and Engineering, Southeast University, Nanjing, 210000, Jiangsu, China
| | - Juan Liu
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China
| | - Shanbao Gao
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China
| | - Yicai Yin
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China
| | - Ling Zhang
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China
| | - Yongchao Han
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, 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, 250000, Shandong, China.
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250000, Shandong, China.
- Centre de Recherche en Information, BioMdicale Sino-Franais, Nanjing, China.
- Centre de Recherche en Information, BioMdicale Sino-Franais, 35000, Rennes, France.
| | - Shuangshuang Li
- The Comprehensive Cancer Centre of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210000, Jiangsu, China.
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Liu C, Liu H, Li Y, Xiao Z, Wang Y, Guo H, Luo J. Establishing a 4D-CT lung function related volumetric dose model to reduce radiation pneumonia. Sci Rep 2024; 14:12589. [PMID: 38824238 PMCID: PMC11144207 DOI: 10.1038/s41598-024-63251-0] [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: 01/03/2024] [Accepted: 05/27/2024] [Indexed: 06/03/2024] Open
Abstract
In order to study how to use pulmonary functional imaging obtained through 4D-CT fusion for radiotherapy planning, and transform traditional dose volume parameters into functional dose volume parameters, a functional dose volume parameter model that may reduce level 2 and above radiation pneumonia was obtained. 41 pulmonary tumor patients who underwent 4D-CT in our department from 2020 to 2023 were included. MIM Software (MIM 7.0.7; MIM Software Inc., Cleveland, OH, USA) was used to register adjacent phase CT images in the 4D-CT series. The three-dimensional displacement vector of CT pixels was obtained when changing from one respiratory state to another respiratory state, and this three-dimensional vector was quantitatively analyzed. Thus, a color schematic diagram reflecting the degree of changes in lung CT pixels during the breathing process, namely the distribution of ventilation function strength, is obtained. Finally, this diagram is fused with the localization CT image. Select areas with Jacobi > 1.2 as high lung function areas and outline them as fLung. Import the patient's DVH image again, fuse the lung ventilation image with the localization CT image, and obtain the volume of fLung different doses (V60, V55, V50, V45, V40, V35, V30, V25, V20, V15, V10, V5). Analyze the functional dose volume parameters related to the risk of level 2 and above radiation pneumonia using R language and create a predictive model. By using stepwise regression and optimal subset method to screen for independent variables V35, V30, V25, V20, V15, and V10, the prediction formula was obtained as follows: Risk = 0.23656-0.13784 * V35 + 0.37445 * V30-0.38317 * V25 + 0.21341 * V20-0.10209 * V15 + 0.03815 * V10. These six independent variables were analyzed using a column chart, and a calibration curve was drawn using the calibrate function. It was found that the Bias corrected line and the Apparent line were very close to the Ideal line, The consistency between the predicted value and the actual value is very good. By using the ROC function to plot the ROC curve and calculating the area under the curve: 0.8475, 95% CI 0.7237-0.9713, it can also be determined that the accuracy of the model is very high. In addition, we also used Lasso method and random forest method to filter out independent variables with different results, but the calibration curve drawn by the calibration function confirmed poor prediction performance. The function dose volume parameters V35, V30, V25, V20, V15, and V10 obtained through 4D-CT are key factors affecting radiation pneumonia. Establishing a predictive model can provide more accurate lung restriction basis for clinical radiotherapy planning.
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Affiliation(s)
- Chunmei Liu
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Huizhi Liu
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Yange Li
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Zhiqing Xiao
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Yanqiang Wang
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Han Guo
- Department of Radiation Oncology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China
| | - Jianmin Luo
- Department of Hematology, The Second Hospital of Hebei Medical University, 215 West Heping Road, Shijiazhuang, 050000, Hebei, China.
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5
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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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Zhang S, Simard M, Lapointe A, Filion É, Campeau MP, Vu TTT, Roberge D, Carrier JF, Blais D, Bedwani S, Bahig H. Evaluation of Radiation Dose Effect on Lung Function Using Iodine Maps Derived From Dual-Energy Computed Tomography. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00609-6. [PMID: 38705488 DOI: 10.1016/j.ijrobp.2024.04.069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/04/2024] [Accepted: 04/25/2024] [Indexed: 05/07/2024]
Abstract
PURPOSE There is interest in using dual-energy computed tomography (DECT) to evaluate organ function before and after radiation therapy (RT). The purpose of this study (trial identifier: NCT04863027) is to assess longitudinal changes in lung perfusion using iodine maps derived from DECT in patients with lung cancer treated with conventional or stereotactic RT. METHODS AND MATERIALS For 48 prospectively enrolled patients with lung cancer, a contrast-enhanced DECT using a dual-source CT simulator was acquired pretreatment and at 6 and 12 months posttreatment. Pulmonary functions tests (PFT) were obtained at baseline and at 6 and 12 months posttreatment. Iodine maps were extracted from the DECT images using a previously described 2-material decomposition framework. Longitudinal iodine maps were normalized using a reference region defined as all voxels with perfusion in the top 10% outside of the 5 Gy isodose volume. Normalized functional responses (NFR) were calculated for 3 dose ranges: <5, 5 to 20, and >20 Gy. Mixed model analysis was used to assess the correlation between dose metrics and NFR. Pearson correlation was used to assess if NFRs were correlated with PFT changes. RESULTS Out of the 48 patients, 21 (44%) were treated with stereotactic body RT and 27 (56%) were treated with conventionally fractionated intensity-modulated RT. Thirty-one out of these 48 patients were ultimately included in data analysis. It was found that NFR is linearly correlated with dose (P < .001) for both groups. The number of months elapsed post-RT was also found to correlate with NFR (P = .029), although this correlation was not observed for the stereotactic body RT subgroup. The NFR was not found to correlate with PFT changes. CONCLUSIONS DECT-derived iodine maps are a promising method for detailed anatomic evaluation of radiation effect on lung function, including potentially subclinical changes.
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Affiliation(s)
- Shen Zhang
- Département de radio-oncologie, Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada
| | - Mikaël Simard
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada; Département de physique, Université de Montréal, Montréal, Quebec, Canada; Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Andréanne Lapointe
- Département de radio-oncologie, Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada; Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada
| | - Édith Filion
- Département de radio-oncologie, Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada
| | - Marie-Pierre Campeau
- Département de radio-oncologie, Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada
| | - Thi Trinh Thuc Vu
- Département de radio-oncologie, Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada
| | - David Roberge
- Département de radio-oncologie, Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada
| | - Jean-François Carrier
- Département de radio-oncologie, Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada; Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada; Département de physique, Université de Montréal, Montréal, Quebec, Canada
| | - Danis Blais
- Département de radio-oncologie, Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada; Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada
| | - Stéphane Bedwani
- Département de radio-oncologie, Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada; Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada
| | - Houda Bahig
- Département de radio-oncologie, Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada.
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Madesta F, Sentker T, Gauer T, Werner R. Deep learning-based conditional inpainting for restoration of artifact-affected 4D CT images. Med Phys 2024; 51:3437-3454. [PMID: 38055336 DOI: 10.1002/mp.16851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 10/12/2023] [Accepted: 10/16/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND 4D CT imaging is an essential component of radiotherapy of thoracic and abdominal tumors. 4D CT images are, however, often affected by artifacts that compromise treatment planning quality and image information reliability. PURPOSE In this work, deep learning (DL)-based conditional inpainting is proposed to restore anatomically correct image information of artifact-affected areas. METHODS The restoration approach consists of a two-stage process: DL-based detection of common interpolation (INT) and double structure (DS) artifacts, followed by conditional inpainting applied to the artifact areas. In this context, conditional refers to a guidance of the inpainting process by patient-specific image data to ensure anatomically reliable results. The study is based on 65 in-house 4D CT images of lung cancer patients (48 with only slight artifacts, 17 with pronounced artifacts) and two publicly available 4D CT data sets that serve as independent external test sets. RESULTS Automated artifact detection revealed a ROC-AUC of 0.99 for INT and of 0.97 for DS artifacts (in-house data). The proposed inpainting method decreased the average root mean squared error (RMSE) by 52 % (INT) and 59 % (DS) for the in-house data. For the external test data sets, the RMSE improvement is similar (50 % and 59 %, respectively). Applied to 4D CT data with pronounced artifacts (not part of the training set), 72 % of the detectable artifacts were removed. CONCLUSIONS The results highlight the potential of DL-based inpainting for restoration of artifact-affected 4D CT data. Compared to recent 4D CT inpainting and restoration approaches, the proposed methodology illustrates the advantages of exploiting patient-specific prior image information.
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Affiliation(s)
- Frederic Madesta
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Thilo Sentker
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tobias Gauer
- Department of Radiotherapy and Radiation Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - René Werner
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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8
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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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9
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Lombardo J, Castillo E, Castillo R, Miller R, Jones B, Miften M, Kavanagh B, Dicker A, Boyle C, Leiby B, Banks J, Simone NL, Movsas B, Grills I, Guerrero T, Rusthoven CG, Vinogradskiy Y. Prospective Trial of Functional Lung Avoidance Radiation Therapy for Lung Cancer: Quality of Life Report. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00476-0. [PMID: 38614278 DOI: 10.1016/j.ijrobp.2024.03.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/26/2024] [Accepted: 03/29/2024] [Indexed: 04/15/2024]
Abstract
PURPOSE A novel form of lung function imaging has been developed that uses 4-dimensional computed tomography (4DCT) data to generate lung ventilation images (4DCT-ventilation). Functional avoidance uses 4DCT-ventilation to reduce doses to functional lung with the aim of reducing pulmonary side effects. A phase 2, multicenter 4DCT-ventilation functional avoidance clinical trial was completed. The purpose of this work was to quantify changes in patient-reported outcomes (PROs) for patients treated with functional avoidance and determine which metrics are predictive of PRO changes. MATERIALS AND METHODS Patients with locally advanced lung cancer receiving curative-intent radiation therapy were accrued. Each patient had a 4DCT-ventilation image generated using 4DCT data and image processing. PRO instruments included the Functional Assessment of Cancer Therapy-Lung (FACT-L) questionnaire administered pretreatment; at the end of treatment; and at 3, 6, and 12 months posttreatment. Using the FACT-Trial Outcome Index and the FACT-Lung Cancer Subscale results, the percentage of clinically meaningful declines (CMDs) were determined. A linear mixed-effects model was used to determine which patient, clinical, dose, and dose-function metrics were predictive of PRO decline. RESULTS Of the 59 patients who completed baseline PRO surveys. 83% had non-small cell lung cancer, with 75% having stage 3 disease. The median dose was 60 Gy in 30 fractions. CMD FACT-Trial Outcome Index decline was 46.3%, 38.5%, and 26.8%, at 3, 6, and 12 months, respectively. CMD FACT-Lung Cancer Subscale decline was 33.3%, 33.3%, and 29.3%, at 3, 6, and 12 months, respectively. Although an increase in most dose and dose-function parameters was associated with a modest decline in PROs, none of the results were significant (all P > .053). CONCLUSIONS The current work presents an innovative combination of use of functional avoidance and PRO assessment and is the first report of PROs for patients treated with prospective 4DCT-ventilation functional avoidance. Approximately 30% of patients had clinically significant decline in PROs at 12 months posttreatment. The study provides additional data on outcomes with 4DCT-ventilation functional avoidance.
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Affiliation(s)
- Joseph Lombardo
- Thomas Jefferson University, Radiation Oncology, Philadelphia, Pennsylvania
| | - Edward Castillo
- UT Austin, Department of Biomedical Engineering, Austin, Texas
| | - Richard Castillo
- Emory University School of Medicine, Radiation Oncology, Atlanta, Georgia
| | - Ryan Miller
- Thomas Jefferson University, Radiation Oncology, Philadelphia, Pennsylvania
| | - Bernard Jones
- University of Colorado, Radiation Oncology, Denver, Colorado
| | - Moyed Miften
- University of Colorado, Radiation Oncology, Denver, Colorado
| | - Brian Kavanagh
- University of Colorado, Radiation Oncology, Denver, Colorado
| | - Adam Dicker
- Thomas Jefferson University, Radiation Oncology, Philadelphia, Pennsylvania
| | - Cullen Boyle
- Thomas Jefferson University, Radiation Oncology, Philadelphia, Pennsylvania
| | - Benjamin Leiby
- Thomas Jefferson University, Department of Pharmacology, Physiology, and Cancer Biology, Philadelphia, Pennsylvania
| | - Joshua Banks
- Thomas Jefferson University, Department of Pharmacology, Physiology, and Cancer Biology, Philadelphia, Pennsylvania
| | - Nicole L Simone
- Thomas Jefferson University, Radiation Oncology, Philadelphia, Pennsylvania
| | - Benjamin Movsas
- Henry Ford Cancer Institute, Radiation Oncology, Detroit, Michigan
| | - Inga Grills
- Beaumont Health, Radiation Oncology, Royal Oak, Michigan
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Chen Y, Pahlavian SH, Jacobs P, Neupane T, Forghani-Arani F, Castillo E, Castillo R, Vinogradskiy Y. Systematic Evaluation of the Impact of Lung Segmentation Methods on 4-Dimensional Computed Tomography Ventilation Imaging Using a Large Patient Database. Int J Radiat Oncol Biol Phys 2024; 118:242-252. [PMID: 37607642 PMCID: PMC10842520 DOI: 10.1016/j.ijrobp.2023.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 08/04/2023] [Accepted: 08/08/2023] [Indexed: 08/24/2023]
Abstract
PURPOSE A novel form of lung functional imaging applied for functional avoidance radiation therapy has been developed that uses 4-dimensional computed tomography (4DCT) data and image processing techniques to calculate lung ventilation (4DCT-ventilation). Lung segmentation is a common step to define a region of interest for 4DCT-ventilation generation. The purpose of this study was to quantitatively evaluate the sensitivity of 4DCT-ventilation imaging using different lung segmentation methods. METHODS AND MATERIALS The 4DCT data of 350 patients from 2 institutions were used. Lung contours were generated using 3 methods: (1) reference segmentations that removed airways and pulmonary vasculature manually (Lung-Manual), (2) standard lung contours used for planning (Lung-RadOnc), and (3) artificial intelligence (AI)-based contours that removed the airways and pulmonary vasculature (Lung-AI). The AI model was based on a residual 3-dimensional U-Net and was trained using the Lung-Manual contours of 279 patients. We compared the Lung-RadOnc or Lung-AI with Lung-Manual contours for the entire 4DCT-ventilation functional avoidance process including lung segmentation (surface Dice similarity coefficient [Surface DSC]), 4DCT-ventilation generation (correlation), and subanalysis of 10 patients on a dosimetric endpoint (percentage of high functional volume of lung receiving ≥20 Gy [fV20{%}]). RESULTS Surface DSC comparing Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI contours was 0.40 ± 0.06 and 0.86 ± 0.04, respectively. The correlation between 4DCT-ventilation images generated with Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI were 0.48 ± 0.14 and 0.85 ± 0.14, respectively. The difference in fV20[%] between 4DCT-ventilation generated with Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI was 2.5% ± 4.1% and 0.3% ± 0.5%, respectively. CONCLUSIONS Our work showed that using standard planning lung contours can result in significantly variable 4DCT-ventilation images. The study demonstrated that AI-based segmentations generate lung contours and 4DCT-ventilation images that are similar to those generated using manual methods. The significance of the study is that it characterizes the lung segmentation sensitivity of the 4DCT-ventilation process and develops methods that can facilitate the integration of this novel imaging in busy clinics.
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Affiliation(s)
- Yingxuan Chen
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | | | | | - Taindra Neupane
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | | | - Edward Castillo
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, Georgia
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania.
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11
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Populaire P, Defraene G, Nafteux P, Depypere L, Moons J, Isebaert S, Haustermans K. Clinical implications of dose to functional lung volumes in the trimodality treatment of esophageal cancer. Acta Oncol 2023; 62:1488-1495. [PMID: 37643135 DOI: 10.1080/0284186x.2023.2251091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 08/17/2023] [Indexed: 08/31/2023]
Abstract
BACKGROUND Trimodality treatment, i.e., neoadjuvant chemoradiotherapy (nCRT) followed by surgery, for locally advanced esophageal cancer (EC) improves overall survival but also increases the risk of postoperative pulmonary complications. Here, we tried to identify a relation between dose to functional lung volumes (FLV) as determined by 4D-CT scans in EC patients and treatment-related lung toxicity. MATERIALS AND METHODS All patients with EC undergoing trimodality treatment between 2017 and 2022 in UZ Leuven and scanned with 4D-CT-simulation were selected. FLVs were determined based on Jacobian determinants of deformable image registration between maximum inspiration and expiration phases. Dose/volume parameters of the anatomical lung volume (ALV) and FLV were compared between patients with versus without postoperative pulmonary complications. Results of pre- and post-nCRT pulmonary function tests (PFTs) were collected and compared in relation to radiation dose. RESULTS Twelve out of 51 EC patients developed postoperative pulmonary complications. ALV was smaller while FLV10Gy and FLV20Gy were larger in patients with complications (respectively 3141 ± 858mL vs 3601 ± 635mL, p = 0.025; 360 ± 216mL vs 264 ± 139mL, p = 0.038; 166 ± 106mL vs 118 ± 63mL, p = 0.030). No differences in ALV dose-volume parameters were detected. Baseline FEV1 and TLC were significantly lower in patients with complications (respectively 90 ± 17%pred vs 102 ± 20%pred, p = 0.033 and 93 ± 17%pred vs 110 ± 13%pred, p = 0.001), though no other PFTs were significantly different between both groups. DLCO was the only PFT that had a meaningful decrease after nCRT (85 ± 17%pred vs 68 ± 15%pred, p < 0.001) but was not related to dose to ALV/FLV. CONCLUSION Small ALV and increasing FLV exposed to intermediate (10 to 20 Gy) dose are associated to postoperative pulmonary complications. Changes of DLCO occur during nCRT but do not seem to be related to radiation dose to ALV or FLV. This information could attribute towards toxicity risk prediction and reduction strategies for EC.
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Affiliation(s)
- Pieter Populaire
- Department of Radiation Oncology, UZ Leuven, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | | | - Philippe Nafteux
- Department of Thoracic Surgery, UZ Leuven, Leuven, Belgium
- Department of Chronic Diseases and Metabolism (CHROMETA), KU Leuven, Leuven, Belgium
| | - Lieven Depypere
- Department of Thoracic Surgery, UZ Leuven, Leuven, Belgium
- Department of Chronic Diseases and Metabolism (CHROMETA), KU Leuven, Leuven, Belgium
| | - Johnny Moons
- Department of Thoracic Surgery, UZ Leuven, Leuven, Belgium
| | - Sofie Isebaert
- Department of Radiation Oncology, UZ Leuven, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Karin Haustermans
- Department of Radiation Oncology, UZ Leuven, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
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12
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Ghassemi N, Castillo R, Castillo E, Jones BL, Miften M, Kavanagh B, Werner-Wasik M, Miller R, Barta JA, Grills I, Leiby BE, Guerrero T, Rusthoven CG, Vinogradskiy Y. Evaluation of variables predicting PFT changes for lung cancer patients treated on a prospective 4DCT-ventilation functional avoidance clinical trial. Radiother Oncol 2023; 187:109821. [PMID: 37516361 PMCID: PMC10529225 DOI: 10.1016/j.radonc.2023.109821] [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: 01/24/2023] [Revised: 06/09/2023] [Accepted: 07/18/2023] [Indexed: 07/31/2023]
Abstract
PURPOSE Functional avoidance radiotherapy uses functional imaging to reduce pulmonary toxicity by designing radiotherapy plans that reduce doses to functional regions of the lung. A phase-II, multi-center, prospective study of 4DCT-ventilation functional avoidance was completed. Pre and post-treatment pulmonary function tests (PFTs) were acquired and assessed pulmonary function change. This study aims to evaluate which clinical, dose and dose-function factors predict PFT changes for patients treated with 4DCT-ventilation functional avoidance radiotherapy. MATERIALS AND METHODS 56 patients with locally advanced lung cancer receiving radiotherapy were accrued. PFTs were obtained at baseline and three months following radiotherapy and included forced expiratory volume in 1-second (FEV1), forced vital capacity (FVC), and FEV1/FVC. The ability of patient, clinical, dose (lung and heart), and dose-function metrics (metrics that combine dose and 4DCT-ventilation-based function) to predict PFT changes were evaluated using univariate and multivariate linear regression. RESULTS Univariate analysis showed that only dose-function metrics and the presence of chronic obstructive pulmonary disease (COPD) were significant (p<0.05) in predicting FEV1 decline. Multivariate analysis identified a combination of clinical (immunotherapy status, presence of thoracic comorbidities, smoking status, and age), along with lung dose, heart dose, and dose-function metrics in predicting FEV1 and FEV1/FVC changes. CONCLUSION The current work evaluated factors predicting PFT changes for patients treated in a prospective functional avoidance radiotherapy study. The data revealed that lung dose- function metrics could predict PFT changes, validating the significance of reducing the dose to the functional lung to mitigate the decline in pulmonary function and providing guidance for future clinical trials.
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Affiliation(s)
- Nader Ghassemi
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA.
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | | | - Bernard L Jones
- 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
| | - Brian Kavanagh
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Maria Werner-Wasik
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Ryan Miller
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Julie A Barta
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA
| | - Inga Grills
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Benjamin E Leiby
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA.
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Chad G Rusthoven
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA.
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Zhou P, Du Y, Zhang Y, Zhu M, Li T, Tian W, Wu T, Xiao Z. Efficacy and Safety in Proton Therapy and Photon Therapy for Patients With Esophageal Cancer: A Meta-Analysis. JAMA Netw Open 2023; 6:e2328136. [PMID: 37581887 PMCID: PMC10427943 DOI: 10.1001/jamanetworkopen.2023.28136] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 06/29/2023] [Indexed: 08/16/2023] Open
Abstract
Importance Radiotherapy plays an important role in the treatment of esophageal cancer. Proton therapy has unique physical properties and higher relative biological effectiveness. However, whether proton therapy has greater benefit than photon therapy is still unclear. Objective To evaluate whether proton was associated with better efficacy and safety outcomes, including dosimetric, prognosis, and toxic effects outcomes, compared with photon therapy and to evaluate the efficacy and safety of proton therapy singly. Data Sources A systematic search of PubMed, Embase, the Cochrane Library, Web of Science, SinoMed, and China National Knowledge Infrastructure databases was conducted for articles published through November 25, 2021, and updated to March 25, 2023. Study Selection For the comparison of proton and photon therapy, studies including dosimetric, prognosis, and associated toxic effects outcomes were included. The separate evaluation of proton therapy evaluated the same metrics. Data Extraction and Synthesis Data on study design, individual characteristics, and outcomes were extracted. If I2 was greater than 50%, the random-effects model was selected. This meta-analysis is reported following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Main Outcomes and Measures The main outcomes were organs at risk (OARs) dosimetric outcomes, prognosis (overall survival [OS], progression-free survival [PFS], and objective response rate [ORR]), and radiation-related toxic effects. Results A total of 45 studies were included in the meta-analysis. For dosimetric analysis, proton therapy was associated with significantly reduced OARs dose. Meta-analysis showed that photon therapy was associated with poor OS (hazard ratio [HR], 1.31; 95% CI, 1.07-1.61; I2 = 11%), but no difference in PFS was observed. Subgroup analysis showed worse OS (HR, 1.42; 95% CI, 1.14-1.78; I2 = 34%) and PFS (HR, 1.48; 95% CI, 1.06-2.08; I2 = 7%) in the radical therapy group with photon therapy. The pathological complete response rate was similar between groups. Proton therapy was associated with significantly decreased grade 2 or higher radiation pneumonitis and pericardial effusion, and grade 4 or higher lymphocytopenia. Single-rate analysis of proton therapy found 89% OS and 65% PFS at 1 year, 71% OS and 56% PFS at 2 years, 63% OS and 48% PFS at 3 years, and 56% OS and 42% PFS at 5 years. The incidence of grade 2 or higher radiation esophagitis was 50%, grade 2 or higher radiation pneumonitis was 2%, grade 2 or higher pleural effusion was 4%, grade 2 or higher pericardial effusion was 3%, grade 3 or higher radiation esophagitis was 8%, and grade 4 or higher lymphocytopenia was 17%. Conclusions and Relevance In this meta-analysis, proton therapy was associated with reduced OARs doses and toxic effects and improved prognosis compared with photon therapy for esophageal cancer, but caution is warranted. In the future, these findings should be further validated in randomized clinical trials.
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Affiliation(s)
- Pixiao Zhou
- Department of Oncology, Changde Hospital, Xiangya School of Medicine, Central South University, Changde, China
| | - Yangfeng Du
- Department of Oncology, Changde Hospital, Xiangya School of Medicine, Central South University, Changde, China
| | - Ying Zhang
- The Second People’s Hospital of Yibin, Yibin, China
| | - Mei Zhu
- Department of Oncology, Changde Hospital, Xiangya School of Medicine, Central South University, Changde, China
| | - Ting Li
- Department of Oncology, Changde Hospital, Xiangya School of Medicine, Central South University, Changde, China
| | - Wei Tian
- Department of Oncology, Changde Hospital, Xiangya School of Medicine, Central South University, Changde, China
| | - Tao Wu
- Department of Oncology, Changde Hospital, Xiangya School of Medicine, Central South University, Changde, China
| | - Zemin Xiao
- Department of Oncology, Changde Hospital, Xiangya School of Medicine, Central South University, Changde, China
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Karmali D, Sowho M, Bose S, Pearce J, Tejwani V, Diamant Z, Yarlagadda K, Ponce E, Eikelis N, Otvos T, Khan A, Lester M, Fouras A, Kirkness J, Siddharthan T. Functional imaging for assessing regional lung ventilation in preclinical and clinical research. Front Med (Lausanne) 2023; 10:1160292. [PMID: 37261124 PMCID: PMC10228734 DOI: 10.3389/fmed.2023.1160292] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 04/17/2023] [Indexed: 06/02/2023] Open
Abstract
Dynamic heterogeneity in lung ventilation is an important measure of pulmonary function and may be characteristic of early pulmonary disease. While standard indices like spirometry, body plethysmography, and blood gases have been utilized to assess lung function, they do not provide adequate information on regional ventilatory distribution nor function assessments of ventilation during the respiratory cycle. Emerging technologies such as xenon CT, volumetric CT, functional MRI and X-ray velocimetry can assess regional ventilation using non-invasive radiographic methods that may complement current methods of assessing lung function. As a supplement to current modalities of pulmonary function assessment, functional lung imaging has the potential to identify respiratory disease phenotypes with distinct natural histories. Moreover, these novel technologies may offer an optimal strategy to evaluate the effectiveness of novel therapies and therapies targeting localized small airways disease in preclinical and clinical research. In this review, we aim to discuss the features of functional lung imaging, as well as its potential application and limitations to adoption in research.
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Affiliation(s)
- Dipan Karmali
- Division of Pulmonary and Critical Care, Leonard M. Miller School of Medicine, University of Miami, Coral Gables, FL, United States
| | - Mudiaga Sowho
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Sonali Bose
- Division of Pulmonary and Critical Care, Icahn School of Medicine, Mount Sinai, NY, United States
| | - Jackson Pearce
- School of Medicine, Medical University of South Carolina, Charleston, SC, United States
| | - Vickram Tejwani
- Respiratory Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Zuzana Diamant
- Department of Microbiology Immunology and Transplantation, KU Leuven, Catholic University of Leuven, Leuven, Belgium
- Department of Respiratory Medicine and Allergology, Institute for Clinical Science, Skane University Hospital, Lund University, Lund, Sweden
- Department Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Keerthi Yarlagadda
- Division of Pulmonary and Critical Care, Leonard M. Miller School of Medicine, University of Miami, Coral Gables, FL, United States
| | - Erick Ponce
- Division of Pulmonary and Critical Care, Leonard M. Miller School of Medicine, University of Miami, Coral Gables, FL, United States
| | | | | | - Akram Khan
- Division of Pulmonary and Critical Care, Oregon Health and Science University, Portland, OR, United States
| | - Michael Lester
- Department of Pulmonary and Critical Care Medicine, Vanderbilt Medical Center, Nashville, CA, United States
| | | | | | - Trishul Siddharthan
- Division of Pulmonary and Critical Care, Leonard M. Miller School of Medicine, University of Miami, Coral Gables, FL, United States
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15
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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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16
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Yamamoto T, Kabus S, Bal M, Keall PJ, Moran A, Wright C, Benedict SH, Holland D, Mahaffey N, Qi L, Daly ME. Four-Dimensional Computed Tomography Ventilation Image-Guided Lung Functional Avoidance Radiation Therapy: A Single-Arm Prospective Pilot Clinical Trial. Int J Radiat Oncol Biol Phys 2023; 115:1144-1154. [PMID: 36427643 DOI: 10.1016/j.ijrobp.2022.11.026] [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: 07/19/2022] [Revised: 10/28/2022] [Accepted: 11/09/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE The primary objective of this prospective pilot trial was to assess the safety and feasibility of lung functional avoidance radiation therapy (RT) with 4-dimensional (4D) computed tomography (CT) ventilation imaging. METHODS AND MATERIALS Patients with primary lung cancer or metastatic disease to the lungs to receive conventionally fractionated RT (CFRT) or stereotactic body RT (SBRT) were eligible. Standard-of-care 4D-CT scans were used to generate ventilation images through image processing/analysis. Each patient required a standard intensity modulated RT plan and ventilation image guided functional avoidance plan. The primary endpoint was the safety of functional avoidance RT, defined as the rate of grade ≥3 adverse events (AEs) that occurred ≤12 months after treatment. Protocol treatment was considered safe if the rates of grade ≥3 pneumonitis and esophagitis were <13% and <21%, respectively for CFRT, and if the rate of any grade ≥3 AEs was <28% for SBRT. Feasibility of functional avoidance RT was assessed by comparison of dose metrics between the 2 plans using the Wilcoxon signed-rank test. RESULTS Between May 2015 and November 2019, 34 patients with non-small cell lung cancer were enrolled, and 33 patients were evaluable (n = 24 for CFRT; n = 9 for SBRT). Median follow-up was 14.7 months. For CFRT, the rates of grade ≥3 pneumonitis and esophagitis were 4.2% (95% confidence interval, 0.1%-21.1%) and 12.5% (2.7%-32.4%). For SBRT, no patients developed grade ≥3 AEs. Compared with the standard plans, the functional avoidance plans significantly (P < .01) reduced the lung dose-function metrics without compromising target coverage or adherence to standard organs at risk constraints. CONCLUSIONS This study, representing one of the first prospective investigations on lung functional avoidance RT, demonstrated that the 4D-CT ventilation image guided functional avoidance RT that significantly reduced dose to ventilated lung regions could be safely administered, adding to the growing body of evidence for its clinical utility.
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Affiliation(s)
- Tokihiro Yamamoto
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, California.
| | - Sven Kabus
- Department of Medical Image Processing & Analytics, Philips Research, Hamburg, Germany
| | | | - Paul J Keall
- ACRF Image X Institute, University of Sydney, Sydney, New South Wales, Australia
| | - Angel Moran
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, California
| | - Cari Wright
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, California
| | - Stanley H Benedict
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, California
| | - Devin Holland
- Office of Clinical Research, University of California Davis Comprehensive Cancer Center, Sacramento, California
| | - Nichole Mahaffey
- Office of Clinical Research, University of California Davis Comprehensive Cancer Center, Sacramento, California
| | - Lihong Qi
- Department of Public Health Sciences, University of California, Davis, California
| | - Megan E Daly
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, California
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17
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Miller R, Castillo R, Castillo E, Jones BL, Miften M, Kavanagh B, Lu B, Werner-Wasik M, Ghassemi N, Lombardo J, Barta J, Grills I, Rusthoven CG, Guerrero T, Vinogradskiy Y. Characterizing Pulmonary Function Test Changes for Patients With Lung Cancer Treated on a 2-Institution, 4-Dimensional Computed Tomography-Ventilation Functional Avoidance Prospective Clinical Trial. Adv Radiat Oncol 2023; 8:101133. [PMID: 36618762 PMCID: PMC9816902 DOI: 10.1016/j.adro.2022.101133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 11/17/2022] [Indexed: 12/05/2022] Open
Abstract
Purpose Four-dimensional computed tomography (4DCT)-ventilation-based functional avoidance uses 4DCT images to generate plans that avoid functional regions of the lung with the goal of reducing pulmonary toxic effects. A phase 2, multicenter, prospective study was completed to evaluate 4DCT-ventilation functional avoidance radiation therapy. The purpose of this study was to report the results for pretreatment to posttreatment pulmonary function test (PFT) changes for patients treated with functional avoidance radiation therapy. Methods and Materials Patients with locally advanced lung cancer receiving chemoradiation were accrued. Functional avoidance plans based on 4DCT-ventilation images were generated. PFTs were obtained at baseline and 3 months after chemoradiation. Differences for PFT metrics are reported, including diffusing capacity for carbon monoxide (DLCO), forced expiratory volume in 1 second (FEV1), and forced vital capacity (FVC). PFT metrics were compared for patients who did and did not experience grade 2 or higher pneumonitis. Results Fifty-six patients enrolled on the study had baseline and posttreatment PFTs evaluable for analysis. The mean change in DLCO, FEV1, and FVC was -11.6% ± 14.2%, -5.6% ± 16.9%, and -9.0% ± 20.1%, respectively. The mean change in DLCO was -15.4% ± 14.4% for patients with grade 2 or higher radiation pneumonitis and -10.8% ± 14.1% for patients with grade <2 radiation pneumonitis (P = .37). The mean change in FEV1 was -14.3% ± 22.1% for patients with grade 2 or higher radiation pneumonitis and -3.9% ± 15.4% for patients with grade <2 radiation pneumonitis (P = .09). Conclusions The current work is the first to quantitatively characterize PFT changes for patients with lung cancer treated on a prospective functional avoidance radiation therapy study. In comparison with patients treated with standard thoracic radiation planning, the data qualitatively show that functional avoidance resulted in less of a decline in DLCO and FEV1. The presented data can help elucidate the potential pulmonary function improvement with functional avoidance radiation therapy.
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Affiliation(s)
- Ryan Miller
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, Georgia
| | - Edward Castillo
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas
| | - Bernard L. Jones
- 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
| | - Brian Kavanagh
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Bo Lu
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Maria Werner-Wasik
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Nader Ghassemi
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Joseph Lombardo
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Julie Barta
- Department of Thoracic Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Inga Grills
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Chad G. Rusthoven
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
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18
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Wallat EM, Wuschner AE, Flakus MJ, Gerard SE, Christensen GE, Reinhardt JM, Bayouth JE. Predicting pulmonary ventilation damage after radiation therapy for nonsmall cell lung cancer using a ResNet generative adversarial network. Med Phys 2023; 50:3199-3209. [PMID: 36779695 DOI: 10.1002/mp.16311] [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: 08/27/2022] [Revised: 02/01/2023] [Accepted: 02/06/2023] [Indexed: 02/14/2023] Open
Abstract
BACKGROUND Functional lung avoidance radiation therapy (RT) is a technique being investigated to preferentially avoid specific regions of the lung that are predicted to be more susceptible to radiation-induced damage. Reducing the dose delivered to high functioning regions may reduce the occurrence radiation-induced lung injuries (RILIs) and toxicities. However, in order to develop effective lung function-sparing plans, accurate predictions of post-RT ventilation change are needed to determine which regions of the lung should be spared. PURPOSE To predict pulmonary ventilation change following RT for nonsmall cell lung cancer using machine learning. METHODS A conditional generative adversarial network (cGAN) was developed with data from 82 human subjects enrolled in a randomized clinical trial approved by the institution's IRB to predict post-RT pulmonary ventilation change. The inputs to the network were the pre-RT pulmonary ventilation map and radiation dose distribution. The loss function was a combination of the binary cross-entropy loss and an asymmetrical structural similarity index measure (aSSIM) function designed to increase penalization of under-prediction of ventilation damage. Network performance was evaluated against a previously developed polynomial regression model using a paired sample t-test for comparison. Evaluation was performed using eight-fold cross-validation. RESULTS From the eight-fold cross-validation, we found that relative to the polynomial model, the cGAN model significantly improved predicting regions of ventilation damage following radiotherapy based on true positive rate (TPR), 0.14±0.15 to 0.72±0.21, and Dice similarity coefficient (DSC), 0.19±0.16 to 0.46±0.14, but significantly declined in true negative rate, 0.97±0.05 to 0.62±0.21, and accuracy, 0.79±0.08 to 0.65±0.14. Additionally, the average true positive volume increased from 104±119 cc in the POLY model to 565±332 cc in the cGAN model, and the average false negative volume decreased from 654±361 cc in the POLY model to 193±163 cc in the cGAN model. CONCLUSIONS The proposed cGAN model demonstrated significant improvement in TPR and DSC. The higher sensitivity of the cGAN model can improve the clinical utility of functional lung avoidance RT by identifying larger volumes of functional lung that can be spared and thus decrease the probability of the patient developing RILIs.
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Affiliation(s)
- Eric M Wallat
- 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
| | - Mattison J Flakus
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Sarah E Gerard
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, 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.,Department of Radiology, University of Iowa, Iowa City, Iowa, USA
| | - John E Bayouth
- Department of Radiation Medicine, Oregon Health & Science University, Portland, Oregon, USA
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19
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Xenon-Enhanced Ventilation Computed Tomography for Functional Lung Avoidance Radiation Therapy in Patients With Lung Cancer. Int J Radiat Oncol Biol Phys 2023; 115:356-365. [PMID: 36029910 DOI: 10.1016/j.ijrobp.2022.07.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 06/22/2022] [Accepted: 07/19/2022] [Indexed: 01/14/2023]
Abstract
PURPOSE This phase 2 trial aimed to determine whether xenon-enhanced ventilation computed tomography (XeCT)-guided functional-lung-avoidance radiation therapy could reduce the radiation pneumonitis (RP) rate in patients with lung cancer undergoing definitive chemoradiation therapy. METHODS AND MATERIALS Functional lung ventilation was measured via pulmonary function testing (PFT) and XeCT. A standard plan (SP) without reference to XeCT and a functional-lung-avoidance plan (fAP) optimized for lowering the radiation dose to the functional lung at the guidance of XeCT were designed. Dosimetric parameters and predicted RP risks modeled by biological evaluation were compared between the 2 plans in a treatment planning system (TPS). All patients received the approved fAP. The primary endpoint was the rate of grade ≥2 RP, and the secondary endpoints were the survival outcomes. The study hypothesis was that fAP could reduce the rate of grade ≥2 RP to 12% compared with a 30% historical rate. RESULTS Thirty-six patients were evaluated. Xenon-enhanced total functional lung volumes positively correlated with PFT ventilation parameters (forced vital capacity, P = .012; forced expiratory volume in 1 second, P = .035), whereas they were not correlated with the diffusion capacity parameter. We observed a 17% rate of grade ≥2 RP (6 of 36 patients), which was significantly different (P = .040) compared with the historical control. Compared with the SP, the fAP significantly spared the total ventilated lung, leading to a reduction in predicted grade ≥2 RP (P = .001) by TPS biological evaluation. The median follow-up was 15.2 months. The 1-year local control (LC), disseminated failure-free survival (DFFS), and overall survival (OS) rates were 88%, 66%, and 91%, respectively. The median LC and OS were not reached, and the median DFFS was 24.0 months (95% confidence interval, 15.7-32.3 months). CONCLUSIONS This report of XeCT-guided functional-lung-avoidance radiation therapy provided evidence showing its feasibility in clinical practice. Its benefit should be assessed in a broader multicenter trial setting.
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20
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Gaudreault M, Korte J, Bucknell N, Jackson P, Sakyanun P, McIntosh L, Woon B, Buteau JP, Hofman MS, Mulcahy T, Kron T, Siva S, Hardcastle N. Comparison of dual-energy CT with positron emission tomography for lung perfusion imaging in patients with non-small cell lung cancer. Phys Med Biol 2023; 68. [PMID: 36623318 DOI: 10.1088/1361-6560/acb198] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 01/09/2023] [Indexed: 01/11/2023]
Abstract
Objective.Functional lung avoidance (FLA) radiotherapy treatment aims to spare lung regions identified as functional from imaging. Perfusion contributes to lung function and can be measured from the determination of pulmonary blood volume (PBV). An advantageous alternative to the current determination of PBV from positron emission tomography (PET) may be from dual energy CT (DECT), due to shorter examination time and widespread availability. This study aims to determine the correlation between PBV determined from DECT and PET in the context of FLA radiotherapy.Approach.DECT and PET acquisitions at baseline of patients enrolled in the HI-FIVE clinical trial (ID: NCT03569072) were reviewed. Determination of PBV from PET imaging (PBVPET), from DECT imaging generated from a commercial software (Syngo.via, Siemens Healthineers, Forchheim, Germany) with its lowest (PBVsyngoR=1) and highest (PBVsyngoR=10) smoothing level parameter value (R), and from a two-material decomposition (TMD) method (PBVTMDL) with variable median filter kernel size (L) were compared. Deformable image registration between DECT images and the CT component of the PET/CT was applied to PBV maps before resampling to the PET resolution. The Spearman correlation coefficient (rs) between PBV determinations was calculated voxel-wise in lung subvolumes.Main results.Of this cohort of 19 patients, 17 had a DECT acquisition at baseline. PBV maps determined from the commercial software and the TMD method were very strongly correlated [rs(PBVsyngoR=1,PBVTMDL=1) = 0.94 ± 0.01 andrs(PBVsyngoR=10,PBVTMDL=9) = 0.94 ± 0.02].PBVPETwas strongly correlated withPBVTMDL[rs(PBVPET,PBVTMDL=28) = 0.67 ± 0.11]. Perfusion patterns differed along the posterior-anterior direction [rs(PBVPET,PBVTMDL=28) = 0.77 ± 0.13/0.57 ± 0.16 in the anterior/posterior region].Significance. A strong correlation between DECT and PET determination of PBV was observed. Streak and smoothing effects in DECT and gravitational artefacts and misregistration in PET reduced the correlation posteriorly.
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Affiliation(s)
- Mathieu Gaudreault
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia
| | - James Korte
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia.,Department of Biomedical Engineering, School of Chemical and Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
| | - Nicholas Bucknell
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia.,Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia
| | - Price Jackson
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia
| | - Pitchaya Sakyanun
- Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia.,Department of Radiation Oncology, Phramongkutklao Hospital, Bangkok, Thailand
| | - Lachlan McIntosh
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia
| | - Beverley Woon
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia.,Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia
| | - James P Buteau
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia.,Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia.,Molecular Imaging and Therapeutic Nuclear Medicine; Prostate Cancer Theranostics and Imaging Centre of Excellence (ProsTIC) , Peter MacCallum Cancer Centre, Melbourne, Victoria, 3000, Australia
| | - Michael S Hofman
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia.,Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia.,Molecular Imaging and Therapeutic Nuclear Medicine; Prostate Cancer Theranostics and Imaging Centre of Excellence (ProsTIC) , Peter MacCallum Cancer Centre, Melbourne, Victoria, 3000, Australia
| | - Tony Mulcahy
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia
| | - Tomas Kron
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia.,Centre for Medical Radiation Physics, University of Wollongong, NSW, 2522, Australia
| | - Shankar Siva
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia.,Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia
| | - Nicholas Hardcastle
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia.,Centre for Medical Radiation Physics, University of Wollongong, NSW, 2522, Australia
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21
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Chen Z, Huang YH, Kong FM, Ho WY, Ren G, Cai J. A super-voxel-based method for generating surrogate lung ventilation images from CT. Front Physiol 2023; 14:1085158. [PMID: 37179833 PMCID: PMC10171197 DOI: 10.3389/fphys.2023.1085158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 04/06/2023] [Indexed: 05/15/2023] Open
Abstract
Purpose: This study aimed to develop and evaluate CTVISVD , a super-voxel-based method for surrogate computed tomography ventilation imaging (CTVI). Methods and Materials: The study used four-dimensional CT (4DCT) and single-photon emission computed tomography (SPECT) images and corresponding lung masks from 21 patients with lung cancer obtained from the Ventilation And Medical Pulmonary Image Registration Evaluation dataset. The lung volume of the exhale CT for each patient was segmented into hundreds of super-voxels using the Simple Linear Iterative Clustering (SLIC) method. These super-voxel segments were applied to the CT and SPECT images to calculate the mean density values (D mean) and mean ventilation values (Vent mean), respectively. The final CT-derived ventilation images were generated by interpolation from the D mean values to yield CTVISVD. For the performance evaluation, the voxel- and region-wise differences between CTVISVD and SPECT were compared using Spearman's correlation and the Dice similarity coefficient index. Additionally, images were generated using two deformable image registration (DIR)-based methods, CTVIHU and CTVIJac, and compared with the SPECT images. Results: The correlation between the D mean and Vent mean of the super-voxel was 0.59 ± 0.09, representing a moderate-to-high correlation at the super-voxel level. In the voxel-wise evaluation, the CTVISVD method achieved a stronger average correlation (0.62 ± 0.10) with SPECT, which was significantly better than the correlations achieved with the CTVIHU (0.33 ± 0.14, p < 0.05) and CTVIJac (0.23 ± 0.11, p < 0.05) methods. For the region-wise evaluation, the Dice similarity coefficient of the high functional region for CTVISVD (0.63 ± 0.07) was significantly higher than the corresponding values for the CTVIHU (0.43 ± 0.08, p < 0.05) and CTVIJac (0.42 ± 0.05, p < 0.05) methods. Conclusion: The strong correlation between CTVISVD and SPECT demonstrates the potential usefulness of this novel method of ventilation estimation for surrogate ventilation imaging.
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Affiliation(s)
- Zhi Chen
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Feng-Ming Kong
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, China
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, China
| | - Wai Yin Ho
- Department of Nuclear Medicine, Queen Mary Hospital, Hong Kong, China
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- *Correspondence: Ge Ren, ; Jing Cai,
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- *Correspondence: Ge Ren, ; Jing Cai,
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22
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Zhou P, Wang R, Yu H, Liao Z, Zhang Y, Huang Z, Zhang S. 4DCT ventilation function image-based functional lung protection for esophageal cancer radiotherapy. Strahlenther Onkol 2022; 199:445-455. [PMID: 36331584 DOI: 10.1007/s00066-022-02012-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 09/25/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND 4DCT (four-dimensional computed tomography) can effectively obtain functional lung ventilation images for patients and integrate them into radiotherapy treatment planning. Studies have not been performed on esophageal cancer, and there is no clear consensus on the optimal functional lung threshold for functional lung. METHODS Functional lung images were generated for 11 patients with esophageal cancer. The correlation between the dose-volume parameters of functional lung (FL) as defined by different thresholds and the change of PFT/PDFT (pulmonary [diffusion] function test) metrics before and after radiotherapy were evaluated. FL-sparing planning was generated for each patient to preserve the functional lung and compared to conventional anatomical CT (non-sparing) planning. RESULTS There was a significant positive correlation between the FL0.8 (defined Jacobian value ≤ 0.8), FL0.84, and FL0.9 dose-volume parameters and ΔFEV1/FVC (reduction before and after radiotherapy), and the FL0.8‑V30 correlation was the strongest (r = 0.819, P < 0.01). The FL-sparing planning had a target area conformity index and homogeneity index comparable to the non-sparing planning (P > 0.05). For FL, the FL-sparing planning achieved lower FL-MLD (6.30 ± 2.14 Gy vs. 7.83 ± 2.70 Gy), V10 (17.13 ± 7.70% vs. 27.40 ± 9.48%), and V20 (6.96 ± 3.85% vs. 11.63 ± 7.19%) compared to the non-sparing planning (P < 0.05), while heart and spinal cord doses were not significantly different between the two planning groups. CONCLUSION The 4DCT-based FL irradiation dose for esophageal cancer was significantly associated with a decrease in FEV1/FVC. The optimal FL defined as a Jacobian value ≤ 0.8 or about 21% of the whole lung volume may be a good choice. FL-sparing planning significantly reduced the FL dose without compromising target area coverage.
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Affiliation(s)
- Pixiao Zhou
- Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Ruihao Wang
- Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Hui Yu
- Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Zhiwei Liao
- Department of Radiotherapy, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Ying Zhang
- Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Zouqin Huang
- Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Shuxu Zhang
- Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China.
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23
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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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24
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Zhou PX, Wang RH, Yu H, Zhang Y, Zhang GQ, Zhang SX. Different functional lung-sparing strategies and radiotherapy techniques for patients with esophageal cancer. Front Oncol 2022; 12:898141. [PMID: 36091164 PMCID: PMC9459335 DOI: 10.3389/fonc.2022.898141] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 07/29/2022] [Indexed: 12/24/2022] Open
Abstract
BackgroundIntegration of 4D-CT ventilation function images into esophageal cancer radiation treatment planning aimed to assess dosimetric differences between different functional lung (FL) protection strategies and radiotherapy techniques.MethodsA total of 15 patients with esophageal cancer who had 4D-CT scans were included. Lung ventilation function images based on Jacobian values were obtained by deformation image registration and ventilation imaging algorithm. Several different plans were designed for each patient: clinical treatment planning (non-sparing planning), the same beam distribution to FL-sparing planning, three fixed-beams FL-sparing intensity-modulated radiation therapy (IMRT) planning (5F-IMRT, 7F-IMRT, 9F-IMRT), and two FL-sparing volumetric modulated arc therapy (VMAT) planning [1F-VMAT (1-Arc), 2F-VMAT (2-Arc)]. The dosimetric parameters of the planning target volume (PTV) and organs at risk (OARs) were compared and focused on dosimetric differences in FL.ResultsThe FL-sparing planning compared with the non-sparing planning significantly decreased the FL-Dmean, V5-30 and Lungs-Dmean, V10-30 (Vx: volume of receiving ≥X Gy), although it slightly compromised PTV conformability and increased Heart-V40 (P< 0.05). The 5F-IMRT had the lowest PTV-conformability index (CI) but had a lower Lungs and Heart irradiation dose compared with those of the 7F-IMRT and 9F-IMRT (P< 0.05). The 2F-VMAT had higher PTV-homogeneity index (HI) and reduced irradiation dose to FL, Lungs, and Heart compared to those of the 1F-VMAT planning (P< 0.05). The 2F-VMAT had higher PTV conformability and homogeneity and decreased FL-Dmean, V5-20 and Lungs-Dmean, V5-10 but correspondingly increased spinal cord-Dmean compared with those of the 5F-IMRT planning (P< 0.05).ConclusionIn this study, 4D-CT ventilation function image-based FL-sparing planning for esophageal cancer can effectively reduce the dose of the FL. The 2F-VMAT planning is better than the 5F-IMRT planning in reducing the dose of FL.
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Crockett C, Lorimer C. OncoFlash – Research Updates in a Flash! (March 2022 Edition). Clin Oncol (R Coll Radiol) 2022; 34:205-206. [DOI: 10.1016/j.clon.2022.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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