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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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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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Wang H, Li X, Xu L, Kuang Y. PET/SPECT/spectral-CT/CBCT imaging in a small-animal radiation therapy platform: A Monte Carlo study-Part I: Quad-modal imaging. Med Phys 2024; 51:2941-2954. [PMID: 38421665 DOI: 10.1002/mp.17007] [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: 08/18/2023] [Revised: 01/16/2024] [Accepted: 02/12/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND In spite of the tremendous potential of game-changing biological image- and/or biologically guided radiation therapy (RT) and adaptive radiation therapy for cancer treatment, existing limited strategies for integrating molecular imaging and/or biological information with RT have impeded the translation of preclinical research findings to clinical applications. Additionally, there is an urgent need for a highly integrated small-animal radiation therapy (SART) platform that can seamlessly combine therapeutic and diagnostic capabilities to comprehensively enhance RT for cancer treatment. PURPOSE We investigated a highly integrated quad-modal on-board imaging configuration combining positron emission tomography (PET), single-photon emission computed tomography (SPECT), photon-counting spectral CT, and cone-beam computed tomography (CBCT) in a SART platform using a Monte Carlo model as a proof-of-concept. METHODS The quad-modal on-board imaging configuration of the SART platform was designed and evaluated by using the GATE Monte Carlo code. A partial-ring on-board PET imaging subsystem, utilizing advanced semiconductor thallium bromide detector technology, was designed to achieve high sensitivity and spatial resolution. On-board SPECT, photon-counting spectral-CT, and CBCT imaging were performed using a single cadmium zinc telluride flat detector panel. The absolute peak sensitivity and scatter fraction of the PET subsystem were estimated by using simulated phantoms described in the NEMA NU-4 standard. The spatial resolution of the PET image of the platform was evaluated by imaging a simulated micro-Derenzo hot-rod phantom. To evaluate the quantitative imaging capability of the system's spectral CT, the Bayesian eigentissue decomposition (ETD) method was utilized to quantitatively decompose the virtual noncontrast (VNC) electron densities and iodine contrast agent fractions in the Kidney1 inserts mixed with the iodine contrast agent within the simulated phantoms. The performance of the proposed quad-model imaging in the platform was validated by imaging a simulated phantom with multiple imaging probes, including an iodine contrast agent and radioisotopes of 18F and 99mTc. RESULTS The PET subsystem demonstrated an absolute peak sensitivity of 18.5% at the scanner center, with an energy window of 175-560 KeV, and a scatter fraction of only 3.5% for the mouse phantom, with a default energy window of 480-540 KeV. The spatial resolution of PET on-board imaging exceeded 1.2 mm. All imaging probes were identified clearly within the phantom. The PET and SPECT images agreed well with the actual spatial distributions of the tracers within the phantom. Average relative errors on electron density and iodine contrast agent fraction in the Kidney1 inserts were less than 3%. High-quality PET images, SPECT images, spectral-CT images (including iodine contrast agent fraction images and VNC electron density images), and CBCT images of the simulated phantom demonstrated the comprehensive multimodal imaging capability of the system. CONCLUSIONS The results demonstrated the feasibility of the proposed quad-modal imaging configuration in a SART platform. The design incorporates anatomical, molecular, and functional information about tumors, thereby facilitating successful translation of preclinical studies into clinical practices.
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Affiliation(s)
- Hui Wang
- Medical Imaging and Translational Medicine Laboratory, Department of Radiotherapy, Affiliated Hangzhou Cancer Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
- Medical Physics Program, University of Nevada, Las Vegas, Nevada, USA
| | - Xiadong Li
- Medical Imaging and Translational Medicine Laboratory, Department of Radiotherapy, Affiliated Hangzhou Cancer Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
| | - Lixia Xu
- Medical Imaging and Translational Medicine Laboratory, Department of Radiotherapy, Affiliated Hangzhou Cancer Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China
| | - Yu Kuang
- Medical Physics Program, University of Nevada, Las Vegas, Nevada, USA
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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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Gates EDH, Hippe DS, Vesselle HJ, Zeng J, Bowen SR. Independent association of metabolic tumor response on FDG-PET with pulmonary toxicity following risk-adaptive chemoradiation for unresectable non-small cell lung cancer: Inherent radiosensitivity or immune response? Radiother Oncol 2023; 185:109720. [PMID: 37244360 PMCID: PMC10525017 DOI: 10.1016/j.radonc.2023.109720] [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: 03/20/2023] [Revised: 05/16/2023] [Accepted: 05/18/2023] [Indexed: 05/29/2023]
Abstract
BACKGROUND In the context of a phase II trial of risk-adaptive chemoradiation, we evaluated whether tumor metabolic response could serve as a correlate of treatment sensitivity and toxicity. METHODS Forty-five patients with AJCCv7 stage IIB-IIIB NSCLC enrolled on the FLARE-RT phase II trial (NCT02773238). [18F]fluorodeoxyglucose (FDG) PET-CT images were acquired prior to treatment and after 24 Gy during week 3. Patients with unfavorable on-treatment tumor response received concomitant boosts to 74 Gy total over 30 fractions rather than standard 60 Gy. Metabolic tumor volume and mean standardized uptake value (SUVmean) were calculated semi-automatically. Risk factors of pulmonary toxicity included concurrent chemotherapy regimen, adjuvant anti-PDL1 immunotherapy, and lung dosimetry. Incidence of CTCAE v4 grade 2+ pneumonitis was analyzed using the Fine-Gray method with competing risks of metastasis or death. Peripheral germline DNA microarray sequencing measured predefined candidate genes from distinct pathways: 96 DNA repair, 53 immunology, 38 oncology, 27 lung biology. RESULTS Twenty-four patients received proton therapy, 23 received ICI, 26 received carboplatin-paclitaxel, and 17 pneumonitis events were observed. Pneumonitis risk was significantly higher for patients with COPD (HR 3.78 [1.48, 9.60], p = 0.005), those treated with immunotherapy (HR 2.82 [1.03, 7.71], p = 0.043) but not with carboplatin-paclitaxel (HR 1.98 [0.71, 5.54], p = 0.19). Pneumonitis rates were similar among selected patients receiving 74 Gy radiation vs 60 Gy (p = 0.33), proton therapy vs photon (p = 0.60), or with higher lung dosimetric V20 (p = 0.30). Patients in the upper quartile decrease in SUVmean (>39.7%) were at greater risk for pneumonitis (HR 4.00 [1.54, 10.44], p = 0.005) and remained significant in multivariable analysis (HR 3.34 [1.23, 9.10], p = 0.018). Germline DNA gene alterations in immunology pathways were most frequently associated with pneumonitis. CONCLUSION Tumor metabolic response as measured by mean SUV is associated with increased pneumonitis risk in a clinical trial cohort of NSCLC patients independent of treatment factors. This may be partially attributed to patient-specific differences in immunogenicity.
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Affiliation(s)
- Evan D H Gates
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, United States
| | - Daniel S Hippe
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Hubert J Vesselle
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, United States
| | - Jing Zeng
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, United States
| | - Stephen R Bowen
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, United States; Department of Radiology, University of Washington School of Medicine, Seattle, WA, United States.
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Froger C, Jolivet C, Budzinski H, Pierdet M, Caria G, Saby NPA, Arrouays D, Bispo A. Pesticide Residues in French Soils: Occurrence, Risks, and Persistence. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:7818-7827. [PMID: 37172312 DOI: 10.1021/acs.est.2c09591] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Contamination of the environment by pesticide residues is a growing concern given their widespread presence in the environment and their effects on ecosystems. Only a few studies have addressed the occurrence of pesticides in soils, and their results highlighted the need for further research on the persistence and risks induced by those substances. We monitored 111 pesticide residues (48 fungicides, 36 herbicides, 25 insecticides and/or acaricides, and two safeners) in 47 soils sampled across France under various land uses (arable lands, vineyards, orchards, forests, grasslands, and brownfields). Pesticides were found in 98% of the sites (46 of the 47 sampled), including untreated areas such as organic fields, forests, grasslands, and brownfields, with up to 33 different substances detected in one sample, mostly fungicides and herbicides. The concentrations of herbicides were the highest in soils with glyphosate, and its transformation product, AMPA, contributed 70% of the cumulative herbicides. Risk assessment underlined a moderate to high risk for earthworms in arable soils mostly attributed to insecticides and/or acaricides. Finally, the comparison with pesticide application by farmers underlines the presence of some residues long after their supposed 90% degradation and at concentrations higher than predicted environmental concentrations, leading to questions their real persistence in soils.
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Affiliation(s)
| | | | - Hélène Budzinski
- Bordeaux University, EPOC-LPTC, UMR 5805 CNRS, 33405 Talence Cedex, France
| | - Manon Pierdet
- Bordeaux University, EPOC-LPTC, UMR 5805 CNRS, 33405 Talence Cedex, France
| | - Giovanni Caria
- INRAE, US0010 Laboratoire d'analyses des sols, 700 Avenue d'Immercourt, 62223 Saint-Laurent-Blangy, France
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Mid-treatment adaptive planning during thoracic radiation using 68 Ventilation-Perfusion Positron emission tomography. Clin Transl Radiat Oncol 2023; 40:100599. [PMID: 36879654 PMCID: PMC9984948 DOI: 10.1016/j.ctro.2023.100599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 02/11/2023] [Accepted: 02/12/2023] [Indexed: 02/17/2023] Open
Abstract
Four-Dimensional Gallium 68 Ventilation-Perfusion Positron Emission Tomography (68Ga-4D-V/Q PET/CT) allows for dynamic imaging of lung function. To date there has been no assessment of the feasibility of adapting radiation therapy plans to changes in lung function imaged at mid-treatment function using 68Ga-4D-V/Q PET/CT. This study assessed the potential reductions of dose to the functional lung when radiation therapy plans were adapted to avoid functional lung at the mid-treatment timepoint using volumetric arc radiotherapy (VMAT). Methods A prospective clinical trial (U1111-1138-4421) was performed in patients undergoing conventionally fractionated radiation therapy for non-small cell lung cancer (NSCLC). A 68Ga-4D-V/Q PET/CT was acquired at baseline and in the 4th week of treatment. Functional lung target volumes using the ventilated and perfused lung were created. Baseline functional volumes were compared to the week 4 V/Q functional volumes to describe the change in function over time. For each patient, 3 VMAT plans were created and optimised to spare ventilated, perfused or anatomical lung. All key dosimetry metrics were then compared including dose to target volumes, dose to organs at risk and dose to the anatomical and functional sub-units of lung. Results 25 patients had both baseline and 4 week mid treatment 68Ga-4D-V/Q PET/CT imaging. This resulted in a total of 75 adapted VMAT plans. The HPLung volume decreased in 16/25 patients with a mean of the change in volume (cc) -28 ± 515 cc [±SD, range -996 cc to 1496 cc]. The HVLung volume increased in 13/25 patients with mean of the change in volume (cc) + 112 ± 590 cc. [±SD, range -1424 cc to 950 cc]. The functional lung sparing technique was found to be feasible with no significant differences in dose to anatomically defined organs at risk. Most patients did derive a benefit with a reduction in functional volume receiving 20 Gy (fV20) and/or functional mean lung dose (fMLD) in either perfusion and/or ventilation. Patients with the most reduction in fV20 and fMLD were those with stage III NSCLC. Conclusion Functional lung volumes change during treatment. Some patients benefit from using 68Ga-4D-V/Q PET/CT in the 4th week of radiation therapy to adapt radiation plans. In these patients, the role of mid-treatment adaptation requires further prospective investigation.
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Matviichuk O, Mondamert L, Geffroy C, Dagot C, Labanowski J. Life in an unsuspected antibiotics world: River biofilms. WATER RESEARCH 2023; 231:119611. [PMID: 36716569 DOI: 10.1016/j.watres.2023.119611] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 12/20/2022] [Accepted: 01/13/2023] [Indexed: 06/18/2023]
Abstract
Waterborne bacteria that naturally live in biofilms are continuously exposed to pharmaceutical residues, regularly released into the freshwater environment. At the source level, the discharge of antibiotics into rivers has already been repeatedly linked to the development of antimicrobial resistance. But what about biofilms away from the discharge point? Two rivers, with sites subject to dispersed contamination of medium intensity, were studied as typical representatives of high- and middle-income countries. The biofilms developed on rocks indigenous to rivers are perfectly representative of environmental exposure. Our results show that away from the hotspots, the amount of antibiotics in the biofilms studied favours the maintenance and enrichment of existing resistant strains as well as the selection of new resistant mutants, and these favourable conditions remain over a period of time. Thus, in this type of river, the environmental risk of selection pressure is not only present downstream of urbanized areas but is also possible upstream and far downstream of wastewater treatment plant discharges. Despite this, correlation analysis found no strong positive correlation between antibiotic concentrations and the abundance of measured integrons and their corresponding resistance genes. Nevertheless, this work highlights the need to consider the risks of antibiotics beyond hotspots as well.
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Affiliation(s)
- Olha Matviichuk
- Institut de Chimie des Milieux et Matériaux de Poitiers, UMR CNRS 7285, University of Poitiers, France; University of Limoges, Inserm, CHU Limoges, RESINFIT, U 1092, F-87000 Limoges, France
| | - Leslie Mondamert
- Institut de Chimie des Milieux et Matériaux de Poitiers, UMR CNRS 7285, University of Poitiers, France
| | - Claude Geffroy
- Institut de Chimie des Milieux et Matériaux de Poitiers, UMR CNRS 7285, University of Poitiers, France
| | - Christophe Dagot
- University of Limoges, Inserm, CHU Limoges, RESINFIT, U 1092, F-87000 Limoges, France
| | - Jérôme Labanowski
- Institut de Chimie des Milieux et Matériaux de Poitiers, UMR CNRS 7285, University of Poitiers, France.
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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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10
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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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11
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Thorwarth D. Clinical use of positron emission tomography for radiotherapy planning - Medical physics considerations. Z Med Phys 2023; 33:13-21. [PMID: 36272949 PMCID: PMC10068574 DOI: 10.1016/j.zemedi.2022.09.001] [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: 04/13/2022] [Revised: 08/17/2022] [Accepted: 09/21/2022] [Indexed: 11/06/2022]
Abstract
PET/CT imaging plays an increasing role in radiotherapy treatment planning. The aim of this article was to identify the major use cases and technical as well as medical physics challenges during integration of these data into treatment planning. Dedicated aspects, such as (i) PET/CT-based radiotherapy simulation, (ii) PET-based target volume delineation, (iii) functional avoidance to optimized organ-at-risk sparing and (iv) functionally adapted individualized radiotherapy are discussed in this article. Furthermore, medical physics aspects to be taken into account are summarized and presented in form of check-lists.
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Affiliation(s)
- Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Tübingen, Germany; German Cancer Consortium (DKTK), partner site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany.
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12
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Li B, Zheng X, Zhang J, Lam S, Guo W, Wang Y, Cui S, Teng X, Zhang Y, Ma Z, Zhou T, Lou Z, Meng L, Ge H, Cai J. Lung Subregion Partitioning by Incremental Dose Intervals Improves Omics-Based Prediction for Acute Radiation Pneumonitis in Non-Small-Cell Lung Cancer Patients. Cancers (Basel) 2022; 14:cancers14194889. [PMID: 36230812 PMCID: PMC9564373 DOI: 10.3390/cancers14194889] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 09/19/2022] [Accepted: 09/27/2022] [Indexed: 11/16/2022] Open
Abstract
Purpose: To evaluate the effectiveness of features obtained from our proposed incremental-dose-interval-based lung subregion segmentation (IDLSS) for predicting grade ≥ 2 acute radiation pneumonitis (ARP) in lung cancer patients upon intensity-modulated radiotherapy (IMRT). (1) Materials and Methods: A total of 126 non-small-cell lung cancer patients treated with IMRT were retrospectively analyzed. Five lung subregions (SRs) were generated by the intersection of the whole lung (WL) and five sub-regions receiving incremental dose intervals. A total of 4610 radiomics features (RF) from pre-treatment planning computed tomographic (CT) and 213 dosiomics features (DF) were extracted. Six feature groups, including WL-RF, WL-DF, SR-RF, SR-DF, and the combined feature sets of WL-RDF and SR-RDF, were generated. Features were selected by using a variance threshold, followed by a Student t-test. Pearson’s correlation test was applied to remove redundant features. Subsequently, Ridge regression was adopted to develop six models for ARP using the six feature groups. Thirty iterations of resampling were implemented to assess overall model performance by using the area under the Receiver-Operating-Characteristic curve (AUC), accuracy, precision, recall, and F1-score. (2) Results: The SR-RDF model achieved the best classification performance and provided significantly better predictability than the WL-RDF model in training cohort (Average AUC: 0.98 ± 0.01 vs. 0.90 ± 0.02, p < 0.001) and testing cohort (Average AUC: 0.88 ± 0.05 vs. 0.80 ± 0.04, p < 0.001). Similarly, predictability of the SR-DF model was significantly stronger than that of the WL-DF model in training cohort (Average AUC: 0.88 ± 0.03 vs. 0.70 ± 0.030, p < 0.001) and in testing cohort (Average AUC: 0.74 ± 0.08 vs. 0.65 ± 0.06, p < 0.001). By contrast, the SR-RF model significantly outperformed the WL-RF model only in the training set (Average AUC: 0.93 ± 0.02 vs. 0.85 ± 0.03, p < 0.001), but not in the testing set (Average AUC: 0.79 ± 0.05 vs. 0.77 ± 0.07, p = 0.13). (3) Conclusions: Our results demonstrated that the IDLSS method improved model performance for classifying ARP with grade ≥ 2 when using dosiomics or combined radiomics-dosiomics features.
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Affiliation(s)
- Bing Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Xiaoli Zheng
- Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Saikit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Wei Guo
- Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Yunhan Wang
- Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Sunan Cui
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, Stanford, CA 94305, USA
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Yuanpeng Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Zongrui Ma
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ta Zhou
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Zhaoyang Lou
- Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Lingguang Meng
- Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
| | - Hong Ge
- Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450008, China
- Correspondence: (H.G.); (J.C.); Tel.: +852-3400-8645 (J.C.)
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- Correspondence: (H.G.); (J.C.); Tel.: +852-3400-8645 (J.C.)
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13
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Thomas HMT, Hippe DS, Forouzannezhad P, Sasidharan BK, Kinahan PE, Miyaoka RS, Vesselle HJ, Rengan R, Zeng J, Bowen SR. Radiation and immune checkpoint inhibitor-mediated pneumonitis risk stratification in patients with locally advanced non-small cell lung cancer: role of functional lung radiomics? Discov Oncol 2022; 13:85. [PMID: 36048266 PMCID: PMC9437196 DOI: 10.1007/s12672-022-00548-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/23/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Patients undergoing chemoradiation and immune checkpoint inhibitor (ICI) therapy for locally advanced non-small cell lung cancer (NSCLC) experience pulmonary toxicity at higher rates than historical reports. Identifying biomarkers beyond conventional clinical factors and radiation dosimetry is especially relevant in the modern cancer immunotherapy era. We investigated the role of novel functional lung radiomics, relative to functional lung dosimetry and clinical characteristics, for pneumonitis risk stratification in locally advanced NSCLC. METHODS Patients with locally advanced NSCLC were prospectively enrolled on the FLARE-RT trial (NCT02773238). All received concurrent chemoradiation using functional lung avoidance planning, while approximately half received consolidation durvalumab ICI. Within tumour-subtracted lung regions, 110 radiomics features (size, shape, intensity, texture) were extracted on pre-treatment [99mTc]MAA SPECT/CT perfusion images using fixed-bin-width discretization. The performance of functional lung radiomics for pneumonitis (CTCAE v4 grade 2 or higher) risk stratification was benchmarked against previously reported lung dosimetric parameters and clinical risk factors. Multivariate least absolute shrinkage and selection operator Cox models of time-varying pneumonitis risk were constructed, and prediction performance was evaluated using optimism-adjusted concordance index (c-index) with 95% confidence interval reporting throughout. RESULTS Thirty-nine patients were included in the study and pneumonitis occurred in 16/39 (41%) patients. Among clinical characteristics and anatomic/functional lung dosimetry variables, only the presence of baseline chronic obstructive pulmonary disease (COPD) was significantly associated with the development of pneumonitis (HR 4.59 [1.69-12.49]) and served as the primary prediction benchmark model (c-index 0.69 [0.59-0.80]). Discrimination of time-varying pneumonitis risk was numerically higher when combining COPD with perfused lung radiomics size (c-index 0.77 [0.65-0.88]) or shape feature classes (c-index 0.79 [0.66-0.91]) but did not reach statistical significance compared to benchmark models (p > 0.26). COPD was associated with perfused lung radiomics size features, including patients with larger lung volumes (AUC 0.75 [0.59-0.91]). Perfused lung radiomic texture features were correlated with lung volume (adj R2 = 0.84-1.00), representing surrogates rather than independent predictors of pneumonitis risk. CONCLUSIONS In patients undergoing chemoradiation with functional lung avoidance therapy and optional consolidative immune checkpoint inhibitor therapy for locally advanced NSCLC, the strongest predictor of pneumonitis was the presence of baseline chronic obstructive pulmonary disease. Results from this novel functional lung radiomics exploratory study can inform future validation studies to refine pneumonitis risk models following combinations of radiation and immunotherapy. Our results support functional lung radiomics as surrogates of COPD for non-invasive monitoring during and after treatment. Further study of clinical, dosimetric, and radiomic feature combinations for radiation and immune-mediated pneumonitis risk stratification in a larger patient population is warranted.
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Affiliation(s)
- Hannah M T Thomas
- Department of Radiation Oncology, University of Washington School of Medicine, 1959 NE Pacific St, Box 356043, Seattle, WA, 98195, USA
- Department of Radiation Oncology, Christian Medical College Vellore, Vellore, Tamil Nadu, India
| | - Daniel S Hippe
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Parisa Forouzannezhad
- Department of Radiation Oncology, University of Washington School of Medicine, 1959 NE Pacific St, Box 356043, Seattle, WA, 98195, USA
| | - Balu Krishna Sasidharan
- Department of Radiation Oncology, Christian Medical College Vellore, Vellore, Tamil Nadu, India
| | - Paul E Kinahan
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Robert S Miyaoka
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Hubert J Vesselle
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Ramesh Rengan
- Department of Radiation Oncology, University of Washington School of Medicine, 1959 NE Pacific St, Box 356043, Seattle, WA, 98195, USA
| | - Jing Zeng
- Department of Radiation Oncology, University of Washington School of Medicine, 1959 NE Pacific St, Box 356043, Seattle, WA, 98195, USA
| | - Stephen R Bowen
- Department of Radiation Oncology, University of Washington School of Medicine, 1959 NE Pacific St, Box 356043, Seattle, WA, 98195, USA.
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA.
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14
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Ren G, Li B, Lam SK, Xiao H, Huang YH, Cheung ALY, Lu Y, Mao R, Ge H, Kong FM(S, Ho WY, Cai J. A Transfer Learning Framework for Deep Learning-Based CT-to-Perfusion Mapping on Lung Cancer Patients. Front Oncol 2022; 12:883516. [PMID: 35847874 PMCID: PMC9283770 DOI: 10.3389/fonc.2022.883516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 06/02/2022] [Indexed: 11/18/2022] Open
Abstract
Purpose Deep learning model has shown the feasibility of providing spatial lung perfusion information based on CT images. However, the performance of this method on lung cancer patients is yet to be investigated. This study aims to develop a transfer learning framework to evaluate the deep learning based CT-to-perfusion mapping method specifically on lung cancer patients. Methods SPECT/CT perfusion scans of 33 lung cancer patients and 137 non-cancer patients were retrospectively collected from two hospitals. To adapt the deep learning model on lung cancer patients, a transfer learning framework was developed to utilize the features learned from the non-cancer patients. These images were processed to extract features from three-dimensional CT images and synthesize the corresponding CT-based perfusion images. A pre-trained model was first developed using a dataset of patients with lung diseases other than lung cancer, and subsequently fine-tuned specifically on lung cancer patients under three-fold cross-validation. A multi-level evaluation was performed between the CT-based perfusion images and ground-truth SPECT perfusion images in aspects of voxel-wise correlation using Spearman’s correlation coefficient (R), function-wise similarity using Dice Similarity Coefficient (DSC), and lobe-wise agreement using mean perfusion value for each lobe of the lungs. Results The fine-tuned model yielded a high voxel-wise correlation (0.8142 ± 0.0669) and outperformed the pre-trained model by approximately 8%. Evaluation of function-wise similarity indicated an average DSC value of 0.8112 ± 0.0484 (range: 0.6460-0.8984) for high-functional lungs and 0.8137 ± 0.0414 (range: 0.6743-0.8902) for low-functional lungs. Among the 33 lung cancer patients, high DSC values of greater than 0.7 were achieved for high functional volumes in 32 patients and low functional volumes in all patients. The correlations of the mean perfusion value on the left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe were 0.7314, 0.7134, 0.5108, 0.4765, and 0.7618, respectively. Conclusion For lung cancer patients, the CT-based perfusion images synthesized by the transfer learning framework indicated a strong voxel-wise correlation and function-wise similarity with the SPECT perfusion images. This suggests the great potential of the deep learning method in providing regional-based functional information for functional lung avoidance radiation therapy.
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Affiliation(s)
- Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Bing Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China
| | - Sai-kit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Andy Lai-yin Cheung
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
| | - Yufei Lu
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China
| | - Ronghu Mao
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China
| | - Hong Ge
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China
| | - Feng-Ming (Spring) Kong
- Department of Clinical Oncology, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Wai-yin Ho
- Department of Nuclear Medicine, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
- *Correspondence: Jing Cai,
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15
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Bucknell NW, Belderbos J, Palma DA, Iyengar P, Samson P, Chua K, Gomez D, McDonald F, Louie AV, Faivre-Finn C, Hanna GG, Siva S. Avoiding toxicity with lung radiation therapy: An IASLC perspective. J Thorac Oncol 2022; 17:961-973. [DOI: 10.1016/j.jtho.2022.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 05/10/2022] [Accepted: 05/12/2022] [Indexed: 11/25/2022]
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16
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Vinogradskiy Y, Castillo R, Castillo E, Schubert L, Jones BL, Faught A, Gaspar LE, Kwak J, Bowles DW, Waxweiler T, Dougherty JM, Gao D, Stevens C, Miften M, Kavanagh B, Grills I, Rusthoven CG, Guerrero T. Results of a Multi-Institutional Phase 2 Clinical Trial for 4DCT-Ventilation Functional Avoidance Thoracic Radiation Therapy. Int J Radiat Oncol Biol Phys 2022; 112:986-995. [PMID: 34767934 PMCID: PMC8863640 DOI: 10.1016/j.ijrobp.2021.10.147] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 10/07/2021] [Accepted: 10/22/2021] [Indexed: 12/25/2022]
Abstract
PURPOSE Radiation pneumonitis remains a major limitation in the radiation therapy treatment of patients with lung cancer. Functional avoidance radiation therapy uses functional imaging to reduce pulmonary toxic effects by designing radiation therapy plans that reduce doses to functional regions of the lung. Lung functional imaging has been developed that uses 4-dimensional computed tomography (4DCT) imaging to calculate 4DCT-based lung ventilation (4DCT-ventilation). A phase 2 multicenter study was initiated to evaluate 4DCT-ventilation functional avoidance radiation therapy. The study hypothesis was that functional avoidance radiation therapy could reduce the rate of grade ≥2 radiation pneumonitis to 12% compared with a 25% historical rate, with the trial being positive if ≤16.4% of patients experienced grade ≥2 pneumonitis. METHODS AND MATERIALS Lung cancer patients receiving curative-intent radiation therapy (prescription doses of 45-75 Gy) and chemotherapy were accrued. Patient 4DCT scans were used to generate 4DCT-ventilation images. The 4DCT-ventilation images were used to generate functional avoidance plans that reduced doses to functional portions of the lung while delivering the prescribed tumor dose. Pneumonitis was evaluated by a clinician at 3, 6, and 12 months after radiation therapy. RESULTS Sixty-seven evaluable patients were accrued between April 2015 and December 2019. The median prescription dose was 60 Gy (range, 45-66 Gy) delivered in 30 fractions (range, 15-33 fractions). The average reduction in the functional volume of lung receiving ≥20 Gy with functional avoidance was 3.5% (range, 0%-12.8%). The median follow-up was 312 days. The rate of grade ≥2 radiation pneumonitis was 10 of 67 patients (14.9%; 95% upper CI, 24.0%), meeting the phase 2 criteria. CONCLUSIONS 4DCT-ventilation offers an imaging modality that is convenient and provides functional imaging without an extra procedure necessary. This first report of a multicenter study of 4DCT-ventilation functional avoidance radiation therapy provided data showing that the trial met phase 2 criteria and that evaluation in a phase 3 study is warranted.
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Affiliation(s)
- Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado; Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania.
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, Georgia
| | - Edward Castillo
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Leah Schubert
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Bernard L Jones
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Austin Faught
- Department of Radiation Oncology, St Jude Children's Research Hospital, Memphis, Tennessee
| | - Laurie E Gaspar
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Jennifer Kwak
- Department of Radiology, University of Colorado School of Medicine, Aurora, Colorado
| | - Daniel W Bowles
- Division of Medical Oncology, University of Colorado School of Medicine, Aurora, Colorado; Rocky Mountain Regional VA Medical Center, Aurora, Colorado
| | - Timothy Waxweiler
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | | | - Dexiang Gao
- Departments of Pediatrics and Biostatistics and Informatics, University of Colorado School of Medicine, Aurora, Colorado
| | - Craig Stevens
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Brian Kavanagh
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Inga Grills
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
| | - Chad G Rusthoven
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, Michigan
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17
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Bowen SR, Hippe DS, Thomas HM, Sasidharan B, Lampe PD, Baik CS, Eaton KD, Lee S, Martins RG, Santana-Davila R, Chen DL, Kinahan PE, Miyaoka RS, Vesselle HJ, Houghton AM, Rengan R, Zeng J. Prognostic Value of Early Fluorodeoxyglucose-Positron Emission Tomography Response Imaging and Peripheral Immunologic Biomarkers: Substudy of a Phase II Trial of Risk-Adaptive Chemoradiation for Unresectable Non-Small Cell Lung Cancer. Adv Radiat Oncol 2022; 7:100857. [PMID: 35387421 PMCID: PMC8977846 DOI: 10.1016/j.adro.2021.100857] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 10/29/2021] [Indexed: 11/24/2022] Open
Abstract
Purpose We sought to examine the prognostic value of fluorodeoxyglucose-positron emission tomography (PET) imaging during chemoradiation for unresectable non-small cell lung cancer for survival and hypothesized that tumor PET response is correlated with peripheral T-cell function. Methods and Materials Forty-five patients with American Joint Committee on Cancer version 7 stage IIB-IIIB non-small cell lung cancer enrolled in a phase II trial and received platinum-doublet chemotherapy concurrent with 6 weeks of radiation (NCT02773238). Fluorodeoxyglucose-PET was performed before treatment start and after 24 Gy of radiation (week 3). PET response status was prospectively defined by multifactorial radiologic interpretation. PET responders received 60 Gy in 30 fractions, while nonresponders received concomitant boosts to 74 Gy in 30 fractions. Peripheral blood was drawn synchronously with PET imaging, from which germline DNA sequencing, T-cell receptor sequencing, and plasma cytokine analysis were performed. Results Median follow-up was 18.8 months, 1-year overall survival (OS) 82%, 1-year progression-free survival 53%, and 1-year locoregional control 88%. Higher midtreatment PET total lesion glycolysis was detrimental to OS (1 year 87% vs 63%, P < .001), progression-free survival (1 year 60% vs 26%, P = .044), and locoregional control (1 year 94% vs 65%, P = .012), even after adjustment for clinical/treatment factors. Twenty-nine of 45 patients (64%) were classified as PET responders based on a priori definition. Higher tumor programmed death-ligand 1 expression was correlated with response on PET (P = .017). Higher T-cell receptor richness and clone distribution slope were associated with improved OS (P = .018-0.035); clone distribution slope was correlated with PET response (P = .031). Conclusions Midchemoradiation PET imaging is prognostic for survival; PET response may be linked to tumor and peripheral T-cell biomarkers.
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Affiliation(s)
- Stephen R. Bowen
- Radiation Oncology and
- Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Daniel S. Hippe
- Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Hannah M. Thomas
- Department of Radiation Oncology, Christian Medical College, Vellore, India
| | | | - Paul D. Lampe
- Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Christina S. Baik
- Division of Medical Oncology, Department of Medicine, University of Washington School of Medicine, Seattle, Washington
| | - Keith D. Eaton
- Division of Medical Oncology, Department of Medicine, University of Washington School of Medicine, Seattle, Washington
| | - Sylvia Lee
- Division of Medical Oncology, Department of Medicine, University of Washington School of Medicine, Seattle, Washington
| | - Renato G. Martins
- Division of Medical Oncology, Department of Medicine, University of Washington School of Medicine, Seattle, Washington
| | - Rafael Santana-Davila
- Division of Medical Oncology, Department of Medicine, University of Washington School of Medicine, Seattle, Washington
| | - Delphine L. Chen
- Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Paul E. Kinahan
- Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Robert S. Miyaoka
- Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Hubert J. Vesselle
- Radiology, University of Washington School of Medicine, Seattle, Washington
| | - A. McGarry Houghton
- Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Ramesh Rengan
- Radiation Oncology and
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
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18
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Horn KP, Thomas HMT, Vesselle HJ, Kinahan PE, Miyaoka RS, Rengan R, Zeng J, Bowen SR. Reliability of Quantitative 18F-FDG PET/CT Imaging Biomarkers for Classifying Early Response to Chemoradiotherapy in Patients With Locally Advanced Non-Small Cell Lung Cancer. Clin Nucl Med 2021; 46:861-871. [PMID: 34172602 PMCID: PMC8490284 DOI: 10.1097/rlu.0000000000003774] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF THE REPORT We evaluated the reliability of 18F-FDG PET imaging biomarkers to classify early response status across observers, scanners, and reconstruction algorithms in support of biologically adaptive radiation therapy for locally advanced non-small cell lung cancer. PATIENTS AND METHODS Thirty-one patients with unresectable locally advanced non-small cell lung cancer were prospectively enrolled on a phase 2 trial (NCT02773238) and underwent 18F-FDG PET on GE Discovery STE (DSTE) or GE Discovery MI (DMI) PET/CT systems at baseline and during the third week external beam radiation therapy regimens. All PET scans were reconstructed using OSEM; GE-DMI scans were also reconstructed with BSREM-TOF (block sequential regularized expectation maximization reconstruction algorithm incorporating time of flight). Primary tumors were contoured by 3 observers using semiautomatic gradient-based segmentation. SUVmax, SUVmean, SUVpeak, MTV (metabolic tumor volume), and total lesion glycolysis were correlated with midtherapy multidisciplinary clinical response assessment. Dice similarity of contours and response classification areas under the curve were evaluated across observers, scanners, and reconstruction algorithms. LASSO logistic regression models were trained on DSTE PET patient data and independently tested on DMI PET patient data. RESULTS Interobserver variability of PET contours was low for both OSEM and BSREM-TOF reconstructions; intraobserver variability between reconstructions was slightly higher. ΔSUVpeak was the most robust response predictor across observers and image reconstructions. LASSO models consistently selected ΔSUVpeak and ΔMTV as response predictors. Response classification models achieved high cross-validated performance on the DSTE cohort and more variable testing performance on the DMI cohort. CONCLUSIONS The variability FDG PET lesion contours and imaging biomarkers was relatively low across observers, scanners, and reconstructions. Objective midtreatment PET response assessment may lead to improved precision of biologically adaptive radiation therapy.
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Affiliation(s)
- Kevin P. Horn
- Radiology, Division of Nuclear Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Hannah M. T. Thomas
- Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Hubert J. Vesselle
- Radiology, Division of Nuclear Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Paul E. Kinahan
- Radiology, Division of Nuclear Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Robert S. Miyaoka
- Radiology, Division of Nuclear Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Ramesh Rengan
- Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Jing Zeng
- Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Stephen R. Bowen
- Radiology, Division of Nuclear Medicine, University of Washington School of Medicine, Seattle, WA, USA
- Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
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19
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Matrosic CK, Owen DR, Polan D, Sun Y, Jolly S, Schonewolf C, Schipper M, Haken RKT, Galban CJ, Matuszak M. Feasibility of function-guided lung treatment planning with parametric response mapping. J Appl Clin Med Phys 2021; 22:80-89. [PMID: 34697884 PMCID: PMC8598143 DOI: 10.1002/acm2.13436] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 08/04/2021] [Accepted: 09/14/2021] [Indexed: 11/18/2022] Open
Abstract
Purpose Recent advancements in functional lung imaging have been developed to improve clinicians’ knowledge of patient pulmonary condition prior to treatment. Ultimately, it may be possible to employ these functional imaging modalities to tailor radiation treatment plans to optimize patient outcome and mitigate pulmonary complications. Parametric response mapping (PRM) is a computed tomography (CT)–based functional lung imaging method that utilizes a voxel‐wise image analysis technique to classify lung abnormality phenotypes, and has previously been shown to be effective at assessing lung complication risk in diagnostic applications. The purpose of this work was to demonstrate the implementation of PRM guidance in radiotherapy treatment planning. Methods and materials A retrospective study was performed with 18 lung cancer patients to test the incorporation of PRM into a radiotherapy planning workflow. Paired inspiration/expiration pretreatment CT scans were acquired and PRM analysis was utilized to classify each voxel as normal, parenchymal disease, small airway disease, and emphysema. Density maps were generated for each PRM classification to contour high density regions of pulmonary abnormalities. Conventional volumetric‐modulated arc therapy and PRM‐guided treatment plans were designed for each patient. Results PRM guidance was successfully implemented into the treatment planning process. The inclusion of PRM priorities resulted in statistically significant (p < 0.05) improvements to the V20Gy within the PRM avoidance contours. On average, reductions of 5.4% in the V20Gy(%) were found. The PRM‐guided treatment plans did not significantly increase the dose to the organs at risk or result in insufficient planning target volume coverage, but did increase plan complexity. Conclusions PRM guidance was successfully implemented into a treatment planning workflow and shown to be effective for dose redistribution within the lung. This work has provided a framework for the potential clinical implementation of PRM‐guided treatment planning.
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Affiliation(s)
- Charles K Matrosic
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - D Rocky Owen
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Daniel Polan
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Yilun Sun
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.,School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Caitlin Schonewolf
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Matthew Schipper
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.,School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Craig J Galban
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Martha Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
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20
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Hyperpolarized 129Xe Magnetic Resonance Imaging for Functional Avoidance Treatment Planning in Thoracic Radiation Therapy: A Comparison of Ventilation- and Gas Exchange-Guided Treatment Plans. Int J Radiat Oncol Biol Phys 2021; 111:1044-1057. [PMID: 34265395 DOI: 10.1016/j.ijrobp.2021.07.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 05/19/2021] [Accepted: 07/02/2021] [Indexed: 12/14/2022]
Abstract
PURPOSE To present a methodology to use pulmonary gas exchange maps to guide functional avoidance treatment planning in radiation therapy (RT) and evaluate its efficacy compared with ventilation-guided treatment planning. METHODS AND MATERIALS Before receiving conventional RT for non-small cell lung cancer, 11 patients underwent hyperpolarized 129Xe gas exchange magnetic resonance imaging to map the distribution of xenon in its gas phase (ventilation) and transiently bound to red blood cells in the alveolar capillaries (gas exchange). Both ventilation and gas exchange maps were independently used to guide development of new functional avoidance treatment plans for every patient, while adhering to institutional dose-volume constraints for normal tissues and target coverage. Furthermore, dose-volume histogram (DVH)-based reoptimizations of the clinical plan, with reductions in mean lung dose (MLD) equal to the functional avoidance plans, were created to serve as the control group. To evaluate each plan (regardless of type), gas exchange maps, representing end-to-end lung function, were used to calculate gas exchange-weighted MLD (fMLD), gas exchange-weighted volume receiving ≥20 Gy (fV20), and mean dose in the highest gas exchanging 33% and 50% volumes of lung (MLD-f33% and MLD-f50%). Using each clinically approved plan as a baseline, the reductions in functional metrics were compared for ventilation-optimization, gas exchange optimization, and DVH-based reoptimization. Statistical significance was determined using the Freidman test, with subsequent subdivision when indicated by P values less than .10 and post hoc testing with Wilcoxon signed rank tests to determine significant differences (P < .05). Toxicity modeling was performed using an established function-based model to estimate clinical significance of the results. RESULTS Compared with DVH-based reoptimization of the clinically approved plans, gas exchange-guided functional avoidance planning more effectively reduced the gas exchange-weighted metrics fMLD (average ± SD, -78 ± 79 cGy for gas exchange, compared with -45 ± 34 cGy for DVH-based; P = .03), MLD-f33% (-135 ± 136 cGy, compared with -52 ± 47 cGy; P = .004), and MLD-f50% (-96 ± 95 cGy, compared with -47 ± 40 cGy; P = .01). Comparing the 2 functional planning types, gas exchange-guided planning more effectively reduced MLD-f33% compared with ventilation-guided planning (-64 ± 95; P = .009). For some patients, gas exchange-guided functional avoidance plans demonstrated clinically significant reductions in model-predicted toxicity, more so than the accompanying ventilation-guided plans and DVH-based reoptimizations. CONCLUSION Gas exchange-guided planning effectively reduced dose to high gas exchanging regions of lung while maintaining clinically acceptable plan quality. In many patients, ventilation-guided planning incidentally reduced dose to higher gas exchange regions, to a lesser extent. This methodology enables future prospective trials to examine patient outcomes.
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21
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Forghani F, Patton T, Kwak J, Thomas D, Diot Q, Rusthoven C, Castillo R, Castillo E, Grills I, Guerrero T, Miften M, Vinogradskiy Y. Characterizing spatial differences between SPECT-ventilation and SPECT-perfusion in patients with lung cancer undergoing radiotherapy. Radiother Oncol 2021; 160:120-124. [PMID: 33964328 PMCID: PMC8489737 DOI: 10.1016/j.radonc.2021.04.022] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/24/2021] [Accepted: 04/28/2021] [Indexed: 12/25/2022]
Abstract
This study investigates agreement between ventilation and perfusion for lung cancer patients undergoing radiotherapy. Ventilation-perfusion scans of nineteen patients with stage III lung cancer from a prospective protocol were compared using voxel-wise Spearman correlation-coefficients. The presented results show in about 25% of patients, ventilation and perfusion exhibit lower agreement.
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Affiliation(s)
- Farnoush Forghani
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Taylor Patton
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States, United States(1); Department of Radiation Oncology, Mayo Clinic, Rochester, MN, United States(2)
| | - Jennifer Kwak
- Department of Radiology, University of Colorado School of Medicine, Aurora, CO, United States
| | - David Thomas
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Quentin Diot
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Chad Rusthoven
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States
| | - Edward Castillo
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, United States
| | - Inga Grills
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, United States
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, United States
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States
| | - Yevgeniy Vinogradskiy
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, United States, United States(1); Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, United States(2)
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22
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Zeng J, Bowen SR. Treatment Intensification in Locally Advanced/Unresectable NSCLC Through Combined Modality Treatment and Precision Dose Escalation. Semin Radiat Oncol 2021; 31:105-111. [PMID: 33610266 DOI: 10.1016/j.semradonc.2020.11.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The best survival for patients with unresectable, locally advanced NSCLC is currently achieved through concurrent chemoradiation followed by durvalumab for a year. Despite the best standard of care treatment, the majority of patients still develop disease recurrence, which could be distant and/or local. Trials continue to try and improve outcomes for patients with unresectable NSCLC, typically through treatment intensification, with the addition of more systemic agents, or more radiation dose to the tumor. Although RTOG 0617 showed that uniform dose escalation across an unselected population of patients undergoing chemoradiation is not beneficial, efforts continue to select patients and tumor subsets that are likely to benefit from dose escalation. This review describes some of the ongoing therapeutic trials in unresectable NSCLC, with an emphasis on quantitative imaging and precision radiation dose escalation.
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Affiliation(s)
- Jing Zeng
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA.
| | - Stephen R Bowen
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA; Department of Radiology, University of Washington School of Medicine, Seattle, WA
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23
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Ren G, Lam SK, Zhang J, Xiao H, Cheung ALY, Ho WY, Qin J, Cai J. Investigation of a Novel Deep Learning-Based Computed Tomography Perfusion Mapping Framework for Functional Lung Avoidance Radiotherapy. Front Oncol 2021; 11:644703. [PMID: 33842356 PMCID: PMC8024641 DOI: 10.3389/fonc.2021.644703] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/02/2021] [Indexed: 11/25/2022] Open
Abstract
Functional lung avoidance radiation therapy aims to minimize dose delivery to the normal lung tissue while favoring dose deposition in the defective lung tissue based on the regional function information. However, the clinical acquisition of pulmonary functional images is resource-demanding, inconvenient, and technically challenging. This study aims to investigate the deep learning-based lung functional image synthesis from the CT domain. Forty-two pulmonary macro-aggregated albumin SPECT/CT perfusion scans were retrospectively collected from the hospital. A deep learning-based framework (including image preparation, image processing, and proposed convolutional neural network) was adopted to extract features from 3D CT images and synthesize perfusion as estimations of regional lung function. Ablation experiments were performed to assess the effects of each framework component by removing each element of the framework and analyzing the testing performances. Major results showed that the removal of the CT contrast enhancement component in the image processing resulted in the largest drop in framework performance, compared to the optimal performance (~12%). In the CNN part, all the three components (residual module, ROI attention, and skip attention) were approximately equally important to the framework performance; removing one of them resulted in a 3–5% decline in performance. The proposed CNN improved ~4% overall performance and ~350% computational efficiency, compared to the U-Net model. The deep convolutional neural network, in conjunction with image processing for feature enhancement, is capable of feature extraction from CT images for pulmonary perfusion synthesis. In the proposed framework, image processing, especially CT contrast enhancement, plays a crucial role in the perfusion synthesis. This CTPM framework provides insights for relevant research studies in the future and enables other researchers to leverage for the development of optimized CNN models for functional lung avoidance radiation therapy.
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Affiliation(s)
- Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Sai-Kit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Haonan Xiao
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Andy Lai-Yin Cheung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Wai-Yin Ho
- Department of Nuclear Medicine, Queen Mary Hospital, Hong Kong, Hong Kong
| | - Jing Qin
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
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24
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Owen DR, Sun Y, Boonstra PS, McFarlane M, Viglianti BL, Balter JM, El Naqa I, Schipper MJ, Schonewolf CA, Ten Haken RK, Kong FMS, Jolly S, Matuszak MM. Investigating the SPECT Dose-Function Metrics Associated With Radiation-Induced Lung Toxicity Risk in Patients With Non-small Cell Lung Cancer Undergoing Radiation Therapy. Adv Radiat Oncol 2021; 6:100666. [PMID: 33817412 PMCID: PMC8010578 DOI: 10.1016/j.adro.2021.100666] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 01/22/2021] [Indexed: 12/14/2022] Open
Abstract
Purpose Dose to normal lung has commonly been linked with radiation-induced lung toxicity (RILT) risk, but incorporating functional lung metrics in treatment planning may help further optimize dose delivery and reduce RILT incidence. The purpose of this study was to investigate the impact of the dose delivered to functional lung regions by analyzing perfusion (Q), ventilation (V), and combined V/Q single-photon-emission computed tomography (SPECT) dose-function metrics with regard to RILT risk in patients with non-small cell lung cancer (NSCLC) patients who received radiation therapy (RT). Methods and Materials SPECT images acquired from 88 patients with locally advanced NSCLC before undergoing conventionally fractionated RT were retrospectively analyzed. Dose was converted to the nominal dose equivalent per 2 Gy fraction, and SPECT intensities were normalized. Regional lung segments were defined, and the average dose delivered to each lung region was quantified. Three functional categorizations were defined to represent low-, normal-, and high-functioning lungs. The percent of functional lung category receiving ≥20 Gy and mean functional intensity receiving ≥20 Gy (iV20) were calculated. RILT was defined as grade 2+ radiation pneumonitis and/or clinical radiation fibrosis. A logistic regression was used to evaluate the association between dose-function metrics and risk of RILT. Results By analyzing V/Q normalized intensities and functional distributions across the population, a wide range in functional capability (especially in the ipsilateral lung) was observed in patients with NSCLC before RT. Through multivariable regression models, global lung average dose to the lower lung was found to be significantly associated with RILT, and Q and V iV20 were correlated with RILT when using ipsilateral lung metrics. Through a receiver operating characteristic analysis, combined V/Q low-function receiving ≥20 Gy (low-functioning V/Q20) in the ipsilateral lung was found to be the best predictor (area under the curce: 0.79) of RILT risk. Conclusions Irradiation of the inferior lung appears to be a locational sensitivity for RILT risk. The multivariable correlation between ipsilateral lung iV20 and RILT, as well as the association of low-functioning V/Q20 and RILT, suggest that irradiating low-functioning regions in the lung may lead to higher toxicity rates.
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Affiliation(s)
- Daniel R Owen
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Yilun Sun
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.,Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Philip S Boonstra
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Matthew McFarlane
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Benjamin L Viglianti
- Department of Radiology, University of Michigan, Ann Arbor, Michigan.,Veterans Administration, Nuclear Medicine Service, Ann Arbor Michigan
| | - James M Balter
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Matthew J Schipper
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | | | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Feng-Ming S Kong
- Hong Kong University Shenzhen Hospital and Queen Mary Hospital, Hong Kong University Li Ka Shing Medical School, Department of Clinical Oncology, Hong Kong.,Department of Radiation Oncology, Case Western Reserve University, Cleveland, Ohio
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Martha M Matuszak
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
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25
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Mycoremediation of Soils Polluted with Trichloroethylene: First Evidence of Pleurotus Genus Effectiveness. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11041354] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Trichloroethylene (TCE) is a proven carcinogenic chlorinated organic compound widely used as a solvent in industrial cleaning solutions; it is easily found in the soil, air, and water and is a hazardous environmental pollutant. Most studies have attempted to remove TCE from air and water using different anaerobic bacteria species. In addition, a few have used white-rot fungi, although there are hardly any in soil. The objective of the present work is to assess TCE removal efficiency using two species of the genus Pleurotus that have not been tested before: Pleurotus ostreatus and Pleurotus eryngii, growing on a sandy loam soil. These fungi presented different intra- and extracellular enzymatic systems (chytochrome P450 (CYP450), laccase, Mn peroxidase (MnP)) capable of aerobically degrading TCE to less harmful compounds. The potential toxicity of TCE to P. ostreatus and P. eryngii was firstly tested in a TCE-spiked liquid broth (70 mg L−1 and 140 mg L−1) for 14 days. Then, both fungi were assessed for their ability to degrade the pollutant in sandy loam soil spiked with 140 mg kg−1 of TCE. P. ostreatus and P. eryngii improved the natural dissipation of TCE from soil by 44%. Extracellular enzymes were poorly expressed, but mainly in the presence of the contaminant, in accordance with the hypothesis of the involvement of CYP450.
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26
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Gajewicz-Skretna A, Gromelski M, Wyrzykowska E, Furuhama A, Yamamoto H, Suzuki N. Aquatic toxicity (Pre)screening strategy for structurally diverse chemicals: global or local classification tree models? ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2021; 208:111738. [PMID: 33396066 DOI: 10.1016/j.ecoenv.2020.111738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 11/23/2020] [Accepted: 11/25/2020] [Indexed: 06/12/2023]
Abstract
With an ever-increasing number of synthetic chemicals being manufactured, it is unrealistic to expect that they will all be subjected to comprehensive and effective risk assessment. A shift from conventional animal testing to computer-aided methods is therefore an important step towards advancing the environmental risk assessments of chemicals. The aims of this study are two-fold: firstly, it examines the relationships between structural and physicochemical features of a diverse set of organic chemicals, and their acute aquatic toxicity towards Daphnia magna and Oryzias latipes using a classification tree approach. Secondly, it compares the efficiency and accuracy of the predictions of two modeling schemes: local models that are inherently restricted to a smaller subset of structurally-related substances, and a global model that covers a wider chemical space and a number of modes of toxic action. The classification tree-based models differentiate the organic chemicals into either 'highly toxic' or 'low to non-toxic' classes, based on internal and external validation criteria. These mechanistically-driven models, which demonstrate good performance, reveal that the key factors driving acute aquatic toxicity are lipophilicity, electrophilic reactivity, molecular polarizability and size. A comparative analysis of the performance of the two modeling schemes indicates that the local models, trained on homogeneous data sets, are less error prone, and therefore superior to the global model. Although the global models showed worse performance metrics compared to the local ones, their applicability domain is much wider, thereby significantly increasing their usefulness in practical applications for regulatory purposes. This demonstrates their advantage over local models and shows they are an invaluable tool for modeling heterogeneous chemical data sets.
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Affiliation(s)
- Agnieszka Gajewicz-Skretna
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland.
| | - Maciej Gromelski
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Ewelina Wyrzykowska
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Ayako Furuhama
- Division of Genetics and Mutagenesis, National Institute of Health Sciences (NIHS), 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 210-9501, Japan; Center for Health and Environmental Risk Research, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba 305-8506, Japan
| | - Hiroshi Yamamoto
- Center for Health and Environmental Risk Research, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba 305-8506, Japan
| | - Noriyuki Suzuki
- Center for Health and Environmental Risk Research, National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba 305-8506, Japan
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27
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Duan C, Chaovalitwongse WA, Bai F, Hippe DS, Wang S, Thammasorn P, Pierce LA, Liu X, You J, Miyaoka RS, Vesselle HJ, Kinahan PE, Rengan R, Zeng J, Bowen SR. Sensitivity analysis of FDG PET tumor voxel cluster radiomics and dosimetry for predicting mid-chemoradiation regional response of locally advanced lung cancer. Phys Med Biol 2020; 65:205007. [PMID: 33027064 PMCID: PMC7593986 DOI: 10.1088/1361-6560/abb0c7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
We investigated the sensitivity of regional tumor response prediction to variability in voxel clustering techniques, imaging features, and machine learning algorithms in 25 patients with locally advanced non-small cell lung cancer (LA-NSCLC) enrolled on the FLARE-RT clinical trial. Metabolic tumor volumes (MTV) from pre-chemoradiation (PETpre) and mid-chemoradiation fluorodeoxyglucose-positron emission tomography (FDG PET) images (PETmid) were subdivided into K-means or hierarchical voxel clusters by standardized uptake values (SUV) and 3D-positions. MTV cluster separability was evaluated by CH index, and morphologic changes were captured by Dice similarity and centroid Euclidean distance. PETpre conventional features included SUVmean, MTV/MTV cluster size, and mean radiation dose. PETpre radiomics consisted of 41 intensity histogram and 3D texture features (PET Oncology Radiomics Test Suite) extracted from MTV or MTV clusters. Machine learning models (multiple linear regression, support vector regression, logistic regression, support vector machines) of conventional features or radiomic features were constructed to predict PETmid response. Leave-one-out-cross-validated root-mean-squared-error (RMSE) for continuous response regression (ΔSUVmean) and area-under-receiver-operating-characteristic-curve (AUC) for binary response classification were calculated. K-means MTV 2-clusters (MTVhi, MTVlo) achieved maximum CH index separability (Friedman p < 0.001). Between PETpre and PETmid, MTV cluster pairs overlapped (Dice 0.70-0.87) and migrated 0.6-1.1 cm. PETmid ΔSUVmean response prediction was superior in MTV and MTVlo (RMSE = 0.17-0.21) compared to MTVhi (RMSE = 0.42-0.52, Friedman p < 0.001). PETmid ΔSUVmean response class prediction performance trended higher in MTVlo (AUC = 0.83-0.88) compared to MTVhi (AUC = 0.44-0.58, Friedman p = 0.052). Models were more sensitive to MTV/MTV cluster regions (Friedman p = 0.026) than feature sets/algorithms (Wilcoxon signed-rank p = 0.36). Top-ranked radiomic features included GLZSM-LZHGE (large-zone-high-SUV), GTSDM-CP (cluster-prominence), GTSDM-CS (cluster-shade) and NGTDM-CNT (contrast). Top-ranked features were consistent between MTVhi and MTVlo cluster pairs but varied between MTVhi-MTVlo clusters, reflecting distinct regional radiomic phenotypes. Variability in tumor voxel cluster response prediction can inform robust radiomic target definition for risk-adaptive chemoradiation in patients with LA-NSCLC. FLARE-RT trial: NCT02773238.
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Affiliation(s)
- Chunyan Duan
- Department of Mechanical Engineering, Tongji University School of Mechanical Engineering, Shanghai China
- Department of Industrial Engineering, University of Arkansas College of Engineering, Fayetteville AR
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle WA
| | - W. Art Chaovalitwongse
- Department of Industrial Engineering, University of Arkansas College of Engineering, Fayetteville AR
| | - Fangyun Bai
- Department of Management Science and Engineering, Tongji University School of Economics and Management, Shanghai China
- Department of Industrial, Manufacturing, & Systems Engineering, University of Texas at Arlington College of Engineering, Arlington, TX
| | - Daniel S. Hippe
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Shouyi Wang
- Department of Industrial, Manufacturing, & Systems Engineering, University of Texas at Arlington College of Engineering, Arlington, TX
| | - Phawis Thammasorn
- Department of Industrial Engineering, University of Arkansas College of Engineering, Fayetteville AR
| | - Larry A. Pierce
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Xiao Liu
- Department of Industrial Engineering, University of Arkansas College of Engineering, Fayetteville AR
| | - Jianxin You
- Department of Management Science and Engineering, Tongji University School of Economics and Management, Shanghai China
| | - Robert S. Miyaoka
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Hubert J. Vesselle
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Paul E. Kinahan
- Department of Radiology, University of Washington School of Medicine, Seattle WA
| | - Ramesh Rengan
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle WA
| | - Jing Zeng
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle WA
| | - Stephen R. Bowen
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle WA
- Department of Radiology, University of Washington School of Medicine, Seattle WA
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Thureau S, Briens A, Decazes P, Castelli J, Barateau A, Garcia R, Thariat J, de Crevoisier R. PET and MRI guided adaptive radiotherapy: Rational, feasibility and benefit. Cancer Radiother 2020; 24:635-644. [PMID: 32859466 DOI: 10.1016/j.canrad.2020.06.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 06/22/2020] [Indexed: 02/07/2023]
Abstract
Adaptive radiotherapy (ART) corresponds to various replanning strategies aiming to correct for anatomical variations occurring during the course of radiotherapy. The goal of the article was to report the rational, feasibility and benefit of using PET and/or MRI to guide this ART strategy in various tumor localizations. The anatomical modifications defined by scanner taking into account tumour mobility and volume variation are not always sufficient to optimise treatment. The contribution of functional imaging by PET or the precision of soft tissue by MRI makes it possible to consider optimized ART. Today, the most important data for both PET and MRI are for lung, head and neck, cervical and prostate cancers. PET and MRI guided ART appears feasible and safe, however in a very limited clinical experience. Phase I/II studies should be therefore performed, before proposing cost-effectiveness comparisons in randomized trials and before using the approach in routine practice.
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Affiliation(s)
- S Thureau
- Département de radiothérapie et de physique médicale, centre Henri-Becquerel, QuantIF EA 4108, université de Rouen, 76000 Rouen, France.
| | - A Briens
- Département de radiothérapie, centre Eugène-Marquis, rue de la Bataille-Flandres-Dunkerque, CS 44229, 35042 Rennes cedex, France
| | - P Decazes
- Département de médecine nucléaire, center Henri-Becquerel, QuantIF EA 4108, université de Rouen, Rouen, France
| | - J Castelli
- Département de radiothérapie, centre Eugène Marquis, rue de la Bataille-Flandres-Dunkerque, CS 44229, 35042 Rennes cedex, France; CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, université de Rennes, 35000 Rennes, France
| | - A Barateau
- Département de radiothérapie, centre Eugène Marquis, rue de la Bataille-Flandres-Dunkerque, CS 44229, 35042 Rennes cedex, France; CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, université de Rennes, 35000 Rennes, France
| | - R Garcia
- Service de physique médicale, institut Sainte-Catherine, 84918 Avignon, France
| | - J Thariat
- Department of radiation oncology, centre François-Baclesse, 14000 Caen, France; Laboratoire de physique corpusculaire IN2P3/ENSICAEN-UMR6534-Unicaen-Normandie université, 14000 Caen, France; ARCHADE Research Community, 14000 Caen, France
| | - R de Crevoisier
- Département de radiothérapie, centre Eugène-Marquis, rue de la Bataille-Flandres-Dunkerque, CS 44229, 35042 Rennes cedex, France; CLCC Eugène Marquis, Inserm, LTSI-UMR 1099, université de Rennes, 35000 Rennes, France
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Vicente E, Modiri A, Kipritidis J, Hagan A, Yu K, Wibowo H, Yan Y, Owen DR, Matuszak MM, Mohindra P, Timmerman R, Sawant A. Functionally weighted airway sparing (FWAS): a functional avoidance method for preserving post-treatment ventilation in lung radiotherapy. Phys Med Biol 2020; 65:165010. [PMID: 32575096 DOI: 10.1088/1361-6560/ab9f5d] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Recent changes to the guidelines for screening and early diagnosis of lung cancer have increased the interest in preserving post-radiotherapy lung function. Current investigational approaches are based on spatially mapping functional regions and generating regional avoidance plans that preferentially spare highly ventilated/perfused lung. A potentially critical, yet overlooked, aspect of functional avoidance is radiation injury to peripheral airways, which serve as gas conduits to and from functional lung regions. Dose redistribution based solely on regional function may cause irreparable damage to the 'supply chain'. To address this deficiency, we propose the functionally weighted airway sparing (FWAS) method. FWAS (i) maps the bronchial pathways to each functional sub-lobar lung volume; (ii) assigns a weighting factor to each airway based on the relative contribution of the sub-volume to overall lung function; and (iii) creates a treatment plan that aims to preserve these functional pathways. To evaluate it, we used four cases from a retrospective cohort of SAbR patients treated for lung cancer. Each patient's airways were auto-segmented from a diagnostic-quality breath-hold CT using a research virtual bronchoscopy software. A ventilation map was generated from the planning 4DCT to map regional lung function. For each terminal airway, as resolved by the segmentation software, the total ventilation within the sub-lobar volume supported by that airway was estimated and used as a function-based weighting factor. Upstream airways were weighted based on the cumulative volumetric ventilation supported by corresponding downstream airways. Using a previously developed model for airway radiosensitivity, dose constraints were determined for each airway corresponding to a <5% probability of airway collapse. Airway dose constraints, ventilation scores, and clinical dose constraints were input to a swarm optimization-based inverse planning engine to create a 3D conformal SAbR plan (CRT). The FWAS plans were compared to the patients' prescribed CRT clinical plans and the inverse-optimized clinical plans. Depending on the size and location of the tumour, the FWAS plan showed superior preservation of ventilation due to airflow preservation through open pathways (i.e. cumulative ventilation score from the sub-lobar volumes of open pathways). Improvements ranged between 3% and 23%, when comparing to the prescribed clinical plans, and between 3% and 35%, when comparing to the inverse-optimized clinical plans. The three plans satisfied clinical requirements for PTV coverage and OAR dose constraints. These initial results suggest that by sparing pathways to high-functioning lung subregions it is possible to reduce post-SAbR loss of respiratory function.
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Affiliation(s)
- E Vicente
- University of Maryland School of Medicine, Baltimore, MD, United States of America
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O’Reilly S, Jain V, Huang Q, Cheng C, Teo BKK, Yin L, Zhang M, Diffenderfer E, Li T, Levin W, Xiao Y, Dong L, Feigenberg S, Berman AT, Zou W. Dose to Highly Functional Ventilation Zones Improves Prediction of Radiation Pneumonitis for Proton and Photon Lung Cancer Radiation Therapy. Int J Radiat Oncol Biol Phys 2020; 107:79-87. [DOI: 10.1016/j.ijrobp.2020.01.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 12/08/2019] [Accepted: 01/10/2020] [Indexed: 12/14/2022]
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Thomas HMT, Zeng J, Lee, Jr HJ, Sasidharan BK, Kinahan PE, Miyaoka RS, Vesselle HJ, Rengan R, Bowen SR. Comparison of regional lung perfusion response on longitudinal MAA SPECT/CT in lung cancer patients treated with and without functional tissue-avoidance radiation therapy. Br J Radiol 2019; 92:20190174. [PMID: 31364397 PMCID: PMC6849661 DOI: 10.1259/bjr.20190174] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 06/28/2019] [Accepted: 07/23/2019] [Indexed: 12/25/2022] Open
Abstract
OBJECTIVE The effect of functional lung avoidance planning on radiation dose-dependent changes in regional lung perfusion is unknown. We characterized dose-perfusion response on longitudinal perfusion single photon emission computed tomography (SPECT)/CT in two cohorts of lung cancer patients treated with and without functional lung avoidance techniques. METHODS The study included 28 primary lung cancer patients: 20 from interventional (NCT02773238) (FLARE-RT) and eight from observational (NCT01982123) (LUNG-RT) clinical trials. FLARE-RT treatment plans included perfused lung dose constraints while LUNG-RT plans adhered to clinical standards. Pre- and 3 month post-treatment macro-aggregated albumin (MAA) SPECT/CT scans were rigidly co-registered to planning four-dimensional CT scans. Tumour-subtracted lung dose was converted to EQD2 and sorted into 5 Gy bins. Mean dose and percent change between pre/post-RT MAA-SPECT uptake (%ΔPERF), normalized to total tumour-subtracted lung uptake, were calculated in each binned dose region. Perfusion frequency histograms of pre/post-RT MAA-SPECT were analyzed. Dose-response data were parameterized by sigmoid logistic functions to estimate maximum perfusion increase (%ΔPERFmaxincrease), maximum perfusion decrease (%ΔPERFmaxdecrease), dose midpoint (Dmid), and dose-response slope (k). RESULTS Differences in MAA perfusion frequency distribution shape between time points were observed in 11/20 (55%) FLARE-RT and 2/8 (25%) LUNG-RT patients (p < 0.05). FLARE-RT dose response was characterized by >10% perfusion increase in the 0-5 Gy dose bin for 8/20 patients (%ΔPERFmaxincrease = 10-40%), which was not observed in any LUNG-RT patients (p = 0.03). The dose midpoint Dmid at which relative perfusion declined by 50% trended higher in FLARE-RT compared to LUNG-RT cohorts (35 GyEQD2 vs 21 GyEQD2, p = 0.09), while the dose-response slope k was similar between FLARE-RT and LUNG-RT cohorts (3.1-3.2, p = 0.86). CONCLUSION Functional lung avoidance planning may promote increased post-treatment perfusion in low dose regions for select patients, though inter-patient variability remains high in unbalanced cohorts. These preliminary findings form testable hypotheses that warrant subsequent validation in larger cohorts within randomized or case-matched control investigations. ADVANCES IN KNOWLEDGE This novel preliminary study reports differences in dose-response relationships between patients receiving functional lung avoidance radiation therapy (FLARE-RT) and those receiving conventionally planned radiation therapy (LUNG-RT). Following further validation and testing of these effects in larger patient populations, individualized estimation of regional lung perfusion dose-response may help refine future risk-adaptive strategies to minimize lung function deficits and toxicity incidence.
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Affiliation(s)
- Hannah Mary T Thomas
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, USA
| | - Jing Zeng
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, USA
| | - Howard J Lee, Jr
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, USA
| | | | - Paul E Kinahan
- Department of Radiology, University of Washington School of Medicine, Seattle, USA
| | - Robert S Miyaoka
- Department of Radiology, University of Washington School of Medicine, Seattle, USA
| | - Hubert J. Vesselle
- Department of Radiology, University of Washington School of Medicine, Seattle, USA
| | - Ramesh Rengan
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, USA
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Mounessi FS, Eckardt J, Holstein A, Ewig S, Könemann S. Image-based lung functional radiotherapy planning for non-small cell lung cancer. Strahlenther Onkol 2019; 196:151-158. [DOI: 10.1007/s00066-019-01518-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 09/10/2019] [Indexed: 12/25/2022]
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Das SK, McGurk R, Miften M, Mutic S, Bowsher J, Bayouth J, Erdi Y, Mawlawi O, Boellaard R, Bowen SR, Xing L, Bradley J, Schoder H, Yin FF, Sullivan DC, Kinahan P. Task Group 174 Report: Utilization of [ 18 F]Fluorodeoxyglucose Positron Emission Tomography ([ 18 F]FDG-PET) in Radiation Therapy. Med Phys 2019; 46:e706-e725. [PMID: 31230358 DOI: 10.1002/mp.13676] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 04/30/2019] [Accepted: 06/06/2019] [Indexed: 02/03/2023] Open
Abstract
The use of positron emission tomography (PET) in radiation therapy (RT) is rapidly increasing in the areas of staging, segmentation, treatment planning, and response assessment. The most common radiotracer is 18 F-fluorodeoxyglucose ([18 F]FDG), a glucose analog with demonstrated efficacy in cancer diagnosis and staging. However, diagnosis and RT planning are different endeavors with unique requirements, and very little literature is available for guiding physicists and clinicians in the utilization of [18 F]FDG-PET in RT. The two goals of this report are to educate and provide recommendations. The report provides background and education on current PET imaging systems, PET tracers, intensity quantification, and current utilization in RT (staging, segmentation, image registration, treatment planning, and therapy response assessment). Recommendations are provided on acceptance testing, annual and monthly quality assurance, scanning protocols to ensure consistency between interpatient scans and intrapatient longitudinal scans, reporting of patient and scan parameters in literature, requirements for incorporation of [18 F]FDG-PET in treatment planning systems, and image registration. The recommendations provided here are minimum requirements and are not meant to cover all aspects of the use of [18 F]FDG-PET for RT.
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Affiliation(s)
- Shiva K Das
- Department of Radiation Oncology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Ross McGurk
- Department of Radiation Oncology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Moyed Miften
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Sasa Mutic
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - James Bowsher
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - John Bayouth
- Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Yusuf Erdi
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Osama Mawlawi
- Department of Imaging Physics, University of Texas, M D Anderson Cancer Center, Houston, TX, USA
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Stephen R Bowen
- Department of Radiation Oncology, University of Washington, Seattle, WA, USA
| | - Lei Xing
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jeffrey Bradley
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Heiko Schoder
- Molecular Imaging and Therapy Service, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Daniel C Sullivan
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Paul Kinahan
- Department of Radiology, University of Washington, Seattle, WA, USA
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Functional perfusion image guided radiation treatment planning for locally advanced lung cancer. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2019; 11:76-81. [PMID: 33458283 PMCID: PMC7807615 DOI: 10.1016/j.phro.2019.08.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 08/28/2019] [Accepted: 08/30/2019] [Indexed: 01/28/2023]
Abstract
Background and purpose Functional avoidance radiation therapy (RT) aims at sparing functional lung regions. The purpose of this simulation study was to evaluate the feasibility of functional lung avoidance methodology in RT of lung cancer and to characterize the achievable dosimetry of single photon emission computed tomography (SPECT) guided treatment planning. Materials and methods Fifteen consecutive lung cancer patients were included and planned for definitive RT of 60–66 Gy in 2-Gy fractions. Two plans were optimized: a standard CT-plan, and functional SPECT-plan. The objective was to reduce dose to the highly functional lung subvolumes without compromising tumour coverage, and respecting dose to other organs at risk. For each patient a 3D-conformal, intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy plans were created for standard and functional avoidance. Standard versus functional dose-volume parameters for functional lung (FL) subvolumes, organs at risk and tumour coverage were compared. Results The largest dose reduction was achieved with IMRT plans. Functional plans resulted in dose reduction from 9.0 Gy to 6.7 Gy (mean reduction of 2.3 Gy or 26%) to the highest functional subvolume FL80% (95%CI 1.1; 3.5). Dose to FL40% was reduced from 13.3 Gy to 11.6 Gy with functional planning. Dose reduction to FL40% was 1.7 Gy (95%CI 0.9; 2.6). Functional volume of lung receiving over 20 Gy improved by 5% (standard 22%, functional 17%). Dose to organs at risk and tumour coverage were not significantly different between plans. Conclusions SPECT/CT-guided planning resulted in improved dose-volumetric outcomes for functional lung. This methodology may lead to potential reduction in radiation-induced lung toxicity.
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Bowen SR, Hippe DS, Chaovalitwongse WA, Duan C, Thammasorn P, Liu X, Miyaoka RS, Vesselle HJ, Kinahan PE, Rengan R, Zeng J. Voxel Forecast for Precision Oncology: Predicting Spatially Variant and Multiscale Cancer Therapy Response on Longitudinal Quantitative Molecular Imaging. Clin Cancer Res 2019; 25:5027-5037. [PMID: 31142507 DOI: 10.1158/1078-0432.ccr-18-3908] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 03/28/2019] [Accepted: 05/17/2019] [Indexed: 12/25/2022]
Abstract
PURPOSE Prediction of spatially variant response to cancer therapies can inform risk-adaptive management within precision oncology. We developed the "Voxel Forecast" multiscale regression framework for predicting spatially variant tumor response to chemoradiotherapy on fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) imaging. EXPERIMENTAL DESIGN Twenty-five patients with locally advanced non-small cell lung cancer, enrolled on the FLARE-RT phase II trial (NCT02773238), underwent FDG PET/CT imaging prior to (PETpre) and during week 3 (PETmid) of concurrent chemoradiotherapy. Voxel Forecast was designed to predict tumor voxel standardized uptake value (SUV) on PETmid from baseline patient-level and voxel-level covariates using a custom generalized least squares (GLS) algorithm. Matérn covariance matrices were fit to patient- specific empirical variograms of distance-dependent intervoxel correlation. Regression coefficients from variogram-based weights and corresponding standard errors were estimated using the jackknife technique. The framework was validated using statistical simulations of known spatially variant tumor response. Mean absolute prediction errors (MAEs) of Voxel Forecast models were calculated under leave-one-patient-out cross-validation. RESULTS Patient-level forecasts resulted in tumor voxel SUV MAE on PETmid of 1.5 g/mL while combined patient- and voxel-level forecasts achieved lower MAE of 1.0 g/mL (P < 0.0001). PETpre voxel SUV was the most important predictor of PETmid voxel SUV. Patients with a greater percentage of under-responding tumor voxels were classified as PETmid nonresponders (P = 0.030) with worse overall survival prognosis (P < 0.001). CONCLUSIONS Voxel Forecast multiscale regression provides a statistical framework to predict voxel-wise response patterns during therapy. Voxel Forecast can be extended to predict spatially variant response on multimodal quantitative imaging and may eventually guide optimized spatial-temporal dose distributions for precision cancer therapy.
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Affiliation(s)
- Stephen R Bowen
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington. .,Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington
| | - Daniel S Hippe
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington
| | - W Art Chaovalitwongse
- Department of Industrial Engineering, University of Arkansas, Fayetteville, Arkansas
| | - Chunyan Duan
- Department of Industrial Engineering, University of Arkansas, Fayetteville, Arkansas.,Department of Management Science and Engineering, Tongji University, Shanghai, China
| | - Phawis Thammasorn
- Department of Industrial Engineering, University of Arkansas, Fayetteville, Arkansas
| | - Xiao Liu
- Department of Industrial Engineering, University of Arkansas, Fayetteville, Arkansas
| | - Robert S Miyaoka
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Hubert J Vesselle
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Paul E Kinahan
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Ramesh Rengan
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington
| | - Jing Zeng
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington
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Zhong Y, Vinogradskiy Y, Chen L, Myziuk N, Castillo R, Castillo E, Guerrero T, Jiang S, Wang J. Technical Note: Deriving ventilation imaging from 4DCT by deep convolutional neural network. Med Phys 2019; 46:2323-2329. [PMID: 30714159 DOI: 10.1002/mp.13421] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 12/20/2018] [Accepted: 01/22/2019] [Indexed: 01/10/2023] Open
Abstract
PURPOSE Ventilation images can be derived from four-dimensional computed tomography (4DCT) by analyzing the change in HU values and deformable vector fields between different respiration phases of computed tomography (CT). As deformable image registration (DIR) is involved, accuracy of 4DCT-derived ventilation image is sensitive to the choice of DIR algorithms. To overcome the uncertainty associated with DIR, we develop a method based on deep convolutional neural network (CNN) to derive ventilation images directly from the 4DCT without explicit image registration. METHODS A total of 82 sets of 4DCT and ventilation images from patients with lung cancer were used in this study. In the proposed CNN architecture, the CT two-channel input data consist of CT at the end of exhale and the end of inhale phases. The first convolutional layer has 32 different kernels of size 5 × 5 × 5, followed by another eight convolutional layers each of which is equipped with an activation layer (ReLU). The loss function is the mean-squared-error (MSE) to measure the intensity difference between the predicted and reference ventilation images. RESULTS The predicted images were comparable to the label images of the test data. The similarity index, correlation coefficient, and Gamma index passing rate averaged over the tenfold cross validation were 0.880 ± 0.035, 0.874 ± 0.024, and 0.806 ± 0.014, respectively. CONCLUSIONS The results demonstrate that deep CNN can generate ventilation imaging from 4DCT without explicit deformable image registration, reducing the associated uncertainty.
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Affiliation(s)
- Yuncheng Zhong
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Liyuan Chen
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Nick Myziuk
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Richard Castillo
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - Edward Castillo
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Thomas Guerrero
- Department of Radiation Oncology, Beaumont Health System, Royal Oak, MI, USA
| | - Steve Jiang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jing Wang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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Ghita M, Dunne V, Hanna GG, Prise KM, Williams JP, Butterworth KT. Preclinical models of radiation-induced lung damage: challenges and opportunities for small animal radiotherapy. Br J Radiol 2019; 92:20180473. [PMID: 30653332 DOI: 10.1259/bjr.20180473] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Despite a major paradigm shift in radiotherapy planning and delivery over the past three decades with continuing refinements, radiation-induced lung damage (RILD) remains a major dose limiting toxicity in patients receiving thoracic irradiations. Our current understanding of the biological processes involved in RILD which includes DNA damage, inflammation, senescence and fibrosis, is based on clinical observations and experimental studies in mouse models using conventional radiation exposures. Whilst these studies have provided vital information on the pulmonary radiation response, the current implementation of small animal irradiators is enabling refinements in the precision and accuracy of dose delivery to mice which can be applied to studies of RILD. This review presents the current landscape of preclinical studies in RILD using small animal irradiators and highlights the challenges and opportunities for the further development of this emerging technology in the study of normal tissue damage in the lung.
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Affiliation(s)
- Mihaela Ghita
- 1 Centre for Cancer Research and Cell Biology, Queen's University Belfast , Belfast , Northern Ireland, UK
| | - Victoria Dunne
- 1 Centre for Cancer Research and Cell Biology, Queen's University Belfast , Belfast , Northern Ireland, UK
| | - Gerard G Hanna
- 1 Centre for Cancer Research and Cell Biology, Queen's University Belfast , Belfast , Northern Ireland, UK.,2 Northern Ireland Cancer Centre, Belfast City Hospital , Belfast , Northern Ireland, UK
| | - Kevin M Prise
- 1 Centre for Cancer Research and Cell Biology, Queen's University Belfast , Belfast , Northern Ireland, UK
| | - Jaqueline P Williams
- 3 University of Rochester Medical Centre, University of Rochester , Rochester , USA
| | - Karl T Butterworth
- 1 Centre for Cancer Research and Cell Biology, Queen's University Belfast , Belfast , Northern Ireland, UK
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Deriving Lung Perfusion Directly from CT Image Using Deep Convolutional Neural Network: A Preliminary Study. ARTIFICIAL INTELLIGENCE IN RADIATION THERAPY 2019. [DOI: 10.1007/978-3-030-32486-5_13] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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The Expanding Role of Physiologic Imaging in Radiation Oncology. Int J Radiat Oncol Biol Phys 2018; 102:694-697. [DOI: 10.1016/j.ijrobp.2018.01.059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 01/16/2018] [Indexed: 11/19/2022]
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Schaub SK, Apisarnthanarax S, Price RG, Nyflot MJ, Chapman TR, Matesan M, Vesselle HJ, Bowen SR. Functional Liver Imaging and Dosimetry to Predict Hepatotoxicity Risk in Cirrhotic Patients With Primary Liver Cancer. Int J Radiat Oncol Biol Phys 2018; 102:1339-1348. [DOI: 10.1016/j.ijrobp.2018.08.029] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Revised: 07/27/2018] [Accepted: 08/18/2018] [Indexed: 12/17/2022]
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Functional lung imaging in radiation therapy for lung cancer: A systematic review and meta-analysis. Radiother Oncol 2018; 129:196-208. [PMID: 30082143 DOI: 10.1016/j.radonc.2018.07.014] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 06/14/2018] [Accepted: 07/18/2018] [Indexed: 12/25/2022]
Abstract
RATIONALE Advanced imaging techniques allow functional information to be derived and integrated into treatment planning. METHODS A systematic review was conducted with the primary objective to evaluate the ability of functional lung imaging to predict risk of radiation pneumonitis. Secondary objectives were to evaluate dose-response relationships on post treatment functional imaging and assess the utility in including functional lung information into treatment planning. A structured search for publications was performed following PRISMA guidelines and registered on PROSPERO. RESULTS 814 articles were screened against review criteria and 114 publications met criteria. Methods of identifying functional lung included using CT, MRI, SPECT and PET to image ventilation or perfusion. Six studies compared differences between functional and anatomical lung imaging at predicting radiation pneumonitis. These found higher predictive values using functional lung imaging. Twenty-one studies identified a dose-response relationship on post-treatment functional lung imaging. Nineteen planning studies demonstrated the ability of functional lung optimised planning techniques to spare regions of functional lung. Meta-analysis of these studies found that mean (95% CI) functional volume receiving 20 Gy was reduced by 4.2% [95% CI: 2.3: 6.0] and mean lung dose by 2.2 Gy [95% CI: 1.2: 3.3] when plans were optimised to spare functional lung. There was significant variation between publications in the definition of functional lung. CONCLUSION Functional lung imaging may have potential utility in radiation therapy planning and delivery, although significant heterogeneity was identified in approaches and reporting. Recommendations have been made based on the available evidence for future functional lung trials.
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Kashani R, Olsen JR. Magnetic Resonance Imaging for Target Delineation and Daily Treatment Modification. Semin Radiat Oncol 2018; 28:178-184. [DOI: 10.1016/j.semradonc.2018.02.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Modeling Patient-Specific Dose-Function Response for Enhanced Characterization of Personalized Functional Damage. Int J Radiat Oncol Biol Phys 2018; 102:1265-1275. [PMID: 30108006 DOI: 10.1016/j.ijrobp.2018.05.049] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 04/25/2018] [Accepted: 05/14/2018] [Indexed: 12/25/2022]
Abstract
PURPOSE Functional-guided radiation therapy (RT) plans have the potential to limit damage to normal tissue and reduce toxicity. Although functional imaging modalities have continued to improve, a limited understanding of the functional response to radiation and its application to personalized therapy has hindered clinical implementation. The purpose of this study was to retrospectively model the longitudinal, patient-specific dose-function response in non-small cell lung cancer patients treated with RT to better characterize the expected functional damage in future, unknown patients. METHODS AND MATERIALS Perfusion single-photon emission computed tomography/computed tomography scans were obtained at baseline (n = 81), midtreatment (n = 74), 3 months post-treatment (n = 51), and 1 year post-treatment (n = 26) and retrospectively analyzed. Patients were treated with conventionally fractionated RT or stereotactic body RT. Normalized perfusion single-photon emission computed tomography voxel intensity was used as a surrogate for local lung function. A patient-specific logistic model was applied to each individual patient's dose-function response to characterize functional reduction at each imaging time point. Patient-specific model parameters were averaged to create a population-level logistic dose-response model. RESULTS A significant longitudinal decrease in lung function was observed after RT by analyzing the voxelwise change in normalized perfusion intensity. Generated dose-function response models represent the expected voxelwise reduction in function, and the associated uncertainty, for an unknown patient receiving conventionally fractionated RT or stereotactic body RT. Differential treatment responses based on the functional status of the voxel at baseline suggest that initially higher functioning voxels are damaged at a higher rate than lower functioning voxels. CONCLUSIONS This study modeled the patient-specific dose-function response in patients with non-small cell lung cancer during and after radiation treatment. The generated population-level dose-function response models were derived from individual patient assessment and have the potential to inform functional-guided treatment plans regarding the expected functional lung damage. This type of patient-specific modeling approach can be applied broadly to other functional response analyses to better capture intrapatient dependencies and characterize personalized functional damage.
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Lee HJ, Zeng J, Vesselle HJ, Patel SA, Rengan R, Bowen SR. Correlation of Functional Lung Heterogeneity and Dosimetry to Radiation Pneumonitis using Perfusion SPECT/CT and FDG PET/CT Imaging. Int J Radiat Oncol Biol Phys 2018; 102:1255-1264. [PMID: 30108002 DOI: 10.1016/j.ijrobp.2018.05.051] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Revised: 04/18/2018] [Accepted: 05/22/2018] [Indexed: 12/25/2022]
Abstract
PURPOSE To apply a previously designed framework for predicting radiation pneumonitis by using pretreatment lung function heterogeneity metrics, anatomic dosimetry, and functional lung dosimetry derived from 2 imaging modalities within the same cohort. METHODS AND MATERIALS Treatment planning computed tomography (CT) scans were co-registered with pretreatment [99mTc] macro-aggregated albumin perfusion single-photon positron emission tomography (SPECT)/CT scans and [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT scans of 28 patients who underwent definitive thoracic radiation. Clinical radiation pneumonitis was defined as grade ≥2 (Common Terminology Criteria for Adverse Events, v. 4). Anatomic dosimetric parameters (mean lung dose [MLD], volume receiving ≥20 Gy [V20]) were collected from treatment planning scans. Baseline functional lung heterogeneity parameters and functional lung dose-volume parameters were calculated from pretreatment SPECT/CT and FDG PET/CT scans. Functional heterogeneity parameters calculated over the tumor-subtracted lung included skewness, kurtosis, and coefficient of variation from perfusion SPECT and FDG PET and the global lung parenchymal glycolysis and mean standardized uptake value from FDG PET. Functional dose-volume parameters calculated in regions of highly functional lung, defined on perfusion (p) or SUV (s) images, included mean lung dose (pMLD, sMLD) and V20 (pV20, sV20). Fraction of integral lung function receiving ≥20 Gy (pF20, sF20) was also calculated. Equivalent doses in 2 Gy per fraction (EQD2) were calculated to account for differences in treatment regimens and dose fractionation (EQD2Lung). RESULTS Two anatomic dosimetric parameters (MLD, V20) and 4 functional dosimetric parameters (pMLD, pV20, pF20, sF20) were significant predictors of grade ≥2 pneumonitis (area under the curve >0.84; P < .05). Dose-independent functional lung heterogeneity metrics were not associated with pneumonitis incidence. At thresholds of 100% sensitivity and 65% to 91% specificity, corresponding to maximum prediction accuracy for pneumonitis, these parameters had the following cutoff values: MLD = 13.6 Gy EQD2Lung, V20 = 25%, pMLD = 13.2 Gy EQD2Lung, pV20 = 15%, pF20 = 17%, and sF20 = 25%. Significant parameters MLD, V20, pF20, and sF20 were not cross-correlated to significant parameters pMLD and pV20, indicating that they may offer independently predictive information (Spearman ρ < 0.7). CONCLUSIONS We reported differences in anatomic and functional lung dosimetry between patients with and without pneumonitis in this limited patient cohort. Adding selected independent functional lung parameters may risk stratify patients for pneumonitis. Validation studies are ongoing in a prospective functional lung avoidance trial at our institution.
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Affiliation(s)
- Howard J Lee
- Duke University School of Medicine, Durham, North Carolina
| | - Jing Zeng
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington
| | - Hubert J Vesselle
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington
| | - Shilpen A Patel
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington
| | - Ramesh Rengan
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington
| | - Stephen R Bowen
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington; Department of Radiology, University of Washington School of Medicine, Seattle, Washington.
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Maes D, Saini J, Zeng J, Rengan R, Wong T, Bowen SR. Advanced proton beam dosimetry part II: Monte Carlo vs. pencil beam-based planning for lung cancer. Transl Lung Cancer Res 2018; 7:114-121. [PMID: 29876310 PMCID: PMC5960654 DOI: 10.21037/tlcr.2018.04.04] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Accepted: 03/28/2018] [Indexed: 12/25/2022]
Abstract
BACKGROUND Proton pencil beam (PB) dose calculation algorithms have limited accuracy within heterogeneous tissues of lung cancer patients, which may be addressed by modern commercial Monte Carlo (MC) algorithms. We investigated clinical pencil beam scanning (PBS) dose differences between PB and MC-based treatment planning for lung cancer patients. METHODS With IRB approval, a comparative dosimetric analysis between RayStation MC and PB dose engines was performed on ten patient plans. PBS gantry plans were generated using single-field optimization technique to maintain target coverage under range and setup uncertainties. Dose differences between PB-optimized (PBopt), MC-recalculated (MCrecalc), and MC-optimized (MCopt) plans were recorded for the following region-of-interest metrics: clinical target volume (CTV) V95, CTV homogeneity index (HI), total lung V20, total lung VRX (relative lung volume receiving prescribed dose or higher), and global maximum dose. The impact of PB-based and MC-based planning on robustness to systematic perturbation of range (±3% density) and setup (±3 mm isotropic) was assessed. Pairwise differences in dose parameters were evaluated through non-parametric Friedman and Wilcoxon sign-rank testing. RESULTS In this ten-patient sample, CTV V95 decreased significantly from 99-100% for PBopt to 77-94% for MCrecalc and recovered to 99-100% for MCopt (P<10-5). The median CTV HI (D95/D5) decreased from 0.98 for PBopt to 0.91 for MCrecalc and increased to 0.95 for MCopt (P<10-3). CTV D95 robustness to range and setup errors improved under MCopt (ΔD95 =-1%) compared to MCrecalc (ΔD95 =-6%, P=0.006). No changes in lung dosimetry were observed for large volumes receiving low to intermediate doses (e.g., V20), while differences between PB-based and MC-based planning were noted for small volumes receiving high doses (e.g., VRX). Global maximum patient dose increased from 106% for PBopt to 109% for MCrecalc and 112% for MCopt (P<10-3). CONCLUSIONS MC dosimetry revealed a reduction in target dose coverage under PB-based planning that was regained under MC-based planning along with improved plan robustness. MC-based optimization and dose calculation should be integrated into clinical planning workflows of lung cancer patients receiving actively scanned proton therapy.
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Affiliation(s)
- Dominic Maes
- Seattle Cancer Care Alliance Proton Therapy Center, Seattle, WA, USA
| | - Jatinder Saini
- Seattle Cancer Care Alliance Proton Therapy Center, Seattle, WA, USA
| | - Jing Zeng
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Ramesh Rengan
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
| | - Tony Wong
- Seattle Cancer Care Alliance Proton Therapy Center, Seattle, WA, USA
| | - Stephen R. Bowen
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA, USA
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
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Thomas HM, Kinahan PE, Samuel JJE, Bowen SR. Impact of tumour motion compensation and delineation methods on FDG PET-based dose painting plan quality for NSCLC radiation therapy. J Med Imaging Radiat Oncol 2017; 62:81-90. [PMID: 29193781 DOI: 10.1111/1754-9485.12693] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Accepted: 10/18/2017] [Indexed: 12/25/2022]
Abstract
INTRODUCTION To quantitatively estimate the impact of different methods for both boost volume delineation and respiratory motion compensation of [18F] FDG PET/CT images on the fidelity of planned non-uniform 'dose painting' plans to the prescribed boost dose distribution. METHODS Six locally advanced non-small cell lung cancer (NSCLC) patients were retrospectively reviewed. To assess the impact of respiratory motion, time-averaged (3D AVG), respiratory phase-gated (4D GATED) and motion-encompassing (4D MIP) PET images were used. The boost volumes were defined using manual contour (MANUAL), fixed threshold (FIXED) and gradient search algorithm (GRADIENT). The dose painting prescription of 60 Gy base dose to the planning target volume and an integral dose of 14 Gy (total 74 Gy) was discretized into seven treatment planning substructures and linearly redistributed according to the relative SUV at every voxel in the boost volume. Fifty-four dose painting plan combinations were generated and conformity was evaluated using quality index VQ0.95-1.05, which represents the sum of planned dose voxels within 5% deviation from the prescribed dose. Trends in plan quality and magnitude of achievable dose escalation were recorded. RESULTS Different segmentation techniques produced statistically significant variations in maximum planned dose (P < 0.02), as well as plan quality between segmentation methods for 4D GATED and 4D MIP PET images (P < 0.05). No statistically significant differences in plan quality and maximum dose were observed between motion-compensated PET-based plans (P > 0.75). Low variability in plan quality was observed for FIXED threshold plans, while MANUAL and GRADIENT plans achieved higher dose with lower plan quality indices. CONCLUSIONS The dose painting plans were more sensitive to segmentation of boost volumes than PET motion compensation in this study sample. Careful consideration of boost target delineation and motion compensation strategies should guide the design of NSCLC dose painting trials.
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Affiliation(s)
- Hannah Mary Thomas
- Department of Radiation Oncology, School of Medicine, University of Washington, Seattle, Washington, USA.,Department of Physics, School of Advanced Sciences, VIT University, Vellore, India
| | - Paul E Kinahan
- Department of Radiology, School of Medicine, University of Washington, Seattle, Washington, USA
| | | | - Stephen R Bowen
- Department of Radiation Oncology, School of Medicine, University of Washington, Seattle, Washington, USA.,Department of Radiology, School of Medicine, University of Washington, Seattle, Washington, USA
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