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Flakus MJ, Wuschner AE, Wallat EM, Graham M, Shao W, Shanmuganayagam D, Christensen GE, Reinhardt JM, Bayouth JE. Validation of CT-based ventilation and perfusion biomarkers with histopathology confirms radiation-induced pulmonary changes in a porcine model. Sci Rep 2023; 13:9377. [PMID: 37296169 PMCID: PMC10256800 DOI: 10.1038/s41598-023-36292-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 05/31/2023] [Indexed: 06/12/2023] Open
Abstract
Imaging biomarkers can assess disease progression or prognoses and are valuable tools to help guide interventions. Particularly in lung imaging, biomarkers present an opportunity to extract regional information that is more robust to the patient's condition prior to intervention than current gold standard pulmonary function tests (PFTs). This regional aspect has particular use in functional avoidance radiation therapy (RT) in which treatment planning is optimized to avoid regions of high function with the goal of sparing functional lung and improving patient quality of life post-RT. To execute functional avoidance, detailed dose-response models need to be developed to identify regions which should be protected. Previous studies have begun to do this, but for these models to be clinically translated, they need to be validated. This work validates two metrics that encompass the main components of lung function (ventilation and perfusion) through post-mortem histopathology performed in a novel porcine model. With these methods validated, we can use them to study the nuanced radiation-induced changes in lung function and develop more advanced models.
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Affiliation(s)
- Mattison J Flakus
- Department of Medical Physics, University of Wisconsin - Madison, Madison, WI, USA.
| | - Antonia E Wuschner
- Department of Medical Physics, University of Wisconsin - Madison, Madison, WI, USA
| | - Eric M Wallat
- Department of Medical Physics, University of Wisconsin - Madison, Madison, WI, USA
| | - Melissa Graham
- Research Animal Resources and Compliance, University of Wisconsin - Madison, Madison, WI, USA
| | - Wei Shao
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - Dhanansayan Shanmuganayagam
- Department of Surgery, University of Wisconsin - Madison, Madison, WI, USA
- Department of Animal and Dairy Sciences, University of Wisconsin - Madison, Madison, WI, USA
| | - Gary E Christensen
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Joseph M Reinhardt
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA
| | - John E Bayouth
- Department of Radiation Medicine, Oregon Health Sciences University, Portland, OR, USA
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2
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Gaudreault M, Korte J, Bucknell N, Jackson P, Sakyanun P, McIntosh L, Woon B, Buteau JP, Hofman MS, Mulcahy T, Kron T, Siva S, Hardcastle N. Comparison of dual-energy CT with positron emission tomography for lung perfusion imaging in patients with non-small cell lung cancer. Phys Med Biol 2023; 68. [PMID: 36623318 DOI: 10.1088/1361-6560/acb198] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 01/09/2023] [Indexed: 01/11/2023]
Abstract
Objective.Functional lung avoidance (FLA) radiotherapy treatment aims to spare lung regions identified as functional from imaging. Perfusion contributes to lung function and can be measured from the determination of pulmonary blood volume (PBV). An advantageous alternative to the current determination of PBV from positron emission tomography (PET) may be from dual energy CT (DECT), due to shorter examination time and widespread availability. This study aims to determine the correlation between PBV determined from DECT and PET in the context of FLA radiotherapy.Approach.DECT and PET acquisitions at baseline of patients enrolled in the HI-FIVE clinical trial (ID: NCT03569072) were reviewed. Determination of PBV from PET imaging (PBVPET), from DECT imaging generated from a commercial software (Syngo.via, Siemens Healthineers, Forchheim, Germany) with its lowest (PBVsyngoR=1) and highest (PBVsyngoR=10) smoothing level parameter value (R), and from a two-material decomposition (TMD) method (PBVTMDL) with variable median filter kernel size (L) were compared. Deformable image registration between DECT images and the CT component of the PET/CT was applied to PBV maps before resampling to the PET resolution. The Spearman correlation coefficient (rs) between PBV determinations was calculated voxel-wise in lung subvolumes.Main results.Of this cohort of 19 patients, 17 had a DECT acquisition at baseline. PBV maps determined from the commercial software and the TMD method were very strongly correlated [rs(PBVsyngoR=1,PBVTMDL=1) = 0.94 ± 0.01 andrs(PBVsyngoR=10,PBVTMDL=9) = 0.94 ± 0.02].PBVPETwas strongly correlated withPBVTMDL[rs(PBVPET,PBVTMDL=28) = 0.67 ± 0.11]. Perfusion patterns differed along the posterior-anterior direction [rs(PBVPET,PBVTMDL=28) = 0.77 ± 0.13/0.57 ± 0.16 in the anterior/posterior region].Significance. A strong correlation between DECT and PET determination of PBV was observed. Streak and smoothing effects in DECT and gravitational artefacts and misregistration in PET reduced the correlation posteriorly.
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Affiliation(s)
- Mathieu Gaudreault
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia
| | - James Korte
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia.,Department of Biomedical Engineering, School of Chemical and Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
| | - Nicholas Bucknell
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia.,Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia
| | - Price Jackson
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia
| | - Pitchaya Sakyanun
- Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia.,Department of Radiation Oncology, Phramongkutklao Hospital, Bangkok, Thailand
| | - Lachlan McIntosh
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia
| | - Beverley Woon
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia.,Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia
| | - James P Buteau
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia.,Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia.,Molecular Imaging and Therapeutic Nuclear Medicine; Prostate Cancer Theranostics and Imaging Centre of Excellence (ProsTIC) , Peter MacCallum Cancer Centre, Melbourne, Victoria, 3000, Australia
| | - Michael S Hofman
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia.,Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia.,Molecular Imaging and Therapeutic Nuclear Medicine; Prostate Cancer Theranostics and Imaging Centre of Excellence (ProsTIC) , Peter MacCallum Cancer Centre, Melbourne, Victoria, 3000, Australia
| | - Tony Mulcahy
- Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia
| | - Tomas Kron
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia.,Centre for Medical Radiation Physics, University of Wollongong, NSW, 2522, Australia
| | - Shankar Siva
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia.,Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia
| | - Nicholas Hardcastle
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria 3000, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria 3000, Australia.,Centre for Medical Radiation Physics, University of Wollongong, NSW, 2522, Australia
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3
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Gao Y, Wang Z, Cui Y, Xu M, Weng L. Emerging Strategies of Engineering and Tracking Dendritic Cells for Cancer Immunotherapy. ACS APPLIED BIO MATERIALS 2023; 6:24-43. [PMID: 36520013 DOI: 10.1021/acsabm.2c00790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Dendritic cells (DCs), a kind of specialized immune cells, play key roles in antitumor immune response and promotion of innate and adaptive immune responses. Recently, many strategies have been developed to utilize DCs in cancer therapy, such as delivering antigens and adjuvants to DCs and using scaffold to recruit and activate DCs. Here we outline how different DC subsets influence antitumor immunity, summarize the FDA-approved vaccines and cancer vaccines under clinical trials, discuss the strategies for engineering DCs and noninvasive tracking of DCs to improve antitumor immunotherapy, and reveal the potential of artificial neural networks for the design of DC based vaccines.
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Affiliation(s)
- Yu Gao
- State Key Laboratory for Organic Electronics and Information Displays & Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Jiangsu National Synergistic Innovation Center for Advanced Materials (SICAM), Nanjing University of Posts and Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Zhixuan Wang
- School of Geography and Biological Information, Nanjing University of Posts and Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Ying Cui
- School of Geography and Biological Information, Nanjing University of Posts and Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Miaomiao Xu
- School of Geography and Biological Information, Nanjing University of Posts and Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Lixing Weng
- State Key Laboratory for Organic Electronics and Information Displays & Jiangsu Key Laboratory for Biosensors, Institute of Advanced Materials (IAM), Jiangsu National Synergistic Innovation Center for Advanced Materials (SICAM), Nanjing University of Posts and Telecommunications, 9 Wenyuan Road, Nanjing 210023, China.,School of Geography and Biological Information, Nanjing University of Posts and Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
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4
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Wuschner AE, Flakus MJ, Wallat EM, Reinhardt JM, Shanmuganayagam D, Christensen GE, Gerard SE, Bayouth JE. CT-derived vessel segmentation for analysis of post-radiation therapy changes in vasculature and perfusion. Front Physiol 2022; 13:1008526. [PMID: 36324304 PMCID: PMC9619090 DOI: 10.3389/fphys.2022.1008526] [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: 07/31/2022] [Accepted: 10/05/2022] [Indexed: 11/22/2022] Open
Abstract
Vessel segmentation in the lung is an ongoing challenge. While many methods have been able to successfully identify vessels in normal, healthy, lungs, these methods struggle in the presence of abnormalities. Following radiotherapy, these methods tend to identify regions of radiographic change due to post-radiation therapytoxicities as vasculature falsely. By combining texture analysis and existing vasculature and masking techniques, we have developed a novel vasculature segmentation workflow that improves specificity in irradiated lung while preserving the sensitivity of detection in the rest of the lung. Furthermore, radiation dose has been shown to cause vascular injury as well as reduce pulmonary function post-RT. This work shows the improvements our novel vascular segmentation method provides relative to existing methods. Additionally, we use this workflow to show a dose dependent radiation-induced change in vasculature which is correlated with previously measured perfusion changes (R2 = 0.72) in both directly irradiated and indirectly damaged regions of perfusion. These results present an opportunity to extend non-contrast CT-derived models of functional change following radiation therapy.
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Affiliation(s)
- Antonia E. Wuschner
- Department of Medical Physics, University of Wisconsin, Madison, WI, United States
- *Correspondence: Antonia E. Wuschner,
| | - Mattison J. Flakus
- Department of Medical Physics, University of Wisconsin, Madison, WI, United States
| | - Eric M. Wallat
- Department of Medical Physics, University of Wisconsin, Madison, WI, United States
| | - Joseph M. Reinhardt
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa, IA, United States
| | | | - Gary E Christensen
- Department of Electrical and Computer Engineering, University of Iowa, Iowa, IA, United States
- Department of Radiation Oncology, University of Iowa, Iowa, IA, United States
| | - Sarah E. Gerard
- Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa, IA, United States
| | - John E. Bayouth
- Department of Radiation Medicine, Oregon Health Sciences University, Portland, OR, United States
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5
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Wuschner AE, Flakus MJ, Wallat EM, Reinhardt JM, Shanmuganayagam D, Christensen GE, Bayouth JE. Measuring Indirect Radiation-Induced Perfusion Change in Fed Vasculature Using Dynamic Contrast CT. J Pers Med 2022; 12:jpm12081254. [PMID: 36013203 PMCID: PMC9410208 DOI: 10.3390/jpm12081254] [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] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 12/13/2022] Open
Abstract
Recent functional lung imaging studies have presented evidence of an “indirect effect” on perfusion damage, where regions that are unirradiated or lowly irradiated but that are supplied by highly irradiated regions observe perfusion damage post-radiation therapy (RT). The purpose of this work was to investigate this effect using a contrast-enhanced dynamic CT protocol to measure perfusion change in five novel swine subjects. A cohort of five Wisconsin Miniature Swine (WMS) were given a research course of 60 Gy in five fractions delivered locally to a vessel in the lung using an Accuray Radixact tomotherapy system with Synchrony motion tracking to increase delivery accuracy. Imaging was performed prior to delivering RT and 3 months post-RT to yield a 28−36 frame image series showing contrast flowing in and out of the vasculature. Using MIM software, contours were placed in six vessels on each animal to yield a contrast flow curve for each vessel. The contours were placed as follows: one at the point of max dose, one low-irradiated (5−20 Gy) branching from the max dose vessel, one low-irradiated (5−20 Gy) not branching from the max dose vessel, one unirradiated (<5 Gy) branching from the max dose vessel, one unirradiated (<5 Gy) not branching from the max dose vessel, and one in the contralateral lung. Seven measurements (baseline-to-baseline time and difference, slope up and down, max rise and value, and area under the curve) were acquired for each vessel’s contrast flow curve in each subject. Paired Student t-tests showed statistically significant (p < 0.05) reductions in the area under the curve in the max dose, and both fed contours indicating an overall reduction in contrast in these regions. Additionally, there were statistically significant reductions observed when comparing pre- and post-RT in slope up and down in the max dose, low-dose fed, and no-dose fed contours but not the low-dose not-fed, no-dose not-fed, or contralateral contours. These findings suggest an indirect damage effect where irradiation of the vasculature causes a reduction in perfusion in irradiated regions as well as regions fed by the irradiated vasculature.
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Affiliation(s)
- Antonia E. Wuschner
- University of Wisconsin, Madison, WI 53706, USA; (M.J.F.); (E.M.W.); (D.S.); (J.E.B.)
- Correspondence:
| | - Mattison J. Flakus
- University of Wisconsin, Madison, WI 53706, USA; (M.J.F.); (E.M.W.); (D.S.); (J.E.B.)
| | - Eric M. Wallat
- University of Wisconsin, Madison, WI 53706, USA; (M.J.F.); (E.M.W.); (D.S.); (J.E.B.)
| | | | | | | | - John E. Bayouth
- University of Wisconsin, Madison, WI 53706, USA; (M.J.F.); (E.M.W.); (D.S.); (J.E.B.)
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6
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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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Borghetti P, Guerini AE, Sangalli C, Piperno G, Franceschini D, La Mattina S, Arcangeli S, Filippi AR. Unmet needs in the management of unresectable stage III non-small cell lung cancer: a review after the 'Radio Talk' webinars. Expert Rev Anticancer Ther 2022; 22:549-559. [PMID: 35450510 DOI: 10.1080/14737140.2022.2069098] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Stage III non-small cell lung cancer (NSCLC) is a variable entity, encompassing bulky primary tumors, nodal involvement or both. Multidisciplinary evaluation is essential to discuss multiple treatment options, to outline optimal management and to examine the main debated topics and critical issues not addressed by current trials and guidelines that influence daily clinical practice. AREAS COVERED From March to May 2021, 5 meetings were scheduled in a webinar format titled 'Radio Talk' due to the COVID-19 pandemic; the faculty was composed of 6 radiation oncologists from 6 different Institutions of Italy, all of them were the referring radiation oncologist for lung cancer treatment at their respective departments and were or had been members of AIRO (Italian Association of Radiation Oncology) Thoracic Oncology Study Group. The topics covered included: pulmonary toxicity, cardiac toxicity, radiotherapy dose, fractionation and volumes, unfit/elderly patients, multidisciplinary management. EXPERT OPINION The debate was focused on the unmet needs triggered by case reports, personal experiences and questions; the answers were often not univocal, however, the exchange of opinion and the contribution of different centers confirmed the role of multidisciplinary management and the necessity that the most critical issues should be investigated in clinical trials.
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Affiliation(s)
- Paolo Borghetti
- Department of Radiation Oncology, University and Spedali Civili Hospital, Piazzale Spedali Civili 1, 25123, Brescia, Italy
| | - Andrea Emanuele Guerini
- Department of Radiation Oncology, University and Spedali Civili Hospital, Piazzale Spedali Civili 1, 25123, Brescia, Italy
| | - Claudia Sangalli
- Department of Radiation Oncology 1, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Gaia Piperno
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Davide Franceschini
- Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Salvatore La Mattina
- Department of Radiation Oncology, University and Spedali Civili Hospital, Piazzale Spedali Civili 1, 25123, Brescia, Italy
| | - Stefano Arcangeli
- Department of Radiation Oncology, School of Medicine and Surgery, University of Milan Bicocca, Milan, Italy
| | - Andrea Riccardo Filippi
- Department of Radiation Oncology, Fondazione IRCCS Policlinico San Matteo and University of Pavia, Pavia, Italy
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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: 4] [Impact Index Per Article: 1.3] [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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9
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Porter EM, Myziuk NK, Quinn TJ, Lozano D, Peterson AB, Quach DM, Siddiqui ZA, Guerrero TM. Synthetic pulmonary perfusion images from 4DCT for functional avoidance using deep learning. Phys Med Biol 2021; 66. [PMID: 34293726 DOI: 10.1088/1361-6560/ac16ec] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 07/22/2021] [Indexed: 01/14/2023]
Abstract
Purpose.To develop and evaluate the performance of a deep learning model to generate synthetic pulmonary perfusion images from clinical 4DCT images for patients undergoing radiotherapy for lung cancer.Methods. A clinical data set of 58 pre- and post-radiotherapy99mTc-labeled MAA-SPECT perfusion studies (32 patients) each with contemporaneous 4DCT studies was collected. Using the inhale and exhale phases of the 4DCT, a 3D-residual network was trained to create synthetic perfusion images utilizing the MAA-SPECT as ground truth. The training process was repeated for a 50-imaging study, five-fold validation with twenty model instances trained per fold. The highest performing model instance from each fold was selected for inference upon the eight-study test set. A manual lung segmentation was used to compute correlation metrics constrained to the voxels within the lungs. From the pre-treatment test cases (N = 5), 50th percentile contours of well-perfused lung were generated from both the clinical and synthetic perfusion images and the agreement was quantified.Results. Across the hold-out test set, our deep learning model predicted perfusion with a Spearman correlation coefficient of 0.70 (IQR: 0.61-0.76) and a Pearson correlation coefficient of 0.66 (IQR: 0.49-0.73). The agreement of the functional avoidance contour pairs was Dice of 0.803 (IQR: 0.750-0.810) and average surface distance of 5.92 mm (IQR: 5.68-7.55).Conclusion. We demonstrate that from 4DCT alone, a deep learning model can generate synthetic perfusion images with potential application in functional avoidance treatment planning.
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Affiliation(s)
- Evan M Porter
- Department of Medical Physics, Wayne State University, Detroit, MI, United States of America.,Beaumont Artificial Intelligence Research Laboratory, Beaumont Health, Royal Oak, MI, United States of America.,Department of Radiation Oncology, UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America
| | - Nicholas K Myziuk
- Beaumont Artificial Intelligence Research Laboratory, Beaumont Health, Royal Oak, MI, United States of America.,Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, United States of America
| | - Thomas J Quinn
- Beaumont Artificial Intelligence Research Laboratory, Beaumont Health, Royal Oak, MI, United States of America.,Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, United States of America
| | - Daniela Lozano
- Beaumont Artificial Intelligence Research Laboratory, Beaumont Health, Royal Oak, MI, United States of America.,Oakland University William Beaumont School of Medicine, Oakland University, Rochester, MI, United States of America
| | - Avery B Peterson
- Department of Medical Physics, Wayne State University, Detroit, MI, United States of America.,Beaumont Artificial Intelligence Research Laboratory, Beaumont Health, Royal Oak, MI, United States of America
| | - Duyen M Quach
- Beaumont Artificial Intelligence Research Laboratory, Beaumont Health, Royal Oak, MI, United States of America.,Oakland University William Beaumont School of Medicine, Oakland University, Rochester, MI, United States of America
| | - Zaid A Siddiqui
- Beaumont Artificial Intelligence Research Laboratory, Beaumont Health, Royal Oak, MI, United States of America.,Department of Radiation Oncology, UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America
| | - Thomas M Guerrero
- Beaumont Artificial Intelligence Research Laboratory, Beaumont Health, Royal Oak, MI, United States of America.,Department of Radiation Oncology, Beaumont Health, Royal Oak, MI, United States of America.,Oakland University William Beaumont School of Medicine, Oakland University, Rochester, MI, United States of America
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10
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Utsumi N, Takahashi T, Hatanaka S, Hariu M, Saito M, Kondo S, Soda R, Nishimura K, Yamano T, Watanabe W, Shimbo M, Honda N. VMAT Planning With Xe-CT Functional Images Enables Radiotherapy Planning With Consideration of Lung Function. CANCER DIAGNOSIS & PROGNOSIS 2021; 1:193-200. [PMID: 35399314 PMCID: PMC8962790 DOI: 10.21873/cdp.10026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 07/25/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND/AIM The most severe adverse event of radiotherapy in lung cancer is radiation pneumonitis (RP). Some indices commonly used to prevent RP are evaluated based on the anatomical lung volume. The irradiation dose may be more accurately assessed by using functional lung volume. We evaluated the usefulness of computed tomography (CT) incorporating functional ventilation images acquired by the inhalation of xenon (Xe) gas (Xe-CT functional images). PATIENTS AND METHODS Two plans were created for twelve patients: volumetric modulated arc therapy (VMAT) planning using conventional chest CT images (anatomical plans) and VMAT planning using Xe-CT functional images (functional plans), and the dosimetric parameters were compared. RESULTS Compared to the anatomical plans, the functional plans had significantly reduced V 20Gy in the high-functional lungs (p=0.005), but significant differences were not seen in the moderate-functional and low-functional lungs. CONCLUSION The incorporation of Xe-CT functional images into VMAT plans enables radiotherapy planning with consideration of lung function.
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Affiliation(s)
- Nobuko Utsumi
- Department of Radiation Oncology, Saitama Medical Center, Saitama Medical University, Saitama, Japan
- Department of Radiation Therapy, JCHO Tokyo Shinjuku Medical Center, Tokyo, Japan
| | - Takeo Takahashi
- Department of Radiation Oncology, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Shogo Hatanaka
- Department of Radiation Oncology, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Masatsugu Hariu
- Department of Radiation Oncology, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Mio Saito
- Department of Radiation Oncology, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Shuichi Kondo
- Department of Radiation Oncology, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Rikana Soda
- Department of Radiation Oncology, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Keiichiro Nishimura
- Department of Radiation Oncology, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Takafumi Yamano
- Department of Radiation Oncology, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Wataru Watanabe
- Department of Radiology, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Munefumi Shimbo
- Department of Radiation Oncology, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Norinari Honda
- Department of Radiology, Saitama Sekishinkai Hospital, Saitama, Japan
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11
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Mohan V, Bruin NM, van de Kamer JB, Sonke JJ, Vogel WV. The increasing potential of nuclear medicine imaging for the evaluation and reduction of normal tissue toxicity from radiation treatments. Eur J Nucl Med Mol Imaging 2021; 48:3762-3775. [PMID: 33687522 PMCID: PMC8484246 DOI: 10.1007/s00259-021-05284-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 02/24/2021] [Indexed: 11/26/2022]
Abstract
Radiation therapy is an effective treatment modality for a variety of cancers. Despite several advances in delivery techniques, its main drawback remains the deposition of dose in normal tissues which can result in toxicity. Common practices of evaluating toxicity, using questionnaires and grading systems, provide little underlying information beyond subjective scores, and this can limit further optimization of treatment strategies. Nuclear medicine imaging techniques can be utilised to directly measure regional baseline function and function loss from internal/external radiation therapy within normal tissues in an in vivo setting with high spatial resolution. This can be correlated with dose delivered by radiotherapy techniques to establish objective dose-effect relationships, and can also be used in the treatment planning step to spare normal tissues more efficiently. Toxicity in radionuclide therapy typically occurs due to undesired off-target uptake in normal tissues. Molecular imaging using diagnostic analogues of therapeutic radionuclides can be used to test various interventional protective strategies that can potentially reduce this normal tissue uptake without compromising tumour uptake. We provide an overview of the existing literature on these applications of nuclear medicine imaging in diverse normal tissue types utilising various tracers, and discuss its future potential.
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Affiliation(s)
- V Mohan
- Department of Nuclear Medicine, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - N M Bruin
- Department of Nuclear Medicine, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - J B van de Kamer
- Department of Nuclear Medicine, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - J-J Sonke
- Department of Nuclear Medicine, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Wouter V Vogel
- Department of Nuclear Medicine, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
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12
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Kai Y, Toya R, Saito T, Matsuyama T, Fukugawa Y, Shiraishi S, Shimohigashi Y, Oya N. Stereotactic Body Radiotherapy Based on 99mTc-GSA SPECT Image-guided Inverse Planning for Hepatocellular Carcinoma. In Vivo 2020; 34:3583-3588. [PMID: 33144471 DOI: 10.21873/invivo.12202] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 09/24/2020] [Accepted: 09/25/2020] [Indexed: 12/29/2022]
Abstract
BACKGROUND/AIM A recent planning study suggested that 99mTc-labelled diethylene triamine pentaacetate-galactosyl human serum albumin (99mTc-GSA) single-photon emission computed tomography (SPECT) image-guided inverse planning (IGIP) shows dosimetric superiority to conventional planning in sparing liver function. Here, we report the first clinical translation of 99mTc-GSA SPECT IGIP for stereotactic body radiotherapy (SBRT) in a patient with hepatocellular carcinoma (HCC). CASE REPORT A 60-year-old male developed obstructive jaundice caused by recurrent HCC in segment 1 after hepatic resection. He underwent repeated radiotherapy (RT) consisting of 45 Gy in 15 fractions 8 years ago and 30 Gy in 5 fractions 2 years ago. We performed SBRT consisting of 40 Gy in 8 fractions using 99mTc-GSA SPECT-IGIP. We confirmed the dosimetric superiority of functional IGIP to conventional planning. He achieved complete response as assessed using the target volume. The patient has remained alive without recurrence for 18 months. He did not experience radiation-induced liver disease. CONCLUSION Recurrent HCC was successfully and safely salvaged via re-irradiation with SBRT using 99mTc-GSA SPECT-IGIP.
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Affiliation(s)
- Yudai Kai
- Department of Radiological Technology, Kumamoto University Hospital, Kumamoto, Japan
| | - Ryo Toya
- Department of Radiation Oncology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Tetsuo Saito
- Department of Radiation Oncology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Tomohiko Matsuyama
- Department of Radiation Oncology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Yoshiyuki Fukugawa
- Department of Radiation Oncology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Shinya Shiraishi
- Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | | | - Natsuo Oya
- Department of Radiation Oncology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
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13
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Lee SJ, Park HJ. Single photon emission computed tomography (SPECT) or positron emission tomography (PET) imaging for radiotherapy planning in patients with lung cancer: a meta-analysis. Sci Rep 2020; 10:14864. [PMID: 32913277 PMCID: PMC7483712 DOI: 10.1038/s41598-020-71445-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 08/13/2020] [Indexed: 12/16/2022] Open
Abstract
Functional imaging modalities enable practitioners to identify functional lung regions. This analysis evaluated the feasibility of nuclear medicine imaging to avoid doses to the functional lung in radiotherapy (RT) planning for patients with lung cancer. This systematic review and meta-analysis was carried out according to PRISMA-P guidelines. A search of EMBASE and PubMed for studies published throughout the last 20 years was performed using the following search criteria: (a) ‘lung cancer’ or ‘lung malignancy’ and (b) ‘radiotherapy’ or ‘radiation therapy’ or ‘RT planning’ and (c) ‘SPECT’ or ‘single positron emission computed tomography’ or ‘functional image.’ The analyzed planning parameters were the volumes of the normal lung that have received ≥ 10 Gy and ≥ 20 Gy of radiation (V10 and V20, respectively) and the mean lung dose (MLD). We compared the planning parameters obtained from anatomical RT planning and functional RT planning using perfusion or ventilation imaging (‘V10, V20 or MLD’ in anatomical plan vs. ‘fV10, fV20 or fMLD’ in functional plan). A total of 309 patients with 344 RT plan sets from 15 publications (11 perfusion SPECT, 2 ventilation SPECT, and 1 SPECT and 1 PET with both perfusion and ventilation) were included in the meta-analysis. The standard mean differences in planning parameters in functional plans using nuclear imaging were significantly reduced compared to those of anatomical plans (P < 0.01 for all): − 0.42 (95% confidence interval (CI) − 0.78 to − 0.07) for ‘V10 vs. fV10′, − 0.41 (95% CI − 0.64 to − 0.17) for ‘V20 vs. fV20′, and − 0.24 (95% CI − 0.45 to − 0.03) for ‘MLD vs. fMLD’. In subgroup analysis, the functional plan using perfusion was significantly lower than the anatomical plan in all planning parameters, but there was no significant difference for ventilation. RT planning with nuclear functional lung imaging has potential to reduce radiation-induced lung injury. Perfusion imaging seems to be more promising than ventilation imaging for all planning parameters. There were not enough studies using ventilation imaging to determine what the effect is on the lung plan parameters.
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Affiliation(s)
- Soo Jin Lee
- Department of Nuclear Medicine, Hanyang University Medical Center, 222-1 Wangsimni-ro, Seongdong-gu, Seoul, 04763, South Korea
| | - Hae Jin Park
- Department of Radiation Oncology, Hanyang University College of Medicine, 222-1 Wangsimni-ro, Seongdong-gu, Seoul, 04763, South Korea.
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