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Mercantepe F, Tumkaya L, Mercantepe T, Rakici S. Histopathological evaluation of the effects of dexmedetomidine against pituitary damage ınduced by X-ray irradiation. Biomarkers 2023; 28:168-176. [PMID: 36453587 DOI: 10.1080/1354750x.2022.2154385] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Background: The present study, aimed to investigate the potential negative effects of x-ray radiation and the effects of the α2-adrenergic receptor agonist dexmedetomidine on the pituitary gland.Methods: Twenty-four Sprague-Dawley rats were divided into three groups: Rats in Group 1 (control group). Group 2 (X-ray irradiation) and group 3 (X-ray irradiation + Dexmedetomidine) were given a total of 10 Gy external beam total body irradiation. Group 3 was given a single intraperitoneal dose of 200 µg/kg dexmedetomidine 30 minutes before RT.Results: In sections obtained from the x-ray irradiation group, we observed many necrotic in adenohypophysis and neurohypophysis. In addition, there were extensive oedematous areas and vascular congestions due to the necrotic cells in both the adenohypophysis and neurohypophysis. In contrast, we observed a reduction in necrotic chromophobic and chromophilic cells in adenohypophyseal tissue and a reduction in necrotic pituicytes in neurohypophyseal tissue in the dexmedetomidine treatment group. In addition, we determined lower caspase-3 and TUNEL expression in the dexmedetomidine treatment group compared with the x-ray irradiation group. Dexmedetomidine reduced x-ray radiation-induced pituitary damage by preventing apoptosis.Conclusions: The present study demonstrated the use of dexmedetomidine in situations related to radiation toxicity and offers the potential for a comprehensive study.
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Affiliation(s)
- Filiz Mercantepe
- Department of Endocrinology and Metabolism Diseases, Faculty of Medicine, Recep Tayyip Erdogan University, Rize, Turkey
| | - Levent Tumkaya
- Department of Histology and Embryology, Faculty of Medicine, Recep Tayyip Erdogan University, Rize, Turkey
| | - Tolga Mercantepe
- Department of Histology and Embryology, Faculty of Medicine, Recep Tayyip Erdogan University, Rize, Turkey
| | - Sema Rakici
- Department of Radiation Oncology, Faculty of Medicine, Recep Tayyip Erdogan University, Rize, Turkey
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2
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Owen DR, Sun Y, Irrer JC, Schipper MJ, Schonewolf CA, Galbán S, Jolly S, Haken RKT, Galbán C, Matuszak M. Investigating the Incidence of Pulmonary Abnormalities as Identified by Parametric Response Mapping in Lung Cancer Patients Prior to Radiation Treatment. Adv Radiat Oncol 2022; 7:100980. [PMID: 35693252 PMCID: PMC9184868 DOI: 10.1016/j.adro.2022.100980] [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: 06/13/2021] [Accepted: 12/14/2021] [Indexed: 12/02/2022] Open
Abstract
Purpose Parametric response mapping (PRM) of high-resolution, paired inspiration and expiration computed tomography (CT) scans is a promising analytical imaging technique that is currently used in diagnostic applications and offers the ability to characterize and quantify certain pulmonary pathologies on a patient-specific basis. As one of the first studies to implement such a technique in the radiation oncology clinic, the goal of this work was to assess the feasibility for PRM analysis to identify pulmonary abnormalities in patients with lung cancer before radiation therapy (RT). Methods and Materials High-resolution, paired inspiration and expiration CT scans were acquired from 23 patients with lung cancer as part of routine treatment planning CT acquisition. When applied to the paired CT scans, PRM analysis classifies lung parenchyma, on a voxel-wise basis, as normal, small airways disease (SAD), emphysema, or parenchymal disease (PD). PRM classifications were quantified as a percent of total lung volume and were evaluated globally and regionally within the lung. Results PRM analysis of pre-RT CT scans was successfully implemented using a workflow that produced patient-specific maps and quantified specific phenotypes of pulmonary abnormalities. Through this study, a large prevalence of SAD and PD was demonstrated in this lung cancer patient population, with global averages of 10% and 17%, respectively. Moreover, PRM-classified normal and SAD in the region with primary tumor involvement were found to be significantly different from global lung values. When present, elevated levels of PD and SAD abnormalities tended to be pervasive in multiple regions of the lung, indicating a large burden of underlying disease. Conclusions Pulmonary abnormalities, as detected by PRM, were characterized in patients with lung cancer scheduled for RT. Although further study is needed, PRM is a highly accessible CT-based imaging technique that has the potential to identify local lung abnormalities associated with chronic obstructive pulmonary disease and interstitial lung disease. Further investigation in the radiation oncology setting may provide strategies for tailoring RT planning and risk assessment based on pre-existing PRM-based pathology.
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Affiliation(s)
- Daniel R. Owen
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
- Corresponding author: Daniel 'Rocky' Owen, PhD
| | - Yilun Sun
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
- Departments of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Jim C. Irrer
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | | | | | - Stefanie Galbán
- Departments of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Shruti Jolly
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | | | - C.J. Galbán
- Departments of Radiology, University of Michigan, Ann Arbor, Michigan
| | - M.M. Matuszak
- Departments of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
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Chandy E, Szmul A, Stavropoulou A, Jacob J, Veiga C, Landau D, Wilson J, Gulliford S, Fenwick JD, Hawkins MA, Hiley C, McClelland JR. Quantitative Analysis of Radiation-Associated Parenchymal Lung Change. Cancers (Basel) 2022; 14:946. [PMID: 35205693 PMCID: PMC8870325 DOI: 10.3390/cancers14040946] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 02/07/2022] [Accepted: 02/08/2022] [Indexed: 02/01/2023] Open
Abstract
We present a novel classification system of the parenchymal features of radiation-induced lung damage (RILD). We developed a deep learning network to automate the delineation of five classes of parenchymal textures. We quantify the volumetric change in classes after radiotherapy in order to allow detailed, quantitative descriptions of the evolution of lung parenchyma up to 24 months after RT, and correlate these with radiotherapy dose and respiratory outcomes. Diagnostic CTs were available pre-RT, and at 3, 6, 12 and 24 months post-RT, for 46 subjects enrolled in a clinical trial of chemoradiotherapy for non-small cell lung cancer. All 230 CT scans were segmented using our network. The five parenchymal classes showed distinct temporal patterns. Moderate correlation was seen between change in tissue class volume and clinical and dosimetric parameters, e.g., the Pearson correlation coefficient was ≤0.49 between V30 and change in Class 2, and was 0.39 between change in Class 1 and decline in FVC. The effect of the local dose on tissue class revealed a strong dose-dependent relationship. Respiratory function measured by spirometry and MRC dyspnoea scores after radiotherapy correlated with the measured radiological RILD. We demonstrate the potential of using our approach to analyse and understand the morphological and functional evolution of RILD in greater detail than previously possible.
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Affiliation(s)
- Edward Chandy
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
- UCL Cancer Institute, University College London, London WC1E 6BT, UK; (D.L.); (C.H.)
- Sussex Cancer Centre, Royal Sussex County Hospital, Brighton BN2 5BE, UK
| | - Adam Szmul
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
| | - Alkisti Stavropoulou
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
| | - Joseph Jacob
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
- UCL Respiratory Department, University College London Hospital, London NW1 2PG, UK
| | - Catarina Veiga
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
| | - David Landau
- UCL Cancer Institute, University College London, London WC1E 6BT, UK; (D.L.); (C.H.)
| | - James Wilson
- Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (J.W.); (S.G.); (M.A.H.)
| | - Sarah Gulliford
- Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (J.W.); (S.G.); (M.A.H.)
| | - John D. Fenwick
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 3GE, UK;
| | - Maria A. Hawkins
- Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (J.W.); (S.G.); (M.A.H.)
| | - Crispin Hiley
- UCL Cancer Institute, University College London, London WC1E 6BT, UK; (D.L.); (C.H.)
| | - Jamie R. McClelland
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; (A.S.); (A.S.); (J.J.); (C.V.); (J.R.M.)
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Feng A, Shao Y, Wang H, Chen H, Gu H, Duan Y, Gan W, Xu Z. A novel lung-avoidance planning strategy based on 4DCT ventilation imaging and CT density characteristics for stage III non-small-cell lung cancer patients. Strahlenther Onkol 2021; 197:1084-1092. [PMID: 34351454 PMCID: PMC8604857 DOI: 10.1007/s00066-021-01821-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 07/02/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Functional planning based merely on 4DCT ventilation imaging has limitations. In this study, we proposed a radiotherapy planning strategy based on 4DCT ventilation imaging and CT density characteristics. MATERIALS AND METHODS For 20 stage III non-small-cell lung cancer (NSCLC) patients, clinical plans and lung-avoidance plans were generated. Through deformable image registration (DIR) and quantitative image analysis, a 4DCT ventilation map was calculated. High-, medium-, and low-ventilation regions of the lung were defined based on the ventilation value. In addition, the total lung was also divided into high-, medium-, and low-density areas according to the HU threshold. The lung-avoidance plan aimed to reduce the dose to functional and high-density lungs while meeting standard target and critical structure constraints. Standard and dose-function metrics were compared between the clinical and lung-avoidance plans. RESULTS Lung avoidance plans led to significant reductions in high-function and high-density lung doses, without significantly increasing other organ at risk (OAR) doses, but at the expense of a significantly degraded homogeneity index (HI) and conformity index (CI; p < 0.05) of the planning target volume (PTV) and a slight increase in monitor units (MU) as well as in the number of segments (p > 0.05). Compared with the clinical plan, the mean lung dose (MLD) in the high-function and high-density areas was reduced by 0.59 Gy and 0.57 Gy, respectively. CONCLUSION A lung-avoidance plan based on 4DCT ventilation imaging and CT density characteristics is feasible and implementable, with potential clinical benefits. Clinical trials will be crucial to show the clinical relevance of this lung-avoidance planning strategy.
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Affiliation(s)
- AiHui Feng
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, NO.241 West Huaihai Road, Xuhui District, 20030, Shanghai, China
| | - Yan Shao
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, NO.241 West Huaihai Road, Xuhui District, 20030, Shanghai, China
| | - Hao Wang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, NO.241 West Huaihai Road, Xuhui District, 20030, Shanghai, China
| | - Hua Chen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, NO.241 West Huaihai Road, Xuhui District, 20030, Shanghai, China
| | - HengLe Gu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, NO.241 West Huaihai Road, Xuhui District, 20030, Shanghai, China
| | - YanHua Duan
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, NO.241 West Huaihai Road, Xuhui District, 20030, Shanghai, China
| | - WuTian Gan
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, NO.241 West Huaihai Road, Xuhui District, 20030, Shanghai, China
| | - ZhiYong Xu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, NO.241 West Huaihai Road, Xuhui District, 20030, Shanghai, China.
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Palma G, Monti S, Pacelli R, Liao Z, Deasy JO, Mohan R, Cella L. Radiation Pneumonitis in Thoracic Cancer Patients: Multi-Center Voxel-Based Analysis. Cancers (Basel) 2021; 13:cancers13143553. [PMID: 34298767 PMCID: PMC8306650 DOI: 10.3390/cancers13143553] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/07/2021] [Accepted: 07/14/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary The pathophysiology of radiation pneumonitis (RP) after thoracic cancer radiation treatments is still not completely understood although the identification of underlying RP mechanisms may improve the therapeutic window of thoracic cancer patients. The aim of our retrospective study was to explore the dose–response patterns associated with RP by a multi-center voxel-based analysis. In a heterogeneously treated population of 382 thoracic cancer patients, we confirmed the previously described heart–lung interaction in the development of RP. The empowerment of VBA with a novel description of dose map spatial properties based on probabilistic independent component analysis (PICA) and connectograms provided valuable additional and independent information on the radiobiology of RP. Abstract This study investigates the dose–response patterns associated with radiation pneumonitis (RP) in patients treated for thoracic malignancies with different radiation modalities. To this end, voxel-based analysis (VBA) empowered by a novel strategy for the characterization of spatial properties of dose maps was applied. Data from 382 lung cancer and mediastinal lymphoma patients from three institutions treated with different radiation therapy (RT) techniques were analyzed. Each planning CT and biologically effective dose map (α/β = 3 Gy) was spatially normalized on a common anatomical reference. The VBA of local dose differences between patients with and without RP was performed and the clusters of voxels with dose differences that significantly correlated with RP at a p-level of 0.05 were generated accordingly. The robustness of VBA inference was evaluated by a novel characterization for spatial properties of dose maps based on probabilistic independent component analysis (PICA) and connectograms. This lays robust foundations to the obtained findings that the lower parts of the lungs and the heart play a prominent role in the development of RP. Connectograms showed that the dataset can support a radiobiological differentiation between the main heart and lung substructures.
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Affiliation(s)
- Giuseppe Palma
- Institute of Biostructures and Bioimaging, National Research Council, 80145 Napoli, Italy;
- Correspondence: (G.P.); (L.C.)
| | - Serena Monti
- Institute of Biostructures and Bioimaging, National Research Council, 80145 Napoli, Italy;
| | - Roberto Pacelli
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Napoli, Italy;
| | - Zhongxing Liao
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Joseph O. Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Radhe Mohan
- Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Laura Cella
- Institute of Biostructures and Bioimaging, National Research Council, 80145 Napoli, Italy;
- Correspondence: (G.P.); (L.C.)
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Voshart DC, Wiedemann J, van Luijk P, Barazzuol L. Regional Responses in Radiation-Induced Normal Tissue Damage. Cancers (Basel) 2021; 13:cancers13030367. [PMID: 33498403 PMCID: PMC7864176 DOI: 10.3390/cancers13030367] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 01/16/2021] [Accepted: 01/18/2021] [Indexed: 12/16/2022] Open
Abstract
Normal tissue side effects remain a major concern in radiotherapy. The improved precision of radiation dose delivery of recent technological developments in radiotherapy has the potential to reduce the radiation dose to organ regions that contribute the most to the development of side effects. This review discusses the contribution of regional variation in radiation responses in several organs. In the brain, various regions were found to contribute to radiation-induced neurocognitive dysfunction. In the parotid gland, the region containing the major ducts was found to be critical in hyposalivation. The heart and lung were each found to exhibit regional responses while also mutually affecting each other's response to radiation. Sub-structures critical for the development of side effects were identified in the pancreas and bladder. The presence of these regional responses is based on a non-uniform distribution of target cells or sub-structures critical for organ function. These characteristics are common to most organs in the body and we therefore hypothesize that regional responses in radiation-induced normal tissue damage may be a shared occurrence. Further investigations will offer new opportunities to reduce normal tissue side effects of radiotherapy using modern and high-precision technologies.
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Affiliation(s)
- Daniëlle C. Voshart
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands; (D.C.V.); (J.W.)
- Department of Biomedical Sciences of Cells & Systems–Section Molecular Cell Biology, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands
| | - Julia Wiedemann
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands; (D.C.V.); (J.W.)
- Department of Biomedical Sciences of Cells & Systems–Section Molecular Cell Biology, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands
| | - Peter van Luijk
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands; (D.C.V.); (J.W.)
- Department of Biomedical Sciences of Cells & Systems–Section Molecular Cell Biology, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands
- Correspondence: (P.v.L.); (L.B.)
| | - Lara Barazzuol
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands; (D.C.V.); (J.W.)
- Department of Biomedical Sciences of Cells & Systems–Section Molecular Cell Biology, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands
- Correspondence: (P.v.L.); (L.B.)
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7
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Dong Y, Kumar H, Tawhai M, Veiga C, Szmul A, Landau D, McClelland J, Lao L, Burrowes KS. In Silico Ventilation Within the Dose-Volume is Predictive of Lung Function Post-radiation Therapy in Patients with Lung Cancer. Ann Biomed Eng 2020; 49:1416-1431. [PMID: 33258090 PMCID: PMC8058012 DOI: 10.1007/s10439-020-02697-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 11/18/2020] [Indexed: 12/24/2022]
Abstract
Lung cancer is a leading cause of death worldwide. Radiation therapy (RT) is one method to treat this disease. A common side effect of RT for lung cancer is radiation-induced lung damage (RILD) which leads to loss of lung function. RILD often compounds pre-existing smoking-related regional lung function impairment. It is difficult to predict patient outcomes due to large variability in individual response to RT. In this study, the capability of image-based modelling of regional ventilation in lung cancer patients to predict lung function post-RT was investigated. Twenty-five patient-based models were created using CT images to define the airway geometry, size and location of tumour, and distribution of emphysema. Simulated ventilation within the 20 Gy isodose volume showed a statistically significant negative correlation with the change in forced expiratory volume in 1 s 12-months post-RT (p = 0.001, R = - 0.61). Patients with higher simulated ventilation within the 20 Gy isodose volume had a greater loss in lung function post-RT and vice versa. This relationship was only evident with the combined impact of tumour and emphysema, with the location of the emphysema relative to the dose-volume being important. Our results suggest that model-based ventilation measures can be used in the prediction of patient lung function post-RT.
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Affiliation(s)
- Yu Dong
- Department of Chemical and Materials Engineering, University of Auckland, Auckland, New Zealand
| | - H Kumar
- Auckland Bioengineering Institute, Level 6, 70 Symonds Street, Auckland, 1010, New Zealand
| | - M Tawhai
- Auckland Bioengineering Institute, Level 6, 70 Symonds Street, Auckland, 1010, New Zealand
| | - C Veiga
- Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering, University College London, London, UK
| | - A Szmul
- Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering, University College London, London, UK
| | - D Landau
- Department of Oncology, University College London Hospital, London, UK
| | - J McClelland
- Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering, University College London, London, UK
| | - L Lao
- Auckland District Health Board, Auckland, New Zealand
| | - K S Burrowes
- Department of Chemical and Materials Engineering, University of Auckland, Auckland, New Zealand. .,Auckland Bioengineering Institute, Level 6, 70 Symonds Street, Auckland, 1010, New Zealand.
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8
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Defraene G, van Elmpt W, De Ruysscher D. Regional lung avoidance by CT numbers to reduce radiation-induced lung damage risk in non-small-cell lung cancer: a simulation study. Acta Oncol 2020; 59:201-207. [PMID: 31549562 DOI: 10.1080/0284186x.2019.1669814] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background: Selective avoidance aims at sparing functional lung regions. Here, we preferentially direct radiation to irreversibly nonfunctional lung areas based on planning CT imaging to reduce functional lung damage.Materials and methods: For 12 stage I-IV NSCLC patients, 5 lung substructures were segmented on the planning CT, combining voxels <-900HU, -900HU to -801HU, -800HU to -701HU, -700HU to -601HU and ≥-600HU (Level 1 to 5). Two VMAT plans were optimized: a reference plan blinded from substructures and a selective avoidance plan (AV) imposing gradually stricter constraints on Level 1-5, based on previously validated associations between lung subvolume baseline density and density increase (ΔHU) after treatment. Characteristics of treatment plans were evaluated, including subvolumes, dose, and predicted ΔHU (with reported 95% CI reflecting prediction model uncertainty).Results: Segmented substructures were on average 477 cc, 1157 cc, 484 cc, 69 cc, and 123 cc (Level 1-5). AV plans could spare Level 3-5, e.g., mean dose decrease of 3.5 Gy (range 0.6 Gy; 6.0 Gy) for Level 5, p<.001. This significantly reduced the average lung mass with predicted ΔHU>20HU by 12.5 g (95% CI: 5.4-16.9) and 27.1 g (95% CI: 10.2-32.9) for a median and upper 10th percentile patient susceptibility for damage simulation, respectively.Conclusions: Lung damage avoidance based on CT density is feasible and easy to implement. A biomarker providing a reliable selection of patients with high susceptibility for lung damage will be crucial to show the clinical relevance of this avoidance planning strategy.
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Affiliation(s)
- Gilles Defraene
- Department of Oncology, Experimental Radiation Oncology, KU Leuven—University of Leuven, Leuven, Belgium
| | - Wouter van Elmpt
- Department of Radiation Oncology (Maastro Clinic), Maastricht University Medical Center, GROW School for Developmental Biology and Oncology, Maastricht, The Netherlands
| | - Dirk De Ruysscher
- Department of Oncology, Experimental Radiation Oncology, KU Leuven—University of Leuven, Leuven, Belgium
- Department of Radiation Oncology (Maastro Clinic), Maastricht University Medical Center, GROW School for Developmental Biology and Oncology, Maastricht, The Netherlands
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9
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Palma G, Monti S, Conson M, Pacelli R, Cella L. Normal tissue complication probability (NTCP) models for modern radiation therapy. Semin Oncol 2019; 46:210-218. [PMID: 31506196 DOI: 10.1053/j.seminoncol.2019.07.006] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 07/31/2019] [Indexed: 02/07/2023]
Abstract
Mathematical models of normal tissue complication probability (NTCP) able to robustly predict radiation-induced morbidities (RIM) play an essential role in the identification of a personalized optimal plan, and represent the key to maximizing the benefits of technological advances in radiation therapy (RT). Most modern RT techniques pose, however, new challenges in estimating the risk of RIM. The aim of this report is to schematically review NTCP models in the framework of advanced radiation therapy techniques. Issues relevant to hypofractionated stereotactic body RT and ion beam therapy are critically reviewed. Reirradiation scenarios for new or recurrent malignances and NTCP are also illustrated. A new phenomenological approach to predict RIM is suggested.
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Affiliation(s)
- Giuseppe Palma
- National Research Council, Institute of Biostructures and Bioimaging, Napoli, Italy
| | - Serena Monti
- National Research Council, Institute of Biostructures and Bioimaging, Napoli, Italy
| | - Manuel Conson
- Department of Advanced Biomedical Sciences, Federico II University School of Medicine, Naples, Italy
| | - Roberto Pacelli
- Department of Advanced Biomedical Sciences, Federico II University School of Medicine, Naples, Italy
| | - Laura Cella
- National Research Council, Institute of Biostructures and Bioimaging, Napoli, Italy.
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10
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Palma G, Monti S, Thor M, Rimner A, Deasy JO, Cella L. Spatial signature of dose patterns associated with acute radiation-induced lung damage in lung cancer patients treated with stereotactic body radiation therapy. Phys Med Biol 2019; 64:155006. [PMID: 31261141 DOI: 10.1088/1361-6560/ab2e16] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Thoracic radiation therapy (RT) is often associated with lung side effects, whose etiology is still controversial. Our aim was to explore correlations between local dose in the thoracic anatomy and the radiation-induced lung damage (RILD). To this end, we designed a robust scheme for voxel-based analysis (VBA) to explore dose patterns associated with RILD in non-small-cell lung cancer (NSCLC) patients receiving stereotactic body RT (SBRT). We analyzed 106 NSCLC SBRT patients (median prescription dose: 50 Gy; range: [40-54] Gy) in 4 fractions (range: [3-5]) with clinical and dosimetric records suitable for the analysis. The incidence of acute G1 RILD (RTOG grade ⩾ 1) was 68%. Each planning CT and dose map was spatially normalized to a common anatomical reference using a B-spline inter-patient registration algorithm after masking the gross tumor volume. The tumor-subtracted dose maps were converted into biologically effective dose maps (α/β = 3 Gy). VBA was performed according to a non-parametric permutation test accounting for multiple comparison, based on a cluster analysis method. The underlying general linear model of RILD was designed to include dose maps and each non-dosimetric variable significantly correlated with RILD. The clusters of voxels with dose differences significantly correlated with RILD at a given p -level (S p ) were generated. The only non-dosimetric variable significantly correlated with RILD was the chronic obstructive pulmonary disease (p = 0.034). Patients with G1 RILD received significantly (p ⩽ 0.05) higher doses in two voxel clusters S 0.05 in the lower-left lung (14 cm3) and in an area (64 cm3) largely included within the ventricles. The applied VBA represents a powerful tool to probe the dose susceptibility of inhomogeneous organs in clinical radiobiology studies. The identified subregions with dose differences associated with G1 RILD in both the heart and lower lungs endorse a trend of previously reported hypotheses on lung toxicity radiobiology.
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Affiliation(s)
- Giuseppe Palma
- Institute of Biostructures and Bioimaging, National Research Council (CNR), Via T. De Amicis, 95, 80145, Napoli, Italy. Author to whom any correspondence should be addressed
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Development and internal validation of a multinomial NTCP model for the severity of acute dyspnea after radiotherapy for lung cancer. Radiother Oncol 2019; 136:176-184. [PMID: 31015122 DOI: 10.1016/j.radonc.2019.03.034] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Revised: 02/22/2019] [Accepted: 03/29/2019] [Indexed: 12/23/2022]
Abstract
BACKGROUND AND PURPOSE Dyspnea evolution after radiotherapy for lung cancer is complex with potential symptom deterioration and improvement from baseline. We developed and internally validated a multinomial normal tissue complication probability (NTCP) model predicting dyspnea grade. MATERIALS AND METHODS Patient-reported dyspnea was collected pre-treatment and during 6 months follow-up for 182 stage I-IV lung cancer patients treated with radical (chemo)radiotherapy. Dyspnea changes (ΔDys) from the baseline grade (Dys0) to the follow-up grade (Dys) were evaluated. A multinomial logistic regression model simultaneously predicting 3 grades of Dys (Dys ≥ 3, Dys = 2 and Dys ≤ 1 (reference level)) was optimized. Reference NTCP models predicting Dys ≥ 2 and Dys ≥ 3 risks irrespective of Dys0 were generated for comparison. Models were shrunken and performance was assessed using optimism-corrected AUC (bootstrapping). RESULTS Rates of ΔDys ≥ 1 (deterioration) and ΔDys ≤ -1 (improvement) at 6 months were 31.9% and 12.6%. Dys ≥ 3, Dys = 2 and Dys ≤ 1 rates were 13.7%, 20.9% and 65.4%, respectively. The multinomial model (combining the risk factors Dys0 and MLD and the protective factor chemotherapy treatment) predicted Dys ≥ 3, Dys = 2 and Dys ≤ 1 with AUC (95% CI) of 0.72 (0.65-0.75) 0.76 (0.72-0.79) and 0.78 (0.74-0.80), respectively. Reference Dys ≥ 2 and Dys ≥ 3 models showed worse AUC: 0.64 (0.59-0.67) and 0.66 (0.50-0.70), respectively. CONCLUSIONS Dyspnea grade could be predicted with high accuracy using a multinomial NTCP model, yielding personalized dyspnea symptom improvement and deterioration risks.
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Palma G, Monti S, Buonanno A, Pacelli R, Cella L. PACE: A Probabilistic Atlas for Normal Tissue Complication Estimation in Radiation Oncology. Front Oncol 2019; 9:130. [PMID: 30918837 PMCID: PMC6424876 DOI: 10.3389/fonc.2019.00130] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 02/13/2019] [Indexed: 12/23/2022] Open
Abstract
In radiation oncology, the need for a modern Normal Tissue Complication Probability (NTCP) philosophy to include voxel-based evidence on organ radio-sensitivity (RS) has been acknowledged. Here a new formalism (Probabilistic Atlas for Complication Estimation, PACE) to predict radiation-induced morbidity (RIM) is presented. The adopted strategy basically consists in keeping the structure of a classical, phenomenological NTCP model, such as the Lyman-Kutcher-Burman (LKB), and replacing the dose distribution with a collection of RIM odds, including also significant non-dosimetric covariates, as input of the model framework. The theory was first demonstrated in silico on synthetic dose maps, classified according to synthetic outcomes. PACE was then applied to a clinical dataset of thoracic cancer patients classified for lung fibrosis. LKB models were trained for comparison. Overall, the obtained learning curves showed that the PACE model outperformed the LKB and predicted synthetic outcomes with an accuracy >0.8. On the real patients, PACE performance, evaluated by both discrimination and calibration, was significantly higher than LKB. This trend was confirmed by cross-validation. Furthermore, the capability to infer the spatial pattern of underlying RS map for the analyzed RIM was successfully demonstrated, thus paving the way to new perspectives of NTCP models as learning tools.
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Affiliation(s)
- Giuseppe Palma
- Institute of Biostructures and Bioimaging, National Research Council, Naples, Italy
| | | | - Amedeo Buonanno
- Department of Engineering, University of Campania Luigi Vanvitelli, Aversa, Italy
| | - Roberto Pacelli
- Department of Advanced Biomedical Sciences, Federico II University School of Medicine, Naples, Italy
| | - Laura Cella
- Institute of Biostructures and Bioimaging, National Research Council, Naples, Italy
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13
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Palma G, Monti S, Xu T, Scifoni E, Yang P, Hahn SM, Durante M, Mohan R, Liao Z, Cella L. Spatial Dose Patterns Associated With Radiation Pneumonitis in a Randomized Trial Comparing Intensity-Modulated Photon Therapy With Passive Scattering Proton Therapy for Locally Advanced Non-Small Cell Lung Cancer. Int J Radiat Oncol Biol Phys 2019; 104:1124-1132. [PMID: 30822531 DOI: 10.1016/j.ijrobp.2019.02.039] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 01/31/2019] [Accepted: 02/20/2019] [Indexed: 12/27/2022]
Abstract
PURPOSE Radiation pneumonitis (RP) is commonly associated with thoracic radiation therapy, and its incidence is related to dose and volume of the normal lung in the path of radiation. Our aim was to investigate dose patterns associated with RP in patients enrolled in a randomized trial of intensity modulated radiation therapy (IMRT) versus passive scattering proton therapy (PSPT) for locally advanced non-small cell lung cancer. METHODS We analyzed 178 patients prospectively treated with PSPT or IMRT for non-small cell lung cancer to a prescribed dose of 66 or 74 Gy in conventional daily fractionation with concurrent chemotherapy. Forty patients (22%) developed clinically symptomatic RP. Voxel-based analysis of local dose differences was done with a nonparametric permutation test accounting for multiple comparisons. From the obtained 3-dimensional significance maps, we derived clusters of voxels that exhibited dose differences between groups at a statistical significance level of 0.05. RESULTS The voxel-based analysis highlighted that (1) significant dose differences between patients with and without RP were found in the lower part of the lungs and in the heart and (2) the anatomic regions significantly spared by PSPT and the clusters in which doses were significantly correlated with RP development were disjoint. CONCLUSIONS The analyzed trial data provide an unprecedented opportunity to substantiate previous hypotheses regarding the role of the heart and the lower lungs in the development of RP. Knowledge of this relationship between RP and thoracic regional radiosensitivity should be considered in clinical practice and in the design of future trials.
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Affiliation(s)
- Giuseppe Palma
- National Research Council, Institute of Biostructures and Bioimaging, Napoli, Italy
| | - Serena Monti
- IRCCS SDN, Image Processing Department, Napoli, Italy
| | - Ting Xu
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Emanuele Scifoni
- Istituto Nazionale di Fisica Nucleare, Trento Institute for Fundamental Physics and Applications, Trento, Italy
| | - Pei Yang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Stephen M Hahn
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Marco Durante
- Biophysics Department, GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt, Germany; Technische Universität Darmstadt, Institut für Festkörperphysik, Darmstadt, Germany
| | - Radhe Mohan
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Zhongxing Liao
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Laura Cella
- National Research Council, Institute of Biostructures and Bioimaging, Napoli, Italy
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Abstract
Radiotherapy is used in >50% of patients with cancer, both for curative and palliative purposes. Radiotherapy uses ionizing radiation to target and kill tumour tissue, but normal tissue can also be damaged, leading to toxicity. Modern and precise radiotherapy techniques, such as intensity-modulated radiotherapy, may prevent toxicity, but some patients still experience adverse effects. The physiopathology of toxicity is dependent on many parameters, such as the location of irradiation or the functional status of organs at risk. Knowledge of the mechanisms leads to a more rational approach for controlling radiotherapy toxicity, which may result in improved symptom control and quality of life for patients. This improved quality of life is particularly important in paediatric patients, who may live for many years with the long-term effects of radiotherapy. Notably, signs and symptoms occurring after radiotherapy may not be due to the treatment but to an exacerbation of existing conditions or to the development of new diseases. Although differential diagnosis may be difficult, it has important consequences for patients.
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Defraene G, La Fontaine M, van Kranen S, Reymen B, Belderbos J, Sonke JJ, De Ruysscher D. Radiation-Induced Lung Density Changes on CT Scan for NSCLC: No Impact of Dose-Escalation Level or Volume. Int J Radiat Oncol Biol Phys 2018; 102:642-650. [PMID: 30244882 DOI: 10.1016/j.ijrobp.2018.06.038] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Revised: 05/23/2018] [Accepted: 06/20/2018] [Indexed: 12/25/2022]
Abstract
PURPOSE Dose-escalation for patients with non-small cell lung cancer (NSCLC) in the positron emission tomography (PET)-boost trial (NCT01024829) exposes portions of normal lung tissue to high radiation doses. The relationship between lung parenchyma dose and density changes on computed tomography (CT) was analyzed. MATERIALS AND METHODS The CT scans of 59 patients with stage IB to III NSCLC, randomized between a boost to the whole primary tumor and an integrated boost to its 50% SUVmax (maximum standardized uptake value) volume. Patients were treated with concurrent or sequential chemoradiation or radiation only. Deformable registration mapped the 3-month follow-up CT to the planning CT. Hounsfield unit differences (ΔHU) were extracted to assess lung parenchyma density changes. Equivalent dose in 2 Gy fractions (EQD2)-ΔHU response was described sigmoidally, and regional response variation was studied by polar analysis. Prognostic factors of ΔHU were obtained through generalized linear modeling. RESULTS Saturation of ΔHU was observed above 60 Gy. No interaction was found between boost dose distribution (D1cc and V70Gy) and ΔHU at lower doses. ΔHU was lowest peripherally from the tumor and peaked posteriorly at 3 cm from the tumor border (3.1 HU/Gy). Right lung location was an independent risk factor for ΔHU (P = .02). CONCLUSIONS No apparent increase of lung density changes at 3-month follow-up was observed above 60 Gy EQD2 for patients with NSCLC treated with (concurrent or sequential chemo) radiation. The mild response observed peripherally in the lung parenchyma might be exploited in plan optimization routines minimizing lung damage.
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Affiliation(s)
- Gilles Defraene
- Department of Oncology, Experimental Radiation Oncology, KU Leuven-University of Leuven, Belgium.
| | - Matthew La Fontaine
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Simon van Kranen
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Bart Reymen
- Maastricht University Medical Center, Maastricht, The Netherlands
| | - José Belderbos
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Dirk De Ruysscher
- Department of Oncology, Experimental Radiation Oncology, KU Leuven-University of Leuven, Belgium; Maastricht University Medical Center, Maastricht, The Netherlands; Department of Radiation Oncology (Maastro Clinic), GROW School for Developmental Biology and Oncology, Maastricht, The Netherlands
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De Ruysscher D, Jin J, Lautenschlaeger T, She JX, Liao Z, Kong FMS. Blood-based biomarkers for precision medicine in lung cancer: precision radiation therapy. Transl Lung Cancer Res 2017; 6:661-669. [PMID: 29218269 DOI: 10.21037/tlcr.2017.09.12] [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] [Indexed: 12/12/2022]
Abstract
Both tumors and patients are complex and models that determine survival and toxicity of radiotherapy or any other treatment ideally must take into account this variability as well as its dynamic state. The genetic features of the tumor and the host, and increasingly also the epi-genetic and proteomic characteristics, are being unraveled. Multiple techniques, including histological examination, blood sampling, measurement of circulating tumor cells (CTCs), and functional and molecular imaging, can be used for this purpose. However, the effects of radiation on the tumor and on organs at risk (OARs) are also influenced by the applied dose and volume of irradiated tissues. Combining all these biological, clinical, imaging, and dosimetric parameters in a validated prognostic or predictive model poses a major challenge. Here we aimed to provide an objective review of the potential of blood markers to guide high precision radiation therapy. A combined biological-mathematical approach opens new doors beyond prognostication of patients, as it allows truly precise oncological treatment. Indeed, the core for individualized and precision medicine is not only selection of patients, but even more the optimization of the therapeutic window on an individual basis. A holistic model will allow for determination of an individual dose-response relationship for each organ at risk for each tumor in each individual patient for the complete oncological treatment package. This includes, but is not limited to, radiotherapy alone. Individualized dose-response curves will allow for consideration of different doses of radiation and combinations with other drugs to plan for both optimal toxicity and complete response. Insights into the interactions between a multitude of parameters will lead to the discovery of new pathways and networks that will fuel new biological research on target discovery.
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Affiliation(s)
- Dirk De Ruysscher
- Department of Radiation Oncology (Maastro Clinic), GROW School of Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands.,KU Leuven Radiation Oncology, Leuven, Belgium
| | - Jianyue Jin
- Department of Radiation Oncology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Tim Lautenschlaeger
- Department of Radiation Oncology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Jin-Xiong She
- Center for Biotechnology and Genomic Medicine and Department of OB/GYN, Augusta University, Augusta, GA, USA
| | - Zhongxing Liao
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Feng-Ming Spring Kong
- Department of Radiation Oncology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
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De Ruysscher D, Lambrecht M, van Baardwijk A, Peeters S, Reymen B, Verhoeven K, Wanders R, Öllers M, van Elmpt W, van Loon J. Standard of care in high-dose radiotherapy for localized non-small cell lung cancer. Acta Oncol 2017; 56:1610-1613. [PMID: 28840754 DOI: 10.1080/0284186x.2017.1349337] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Dirk De Ruysscher
- Department of Radiation Oncology (Maastro Clinic), GROW Research Institute, Maastricht University Medical Center, Maastricht, The Netherlands
- Radiation Oncology, KU Leuven, Leuven, Belgium
| | - Maarten Lambrecht
- Department of Radiation Oncology, University Hospitals Leuven, Leuven, Belgium
| | - Angela van Baardwijk
- Department of Radiation Oncology (Maastro Clinic), GROW Research Institute, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Stéphanie Peeters
- Department of Radiation Oncology (Maastro Clinic), GROW Research Institute, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Bart Reymen
- Department of Radiation Oncology (Maastro Clinic), GROW Research Institute, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Karolien Verhoeven
- Department of Radiation Oncology (Maastro Clinic), GROW Research Institute, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Rinus Wanders
- Department of Radiation Oncology (Maastro Clinic), GROW Research Institute, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Michel Öllers
- Department of Radiation Oncology (Maastro Clinic), GROW Research Institute, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (Maastro Clinic), GROW Research Institute, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Judith van Loon
- Department of Radiation Oncology (Maastro Clinic), GROW Research Institute, Maastricht University Medical Center, Maastricht, The Netherlands
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Avanzo M, Barbiero S, Trovo M, Bissonnette JP, Jena R, Stancanello J, Pirrone G, Matrone F, Minatel E, Cappelletto C, Furlan C, Jaffray DA, Sartor G. Voxel-by-voxel correlation between radiologically radiation induced lung injury and dose after image-guided, intensity modulated radiotherapy for lung tumors. Phys Med 2017; 42:150-156. [PMID: 29173909 DOI: 10.1016/j.ejmp.2017.09.127] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Revised: 07/24/2017] [Accepted: 09/17/2017] [Indexed: 10/18/2022] Open
Abstract
PURPOSE To correlate radiation dose to the risk of severe radiologically-evident radiation-induced lung injury (RRLI) using voxel-by-voxel analysis of the follow-up computed tomography (CT) of patients treated for lung cancer with hypofractionated helical Tomotherapy. METHODS AND MATERIALS The follow-up CT scans from 32 lung cancer patients treated with various regimens (5, 8, and 25 fractions) were registered to pre-treatment CT using deformable image registration (DIR). The change in density was calculated for each voxel within the combined lungs minus the planning target volume (PTV). Parameters of a Probit formula were derived by fitting the occurrences of changes of density in voxels greater than 0.361gcm-3 to the radiation dose. The model's predictive capability was assessed using the area under receiver operating characteristic curve (AUC), the Kolmogorov-Smirnov test for goodness-of-fit, and the permutation test (Ptest). RESULTS The best-fit parameters for prediction of RRLI 6months post RT were D50 of 73.0 (95% CI 59.2.4-85.3.7)Gy, and m of 0.41 (0.39-0.46) for hypofractionated (5 and 8 fractions) and D50 of 96.8 (76.9-123.9)Gy, and m of 0.36 (0.34-0.39) for 25 fractions RT. According to the goodness-of-fit test the null hypothesis of modeled and observed occurrence of RRLI coming from the same distribution could not be rejected. The AUC was 0.581 (0.575-0.583) for fractionated and 0.579 (0.577-0.581) for hypofractionated patients. The predictive models had AUC>upper 95% band of the Ptest. CONCLUSIONS The correlation of voxel-by-voxel density increase with dose can be used as a support tool for differential diagnosis of tumor from benign changes in the follow-up of lung IMRT patients.
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Affiliation(s)
- Michele Avanzo
- Medical Physics, Centro di Riferimento Oncologico IRCCS Aviano, 33081 Aviano, Italy.
| | - Sara Barbiero
- Radiotherapy Department, Casa di Cura S. Rossore, Pisa, Italy
| | - Marco Trovo
- Radiation Oncology Department, Centro di Riferimento Oncologico IRCCS Aviano, 33081 Aviano, Italy; Radiation Oncology Department, Azienda Sanitaria Universitaria Integrata, Udine, Italy
| | - Jean-Pierre Bissonnette
- Department of Radiation Oncology, University of Toronto, Toronto, Canada; Department of Medical Physics, Princess Margaret Cancer Centre, Toronto, Canada
| | - Rajesh Jena
- Department of Oncology, University of Cambridge, Cambridge CB2 0QQ, UK
| | | | - Giovanni Pirrone
- Medical Physics, Centro di Riferimento Oncologico IRCCS Aviano, 33081 Aviano, Italy
| | - Fabio Matrone
- Radiation Oncology Department, Centro di Riferimento Oncologico IRCCS Aviano, 33081 Aviano, Italy
| | - Emilio Minatel
- Radiation Oncology Department, Centro di Riferimento Oncologico IRCCS Aviano, 33081 Aviano, Italy
| | - Cristina Cappelletto
- Medical Physics, Centro di Riferimento Oncologico IRCCS Aviano, 33081 Aviano, Italy
| | - Carlo Furlan
- Radiation Oncology Department, Centro di Riferimento Oncologico IRCCS Aviano, 33081 Aviano, Italy
| | - David A Jaffray
- Department of Radiation Oncology, University of Toronto, Toronto, Canada; Department of Medical Physics, Princess Margaret Cancer Centre, Toronto, Canada
| | - Giovanna Sartor
- Medical Physics, Centro di Riferimento Oncologico IRCCS Aviano, 33081 Aviano, Italy
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