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Tvilum M, Knap MM, Hoffmann L, Khalil AA, Appelt AL, Haraldsen A, Alber M, Grau C, Schmidt HH, Kandi M, Holt MI, Lutz CM, Møller DS. Early radiologic and metabolic tumour response assessment during combined chemo-radiotherapy for locally advanced NSCLC. Clin Transl Radiat Oncol 2024; 45:100737. [PMID: 38317680 PMCID: PMC10839576 DOI: 10.1016/j.ctro.2024.100737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/20/2024] [Accepted: 01/21/2024] [Indexed: 02/07/2024] Open
Abstract
Background The role of early treatment response for patients with locally advanced non-small cell lung cancer (LA-NSCLC) treated with concurrent chemo-radiotherapy (cCRT) is unclear. The study aims to investigate the predictive value of response to induction chemotherapy (iCX) and the correlation with pattern of failure (PoF). Materials and methods Patients with LA-NSCLC treated with cCRT were included for analyses (n = 276). Target delineations were registered from radiotherapy planning PET/CT to diagnostic PET/CT, in between which patients received iCX. Volume, sphericity, and SUVpeak were extracted from each scan. First site of failure was categorised as loco-regional (LR), distant (DM), or simultaneous LR+M (LR+M). Fine and Gray models for PoF were performed: a baseline model (including performance status (PS), stage, and histology), an image model for squamous cell carcinoma (SCC), and an image model for non-SCC. Parameters included PS, volume (VOL) of tumour, VOL of lymph nodes, ΔVOL, sphericity, SUVpeak, ΔSUVpeak, and oligometastatic disease. Results Median follow-up was 7.6 years. SCC had higher sub-distribution hazard ratio (sHR) for LRF (sHR = 2.771 [1.577:4.87], p < 0.01) and decreased sHR for DM (sHR = 0.247 [0.125:0.485], p < 0.01). For both image models, high diagnostic SUVpeak increased risk of LRF (sHR = 1.059 [1.05:1.106], p < 0.01 for SCC, sHR = 1.12 [1.03:1.21], p < 0.01 for non-SCC). Patients with SCC and less decrease in VOL had higher sHR for DM (sHR = 1.025[1.001:1.048] pr. % increase, p = 0.038). Conclusion Poor response in disease volume was correlated with higher sHR of DM for SCC, no other clear correlation of response and PoF was observed. Histology significantly correlated with PoF with SCC prone to LRF and non-SCC prone to DM as first site of failure. High SUVpeak at diagnosis increased the risk of LRF for both histologies.
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Affiliation(s)
- Marie Tvilum
- Department of Oncology, Aarhus University Hospital, Denmark
- Danish Center for Particle Therapy, Aarhus University Hospital, Denmark
| | | | - Lone Hoffmann
- Department of Oncology, Aarhus University Hospital, Denmark
- Department of Clinical Medicine, Faculty of Health Sciences, Aarhus University, Denmark
| | | | - Ane L. Appelt
- Leeds Institute of Medical Research at St James’s, University of Leeds, United Kingdom
- Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
| | - Ate Haraldsen
- Department of Nuclear Medicine and PET-centre, Aarhus University Hospital, Denmark
| | - Markus Alber
- Department of Radiation Oncology, Heidelberg University Hospital, Germany
- Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg University Hospital, Germany
| | - Cai Grau
- Danish Center for Particle Therapy, Aarhus University Hospital, Denmark
| | | | - Maria Kandi
- Department of Oncology, Aarhus University Hospital, Denmark
| | | | | | - Ditte Sloth Møller
- Department of Oncology, Aarhus University Hospital, Denmark
- Department of Clinical Medicine, Faculty of Health Sciences, Aarhus University, Denmark
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Yang P, Shan J, Ge X, Zhou Q, Ding M, Niu T, Du J. Prediction of SBRT response in liver cancer by combining original and delta cone-beam CT radiomics: a pilot study. Phys Eng Sci Med 2024; 47:295-307. [PMID: 38165634 DOI: 10.1007/s13246-023-01366-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 12/06/2023] [Indexed: 01/04/2024]
Abstract
This study aims to explore the feasibility of utilizing a combination of original and delta cone-beam CT (CBCT) radiomics for predicting treatment response in liver tumors undergoing stereotactic body radiation therapy (SBRT). A total of 49 patients are included in this study, with 36 receiving 5-fraction SBRT, 3 receiving 4-fraction SBRT, and 10 receiving 3-fraction SBRT. The CBCT and planning CT images from liver cancer patients who underwent SBRT are collected to extract overall 547 radiomics features. The CBCT features which are reproducible and interchangeable with pCT are selected for modeling analysis. The delta features between fractions are calculated to depict tumor change. The patients with 4-fraction SBRT are only used for screening robust features. In patients receiving 5-fraction SBRT, the predictive ability of both original and delta CBCT features for two-level treatment response (local efficacy vs. local non-efficacy; complete response (CR) vs. partial response (PR)) is assessed by utilizing multivariable logistic regression with leave-one-out cross-validation. Additionally, univariate analysis is conducted to validate the capability of CBCT features in identifying local efficacy in patients receiving 3-fraction SBRT. In patients receiving 5-fraction SBRT, the combined models incorporating original and delta CBCT radiomics features demonstrate higher area under the curve (AUC) values compared to models using either original or delta features alone for both classification tasks. The AUC values for predicting local efficacy vs. local non-efficacy are 0.58 for original features, 0.82 for delta features, and 0.90 for combined features. For distinguishing PR from CR, the respective AUC values for original, delta and combined features are 0.79, 0.80, and 0.89. In patients receiving 3-fraction SBRT, eight valuable CBCT radiomics features are identified for predicting local efficacy. The combination of original and delta radiomics derived from fractionated CBCT images in liver cancer patients undergoing SBRT shows promise in providing comprehensive information for predicting treatment response.
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Affiliation(s)
- Pengfei Yang
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Jingjing Shan
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xin Ge
- School of Science, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Qinxuan Zhou
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Mingchao Ding
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China
| | - Tianye Niu
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China.
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China.
| | - Jichen Du
- Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, 100049, China.
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Luo W, Xiu Z, Wang X, McGarry R, Allen J. A Novel Method for Evaluating Early Tumor Response Based on Daily CBCT Images for Lung SBRT. Cancers (Basel) 2023; 16:20. [PMID: 38201447 PMCID: PMC10778260 DOI: 10.3390/cancers16010020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/07/2023] [Accepted: 12/11/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND We aimed to develop a new tumor response assessment method for lung SBRT. METHODS In total, 132 lung cancer patients with 134 tumors who received SBRT treatment with daily CBCT were included in this study. The information about tumor size (area), contrast (contrast-to-noise ratio (CNR)), and density/attenuation (μ) was derived from the CBCT images for the first and the last fractions. The ratios of tumor area, CNR, and μ (RA, RCNR, Rμ) between the last and first fractions were calculated for comparison. The product of the three rations was defined as a new parameter (R) for assessment. Tumor response was independently assessed by a radiologist based on a comprehensive analysis of the CBCT images. RESULTS R ranged from 0.27 to 1.67 with a mean value of 0.95. Based on the radiologic assessment results, a receiver operation characteristic (ROC) curve with the area under the curve (AUC) of 95% was obtained and the optimal cutoff value (RC) was determined as 1.1. The results based on RC achieved a 94% accuracy, 94% specificity, and 90% sensitivity. CONCLUSION The results show that R was correlated with early tumor response to lung SBRT and that using R for evaluating tumor response to SBRT would be viable and efficient.
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Affiliation(s)
- Wei Luo
- Department of Radiation Medicine, University of Kentucky, 800 Rose Street, Lexington, KY 40536, USA; (Z.X.); (R.M.)
| | - Zijian Xiu
- Department of Radiation Medicine, University of Kentucky, 800 Rose Street, Lexington, KY 40536, USA; (Z.X.); (R.M.)
| | - Xiaoqin Wang
- Department of Radiology, University of Kentucky, 800 Rose Street, Lexington, KY 40536, USA;
| | - Ronald McGarry
- Department of Radiation Medicine, University of Kentucky, 800 Rose Street, Lexington, KY 40536, USA; (Z.X.); (R.M.)
| | - Joshua Allen
- AdventHealth, 2501 N Orange Ave, Orlando, FL 32804, USA;
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Adachi T, Nakamura M, Iramina H, Matsumoto K, Ishihara Y, Tachibana H, Kurokawa S, Cho S, Tanaka K, Fukumoto K, Nishiyama T, Kito S, Mizowaki T. Identification of reproducible radiomic features from on-board volumetric images: A multi-institutional phantom study. Med Phys 2023; 50:5585-5596. [PMID: 36932977 DOI: 10.1002/mp.16376] [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: 10/07/2022] [Revised: 02/25/2023] [Accepted: 02/27/2023] [Indexed: 03/19/2023] Open
Abstract
BACKGROUND Radiomics analysis using on-board volumetric images has attracted research attention as a method for predicting prognosis during treatment; however, the lack of standardization is still one of the main concerns. PURPOSE This study investigated the factors that influence the reproducibility of radiomic features extracted from on-board volumetric images using an anthropomorphic radiomics phantom. Furthermore, a phantom experiment was conducted with different treatment machines from multiple institutions as external validation to identify reproducible radiomic features. METHODS The phantom was designed to be 35 × 20 × 20 cm with eight types of heterogeneous spheres (⌀ = 1, 2, and 3 cm). On-board volumetric images were acquired using 15 treatment machines from eight institutions. Of these, kilovoltage cone-beam computed tomography (kV-CBCT) image data acquired from four treatment machines at one institution were used as an internal evaluation dataset to explore the reproducibility of radiomic features. The remaining image data, including kV-CBCT, megavoltage-CBCT (MV-CBCT), and megavoltage computed tomography (MV-CT) provided by seven different institutions (11 treatment machines), were used as an external validation dataset. A total of 1,302 radiomic features, including 18 first-order, 75 texture, 465 (i.e., 93 × 5) Laplacian of Gaussian (LoG) filter-based, and 744 (i.e., 93 × 8) wavelet filter-based features, were extracted within the spheres. The intraclass correlation coefficient (ICC) was calculated to explore feature repeatability and reproducibility using an internal evaluation dataset. Subsequently, the coefficient of variation (COV) was calculated to validate the feature variability of external institutions. An absolute ICC exceeding 0.85 or COV under 5% was considered indicative of a highly reproducible feature. RESULTS For internal evaluation, ICC analysis showed that the median percentage of radiomic features with high repeatability was 95.2%. The ICC analysis indicated that the median percentages of highly reproducible features for inter-tube current, reconstruction algorithm, and treatment machine were decreased by 20.8%, 29.2%, and 33.3%, respectively. For external validation, the COV analysis showed that the median percentage of reproducible features was 31.5%. A total of 16 features, including nine LoG filter-based and seven wavelet filter-based features, were indicated as highly reproducible features. The gray-level run-length matrix (GLRLM) was classified as containing the most frequent features (N = 8), followed by the gray-level dependence matrix (N = 7) and gray-level co-occurrence matrix (N = 1) features. CONCLUSIONS We developed the standard phantom for radiomics analysis of kV-CBCT, MV-CBCT, and MV-CT images. With this phantom, we revealed that the differences in the treatment machine and image reconstruction algorithm reduce the reproducibility of radiomic features from on-board volumetric images. Specifically, the most reproducible features for external validation were LoG or wavelet filter-based GLRLM features. However, the acceptability of the identified features should be examined in advance at each institution before applying the findings to prognosis prediction.
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Affiliation(s)
- Takanori Adachi
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Japan
| | - Mitsuhiro Nakamura
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Japan
- Department of Advanced Medical Physics, Graduate School of Medicine, Kyoto University, Shogoin, Sakyo-ku, Kyoto, Japan
| | - Hiraku Iramina
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Japan
| | - Kazushige Matsumoto
- Department of Radiology, National Hospital Organization Kyoto Medical Center, Fushimi-ku, Kyoto, Japan
| | - Yoshitomo Ishihara
- Department of Radiation Oncology, Japanese Red Cross Wakayama Medical Center, Wakayama, Japan
| | - Hidenobu Tachibana
- Department of Radiation Oncology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Shogo Kurokawa
- Department of Radiation Oncology, National Cancer Center Hospital East, Kashiwa, Japan
| | - SangYong Cho
- Division of Radiation Oncology, Chiba Cancer Center, Chuo-ku, Chiba, Japan
| | - Kazunori Tanaka
- Department of Radiation Oncology, Kyoto City Hospital, Nakagyo-ku, Kyoto, Japan
| | - Kenta Fukumoto
- Department of Radiation Oncology, Kyoto City Hospital, Nakagyo-ku, Kyoto, Japan
| | - Tomohiro Nishiyama
- Department of Radiation Oncology, Kyoto-Katsura Hospital, Nishikyo-ku, Kyoto, Japan
| | - Satoshi Kito
- Department of Radiotherapy, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Bunkyo-ku, Tokyo, Japan
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Japan
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Pham TT, Whelan B, Oborn BM, Delaney GP, Vinod S, Brighi C, Barton M, Keall P. Magnetic resonance imaging (MRI) guided proton therapy: A review of the clinical challenges, potential benefits and pathway to implementation. Radiother Oncol 2022; 170:37-47. [DOI: 10.1016/j.radonc.2022.02.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 02/09/2022] [Accepted: 02/25/2022] [Indexed: 10/18/2022]
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Zhang R, Cai Z, Luo Y, Wang Z, Wang W. Preliminary exploration of response the course of radiotherapy for stage III non-small cell lung cancer based on longitudinal CT radiomics features. Eur J Radiol Open 2022; 9:100391. [PMID: 34977279 PMCID: PMC8688890 DOI: 10.1016/j.ejro.2021.100391] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/28/2021] [Accepted: 12/09/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose Explore the longitudinal CT-based radiomics to demonstrate the changing trend of radiotherapy response and to determine at which point after the onset of treatment radiomics exhibit the greatest change for stage III NSCLC patients. Methods and materials Ten stage III NSCLC patients in line with inclusion criteria were enrolled retrospectively, each of whom received radiotherapy or concurrent chemo-radiotherapy and performed eight series of follow-up CT imaging. Longitudinal radiomics were extracted on region of interest from the eight registered images, then two steps were conducted to select significant features as indicators of tumor change: 1) stable features were selected by Kendall rank correlation; 2) texture feature types with a steadily changing trend were retained and intensity features with stable change trends were selected to represent the large number of them. Next, the trend and rate of tumor change were analyzed using the Delta method and Curve-fitting method. Finally, the statistics in the distribution of stable features in patients were calculated. Results 675 stable features were selected from a total number of 1371 radiomics features, then 12 texture features types were retained and three intensity features were chosen to represent their own category. Among the final selected feature types, it was found that the two time points were weeks 1 and 3 with the higher rate of change. One patient had very few stable tumor features out of a total of 101 features, and the rate of change of features of another patient was conspicuously higher than the average level with number of 301 features. Conclusion The longitudinal CT radiomics could demonstrate the change trend of tumor and at which point exhibit the greatest change during radiotherapy, and potentially be used for treatment decisions concerning adaptive radiotherapy.
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Key Words
- CBCT, Cone-beam Computed Tomography
- CT, Computed Tomography
- Computed tomography
- GLCM25/GLCM3, Gray Level Co-occurrence Matrix25/Gray Level Co-occurrence Matrix3
- GLRLM25, Gray Level Run Length Matrix25
- GTV, Gross Tumor Volume
- HU, Hounsfield Units
- IBEX, Imaging Biomarker Explorer
- LASSO, Least Absolute Shrinkage and Selection Operator
- Longitudinal radiomics features
- NID25/NID3, Neighborhood Intensity Difference25/Neighborhood Intensity Difference3
- NSCLC, Non-small cell lung carcinoma
- Non-small cell lung cancer
- PCA, Principle Component Analysis
- ROI, Region of Interest
- Radiation therapy
- VMAT, Volumetric Modulated Arc Therapy
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Affiliation(s)
- Ruiping Zhang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhu West Road, Hexi District, Tianjin 300060, China
| | - Zhengting Cai
- School of Automation (Artificial Intelligence), Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou, Zhejiang Province 310018, China
| | - Yan'an Luo
- Department of Physics, Nankai University, Weijin Road, Nankai District, Tianjin 300071, China
| | - Zhizhen Wang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhu West Road, Hexi District, Tianjin 300060, China
| | - Wei Wang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhu West Road, Hexi District, Tianjin 300060, China
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Pandey R, Thimmarayappa A, Ashok N, Mohan A, Sharma S, Pandey S. Tumor regression during radiotherapy as a predictor of response in locally advanced nonsmall cell carcinoma. J Cancer Res Ther 2022; 18:964-970. [DOI: 10.4103/jcrt.jcrt_265_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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Sellami S, Bourbonne V, Hatt M, Tixier F, Bouzid D, Lucia F, Pradier O, Goasduff G, Visvikis D, Schick U. Predicting response to radiotherapy of head and neck squamous cell carcinoma using radiomics from cone-beam CT images. Acta Oncol 2022; 61:73-80. [PMID: 34632924 DOI: 10.1080/0284186x.2021.1983207] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Radiotherapy (RT) for head and neck cancer is now guided by cone-beam computed tomography (CBCT). We aim to identify a CBCT radiomic signature predictive of progression to RT. MATERIAL AND METHODS A cohort of 93 patients was split into training (n = 60) and testing (n = 33) sets. A total of 88 features were extracted from the gross tumor volume (GTV) on each CBCT. Receiver operating characteristic (ROC) curves were used to determine the power of each feature at each week of treatment to predict progression to radio(chemo)therapy. Only features with AUC > 0.65 at each week were pre-selected. Absolute differences were calculated between features from each weekly CBCT and baseline CBCT1 images. The smallest detectable change (C = 1.96 × SD, SD being the standard deviation of differences between feature values calculated on CBCT1 and CBCTn) with its confidence interval (95% confidence interval [CI]) was determined for each feature. The features for which the change was larger than C for at least 5% of patients were then selected. A radiomics-based model was built at the time-point that showed the highest AUC and compared with models relying on clinical variables. RESULTS Seven features had an AUC > 0.65 at each week, and six exhibited a change larger than the predefined CI 95%. After exclusion of inter-correlated features, only one parameter remains, Coarseness. Among clinical variable, only hemoglobin value was significant. AUC for predicting the treatment response were 0.78 (p = .006), 0.85 (p < .001), and 0.99 (p < .001) for clinical, CBCT4-radiomics (Coarseness) and clinical + radiomics based models respectively. The mean AUC of this last model on a 5-fold cross-validation was 0.80 (±0.09). On the testing cohort, the best prediction was given by the combined model (balanced accuracy [BAcc] 0.67 , p < .001). CONCLUSIONS We described a feature selection methodology for delta-radiomics that is able to select reproducible features which are informative due to their change during treatment. A selected delta radiomics feature may improve clinical-based prediction models.
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Affiliation(s)
- S. Sellami
- Radiation Oncology Department, University Hospital, Brest, France
| | - V. Bourbonne
- Radiation Oncology Department, University Hospital, Brest, France
| | - M. Hatt
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - F. Tixier
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - D. Bouzid
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - F. Lucia
- Radiation Oncology Department, University Hospital, Brest, France
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - O. Pradier
- Radiation Oncology Department, University Hospital, Brest, France
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
- Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
| | - G. Goasduff
- Radiation Oncology Department, University Hospital, Brest, France
| | - D. Visvikis
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
| | - U. Schick
- Radiation Oncology Department, University Hospital, Brest, France
- INSERM, UMR 1101, LaTIM, University of Brest, Brest, France
- Faculté de Médecine et des Sciences de la Santé, Université de Bretagne Occidentale, Brest, France
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Amugongo LM, Green A, Cobben D, van Herk M, McWilliam A, Osorio EV. Identification of modes of tumor regression in non-small cell lung cancer patients during radiotherapy. Med Phys 2021; 49:370-381. [PMID: 34724228 DOI: 10.1002/mp.15320] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 09/18/2021] [Accepted: 10/19/2021] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Observed gross tumor volume (GTV) shrinkage during radiotherapy (RT) raises the question of whether to adapt treatment to changes observed on the acquired images. In the literature, two modes of tumor regression have been described: elastic and non-elastic. These modes of tumor regression will affect the safety of treatment adaptation. This study applies a novel approach, using routine cone-beam computed tomography (CBCT) and deformable image registration to automatically distinguish between elastic and non-elastic tumor regression. METHODS In this retrospective study, 150 locally advanced non-small cell lung cancer patients treated with 55 Gray of radiotherapy were included. First, the two modes of tumor regression were simulated. For each mode of tumor regression, one timepoint was simulated. Based on the results of simulated data, the approach used for analysis in real patients was developed. CBCTs were non-rigidly registered to the baseline CBCT using a cubic B-spline algorithm, NiftyReg. Next, the Jacobian determinants were computed from the deformation vector fields. To capture local volume changes, 10 Jacobian values were sampled perpendicular to the surface of the GTV, across the lung-tumor boundary. From the simulated data, we can distinguish elastic from non-elastic tumor regression by comparing the Jacobian values samples between 5 and 12.5 mm inside and 5 and 12.5 mm outside the planning GTV. Finally, morphometric results were compared between tumors of different histologies. RESULTS Most patients (92.3%) in our cohort showed stable disease in the first week of treatment and non-elastic shrinkage in the later weeks of treatment. At week 2, 125 patients (88%) showed stable disease, three patients (2.1%) disease progression, and 11 patients (8%) regression. By treatment completion, 91 patients (64%) had stable disease, one patient (0.7%) progression and 46 patients (32%) regression. A slight difference in the mode of tumor change was observed between tumors of different histologies. CONCLUSION Our novel approach shows that it may be possible to automatically quantify and identify global changes in lung cancer patients during RT, using routine CBCT images. Our results show that different regions of the tumor change in different ways. Therefore, careful consideration should be taken when adapting RT.
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Affiliation(s)
- Lameck Mbangula Amugongo
- Division of Cancer Sciences, University of Manchester, Manchester, UK.,Department of Radiotherapy Related Research, the Christie NHS Foundation Trust, Manchester, UK
| | - Andrew Green
- Division of Cancer Sciences, University of Manchester, Manchester, UK.,Department of Radiotherapy Related Research, the Christie NHS Foundation Trust, Manchester, UK
| | - David Cobben
- The Clatterbridge Cancer Centre NHS Foundation Trust, Clatterbridge Hospital, Birkenhead, UK
| | - Marcel van Herk
- Division of Cancer Sciences, University of Manchester, Manchester, UK.,Department of Radiotherapy Related Research, the Christie NHS Foundation Trust, Manchester, UK
| | - Alan McWilliam
- Division of Cancer Sciences, University of Manchester, Manchester, UK.,Department of Radiotherapy Related Research, the Christie NHS Foundation Trust, Manchester, UK
| | - Eliana Vasquez Osorio
- Division of Cancer Sciences, University of Manchester, Manchester, UK.,Department of Radiotherapy Related Research, the Christie NHS Foundation Trust, Manchester, UK
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Blomain ES, Moding EJ. Liquid Biopsies for Molecular Biology-Based Radiotherapy. Int J Mol Sci 2021; 22:11267. [PMID: 34681925 PMCID: PMC8538046 DOI: 10.3390/ijms222011267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 10/13/2021] [Accepted: 10/15/2021] [Indexed: 11/29/2022] Open
Abstract
Molecular alterations drive cancer initiation and evolution during development and in response to therapy. Radiotherapy is one of the most commonly employed cancer treatment modalities, but radiobiologic approaches for personalizing therapy based on tumor biology and individual risks remain to be defined. In recent years, analysis of circulating nucleic acids has emerged as a non-invasive approach to leverage tumor molecular abnormalities as biomarkers of prognosis and treatment response. Here, we evaluate the roles of circulating tumor DNA and related analyses as powerful tools for precision radiotherapy. We highlight emerging work advancing liquid biopsies beyond biomarker studies into translational research investigating tumor clonal evolution and acquired resistance.
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Affiliation(s)
- Erik S. Blomain
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA;
| | - Everett J. Moding
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA;
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
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Palani D, Shanmugam S, Govindaraj K. Analysing the possibility of utilizing CBCT radiomics as an independent modality: a phantom study. Asian Pac J Cancer Prev 2021; 22:1383-1391. [PMID: 34048165 PMCID: PMC8408395 DOI: 10.31557/apjcp.2021.22.5.1383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Indexed: 11/25/2022] Open
Abstract
Aim: To verify if computed tomography (CT) radiomics were reproducible by cone beam CT (CBCT) radiomics by using Catphan® 504. Materials and Methods: Catphan® 504 was imaged using the default IGRT OBI CBCT imaging protocols and CT scanner. Seven known density image regions of the phantom were segmented and image feature was extracted by Imaging Biomarker Explorer (IBEX) software. The 49 selected features from four feature categories were analyzed by considering each region of interest (ROI) segment as individual image set. Correlation was studies using interclass correlation coefficient (ICC) and Pearson’s correlation coefficient. Results: The ICC of the three feature categories, namely intensity, GLCM, and GLRLM was significant (p-value<0.05) in comparison with CT, while the ICC of the fourth feature category, NID, was no significant. The average absolute Pearson’s correlation coefficient from the features of the images was as follows: CT: r=0.679±0.257, CBCThead: r=0.707±0.231, CBCTthorax: r=0.643±0.260, and CBCTpelvis: r=0.594±0.276. Conclusion: It seems that the various densities of Catphan® 504 ROI image segments of the CT radiomics are reproducible with CBCT radiomics and CBCT radiomics can be used as an independent modality.
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Affiliation(s)
- Dharmendran Palani
- Research and Development Centre, Bharathiar University, Coimbatore, India
| | - Senthilkumar Shanmugam
- Department of Radiotherapy Government Rajaji Hospital & Madurai Medical College, Madurai, Tamil Nadu, India
| | - Kesavan Govindaraj
- Research and Development Centre, Bharathiar University, Coimbatore, India.,Department of Radiotherapy, Vadamalayan Hospitals Integrated Cancer Centre, Madurai, India
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12
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The dynamics and prognostic value of FDG PET-metrics in weekly monitoring of (chemo)radiotherapy for NSCLC. Radiother Oncol 2021; 160:107-114. [PMID: 33872642 DOI: 10.1016/j.radonc.2021.04.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 02/03/2021] [Accepted: 04/08/2021] [Indexed: 12/25/2022]
Abstract
PURPOSE To test if the relative change in FDG-PET SUVmax over the course of treatment was associated with disease progression and overall survival. Additionally, the prognostic values of other first-order PET-metric changes were investigated. METHODS The study included 38 patients with stage II-III NSCLC, who underwent concurrent chemoradiotherapy. Patients received two pre-treatment FDG-PET scans and four during-treatment scans at weekly intervals. SUVmax was normalized to the start of treatment and analyzed using linear regression. Linear regression coefficients of other first order PET-metrics were grouped according to dissimilarity. Associations to patient outcome were analyzed using Cox hazard ratio. RESULTS Twenty-eight patients satisfied the criteria for analysis. All PET-metrics demonstrated a strong linear correlation with time during treatment [median R-range: -0.87: -0.97]. No strong associations (p > 0.10) were found for the relative slope of SUVmax to patient outcomes. Other first-order metrics did correlate with outcome but the single imaging time-point maximizing the association of PET response with outcome varied per PET metric and outcome parameter. CONCLUSION All investigated FDG PET metrics linearly decreased during treatment. Relative change in SUVmax was not associated to patient outcome while several other first order PET-metrics were related to patient outcome. A single optimal imaging time-point could not be identified.
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13
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Kavanaugh J, Roach M, Ji Z, Fontenot J, Hugo GD. A method for predictive modeling of tumor regression for lung adaptive radiotherapy. Med Phys 2021; 48:2083-2094. [PMID: 33035365 DOI: 10.1002/mp.14529] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 08/04/2020] [Accepted: 08/20/2020] [Indexed: 12/28/2022] Open
Abstract
PURPOSE The purpose of this work is to create a decision support methodology to predict when patients undergoing radiotherapy treatment for locally advanced lung cancer would potentially benefit from adaptive radiotherapy. The proposed methodology seeks to eliminate the manual subjective review by developing an automated statistical learning model to predict when tumor regression would trigger implementation of adaptive radiotherapy based on quantified anatomic changes observed in individual patients on-treatment cone beam computed tomographies (CTs). This proposed process seeks to improve the efficacy and efficiency of both the existing manual and automated adaptive review processes for locally advanced stage III lung cancer. METHODS A predictive algorithm was developed as a decision support tool to determine the potential utility of mid-treatment adaptive radiotherapy based on anatomic changes observed on 1158 daily CBCT images across 43 patients. The anatomic changes on each axial slice within specified regions-of-interest were quantified into a single value utilizing imaging similarity criteria comparing the daily CBCT to the initial simulation CT. The range of the quantified metrics for each fraction across all axial slices are reduced to specified quantiles, which are used as the predictive input to train a logistic regression algorithm. A "ground-truth" of the need for adaptive radiotherapy based on tumor regression was evaluated systematically on each of the daily CBCTs and used as the classifier in the logistic regression algorithm. Accuracy of the predictive model was assessed utilizing both a tenfold cross validation and an independent validation dataset, with the sensitivity, specificity, and fractional accuracy compared to the ground-truth. RESULTS The sensitivity and specificity for the individual daily fractions ranged from 87.9%-94.3% and 91.9%-98.6% for a probability threshold of 0.2-0.5, respectively. The corresponding average treatment fraction difference between the model predictions and assessed ART "ground-truth" ranged from -2.25 to -0.07 fractions, with the model predictions consistently predicting the potential need for ART earlier in the treatment course. By initially utilizing a lower probability threshold, the higher sensitivity minimizes the chance of false negative by alerting the clinician to review a higher number of questionable cases. CONCLUSIONS The proposed methodology accurately predicted the first fraction at which individual patients may benefit from ART based on quantified anatomic changes observed in the on-treatment volumetric imaging. The generalizability of the proposed method has potential to expand to additional modes of adaptive radiotherapy for lung cancer patients with observed underlying anatomic changes.
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Affiliation(s)
- James Kavanaugh
- Department of Radiation Oncology, Washington University in St. Louis School of Medicine, St. Louis, MO, 63110, USA
| | - Michael Roach
- Department of Radiation Oncology, Washington University in St. Louis School of Medicine, St. Louis, MO, 63110, USA
| | - Zhen Ji
- Department of Radiation Oncology, Washington University in St. Louis School of Medicine, St. Louis, MO, 63110, USA
| | - Jonas Fontenot
- Department of Physics, Mary Bird Perkins Cancer Center, Baton Rouge, LA, 70809, USA.,Department of Physics and Astronomy, Louisiana State University and Agricultural and Mechanical College, Baton Rouge, LA, 70803-4001, USA
| | - Geoffrey D Hugo
- Department of Radiation Oncology, Washington University in St. Louis School of Medicine, St. Louis, MO, 63110, USA
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A Pilot Study Examining the Prognostic Utility of Tumor Shrinkage on Cone-Beam Computed Tomography (CBCT) for Stage III Locally Advanced Non-Small Cell Lung Cancer Patients Treated with Definitive Chemoradiation. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18063241. [PMID: 33801033 PMCID: PMC8004060 DOI: 10.3390/ijerph18063241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/15/2021] [Accepted: 03/17/2021] [Indexed: 11/29/2022]
Abstract
There has been growing interest in utilizing information from cone-beam computed tomography (CBCT) to help guide both treatment delivery and prognosis. In this assessment of locally advanced unresectable stage III non-small cell lung cancer (NSCLC) treated with definitive chemoradiation, we aimed to determine the survival advantage associated with using CBCT to measure tumor regression. Patient, tumor, and treatment characteristics were collected. The serial tumor shrinkage for each patient was determined from tumor volume contours on weekly CBCTs. Survival analysis was performed using the Kaplan–Meier technique and a Cox proportional hazards model. At least two-thirds of patients had a tumor volume reduction of at least 5% after each week of chemoradiation. A weekly reduction in tumor volume of 5% or greater seen on the CBCT images during radiation therapy was significantly associated with improved overall survival, which remained significant when adjusted for age, histology, grade, and T- and N-stages (p = 0.0036). Additionally, the presence of N3 disease was associated with a five-fold increased risk of recurrence (p = 0.0006) and a nearly three-fold increased risk of death (p = 0.053) compared with N0–N2 disease. Tumor volume shrinkage observed in the CBCT images during definitive chemoradiation holds promise as a prognostic indicator of stage III NSCLC, especially given its affordability, availability, and applicability. Further evaluation in a prospective fashion is warranted to validate the tumor volume shrinkage and its clinical utility.
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15
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Liang S, Li C, Gao Z, Shang D, Yu J, Meng X. The Predictive Value of Tumor Volume and Its Change on Short-Term Outcome for Esophageal Squamous Cell Carcinoma Treated With Radiotherapy or Chemoradiotherapy. Front Oncol 2021; 10:586145. [PMID: 33634014 PMCID: PMC7901880 DOI: 10.3389/fonc.2020.586145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 12/14/2020] [Indexed: 12/09/2022] Open
Abstract
Objectives To investigate the tumor volume and its change on short-term outcome in esophageal squamous cell carcinoma (ESCC) patients who underwent definitive radiotherapy or chemoradiotherapy. Methods and Materials All data were retrospectively collected from 418 ESCC patients who received radiotherapy or chemoradiotherapy at our institution between 2015 and 2019. Short-term outcome using the treatment response evaluation was assessed according to the RECIST 1.1. The tumor volume change rate (TVCR) was defined as follows: TVCR = {1 - [gross tumor volume (GTV) at shrinking irradiation field planning)]/(GTV at the initial treatment planning)} ×100%. Chi square test was used to compare the clinic characteristics in different TVCR groups, and the difference between initial GTV (GTVi) and shrinking GTV (GTVs) was compared using Wilcoxon's sign rank test. Logistic regression analysis and Spearman correlation was performed. Results There was a significant decrease in GTVi compared to GTVs (P < 0.001). In univariate analysis, age, cT-stage, TNM stage, treatment modality, GTVi, and TVCR were associated with short-term outcome (all P < 0.05). In multivariate analysis, gender and TVCR were statistically significant (P = 0.010, <0.001) with short-term outcome, and the combined predictive value of gender and TVCR exceeded that of TVCR (AUC, 0.876 vs 0.855). Conclusions TVCR could serve to forecast short-term outcome of radiotherapy or chemoradiotherapy in ESCC. It was of great significance to guide the individualized treatment of ESCC.
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Affiliation(s)
- Shuai Liang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Chengming Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Zhenhua Gao
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Dongping Shang
- Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jinming Yu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xue Meng
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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16
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Amugongo LM, Osorio EV, Green A, Cobben D, van Herk M, McWilliam A. Identification of patterns of tumour change measured on CBCT images in NSCLC patients during radiotherapy. Phys Med Biol 2020; 65:215001. [PMID: 32693397 DOI: 10.1088/1361-6560/aba7d3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
In this study, we propose a novel approach to investigate changes in the visible tumour and surrounding tissues with the aim of identifying patterns of tumour change during radiotherapy (RT) without segmentation on the follow-up images. On-treatment cone-beam computed tomography (CBCT) images of 240 non-small cell lung cancer (NSCLC) patients who received 55 Gy of RT were included. CBCTs were automatically aligned onto planning computed tomography (planning CT) scan using a two-step rigid registration process. To explore density changes across the lung-tumour boundary, eight shells confined to the shape of the gross tumour volume (GTV) were created. The shells extended 6 mm inside and outside of the GTV border, and each shell is 1.5 mm thick. After applying intensity correction on CBCTs, the mean intensity was extracted from each shell across all CBCTs. Thereafter, linear fits were created, indicating density change over time in each shell during treatment. The slopes of all eight shells were clustered to explore patterns in the slopes that show how tumours change. Seven clusters were obtained, 97% of the patients were clustered into three groups. After visual inspection, we found that these clusters represented patients with little or no density change, progression and regression. For the three groups, the survival curves were not significantly different between the groups, p-value = 0.51. However, the results show that definite patterns of tumour change exist, suggesting that it may be possible to identify patterns of tumour changes from on-treatment CBCT images.
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Affiliation(s)
- Lameck Mbangula Amugongo
- Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom. Department of Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, United Kingdom
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17
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Barrett S, Simpkin AJ, Walls GM, Leech M, Marignol L. Geometric and Dosimetric Evaluation of a Commercially Available Auto-segmentation Tool for Gross Tumour Volume Delineation in Locally Advanced Non-small Cell Lung Cancer: a Feasibility Study. Clin Oncol (R Coll Radiol) 2020; 33:155-162. [PMID: 32798158 DOI: 10.1016/j.clon.2020.07.019] [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: 06/15/2020] [Revised: 06/24/2020] [Accepted: 07/24/2020] [Indexed: 12/25/2022]
Abstract
AIMS To quantify the reliability of a commercially available auto-segmentation tool in locally advanced non-small cell lung cancer using serial four-dimensional computed tomography (4DCT) scans during conventionally fractionated radiotherapy. MATERIALS AND METHODS Eight patients with serial 4DCT scans (n = 44) acquired over the course of radiotherapy were assessed. Each 4DCT had a physician-defined primary tumour manual contour (MC). An auto-contour (AC) and a user-adjusted auto-contour (UA-AC) were created for each scan. Geometric agreement of the AC and the UA-AC to the MC was assessed using the dice similarity coefficient (DSC), the centre of mass (COM) shift from the MC and the structure volume difference from the MC. Bland Altman analysis was carried out to assess agreement between contouring methods. Dosimetric reliability was assessed by comparison of planning target volume dose coverage on the MC and UA-AC. The time trend analysis of the geometric accuracy measures from the initial planning scan through to the final scan for each patient was evaluated using a Wilcoxon signed ranks test to assess the reliability of the UA-AC over the duration of radiotherapy. RESULTS User adjustment significantly improved all geometric comparison metrics over the AC alone. Improved agreement was observed in smaller tumours not abutting normal soft tissue and median values for geometric comparisons to the MC for DSC, tumour volume difference and COM offset were 0.80 (range 0.49-0.89), 0.8 cm3 (range 0.0-5.9 cm3) and 0.16 cm (range 0.09-0.69 cm), respectively. There were no significant differences in dose metrics measured from the MC and the UA-AC after Bonferroni correction. Variation in geometric agreement between the MC and the UA-AC were observed over the course of radiotherapy with both DSC (P = 0.035) and COM shift from the MC (ns) worsening. The median tumour volume difference from the MC improved at the later time point. CONCLUSIONS These findings suggest that the UA-AC can produce geometrically and dosimetrically acceptable contours for appropriately selected patients with non-small cell lung cancer. Larger studies are required to confirm the findings.
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Affiliation(s)
- S Barrett
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity College Dublin, Dublin, Ireland.
| | - A J Simpkin
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway, Ireland
| | - G M Walls
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - M Leech
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity College Dublin, Dublin, Ireland
| | - L Marignol
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity College Dublin, Dublin, Ireland
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18
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den Otter LA, Anakotta RM, Weessies M, Roos CTG, Sijtsema NM, Muijs CT, Dieters M, Wijsman R, Troost EGC, Richter C, Meijers A, Langendijk JA, Both S, Knopf AC. Investigation of inter-fraction target motion variations in the context of pencil beam scanned proton therapy in non-small cell lung cancer patients. Med Phys 2020; 47:3835-3844. [PMID: 32573792 PMCID: PMC7586844 DOI: 10.1002/mp.14345] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 05/01/2020] [Accepted: 06/14/2020] [Indexed: 12/25/2022] Open
Abstract
Purpose For locally advanced‐stage non‐small cell lung cancer (NSCLC), inter‐fraction target motion variations during the whole time span of a fractionated treatment course are assessed in a large and representative patient cohort. The primary objective is to develop a suitable motion monitoring strategy for pencil beam scanning proton therapy (PBS‐PT) treatments of NSCLC patients during free breathing. Methods Weekly 4D computed tomography (4DCT; 41 patients) and daily 4D cone beam computed tomography (4DCBCT; 10 of 41 patients) scans were analyzed for a fully fractionated treatment course. Gross tumor volumes (GTVs) were contoured and the 3D displacement vectors of the centroid positions were compared for all scans. Furthermore, motion amplitude variations in different lung segments were statistically analyzed. The dosimetric impact of target motion variations and target motion assessment was investigated in exemplary patient cases. Results The median observed centroid motion was 3.4 mm (range: 0.2–12.4 mm) with an average variation of 2.2 mm (range: 0.1–8.8 mm). Ten of 32 patients (31.3%) with an initial motion <5 mm increased beyond a 5‐mm motion amplitude during the treatment course. Motion observed in the 4DCBCT scans deviated on average 1.5 mm (range: 0.0–6.0 mm) from the motion observed in the 4DCTs. Larger motion variations for one example patient compromised treatment plan robustness while no dosimetric influence was seen due to motion assessment biases in another example case. Conclusions Target motion variations were investigated during the course of radiotherapy for NSCLC patients. Patients with initial GTV motion amplitudes of < 2 mm can be assumed to be stable in motion during the treatment course. For treatments of NSCLC patients who exhibit motion amplitudes of > 2 mm, 4DCBCT should be considered for motion monitoring due to substantial motion variations observed.
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Affiliation(s)
- Lydia A den Otter
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, 9713 GZ, The Netherlands
| | - Renske M Anakotta
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, 9713 GZ, The Netherlands
| | - Menkedina Weessies
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, 9713 GZ, The Netherlands
| | - Catharina T G Roos
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, 9713 GZ, The Netherlands
| | - Nanna M Sijtsema
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, 9713 GZ, The Netherlands
| | - Christina T Muijs
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, 9713 GZ, The Netherlands
| | - Margriet Dieters
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, 9713 GZ, The Netherlands
| | - Robin Wijsman
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, 9713 GZ, The Netherlands
| | - Esther G C Troost
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden, Rossendorf, Germany.,Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology, OncoRay, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,Partner Site Dresden, and German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany.,National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany
| | - Christian Richter
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden, Rossendorf, Germany.,Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology, OncoRay, Germany.,Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.,Partner Site Dresden, and German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Arturs Meijers
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, 9713 GZ, The Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, 9713 GZ, The Netherlands
| | - Stefan Both
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, 9713 GZ, The Netherlands
| | - Antje-Christin Knopf
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, 9713 GZ, The Netherlands
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Kwint M, Stam B, Proust-Lima C, Philipps V, Hoekstra T, Aalbersberg E, Rossi M, Sonke JJ, Belderbos J, Walraven I. The prognostic value of volumetric changes of the primary tumor measured on Cone Beam-CT during radiotherapy for concurrent chemoradiation in NSCLC patients. Radiother Oncol 2020; 146:44-51. [DOI: 10.1016/j.radonc.2020.02.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 12/05/2019] [Accepted: 02/05/2020] [Indexed: 02/09/2023]
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Qin Q, Shi A, Zhang R, Wen Q, Niu T, Chen J, Qiu Q, Wan Y, Sun X, Xing L. Cone-beam CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients. Thorac Cancer 2020; 11:964-972. [PMID: 32061061 PMCID: PMC7113065 DOI: 10.1111/1759-7714.13349] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 01/20/2020] [Accepted: 01/21/2020] [Indexed: 12/15/2022] Open
Abstract
Background Stereotactic body radiotherapy (SBRT) is the standard care for inoperable early stage non‐small cell lung cancer (NSCLC). The purpose of our study was to investigate whether a prediction model based on cone‐beam CT (CBCT) plus pretreatment CT radiomics features could improve the prediction of tumor control and lung toxicity after SBRT in comparison to a model based on pretreatment CT radiomics features alone. Methods A total of 34 cases of stage I NSCLC patients who received SBRT were included in the study. The pretreatment planning CT and serial CBCT radiomics features were analyzed using the imaging biomarker explorer (IBEX) software platform. Multivariate logistic regression was conducted for the association between progression‐free survival (PFS), lung toxicity and features. The predictive capabilities of the models based on CBCT and CT features were compared using receiver operating characteristic (ROC) curves. Results Five CBCT features and two planning CT features were correlated with disease progression. Six CBCT features and two planning CT features were related to lung injury. The ROC curves indicated that the model based on the CBCT plus planning CT features might be better than the model based on the planning CT features in predicting lung injury. The other ROC curves indicated that the model based on the planning CT features was similar to the model based on the CBCT plus planning CT features in predicting disease progression. Conclusions Both pretreatment CT and CBCT radiomics features could predict disease progression and lung injury. A model with CBCT plus pretreatment CT radiomics features might improve the prediction of lung toxicity in comparison with a model with pretreatment CT features alone. Key points Significant findings of the study: A model with cone‐beam CT radiomics features plus pre‐treatment CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients. What this study adds: In the prediction of PFS and lung toxicity in early‐stage NSCLC patients treated with SBRT, CBCT radiomics could be another effective method.
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Affiliation(s)
- Qingjin Qin
- School of Medicine and Life Sciences, University of Jinan-Shandong Academy of Medical Sciences, Jinan, China.,Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| | - Anhui Shi
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, China.,Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Ran Zhang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China.,Shandong University Cheeloo College of Medicine, Jinan, China
| | - Qiang Wen
- Department of Oncology, Shandong Provincial Hospital, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Tianye Niu
- Nuclear & Radiological Engineering and Medical Physics Programs Woodruff School of Mechanical Engineering Georgia Institute of Technology, Atlanta, Georgia
| | - Jinhu Chen
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| | - Qingtao Qiu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| | - Yidong Wan
- Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang
| | - Xiaorong Sun
- School of Medicine and Life Sciences, University of Jinan-Shandong Academy of Medical Sciences, Jinan, China.,Shandong University Cheeloo College of Medicine, Jinan, China.,Department of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| | - Ligang Xing
- School of Medicine and Life Sciences, University of Jinan-Shandong Academy of Medical Sciences, Jinan, China.,Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
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Shi L, Rong Y, Daly M, Dyer B, Benedict S, Qiu J, Yamamoto T. Cone-beam computed tomography-based delta-radiomics for early response assessment in radiotherapy for locally advanced lung cancer. ACTA ACUST UNITED AC 2020; 65:015009. [DOI: 10.1088/1361-6560/ab3247] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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22
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Mahmood F, Hjorth Johannesen H, Geertsen P, Hansen RH. Diffusion MRI outlined viable tumour volume beats GTV in intra-treatment stratification of outcome. Radiother Oncol 2019; 144:121-126. [PMID: 31805516 DOI: 10.1016/j.radonc.2019.11.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 11/09/2019] [Accepted: 11/12/2019] [Indexed: 12/29/2022]
Abstract
BACKGROUND AND PURPOSE In radiotherapy, treatment response is generally evaluated many weeks after end of the treatment course. If the treatment outcome could be predicted during radiotherapy better tumour control could be achieved through timely adaptation of the treatment strategy. In this study intra-treatment change based on the diffusion MRI outlined viable tumour volume (VTV) was assessed and compared to the standard GTV to study their outcome prediction capacity. MATERIALS AND METHODS Thirty-eight brain metastases from twenty-one cancer patients were analysed in this prospective trial. Diffusion and structural MRI was acquired on a 1 T machine before, during, and at follow-up 2-3 months after radiotherapy. The VTV was defined as a region with high cellularity using high b-value diffusion MRI scans. Further, the diffusivity of the VTV was derived as the apparent diffusion coefficient (ADC). Treatment outcome was determined using RECIST defined bounds in the T1W MRI follow-up scan. Longitudinal statistical analysis was performed using a linear mixed effect model. RESULTS The GTV declined in both responding and non-responding (significantly) tumours with inseparable rates during radiotherapy. The VTV volume fraction reduced significantly in the responding tumours only. The ADC of the VTV increased significantly in responding metastases whereas it decreased in non-responding metastases. Furthermore, no association between baseline tumour size or primary disease and outcome was observed. CONCLUSION GTV size change during radiotherapy is not a reliable predictor of outcome in brain metastases. On the other hand, change in the volume fraction of VTV and diffusivity of VTV shows ability to stratify treatment outcome.
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Affiliation(s)
- Faisal Mahmood
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Denmark; Department of Clinical Research, University of Southern Denmark, Odense C, Denmark; Section of Radiotherapy, Department of Oncology, Herlev Hospital, Denmark.
| | | | - Poul Geertsen
- Section of Radiotherapy, Department of Oncology, Herlev Hospital, Denmark.
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Tumor regression during radiotherapy for non-small cell lung cancer patients using cone-beam computed tomography images. Strahlenther Onkol 2019; 196:159-171. [PMID: 31559481 PMCID: PMC6994551 DOI: 10.1007/s00066-019-01522-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2018] [Accepted: 09/12/2019] [Indexed: 01/25/2023]
Abstract
PURPOSE Previous literature has reported contradicting results regarding the relationship between tumor volume changes during radiotherapy treatment for non-small cell lung cancer (NSCLC) patients and locoregional recurrence-free rate or overall survival. The aim of this study is to validate the results from a previous study by using a different volume extraction procedure and evaluating an external validation dataset. METHODS For two datasets of 94 and 141 NSCLC patients, gross tumor volumes were determined manually to investigate the relationship between tumor volume regression and locoregional control using Kaplan-Meier curves. For both datasets, different subgroups of patients based on histology and chemotherapy regimens were also investigated. For the first dataset (n = 94), automatically determined tumor volumes were available from a previously published study to further compare their correlation with updated clinical data. RESULTS A total of 70 out of 94 patients were classified into the same group as in the previous publication, splitting the dataset based on median tumor regression calculated by the two volume extraction methods. Non-adenocarcinoma patients receiving concurrent chemotherapy with large tumor regression show reduced locoregional recurrence-free rates in both datasets (p < 0.05 in dataset 2). For dataset 2, the opposite behavior is observed for patients not receiving chemotherapy, which was significant for overall survival (p = 0.01) but non-significant for locoregional recurrence-free rate (p = 0.13). CONCLUSION The tumor regression pattern observed during radiotherapy is not only influenced by irradiation but depends largely on the delivered chemotherapy schedule, so it follows that the relationship between patient outcome and the degree of tumor regression is also largely determined by the chemotherapy schedule. This analysis shows that the relationship between tumor regression and outcome is complex, and indicates factors that could explain previously reported contradicting findings. This, in turn, will help guide future studies to fully understand the relationship between tumor regression and outcome.
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Kavanaugh J, Hugo G, Robinson CG, Roach MC. Anatomical Adaptation-Early Clinical Evidence of Benefit and Future Needs in Lung Cancer. Semin Radiat Oncol 2019; 29:274-283. [PMID: 31027644 DOI: 10.1016/j.semradonc.2019.02.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Definitive treatment of locally advanced non-small-cell lung cancer with radiation is challenging. During the course of treatment, anatomical changes such as tumor regression, tumor displacement/deformation, pleural effusion, and/or atelectasis can result in a deviation of the administered radiation dose from the intended prescribed treatment and thereby worsen local control and toxicity. Adaptive radiotherapy can help correct for these changes and can be generally categorized into 3 philosophical paradigms: (1) maintenance of prescribed dose to the initially defined target volume; (2) dose reduction to healthy organs while maintaining initial prescribed dose to a regressing tumor volume; or (3) dose escalation to a regressing tumor volume with isotoxicity to healthy organs. Numerous single institution studies have investigated these methods, and results from large prospective clinical trials will hopefully provide consensus on the method, utility, and efficacy of implementing adaptive radiation therapy (ART) in a clinical setting. Additional development into standardization and automation of the ART workflow, specifically in identifying when ART is warranted and in reducing the manual clinical effort needed to produce an adaptive plan, will be paramount to making ART feasible for the broader radiation therapy community.
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Affiliation(s)
- James Kavanaugh
- Department of Radiation Oncology, Washington University School of Medicine, Saint Louis, MO
| | - Geoffrey Hugo
- Department of Radiation Oncology, Washington University School of Medicine, Saint Louis, MO
| | - Cliff G Robinson
- Department of Radiation Oncology, Washington University School of Medicine, Saint Louis, MO
| | - Michael C Roach
- Department of Radiation Oncology, Washington University School of Medicine, Saint Louis, MO.
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van Timmeren JE, van Elmpt W, Leijenaar RTH, Reymen B, Monshouwer R, Bussink J, Paelinck L, Bogaert E, De Wagter C, Elhaseen E, Lievens Y, Hansen O, Brink C, Lambin P. Longitudinal radiomics of cone-beam CT images from non-small cell lung cancer patients: Evaluation of the added prognostic value for overall survival and locoregional recurrence. Radiother Oncol 2019; 136:78-85. [PMID: 31015133 PMCID: PMC6598851 DOI: 10.1016/j.radonc.2019.03.032] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 03/07/2019] [Accepted: 03/29/2019] [Indexed: 11/23/2022]
Abstract
Longitudinal CBCT radiomics does not show added prognostic information for NSCLC. A CT-radiomics model could be validated in an external validation dataset. Dataset heterogeneity and small cohort sizes could cause poor validation performance.
Background and purpose The prognostic value of radiomics for non-small cell lung cancer (NSCLC) patients has been investigated for images acquired prior to treatment, but no prognostic model has been developed that includes the change of radiomic features during treatment. Therefore, the aim of this study was to investigate the potential added prognostic value of a longitudinal radiomics approach using cone-beam computed tomography (CBCT) for NSCLC patients. Materials and methods This retrospective study includes a training dataset of 141 stage I–IV NSCLC patients and three external validation datasets of 94, 61 and 41 patients, all treated with curative intended (chemo)radiotherapy. The change of radiomic features extracted from CBCT images was summarized as the slope of a linear regression. The CBCT slope-features and CT-extracted features were used as input for a Cox proportional hazards model. Moreover, prognostic performance of clinical parameters was investigated for overall survival and locoregional recurrence. Model performances were assessed using the Kaplan–Meier curves and c-index. Results The radiomics model contained only CT-derived features and reached a c-index of 0.63 for overall survival and could be validated on the first validation dataset. No model for locoregional recurrence could be developed that validated on the validation datasets. The clinical parameters model could not be validated for either overall survival or locoregional recurrence. Conclusion In this study we could not confirm our hypothesis that longitudinal CBCT-extracted radiomic features contribute to improved prognostic information. Moreover, performance of baseline radiomic features or clinical parameters was poor, probably affected by heterogeneity within and between datasets.
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Affiliation(s)
- Janna E van Timmeren
- The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands.
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, the Netherlands
| | - Ralph T H Leijenaar
- The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Bart Reymen
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, the Netherlands
| | - René Monshouwer
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Johan Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Leen Paelinck
- Ghent University Hospital and Ghent University, Ghent, Belgium
| | - Evelien Bogaert
- Ghent University Hospital and Ghent University, Ghent, Belgium
| | | | - Elamin Elhaseen
- Ghent University Hospital and Ghent University, Ghent, Belgium
| | - Yolande Lievens
- Ghent University Hospital and Ghent University, Ghent, Belgium
| | - Olfred Hansen
- Institute of Clinical Research, University of Southern Denmark, Odense, Denmark; Department of Oncology, Odense University Hospital, Odense, Denmark
| | - Carsten Brink
- Institute of Clinical Research, University of Southern Denmark, Odense, Denmark; Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark
| | - Philippe Lambin
- The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
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Clarke E, Curtis J, Brada M. Incidence and evolution of imaging changes on cone-beam CT during and after radical radiotherapy for non-small cell lung cancer. Radiother Oncol 2018; 132:121-126. [PMID: 30825960 DOI: 10.1016/j.radonc.2018.12.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 11/03/2018] [Accepted: 12/07/2018] [Indexed: 10/27/2022]
Abstract
BACKGROUND AND PURPOSE Cone beam CT (CBCT) is used to improve accuracy of radical radiotherapy by adjusting treatment to the observed imaging changes. To ensure appropriate adjustment, image interpretation should precede any changes to treatment delivery. This study provides the methodology for image interpretation and the frequency and evolution of the changes in patients undergoing radical radiotherapy for localised and locally advanced non-small cell lung cancer (NSCLC). PATIENTS AND METHODS From December 2012 to December 2014, 250 patients with localised and locally advanced NSCLC had 2462 chest CBCT scans during the course of fractionated radical radiotherapy (RT) (3-5 daily CBCTs in the first week followed by at least weekly imaging, mean 9.5 per patient, range 1-21). All CBCT images were reviewed describing changes and their evolution using diagnostic imaging definitions and validated by an independent chest radiologist. RESULTS During radical RT for NSCLC 328 imaging changes were identified on CBCT in 180 (72%) patients; 104 (32%) had reduction and 41 (13%) increase in tumour size; 48 (15%) had changes in consolidations contiguous to the primary lesion, 26 (8%) non-contiguous consolidations, 43 (13%) changes in tumour cavitation, 36 (11%) pleural effusion and 30 (9%) changes in atelectasis. In 105 patients imaging changes were noted in continuity with the treated tumour of which only 41 (39%) represented tumour enlargement; others included new or enlarging adjacent consolidation (34%), and new or enlarging atelectasis (19%). The changes evolved during treatment. CONCLUSION Imaging changes on CBCT include real and apparent changes in tumour size and parenchymal changes which evolve during treatment. Correct image interpretation, particularly when occurring adjacent to the tumour, is essential prior to adjustment to treatment delivery.
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Affiliation(s)
- Enrico Clarke
- Department of Radiotherapy, Clatterbridge Cancer Centre NHS Foundation Trust, United Kingdom
| | - John Curtis
- Radiology Department, Aintree University Hospital NHS Foundation Trust, United Kingdom
| | - Michael Brada
- Department of Radiotherapy, Clatterbridge Cancer Centre NHS Foundation Trust, United Kingdom; Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, United Kingdom
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Greater reduction in mid-treatment FDG-PET volume may be associated with worse survival in non-small cell lung cancer. Radiother Oncol 2018; 132:241-249. [PMID: 30389239 DOI: 10.1016/j.radonc.2018.10.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 09/17/2018] [Accepted: 10/08/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND PURPOSE This study tested the hypotheses that 1) changes in mid-treatment fluorodeoxyglucose (FDG)-positron emission tomography (PET) parameters are predictive of overall survival (OS) and 2) mid-treatment FDG-PET-adapted treatment has the potential to improve survival in patients with non-small cell lung cancer (NSCLC). MATERIAL AND METHODS Patients with stage I-III NSCLC requiring daily fractionated radiation were eligible. FDG-PET-CT scans were obtained prior to and mid-treatment with radiotherapy at 40-50 Gy. The normalized maximum standardized uptake value (NSUVmax), normalized mean SUV (NSUVmean), PET-metabolic tumor volume (MTV), total lesion glycolysis (TLG), and computed tomography-based gross tumor volume (CT-GTV) were consistently measured for all patients. The primary study endpoint was OS. RESULTS The study is comprised of 102 patients who received 3-dimensional conformal radiotherapy, among whom 30 patients who received mid-treatment PET-adapted dose escalation radiotherapy. All PET-CT parameters decreased significantly (P < 0.001) mid-treatment, with greater reductions in FDG-volumetric parameters compared to FDG-activity factors. Mid-treatment changes in MTV (P = 0.053) and TLG (P = 0.021) were associated with OS, while changes in NSUVmax, NSUVmean, and CT-GTV were not (all Ps>0.1). Patients receiving conventional radiation (60-70 Gy) with MTV reductions greater than the mean had a median survival of 14 months, compared to those with MTV reductions less than the mean who had a median survival of 22 months. By contrast, patients receiving mid-treatment PET-adapted radiation with MTV reductions greater than the mean had a median survival of 33 months, compared to those with MTV reductions less than the mean who had a median survival of 19 months. Overall, PET-adapted treatment resulted in a 19% better 5-year survival than conventional radiation. CONCLUSION Changes in mid-treatment PET-volumetric parameters were significantly associated with survival in NSCLC. A greater reduction in the mid-treatment MTV was associated with worse survival in patients treated with standard radiation, but with better survival in patients who received mid-treatment PET-adapted treatment.
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Aboudaram A, Khalifa J, Massabeau C, Simon L, Hadj Henni A, Thureau S. [Image-guided radiotherapy in lung cancer]. Cancer Radiother 2018; 22:602-607. [PMID: 30104150 DOI: 10.1016/j.canrad.2018.06.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 06/29/2018] [Indexed: 12/20/2022]
Abstract
Image-guided radiotherapy takes place at every step of the treatment in lung cancer, from treatment planning, with fusion imaging, to daily in-room repositioning. Managing tumoral and surrounding thoracic structures motion has been allowed since the routine use of 4D computed tomography (4DCT). The integration of respiratory motion has been made with "passive" techniques based on reconstruction images from 4DCT planning, or "active" techniques adapted to the patient's breathing. Daily repositioning is based on regular images, weekly or daily, low (kV) or high (MV) energy. MRI and functional imaging also play an important part in lung cancer radiation and open the way for adaptative radiotherapy.
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Affiliation(s)
- A Aboudaram
- Département de radiothérapie, institut universitaire du cancer de Toulouse-oncopôle, 1, avenue Irène-Joliot Curie, 31037 Toulouse, France.
| | - J Khalifa
- Département de radiothérapie, institut universitaire du cancer de Toulouse-oncopôle, 1, avenue Irène-Joliot Curie, 31037 Toulouse, France
| | - C Massabeau
- Département de radiothérapie, institut universitaire du cancer de Toulouse-oncopôle, 1, avenue Irène-Joliot Curie, 31037 Toulouse, France
| | - L Simon
- Département de radiothérapie, institut universitaire du cancer de Toulouse-oncopôle, 1, avenue Irène-Joliot Curie, 31037 Toulouse, France; CRCT UMR 1037 Inserm/UPS, 2, avenue Hubert-Curien, 31037 Toulouse, France
| | - A Hadj Henni
- Département de physique médicale, centre Henri-Becquerel, 1, rue d'Amiens, 76000 Rouen, France
| | - S Thureau
- Département de radiothérapie, centre Henri-Becquerel, 1, rue d'Amiens, 76000 Rouen, France; Laboratoire QuantIF, EA4108-Litis, FR CNRS 3638, 1, rue d'Amiens, 76000 Rouen, France; Département de médecine nucléaire, centre Henri-Becquerel, 1, rue d'Amiens, 76000 Rouen, France
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Guy CL, Weiss E, Christensen GE, Jan N, Hugo GD. CALIPER: A deformable image registration algorithm for large geometric changes during radiotherapy for locally advanced non-small cell lung cancer. Med Phys 2018; 45:2498-2508. [PMID: 29603277 DOI: 10.1002/mp.12891] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 03/06/2018] [Accepted: 03/19/2018] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Locally advanced non-small cell lung cancer (NSCLC) patients may experience dramatic changes in anatomy during radiotherapy and could benefit from adaptive radiotherapy (ART). Deformable image registration (DIR) is necessary to accurately accumulate dose during plan adaptation, but current algorithms perform poorly in the presence of large geometric changes, namely atelectasis resolution. The goal of this work was to develop a DIR framework, named Consistent Anatomy in Lung Parametric imagE Registration (CALIPER), to handle large geometric changes in the thorax. METHODS Registrations were performed on pairs of baseline and mid-treatment CT datasets of NSCLC patients presenting with atelectasis at the start of treatment. Pairs were classified based on atelectasis volume change as either full, partial, or no resolution. The evaluated registration algorithms consisted of several combinations of a hybrid intensity- and feature-based similarity cost function to investigate the ability to simultaneously match healthy lung parenchyma and adjacent atelectasis. These components of the cost function included a mass-preserving intensity cost in the lung parenchyma, use of filters to enhance vascular structures in the lung parenchyma, manually delineated lung lobes as labels, and several intensity cost functions to model atelectasis change. Registration error was quantified with landmark-based target registration error and post-registration alignment of atelectatic lobes. RESULTS The registrations using both lobe labels and vasculature enhancement in addition to intensity of the CT images were found to have the highest accuracy. Of these registrations, the mean (SD) of mean landmark error across patients was 2.50 (1.16) mm, 2.80 (0.70) mm, and 2.04 (0.13) mm for no change, partial resolution, and full atelectasis resolution, respectively. The mean (SD) atelectatic lobe Dice similarity coefficient was 0.91 (0.08), 0.90 (0.08), and 0.89 (0.04), respectively, for the same groups. Registration accuracy was comparable to healthy lung registrations of current state-of-the-art algorithms reported in literature. CONCLUSIONS The CALIPER algorithm developed in this work achieves accurate image registration for challenging cases involving large geometric and topological changes in NSCLC patients, a requirement for enabling ART in this patient group.
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Affiliation(s)
- Christopher L Guy
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, 23298, USA
| | - Elisabeth Weiss
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, 23298, USA
| | - Gary E Christensen
- Department of Electrical and Computer Engineering and Department of Radiation Oncology, University of Iowa, Iowa City, IA, 52242, USA
| | - Nuzhat Jan
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, 23298, USA
| | - Geoffrey D Hugo
- Department of Radiation Oncology, Washington University, St. Louis, MO, 63110, USA
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Is tumor volume reduction during radiotherapy prognostic relevant in patients with stage III non-small cell lung cancer? J Cancer Res Clin Oncol 2018; 144:1165-1171. [DOI: 10.1007/s00432-018-2640-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Accepted: 04/03/2018] [Indexed: 12/26/2022]
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Hudson A, Chan C, Woolf D, McWilliam A, Hiley C, O'Connor J, Bayman N, Blackhall F, Faivre-Finn C. Is heterogeneity in stage 3 non-small cell lung cancer obscuring the potential benefits of dose-escalated concurrent chemo-radiotherapy in clinical trials? Lung Cancer 2018; 118:139-147. [PMID: 29571993 DOI: 10.1016/j.lungcan.2018.02.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2017] [Revised: 01/31/2018] [Accepted: 02/05/2018] [Indexed: 12/22/2022]
Abstract
The current standard of care for the management of inoperable stage 3 non-small cell lung cancer (NSCLC) is concurrent chemoradiotherapy (cCRT) using radiotherapy dose-fractionation and chemotherapy regimens that were established 3 decades ago. In an attempt to improve the chances of long-term control from cCRT, dose-escalation of the radiotherapy dose was assessed in the RTOG 0617 randomised control study comparing the standard 60 Gy in 30 fractions with a high-dose arm receiving 74 Gy in 37 fractions. Following the publication of this trial the thoracic oncology community were surprised to learn that there was worse survival in the dose-escalated arm and that for now the standard of care must remain with the lower dose. In this article we review the RTOG 0617 paper with subsequent analyses and studies to explore why the use of dose-escalated cCRT in stage 3 NSCLC has not shown the benefits that were expected. The overarching theme of this opinion piece is how heterogeneity between stage 3 NSCLC cases in terms of patient, tumour, and clinical factors may obscure the potential benefits of dose-escalation by causing imbalances in the arms of studies such as RTOG 0617. We also examine recent advances in the staging, management, and technological delivery of radiotherapy in NSCLC and how these may be employed to optimise cCRT trials in the future and ensure that any potential benefits of dose-escalation can be detected.
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Affiliation(s)
- Andrew Hudson
- Division of Molecular and Clinical Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK; Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Clara Chan
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - David Woolf
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Alan McWilliam
- Division of Molecular and Clinical Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Crispin Hiley
- Translational Cancer Therapeutics Laboratory, The Francis Crick Institute, London, UK; Division of Cancer Studies, King's College London, London, UK
| | - James O'Connor
- Division of Molecular and Clinical Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Neil Bayman
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Fiona Blackhall
- Division of Molecular and Clinical Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK; Department of Medical Oncology, The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Corinne Faivre-Finn
- Division of Molecular and Clinical Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK; Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
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The Value of CBCT-based Tumor Density and Volume Variations in Prediction of Early Response to Chemoradiation Therapy in Advanced NSCLC. Sci Rep 2017; 7:14650. [PMID: 29116100 PMCID: PMC5676710 DOI: 10.1038/s41598-017-14548-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 10/11/2017] [Indexed: 12/25/2022] Open
Abstract
The correlations between early responses and the variations in physical density and primary tumor volume (TV) according to cone-beam computed tomography (CBCT) during chemoradiotherapy for non-small cell lung cancer (NSCLC) patients were investigated. 54 patients with inoperable and locally advanced NSCLC were included in this study. The CT numbers (CTN) and TV were measured on each of the seven observation points. The changes in the mean CTN values and the variation ratios of TV during the treatment course were analysed and correlated with the clinical outcomes, as evaluated by the RECIST criteria. For patients who responded to treatment, the CTN and TV change ratio decreased by 28.44 ± 13.12 HU and 32.01% (range, 8.46-61.67%); these values were significantly higher than those in the non-responding patients, with 19.63 ± 8.67 HU and 23.20% (range, -15.57-38%) (p = 0.016, p = 0.048), respectively. The area under curve for the combination of CTN and TV was larger than either alone (AUC = 0.751, p = 0.002). The differences between response and non-response were most significant between Fraction 10 and Fraction 15 for CTN changes and between Fraction 5 and Fraction 10 for the TV regression ratio. The changes in CTN and TV obtained from CBCT images have the potential capability to predict an early response of NSCLC.
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van Timmeren JE, Leijenaar RTH, van Elmpt W, Reymen B, Lambin P. Feature selection methodology for longitudinal cone-beam CT radiomics. Acta Oncol 2017; 56:1537-1543. [PMID: 28826307 DOI: 10.1080/0284186x.2017.1350285] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
BACKGROUND Cone-beam CT (CBCT) scans are typically acquired daily for positioning verification of non-small cell lung cancer (NSCLC) patients. Quantitative information, derived using radiomics, can potentially contribute to (early) treatment adaptation. The aims of this study were to (1) describe and investigate a methodology for feature selection of a longitudinal radiomics approach (2) investigate which time-point during treatment is potentially useful for early treatment response assessment. MATERIAL AND METHODS For 90 NSCLC patients CBCT scans of the first two fractions of treatment (considered as 'test-retest' scans) were analyzed, as well as weekly CBCT images. One hundred and sixteen radiomic features were extracted from the GTV of all scans and subsequently absolute and relative differences were calculated between weekly CBCT images and the CBCT of the first fraction. Test-retest scans were used to determine the smallest detectable change (C = 1.96 * SD) allowing for feature selection by choosing a minimum number of patients for which a feature should change more than 'C' to be considered as relevant. Analysis of which features change at which moment during treatment was used to investigate which time-point is potentially relevant to extract longitudinal radiomics information for early treatment response assessment. RESULTS A total of six absolute delta features changed for at least ten patients at week 2 of treatment and increased to 61 at week 3, 79 at week 4 and 85 at week 5. There was 93% overlap between features selected at week 3 and the other weeks. CONCLUSIONS This study describes a feature selection methodology for longitudinal radiomics that is able to select reproducible delta radiomics features that are informative due to their change during treatment, which can potentially be used for treatment decisions concerning adaptive radiotherapy. Nonetheless, the prognostic value of the selected delta radiomic features should be investigated in future studies.
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Affiliation(s)
- Janna E. van Timmeren
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
| | - Ralph T. H. Leijenaar
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
| | - Bart Reymen
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands
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van Timmeren JE, Leijenaar RT, van Elmpt W, Reymen B, Oberije C, Monshouwer R, Bussink J, Brink C, Hansen O, Lambin P. Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images. Radiother Oncol 2017; 123:363-369. [DOI: 10.1016/j.radonc.2017.04.016] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2017] [Revised: 03/20/2017] [Accepted: 04/17/2017] [Indexed: 01/20/2023]
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Gorgisyan J, Perrin R, Lomax AJ, Persson GF, Josipovic M, Engelholm SA, Weber DC, Munck af Rosenschold P. Impact of beam angle choice on pencil beam scanning breath-hold proton therapy for lung lesions. Acta Oncol 2017; 56:853-859. [PMID: 28464744 DOI: 10.1080/0284186x.2017.1287950] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION The breath-hold technique inter alia has been suggested to mitigate the detrimental effect of motion on pencil beam scanned (PBS) proton therapy dose distributions. The aim of this study was to evaluate the robustness of incident proton beam angles to day-to-day anatomical variations in breath-hold. MATERIALS AND METHODS Single field PBS plans at five degrees increments in the transversal plane were made and water-equivalent path lengths (WEPLs) were derived on the planning breath-hold CT (BHCT) for 30 patients diagnosed with locally-advanced non-small cell lung cancer (NSCLC), early stage NSCLC or lung metastasis. Our treatment planning system was subsequently used to recalculate the plans and derive WEPL on a BHCT scan acquired at the end of the treatment. Changes to the V95%, D95 and mean target dose were evaluated. RESULTS The difference in WEPL as a function of the beam angle was highly patient specific, with a median of 3.3 mm (range: 0.0-41.1 mm). Slightly larger WEPL differences were located around the lateral or lateral anterior/posterior beam angles. Linear models revealed that changes in dose were associated to the changes in WEPL and the tumor baseline shift (p < 0.05). CONCLUSIONS WEPL changes and tumor baseline shift can serve as reasonable surrogates for dosimetric uncertainty of the target coverage and are well-suited for routine evaluation of plan robustness. The two lateral beam angles are not recommended to use for PBS proton therapy of lung cancer patients treated in breath-hold, due to the poor robustness for several of the patients evaluated.
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Affiliation(s)
- Jenny Gorgisyan
- Paul Scherrer Institute, Villigen PSI, Switzerland
- Department of Oncology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
| | | | - Antony J. Lomax
- Paul Scherrer Institute, Villigen PSI, Switzerland
- Physics Department, ETH Zurich, Zurich, Switzerland
| | - Gitte F. Persson
- Department of Oncology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Mirjana Josipovic
- Department of Oncology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
| | - Svend Aage Engelholm
- Department of Oncology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Damien C. Weber
- Paul Scherrer Institute, Villigen PSI, Switzerland
- Radiation Oncology Department, University Hospital of Zurich, Zurich, Switzerland
| | - Per Munck af Rosenschold
- Department of Oncology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
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Barros Netto SM, Corrêa Silva A, Lopes H, Cardoso de Paiva A, Acatauassú Nunes R, Gattass M. Statistical tools for the temporal analysis and classification of lung lesions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 142:55-72. [PMID: 28325447 DOI: 10.1016/j.cmpb.2017.02.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Revised: 01/17/2017] [Accepted: 02/08/2017] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE Lung cancer remains one of the most common cancers globally. Temporal evaluation is an important tool for analyzing the malignant behavior of lesions during treatment, or of indeterminate lesions that may be benign. This work proposes a methodology for the analysis, quantification, and visualization of small (local) and large (global) changes in lung lesions. In addition, we extract textural features for the classification of lesions as benign or malignant. METHODS We employ the statistical concept of uncertainty to associate each voxel of a lesion to a probability that changes occur in the lesion over time. We employ the Jensen divergence and hypothesis test locally to verify voxel-to-voxel changes, and globally to capture changes in lesion volumes. RESULTS For the local hypothesis test, we determine that the change in density varies by between 3.84 and 40.01% of the lesion volume in a public database of malignant lesions under treatment, and by between 5.76 and 35.43% in a private database of benign lung nodules. From the texture analysis of regions in which the density changes occur, we are able to discriminate lung lesions with an accuracy of 98.41%, which shows that these changes can indicate the true nature of the lesion. CONCLUSION In addition to the visual aspects of the density changes occurring in the lesions over time, we quantify these changes and analyze the entire set using volumetry. In the case of malignant lesions, large b-divergence values are associated with major changes in lesion volume. In addition, this occurs when the change in volume is small but is associated with significant changes in density, as indicated by the histogram divergence. For benign lesions, the methodology shows that even in cases where the change in volume is small, a change of density occurs. This proves that even in lesions that appear stable, a change in density occurs.
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Affiliation(s)
- Stelmo Magalhães Barros Netto
- Federal University of Maranhão - UFMA, Applied Computing Group - NCA/UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga 65085-580, São Luís, MA, Brazil.
| | - Aristófanes Corrêa Silva
- Federal University of Maranhão - UFMA, Applied Computing Group - NCA/UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga 65085-580, São Luís, MA, Brazil.
| | - Hélio Lopes
- Pontifical Catholic University of Rio de Janeiro - PUC-Rio R. São Vicente, 225, Gávea, 22453-900, Rio de Janeiro, RJ, Brazil.
| | - Anselmo Cardoso de Paiva
- Federal University of Maranhão - UFMA, Applied Computing Group - NCA/UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga 65085-580, São Luís, MA, Brazil.
| | - Rodolfo Acatauassú Nunes
- State University of Rio de Janeiro - UERJ, São Francisco de Xavier, 524, Maracanã, 20550-900, Rio de Janeiro, RJ, Brazil.
| | - Marcelo Gattass
- Pontifical Catholic University of Rio de Janeiro - PUC-Rio R. São Vicente, 225, Gávea, 22453-900, Rio de Janeiro, RJ, Brazil.
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Hugo GD, Weiss E, Sleeman WC, Balik S, Keall PJ, Lu J, Williamson JF. A longitudinal four-dimensional computed tomography and cone beam computed tomography dataset for image-guided radiation therapy research in lung cancer. Med Phys 2017; 44:762-771. [PMID: 27991677 DOI: 10.1002/mp.12059] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2016] [Revised: 11/23/2016] [Accepted: 12/01/2016] [Indexed: 12/21/2022] Open
Abstract
PURPOSE To describe in detail a dataset consisting of serial four-dimensional computed tomography (4DCT) and 4D cone beam CT (4DCBCT) images acquired during chemoradiotherapy of 20 locally advanced, nonsmall cell lung cancer patients we have collected at our institution and shared publicly with the research community. ACQUISITION AND VALIDATION METHODS As part of an NCI-sponsored research study 82 4DCT and 507 4DCBCT images were acquired in a population of 20 locally advanced nonsmall cell lung cancer patients undergoing radiation therapy. All subjects underwent concurrent radiochemotherapy to a total dose of 59.4-70.2 Gy using daily 1.8 or 2 Gy fractions. Audio-visual biofeedback was used to minimize breathing irregularity during all fractions, including acquisition of all 4DCT and 4DCBCT acquisitions in all subjects. Target, organs at risk, and implanted fiducial markers were delineated by a physician in the 4DCT images. Image coordinate system origins between 4DCT and 4DCBCT were manipulated in such a way that the images can be used to simulate initial patient setup in the treatment position. 4DCT images were acquired on a 16-slice helical CT simulator with 10 breathing phases and 3 mm slice thickness during simulation. In 13 of the 20 subjects, 4DCTs were also acquired on the same scanner weekly during therapy. Every day, 4DCBCT images were acquired on a commercial onboard CBCT scanner. An optically tracked external surrogate was synchronized with CBCT acquisition so that each CBCT projection was time stamped with the surrogate respiratory signal through in-house software and hardware tools. Approximately 2500 projections were acquired over a period of 8-10 minutes in half-fan mode with the half bow-tie filter. Using the external surrogate, the CBCT projections were sorted into 10 breathing phases and reconstructed with an in-house FDK reconstruction algorithm. Errors in respiration sorting, reconstruction, and acquisition were carefully identified and corrected. DATA FORMAT AND USAGE NOTES 4DCT and 4DCBCT images are available in DICOM format and structures through DICOM-RT RTSTRUCT format. All data are stored in the Cancer Imaging Archive (TCIA, http://www.cancerimagingarchive.net/) as collection 4D-Lung and are publicly available. DISCUSSION Due to high temporal frequency sampling, redundant (4DCT and 4DCBCT) data at similar timepoints, oversampled 4DCBCT, and fiducial markers, this dataset can support studies in image-guided and image-guided adaptive radiotherapy, assessment of 4D voxel trajectory variability, and development and validation of new tools for image registration and motion management.
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Affiliation(s)
- Geoffrey D Hugo
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, 23298, USA
| | - Elisabeth Weiss
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, 23298, USA
| | - William C Sleeman
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, 23298, USA
| | | | - Paul J Keall
- Radiation Physics Laboratory, The University of Sydney, Camperdown, NSW, Australia
| | - Jun Lu
- University of Mississippi Medical Center, Jackson, MS, 39213, USA
| | - Jeffrey F Williamson
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA, 23298, USA
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Accuracy of dose calculation based on artefact corrected Cone Beam CT images of lung cancer patients. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2017. [DOI: 10.1016/j.phro.2016.11.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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van den Bosch M, Öllers M, Reymen B, van Elmpt W. Automatic selection of lung cancer patients for adaptive radiotherapy using cone-beam CT imaging. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2017. [DOI: 10.1016/j.phro.2017.02.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Michienzi A, Kron T, Callahan J, Plumridge N, Ball D, Everitt S. Cone-beam computed tomography for lung cancer - validation with CT and monitoring tumour response during chemo-radiation therapy. J Med Imaging Radiat Oncol 2016; 61:263-270. [DOI: 10.1111/1754-9485.12551] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2016] [Accepted: 09/02/2016] [Indexed: 11/30/2022]
Affiliation(s)
- Alissa Michienzi
- Faculty of Medicine, Dentistry and Health Sciences; University of Melbourne; Melbourne Victoria Australia
| | - Tomas Kron
- Department of Physical Sciences; Peter MacCallum Cancer Centre; Melbourne Victoria Australia
- Department of Medical Imaging and Radiation Sciences; Monash University; Clayton Victoria Australia
- Sir Peter MacCallum Department of Oncology; University of Melbourne; Melbourne Victoria Australia
| | - Jason Callahan
- Department of Medical Imaging and Radiation Sciences; Monash University; Clayton Victoria Australia
- Centre for Cancer Imaging; Peter MacCallum Cancer Centre; Melbourne Victoria Australia
| | - Nikki Plumridge
- Division of Radiation Oncology; Peter MacCallum Cancer Centre; Melbourne Victoria Australia
| | - David Ball
- Sir Peter MacCallum Department of Oncology; University of Melbourne; Melbourne Victoria Australia
- Division of Radiation Oncology; Peter MacCallum Cancer Centre; Melbourne Victoria Australia
| | - Sarah Everitt
- Department of Medical Imaging and Radiation Sciences; Monash University; Clayton Victoria Australia
- Sir Peter MacCallum Department of Oncology; University of Melbourne; Melbourne Victoria Australia
- Radiation Therapy Services; Peter MacCallum Cancer Centre; Melbourne Victoria Australia
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Tariq I, Chen T, Kirkby NF, Jena R. Modelling and Bayesian adaptive prediction of individual patients' tumour volume change during radiotherapy. Phys Med Biol 2016; 61:2145-61. [PMID: 26907478 DOI: 10.1088/0031-9155/61/5/2145] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The aim of this study is to develop a mathematical modelling method that can predict individual patients’ response to radiotherapy, in terms of tumour volume change during the treatment. The main concept is to start from a population-average model, which is subsequently updated from an individual’s tumour volume measurement. The model becomes increasingly personalized and so too does the prediction it produces. This idea of adaptive prediction was realised by using a Bayesian approach for updating the model parameters. The feasibility of the developed method was demonstrated on the data from 25 non-small cell lung cancer patients treated with helical tomotherapy, during which tumour volume was measured from daily imaging as part of the image-guided radiotherapy. The method could provide useful information for adaptive treatment planning and dose scheduling based on the patient’s personalised response.
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Berthelot K, Thureau S, Giraud P. Détermination des marges du volume cible anatomoclinique au volume cible prévisionnel des cancers bronchiques en radiothérapie conformationnelle tridimensionnelle ou avec modulation d’intensité. Cancer Radiother 2016; 20:616-21. [DOI: 10.1016/j.canrad.2016.08.122] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 08/01/2016] [Indexed: 12/25/2022]
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Thing RS, Bernchou U, Mainegra-Hing E, Hansen O, Brink C. Hounsfield unit recovery in clinical cone beam CT images of the thorax acquired for image guided radiation therapy. Phys Med Biol 2016; 61:5781-802. [DOI: 10.1088/0031-9155/61/15/5781] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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44
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Persoon L, Podesta M, Nijsten S, Troost E, Verhaegen F. Time-Resolved Versus Integrated Transit Planar Dosimetry for Volumetric Modulated Arc Therapy. Technol Cancer Res Treat 2016; 15:NP79-NP87. [PMID: 26655145 DOI: 10.1177/1533034615617668] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2015] [Revised: 09/16/2015] [Accepted: 10/23/2015] [Indexed: 11/16/2022] Open
Abstract
Purpose: It is desirable that dosimetric deviations during radiation treatments are detected. Integrated transit planar dosimetry is commonly used to evaluate external beam treatments such as volumetric-modulated arc therapy. This work focuses on patient geometry changes which result in differences between the planned and the delivered radiation dose. Integrated transit planar dosimetry will average out some deviations. Novel time-resolved transit planar dosimetry compares the delivered dose of volumetric-modulated arc therapy to the planned dose at various time points. Four patient cases are shown where time-resolved transit planar dosimetry detects patient geometry changes during treatment. Methods: A control point to control point comparison between the planned dose and the treatment dose of volumetric-modulated arc therapy beams is calculated using the planning computed tomography and the kV cone-beam computed tomography of the day and evaluated with a time-resolved γ function. Results were computed for 4 patients treated with volumetric-modulated arc therapy, each showing an anatomical change: pleural effusion, rectal gas pockets, and tumor regression. Results: In all cases, the geometrical change was detected by time-resolved transit planar dosimetry, whereas integrated transit planar dosimetry showed minor or no indication of the dose discrepancy. Both tumor regression cases were detected earlier in the treatment with time-resolved planar dosimetry in comparison to integrated transit planar dosimetry. The pleural effusion and the gas pocket were detected exclusively with time-resolved transit planar dosimetry. Conclusions: Clinical cases were presented in this proof-of-principle study in which integrated transit planar dosimetry did not detect dosimetrically relevant deviations to the same extent time-resolved transit planar dosimetry was able to. Time-resolved transit planar dosimetry also provides results that can be presented as a function of arc delivery angle allowing easier interpretation compared to integrated transit planar dosimetry.
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Affiliation(s)
- L.C.G.G. Persoon
- Department of Radiation Oncology (MAASTRO), GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - M. Podesta
- Department of Radiation Oncology (MAASTRO), GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - S.M.J.J.G. Nijsten
- Department of Radiation Oncology (MAASTRO), GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - E.G.C. Troost
- Department of Radiation Oncology (MAASTRO), GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - F. Verhaegen
- Department of Radiation Oncology (MAASTRO), GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, the Netherlands
- Medical Physics Unit, Department of Oncology, McGill University, Montréal, Québec, Canada
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Open source deformable image registration system for treatment planning and recurrence CT scans : Validation in the head and neck region. Strahlenther Onkol 2016; 192:545-51. [PMID: 27323754 DOI: 10.1007/s00066-016-0998-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 05/10/2016] [Indexed: 10/21/2022]
Abstract
BACKGROUND Clinical application of deformable registration (DIR) of medical images remains limited due to sparse validation of DIR methods in specific situations, e. g. in case of cancer recurrences. In this study the accuracy of DIR for registration of planning CT (pCT) and recurrence CT (rCT) images of head and neck squamous cell carcinoma (HNSCC) patients was evaluated. PATIENTS AND MATERIALS Twenty patients treated with definitive IMRT for HNSCC in 2010-2012 were included. For each patient, a pCT and an rCT scan were used. Median interval between the scans was 8.5 months. One observer manually contoured eight anatomical regions-of-interest (ROI) twice on pCT and once on rCT. METHODS pCT and rCT images were deformably registered using the open source software elastix. Mean surface distance (MSD) and Dice similarity coefficient (DSC) between contours were used for validation of DIR. A measure for delineation uncertainty was estimated by assessing MSD from the re-delineations of the same ROI on pCT. DIR and manual contouring uncertainties were correlated with tissue volume and rigidity. RESULTS MSD varied 1-3 mm for different ROIs for DIR and 1-1.5 mm for re-delineated ROIs performed on pCT. DSC for DIR varied between 0.58 and 0.79 for soft tissues and was 0.79 or higher for bony structures, and correlated with the volumes of ROIs (r = 0.5, p < 0.001) and tissue rigidity (r = 0.54, p < 0.001). CONCLUSION DIR using elastix in HNSCC on planning and recurrence CT scans is feasible; an uncertainty of the method is close to the voxel size length of the planning CT images.
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Weiss E, Ford JC, Olsen KM, Karki K, Saraiya S, Groves R, Hugo GD. Apparent diffusion coefficient (ADC) change on repeated diffusion-weighted magnetic resonance imaging during radiochemotherapy for non-small cell lung cancer: A pilot study. Lung Cancer 2016; 96:113-9. [DOI: 10.1016/j.lungcan.2016.04.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Revised: 02/11/2016] [Accepted: 04/03/2016] [Indexed: 12/12/2022]
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Mazzola R, Fiorentino A, Ricchetti F, Giaj Levra N, Fersino S, Di Paola G, Lo Casto A, Ruggieri R, Alongi F. Cone-beam computed tomography in lung stereotactic ablative radiation therapy: predictive parameters of early response. Br J Radiol 2016; 89:20160146. [PMID: 27245138 DOI: 10.1259/bjr.20160146] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVE: To analyze lung lesion volume variations by contouring on cone-beam CT (CBCT) images to evaluate the early predictive parameters of stereotactic ablative radiation therapy (SABR) treatment response. METHODS: The prescribed dose of SABR was varied according to the tumour site (central or peripheral) and maximum diameter of the lesions by using a strategy of risk-adapted dose prescription with a dose range between 48 and 70 Gy in 3-10 consecutive fractions. For the purpose of the analysis, the gross tumour volume (GTV) was recontoured for each patient at first and last CBCT using two lung levels/windows: (a) -600/1000 HU and (b) -1000/250 HU. Univariate analysis was performed to evaluate a correlation between lung lesion variations on CBCT using the two levels/windows and treatment response 6 months after SABR. Independent variables were the number of fractions, time between initial and final fraction, biologically effective dose and pre-SABR GTV. Cut points of lesion volume reduction were evaluated to determine the correlation with complete response 6 months after SABR. RESULTS: 41 lung lesions were evaluated. 82 lung lesions were recontoured for each CBCT level/window. A lung lesion shrinkage of at least 20% was revealed to be statistically related to complete response 6 months after SABR for both the CBCT levels/windows used. The probability of complete response ranged between six and eight times higher in respect to CBCT levels/windows -600/1000 HU and -1000/250 HU, respectively, compared with patients without a lesion shrinkage of 20% at the last session of SABR. CONCLUSION: According to current findings, a lung lesion shrinkage of at least 20% at the last session of SABR could be predictable of complete response 6 months thereafter. Further investigations about this topic are needed. ADVANCES IN KNOWLEDGE: Prediction of the early tumour response could be useful to personalize imaging restaging after the completion of SABR or to incorporate additional therapies in case of poor responders to improve clinical outcomes.
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Affiliation(s)
- Rosario Mazzola
- 1 Division of Radiation Oncology, Sacro Cuore Don Calabria Cancer Care Center, Verona, Italy
| | - Alba Fiorentino
- 1 Division of Radiation Oncology, Sacro Cuore Don Calabria Cancer Care Center, Verona, Italy
| | - Francesco Ricchetti
- 1 Division of Radiation Oncology, Sacro Cuore Don Calabria Cancer Care Center, Verona, Italy
| | - Niccolò Giaj Levra
- 1 Division of Radiation Oncology, Sacro Cuore Don Calabria Cancer Care Center, Verona, Italy
| | - Sergio Fersino
- 1 Division of Radiation Oncology, Sacro Cuore Don Calabria Cancer Care Center, Verona, Italy
| | | | - Antonio Lo Casto
- 3 Sezione di Scienze Radiologiche, DIBIMEL, University of Palermo, Palermo, Italy
| | - Ruggero Ruggieri
- 1 Division of Radiation Oncology, Sacro Cuore Don Calabria Cancer Care Center, Verona, Italy
| | - Filippo Alongi
- 1 Division of Radiation Oncology, Sacro Cuore Don Calabria Cancer Care Center, Verona, Italy
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Netto SMB, Silva AC, Nunes RA, Gattass M. Voxel-based comparative analysis of lung lesions in CT for therapeutic purposes. Med Biol Eng Comput 2016; 55:295-314. [PMID: 27180182 DOI: 10.1007/s11517-016-1510-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Accepted: 04/26/2016] [Indexed: 12/12/2022]
Abstract
Lung cancer remains as one of the most incident types of cancer throughout the world. Temporal evaluation has become a very useful tool when one wishes to analyze some malignancy-indicating behavior. The objective of the present work is to detect changes in the local densities of lung lesions over time (follow-up analysis). From the detected changes, local information as well as extent region of changes can complement the studies regarding the malignant or benign nature of the lesion. Based on this idea, we attempt to use techniques that allow the observation of changes in the lesion over time, based on remote sensing techniques which highlight changes occurring in the environment. The techniques used were the image differencing, image rationing, median filtering, image regression and the fuzzy XOR operator. Based on the global measurement of change percentage in the density, we found density variations which were considered significant in a range from 2.22 to 36.57 % of the volume of the lesion. The results achieved are promising since, besides the visual aspects of the changes in density of the lung lesion over time, we managed to quantify these changes and compare them by volumetric analysis, a more commonly used technique for analysis of changes in lung lesions.
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Affiliation(s)
| | | | | | - Marcelo Gattass
- Pontifical Catholic University of Rio de Janeiro - PUC-Rio, Rio de Janeiro, Brazil
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Kanzaki H, Kataoka M, Nishikawa A, Uwatsu K, Nagasaki K, Nishijima N, Ochi T, Mochizuki T. Impact of early tumor reduction on outcome differs by histological subtype in stage III non-small-cell lung cancer treated with definitive radiotherapy. Int J Clin Oncol 2016; 21:853-861. [PMID: 27125214 DOI: 10.1007/s10147-016-0982-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Accepted: 04/13/2016] [Indexed: 01/05/2023]
Abstract
BACKGROUND We retrospectively investigated the impact on survival of early tumor reduction during definitive radiotherapy for inoperable stage III non-small cell lung cancer (NSCLC) patients, according to their histological subtypes. METHODS Between November 2006 and December 2012, 152 consecutive patients with inoperable stage III NSCLC who underwent definitive radiotherapy were reviewed retrospectively. Forty-one patients were excluded for not satisfying the inclusion criteria. Forty-five (40.5 %) and 48 (43.2 %) patients were diagnosed with squamous cell carcinoma (SQC) and adenocarcinoma (ADC), respectively. The tumor reduction rate (TRR) was defined as follows: TRR = 1-[gross tumor volume (GTV) on computed tomography at shrinking irradiation field planning]/(GTV on computed tomography at the initial treatment planning). The Cox proportional hazard model was used to identify significant prognostic factors for overall survival (OS) and progression-free survival (PFS). RESULTS We evaluated 111 patients, with a median follow-up time of 52.2 months in surviving patients. The median TRR was 45.9 %. In all patients, there were significant associations between TRR and PFS (P = 0.036) on multivariate analysis, although TRR had no correlation with OS (P = 0.141). With respect to histological subtype, multivariate analyses revealed that a higher TRR showed significant associations with better OS and PFS in the SQC group (P = 0.013 and 0.040, respectively). In contrast, a higher TRR was associated with poorer OS in the ADC group (P = 0.030); there was no association between TRR and PFS. CONCLUSION We found that a higher TRR is a promising prognostic factor for better survival and disease control in SQC patients.
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Affiliation(s)
- Hiromitsu Kanzaki
- Department of Radiation Oncology, Shikoku Cancer Center Hospital, National Hospital Organization, Kou-160, Minami-Umenomoto-Machi, Matsuyama, Ehime, 791-0280, Japan. .,Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon-City, Ehime, 791-0295, Japan.
| | - Masaaki Kataoka
- Department of Radiation Oncology, Shikoku Cancer Center Hospital, National Hospital Organization, Kou-160, Minami-Umenomoto-Machi, Matsuyama, Ehime, 791-0280, Japan
| | - Atsushi Nishikawa
- Department of Radiation Oncology, Shikoku Cancer Center Hospital, National Hospital Organization, Kou-160, Minami-Umenomoto-Machi, Matsuyama, Ehime, 791-0280, Japan
| | - Kotaro Uwatsu
- Department of Radiation Oncology, Shikoku Cancer Center Hospital, National Hospital Organization, Kou-160, Minami-Umenomoto-Machi, Matsuyama, Ehime, 791-0280, Japan
| | - Kei Nagasaki
- Department of Radiation Oncology, Shikoku Cancer Center Hospital, National Hospital Organization, Kou-160, Minami-Umenomoto-Machi, Matsuyama, Ehime, 791-0280, Japan
| | - Noriko Nishijima
- Department of Radiation Oncology, Shikoku Cancer Center Hospital, National Hospital Organization, Kou-160, Minami-Umenomoto-Machi, Matsuyama, Ehime, 791-0280, Japan
| | - Takashi Ochi
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon-City, Ehime, 791-0295, Japan
| | - Teruhito Mochizuki
- Department of Radiology, Ehime University Graduate School of Medicine, Shitsukawa, Toon-City, Ehime, 791-0295, Japan
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Hazell I, Bzdusek K, Kumar P, Hansen CR, Bertelsen A, Eriksen JG, Johansen J, Brink C. Automatic planning of head and neck treatment plans. J Appl Clin Med Phys 2016; 17:272-282. [PMID: 26894364 PMCID: PMC5690191 DOI: 10.1120/jacmp.v17i1.5901] [Citation(s) in RCA: 114] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Revised: 09/15/2015] [Accepted: 09/11/2015] [Indexed: 12/13/2022] Open
Abstract
Treatment planning is time‐consuming and the outcome depends on the person performing the optimization. A system that automates treatment planning could potentially reduce the manual time required for optimization and could also provide a method to reduce the variation between persons performing radiation dose planning (dosimetrist) and potentially improve the overall plan quality. This study evaluates the performance of the Auto‐Planning module that has recently become clinically available in the Pinnacle3 radiation therapy treatment planning system. Twenty‐six clinically delivered head and neck treatment plans were reoptimized with the Auto‐Planning module. Comparison of the two types of treatment plans were performed using DVH metrics and a blinded clinical evaluation by two senior radiation oncologists using a scale from one to six. Both evaluations investigated dose coverage of target and dose to healthy tissues. Auto‐Planning was able to produce clinically acceptable treatment plans in all 26 cases. Target coverages in the two types of plans were similar, but automatically generated plans had less irradiation of healthy tissue. In 94% of the evaluations, the autoplans scored at least as high as the previously delivered clinical plans. For all patients, the Auto‐Planning tool produced clinically acceptable head and neck treatment plans without any manual intervention, except for the initial target and OAR delineations. The main benefit of the method is the likely improvement in the overall treatment quality since consistent, high‐quality plans are generated which even can be further optimized, if necessary. This makes it possible for the dosimetrist to focus more time on difficult dose planning goals and to spend less time on the more tedious parts of the planning process. PACS number: 87.55.de
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