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Polf JC, Barajas CA, Peterson SW, Mackin DS, Beddar S, Ren L, Gobbert MK. Applications of Machine Learning to Improve the Clinical Viability of Compton Camera Based in vivo Range Verification in Proton Radiotherapy. Front Phys 2022; 10:838273. [PMID: 36119562 PMCID: PMC9481064 DOI: 10.3389/fphy.2022.838273] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
We studied the application of a deep, fully connected Neural Network (NN) to process prompt gamma (PG) data measured by a Compton camera (CC) during the delivery of clinical proton radiotherapy beams. The network identifies 1) recorded "bad" PG events arising from background noise during the measurement, and 2) the correct ordering of PG interactions in the CC to help improve the fidelity of "good" data used for image reconstruction. PG emission from a tissue-equivalent target during irradiation with a 150 MeV proton beam delivered at clinical dose rates was measured with a prototype CC. Images were reconstructed from both the raw measured data and the measured data that was further processed with a neural network (NN) trained to identify "good" and "bad" PG events and predict the ordering of individual interactions within the good PG events. We determine if NN processing of the CC data could improve the reconstructed PG images to a level in which they could provide clinically useful information about the in vivo range and range shifts of the proton beams delivered at full clinical dose rates. Results showed that a deep, fully connected NN improved the achievable contrast to noise ratio (CNR) in our images by more than a factor of 8x. This allowed the path, range, and lateral width of the clinical proton beam within a tissue equivalent target to easily be identified from the PG images, even at the highest dose rates of a 150 MeV proton beam used for clinical treatments. On average, shifts in the beam range as small as 3 mm could be identified. However, when limited by the amount of PG data measured with our prototype CC during the delivery of a single proton pencil beam (~1 × 109 protons), the uncertainty in the reconstructed PG images limited the identification of range shift to ~5 mm. Substantial improvements in CC images were obtained during clinical beam delivery through NN pre-processing of the measured PG data. We believe this shows the potential of NNs to help improve and push CC-based PG imaging toward eventual clinical application for proton RT treatment delivery verification.
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Affiliation(s)
- Jerimy C. Polf
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Carlos A. Barajas
- Department of Mathematics and Statistics, University of Maryland Baltimore County, Baltimore, MD, United States
| | | | - Dennis S. Mackin
- Department of Medical Physics, University of Texas M.D. Anderson Cancer Center, Houston, TX, United States
| | - Sam Beddar
- Department of Medical Physics, University of Texas M.D. Anderson Cancer Center, Houston, TX, United States
| | - Lei Ren
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Matthias K. Gobbert
- Department of Mathematics and Statistics, University of Maryland Baltimore County, Baltimore, MD, United States
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Barua S, Elhalawani H, Volpe S, Al Feghali KA, Yang P, Ng SP, Elgohari B, Granberry RC, Mackin DS, Gunn GB, Hutcheson KA, Chambers MS, Court LE, Mohamed ASR, Fuller CD, Lai SY, Rao A. Computed Tomography Radiomics Kinetics as Early Imaging Correlates of Osteoradionecrosis in Oropharyngeal Cancer Patients. Front Artif Intell 2021; 4:618469. [PMID: 33898983 PMCID: PMC8063205 DOI: 10.3389/frai.2021.618469] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 03/04/2021] [Indexed: 01/08/2023] Open
Abstract
Osteoradionecrosis (ORN) is a major side-effect of radiation therapy in oropharyngeal cancer (OPC) patients. In this study, we demonstrate that early prediction of ORN is possible by analyzing the temporal evolution of mandibular subvolumes receiving radiation. For our analysis, we use computed tomography (CT) scans from 21 OPC patients treated with Intensity Modulated Radiation Therapy (IMRT) with subsequent radiographically-proven ≥ grade II ORN, at three different time points: pre-IMRT, 2-months, and 6-months post-IMRT. For each patient, radiomic features were extracted from a mandibular subvolume that developed ORN and a control subvolume that received the same dose but did not develop ORN. We used a Multivariate Functional Principal Component Analysis (MFPCA) approach to characterize the temporal trajectories of these features. The proposed MFPCA model performs the best at classifying ORN vs. Control subvolumes with an area under curve (AUC) = 0.74 [95% confidence interval (C.I.): 0.61–0.90], significantly outperforming existing approaches such as a pre-IMRT features model or a delta model based on changes at intermediate time points, i.e., at 2- and 6-month follow-up. This suggests that temporal trajectories of radiomics features derived from sequential pre- and post-RT CT scans can provide markers that are correlates of RT-induced mandibular injury, and consequently aid in earlier management of ORN.
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Affiliation(s)
- Souptik Barua
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Hesham Elhalawani
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Stefania Volpe
- Department of Radiation Oncology, European Institute of Oncology IRCSS, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Karine A Al Feghali
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Pei Yang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Sweet Ping Ng
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Baher Elgohari
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Robin C Granberry
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Dennis S Mackin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - G Brandon Gunn
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Katherine A Hutcheson
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Mark S Chambers
- Department of Oncologic Dentistry and Prosthodontics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Stephen Y Lai
- Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Arvind Rao
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.,Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
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3
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Zhu Y, Mohamed ASR, Lai SY, Yang S, Kanwar A, Wei L, Kamal M, Sengupta S, Elhalawani H, Skinner H, Mackin DS, Shiao J, Messer J, Wong A, Ding Y, Zhang L, Court L, Ji Y, Fuller CD. Imaging-Genomic Study of Head and Neck Squamous Cell Carcinoma: Associations Between Radiomic Phenotypes and Genomic Mechanisms via Integration of The Cancer Genome Atlas and The Cancer Imaging Archive. JCO Clin Cancer Inform 2020; 3:1-9. [PMID: 30730765 DOI: 10.1200/cci.18.00073] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Recent data suggest that imaging radiomic features of a tumor could be indicative of important genomic biomarkers. Understanding the relationship between radiomic and genomic features is important for basic cancer research and future patient care. We performed a comprehensive study to discover the imaginggenomic associations in head and neck squamous cell carcinoma (HNSCC) and explore the potential of predicting tumor genomic alternations using radiomic features. METHODS Our retrospective study integrated whole-genome multiomics data from The Cancer Genome Atlas with matched computed tomography imaging data from The Cancer Imaging Archive for the same set of 126 patients with HNSCC. Linear regression and gene set enrichment analysis were used to identify statistically significant associations between radiomic imaging and genomic features. Random forest classifier was used to predict the status of two key HNSCC molecular biomarkers, human papillomavirus and disruptive TP53 mutation, on the basis of radiomic features. RESULTS Widespread and statistically significant associations were discovered between genomic features (including microRNA expression, somatic mutations, and transcriptional activity, copy number variations, and promoter region DNA methylation changes of pathways) and radiomic features characterizing the size, shape, and texture of tumor. Prediction of human papillomavirus and TP53 mutation status using radiomic features achieved areas under the receiver operating characteristic curve of 0.71 and 0.641, respectively. CONCLUSION Our exploratory study suggests that radiomic features are associated with genomic characteristics at multiple molecular layers in HNSCC and provides justification for continued development of radiomics as biomarkers for relevant genomic alterations in HNSCC.
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Affiliation(s)
- Yitan Zhu
- NorthShore University HealthSystem, Evanston, IL
| | - Abdallah S R Mohamed
- The University of Texas MD Anderson Cancer Center, Houston, TX.,Alexandria University, Alexandria, Egypt
| | - Stephen Y Lai
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - Aasheesh Kanwar
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Lin Wei
- NorthShore University HealthSystem, Evanston, IL
| | - Mona Kamal
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | | | - Heath Skinner
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Dennis S Mackin
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Jay Shiao
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Jay Messer
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Andrew Wong
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Yao Ding
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Lifei Zhang
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Laurence Court
- The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Yuan Ji
- NorthShore University HealthSystem, Evanston, IL.,The University of Chicago, Chicago, IL
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Branco LRF, Ger RB, Mackin DS, Zhou S, Court LE, Layman RR. Technical Note: Proof of concept for radiomics-based quality assurance for computed tomography. J Appl Clin Med Phys 2019; 20:199-205. [PMID: 31609076 PMCID: PMC6839380 DOI: 10.1002/acm2.12750] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 08/14/2019] [Accepted: 08/30/2019] [Indexed: 01/09/2023] Open
Abstract
PURPOSE Routine quality assurance (QA) testing to identify malfunctions in medical imaging devices is a standard practice and plays an important role in meeting quality standards. However, current daily computed tomography (CT) QA techniques have proven to be inadequate for the detection of subtle artifacts on scans. Therefore, we investigated the ability of a radiomics phantom to detect subtle artifacts not detected in conventional daily QA. METHODS An updated credence cartridge radiomics phantom was used in this study, with a focus on two of the cartridges (rubber and cork) in the phantom. The phantom was scanned using a Siemens Definition Flash CT scanner, which was reported to produce a subtle line pattern artifact. Images were then imported into the IBEX software program, and 49 features were extracted from the two cartridges using four different preprocessing techniques. Each feature was then compared with features for the same scanner several months previously and with features from controlled CT scans obtained using 100 scanners. RESULTS Of 196 total features for the test scanner, 79 (40%) from the rubber cartridge and 70 (36%) from the cork cartridge were three or more standard deviations away from the mean of the controlled scan population data. Feature values for the artifact-producing scanner were closer to the population mean when features were preprocessed with Butterworth smoothing. The feature most sensitive to the artifact was co-occurrence matrix maximum probability. The deviation from the mean for this feature was more than seven times greater when the scanner was malfunctioning (7.56 versus 1.01). CONCLUSIONS Radiomics features extracted from a texture phantom were able to identify an artifact-producing scanner as an outlier among 100 CT scanners. This preliminary analysis demonstrated the potential of radiomics in CT QA to identify subtle artifacts not detected using the currently employed daily QA techniques.
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Affiliation(s)
- Luciano R F Branco
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rachel B Ger
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Dennis S Mackin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Shouhao Zhou
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rick R Layman
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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5
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Ger RB, Craft DF, Mackin DS, Zhou S, Layman RR, Jones AK, Elhalawani H, Fuller CD, Howell RM, Li H, Stafford RJ, Court LE. Practical guidelines for handling head and neck computed tomography artifacts for quantitative image analysis. Comput Med Imaging Graph 2018; 69:134-139. [PMID: 30268005 PMCID: PMC6217839 DOI: 10.1016/j.compmedimag.2018.09.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 08/14/2018] [Accepted: 09/06/2018] [Indexed: 12/28/2022]
Abstract
Radiomics studies have demonstrated the potential use of quantitative image features to improve prognostic stratification of patients with head and neck cancer. Imaging protocol parameters that can affect radiomics feature values have been investigated, but the effects of artifacts caused by intrinsic patient factors have not. Two such artifacts that are common in patients with head and neck cancer are streak artifacts caused by dental fillings and beam-hardening artifacts caused by bone. The purpose of this study was to test the impact of these artifacts and if needed, methods for compensating for these artifacts in head and neck radiomics studies. The robustness of feature values was tested by removing slices of the gross tumor volume (GTV) on computed tomography images from 30 patients with head and neck cancer; these images did not have streak artifacts or had artifacts far from the GTV. The range of each feature value over a percentage of the GTV was compared to the inter-patient variability at full volume. To determine the effects of beam-hardening artifacts, we scanned a phantom with 5 cartridges of different materials encased in polystyrene buildup. A cylindrical hole through the cartridges contained either a rod of polylactic acid to simulate water or a rod of polyvinyl chloride to simulate bone. A region of interest was drawn in each cartridge flush with the rod. Most features were robust with up to 50% of the original GTV removed. Most feature values did not significantly differ when measured with the polylactic acid rod or the polyvinyl chloride rod. Of those that did, the size of the difference did not exceed the inter-patient standard deviation in most cases. We conclude that simply removing slices affected by streak artifacts can enable these scans to be included in radiomics studies and that contours of structures can abut bone without being affected by beam hardening if needed.
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Affiliation(s)
- Rachel B Ger
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1420, Houston, Texas 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States.
| | - Daniel F Craft
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1420, Houston, Texas 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States
| | - Dennis S Mackin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1420, Houston, Texas 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States
| | - Shouhao Zhou
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1411, Houston, Texas 77030, United States
| | - Rick R Layman
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, Texas 77030, United States
| | - A Kyle Jones
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, Texas 77030, United States
| | - Hesham Elhalawani
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 0097, Houston, Texas 77030, United States
| | - Clifton D Fuller
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 0097, Houston, Texas 77030, United States
| | - Rebecca M Howell
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1420, Houston, Texas 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States
| | - Heng Li
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1420, Houston, Texas 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States
| | - R Jason Stafford
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, Texas 77030, United States
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1420, Houston, Texas 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, Texas 77030, United States
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Owens CA, Peterson CB, Tang C, Koay EJ, Yu W, Mackin DS, Li J, Salehpour MR, Fuentes DT, Court LE, Yang J. Lung tumor segmentation methods: Impact on the uncertainty of radiomics features for non-small cell lung cancer. PLoS One 2018; 13:e0205003. [PMID: 30286184 PMCID: PMC6171919 DOI: 10.1371/journal.pone.0205003] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 09/18/2018] [Indexed: 01/20/2023] Open
Abstract
Purpose To evaluate the uncertainty of radiomics features from contrast-enhanced breath-hold helical CT scans of non-small cell lung cancer for both manual and semi-automatic segmentation due to intra-observer, inter-observer, and inter-software reliability. Methods Three radiation oncologists manually delineated lung tumors twice from 10 CT scans using two software tools (3D-Slicer and MIM Maestro). Additionally, three observers without formal clinical training were instructed to use two semi-automatic segmentation tools, Lesion Sizing Toolkit (LSTK) and GrowCut, to delineate the same tumor volumes. The accuracy of the semi-automatic contours was assessed by comparison with physician manual contours using Dice similarity coefficients and Hausdorff distances. Eighty-three radiomics features were calculated for each delineated tumor contour. Informative features were identified based on their dynamic range and correlation to other features. Feature reliability was then evaluated using intra-class correlation coefficients (ICC). Feature range was used to evaluate the uncertainty of the segmentation methods. Results From the initial set of 83 features, 40 radiomics features were found to be informative, and these 40 features were used in the subsequent analyses. For both intra-observer and inter-observer reliability, LSTK had higher reliability than GrowCut and the two manual segmentation tools. All observers achieved consistently high ICC values when using LSTK, but the ICC value varied greatly for each observer when using GrowCut and the manual segmentation tools. For inter-software reliability, features were not reproducible across the software tools for either manual or semi-automatic segmentation methods. Additionally, no feature category was found to be more reproducible than another feature category. Feature ranges of LSTK contours were smaller than those of manual contours for all features. Conclusion Radiomics features extracted from LSTK contours were highly reliable across and among observers. With semi-automatic segmentation tools, observers without formal clinical training were comparable to physicians in evaluating tumor segmentation.
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Affiliation(s)
- Constance A. Owens
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, Texas, United States of America
- * E-mail:
| | - Christine B. Peterson
- The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, Texas, United States of America
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Chad Tang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Eugene J. Koay
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Wen Yu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Dennis S. Mackin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, Texas, United States of America
| | - Jing Li
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Mohammad R. Salehpour
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - David T. Fuentes
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Laurence E. Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, Texas, United States of America
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7
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Elhalawani H, Lin TA, Volpe S, Mohamed ASR, White AL, Zafereo J, Wong AJ, Berends JE, AboHashem S, Williams B, Aymard JM, Kanwar A, Perni S, Rock CD, Cooksey L, Campbell S, Yang P, Nguyen K, Ger RB, Cardenas CE, Fave XJ, Sansone C, Piantadosi G, Marrone S, Liu R, Huang C, Yu K, Li T, Yu Y, Zhang Y, Zhu H, Morris JS, Baladandayuthapani V, Shumway JW, Ghosh A, Pöhlmann A, Phoulady HA, Goyal V, Canahuate G, Marai GE, Vock D, Lai SY, Mackin DS, Court LE, Freymann J, Farahani K, Kaplathy-Cramer J, Fuller CD. Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges. Front Oncol 2018; 8:294. [PMID: 30175071 PMCID: PMC6107800 DOI: 10.3389/fonc.2018.00294] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Accepted: 07/16/2018] [Indexed: 12/13/2022] Open
Abstract
Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the "HPV" challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the "local recurrence" challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings.
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Affiliation(s)
- Hesham Elhalawani
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Timothy A. Lin
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Baylor College of Medicine, Houston, TX, United States
| | - Stefania Volpe
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Università degli Studi di Milano, Milan, Italy
| | - Abdallah S. R. Mohamed
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Clinical Oncology and Nuclear Medicine, Alexandria University, Alexandria, Egypt
| | - Aubrey L. White
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- McGovern Medical School, University of Texas, Houston, TX, United States
| | - James Zafereo
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- McGovern Medical School, University of Texas, Houston, TX, United States
| | - Andrew J. Wong
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- School of Medicine, The University of Texas Health Science Center San Antonio, San Antonio, TX, United States
| | - Joel E. Berends
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- School of Medicine, The University of Texas Health Science Center San Antonio, San Antonio, TX, United States
| | - Shady AboHashem
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Bowman Williams
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Furman University, Greenville, SC, United States
| | - Jeremy M. Aymard
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Abilene Christian University, Abilene, TX, United States
| | - Aasheesh Kanwar
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Oncology, Oregon Health and Science University, Portland, OR, United States
| | - Subha Perni
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Crosby D. Rock
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Texas Tech University Health Sciences Center El Paso, El Paso, TX, United States
| | - Luke Cooksey
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- University of North Texas Health Science Center, Fort Worth, TX, United States
| | - Shauna Campbell
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH, United States
| | - Pei Yang
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Baylor College of Medicine, Houston, TX, United States
| | - Khahn Nguyen
- Colgate University, Hamilton City, CA, United States
| | - Rachel B. Ger
- Graduate School of Biomedical Sciences, MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Physics, Graduate School of Biomedical Sciences, MD Anderson Cancer Center, Houston, TX, United States
| | - Carlos E. Cardenas
- Graduate School of Biomedical Sciences, MD Anderson Cancer Center, Houston, TX, United States
- Department of Radiation Physics, Graduate School of Biomedical Sciences, MD Anderson Cancer Center, Houston, TX, United States
| | - Xenia J. Fave
- Moores Cancer Center, University of California, La Jolla, San Diego, CA, United States
| | - Carlo Sansone
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università Degli Studi di Napoli Federico II, Naples, Italy
| | - Gabriele Piantadosi
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università Degli Studi di Napoli Federico II, Naples, Italy
| | - Stefano Marrone
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università Degli Studi di Napoli Federico II, Naples, Italy
| | - Rongjie Liu
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Chao Huang
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Kaixian Yu
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Tengfei Li
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Yang Yu
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Youyi Zhang
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Hongtu Zhu
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Jeffrey S. Morris
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Veerabhadran Baladandayuthapani
- Baylor College of Medicine, Houston, TX, United States
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - John W. Shumway
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Alakonanda Ghosh
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Andrei Pöhlmann
- Fraunhofer-Institut für Fabrikbetrieb und Automatisierung (IFF), Magdeburg, Germany
| | - Hady A. Phoulady
- Department of Computer Science, University of Southern Maine, Portland, OR, United States
| | - Vibhas Goyal
- Indian Institute of Technology Hyderabad, Sangareddy, India
| | | | | | - David Vock
- Department of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States
| | - Stephen Y. Lai
- Department of Head and Neck Surgery, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Dennis S. Mackin
- Colgate University, Hamilton City, CA, United States
- Department of Radiation Physics, Graduate School of Biomedical Sciences, MD Anderson Cancer Center, Houston, TX, United States
| | - Laurence E. Court
- Colgate University, Hamilton City, CA, United States
- Department of Radiation Physics, Graduate School of Biomedical Sciences, MD Anderson Cancer Center, Houston, TX, United States
| | - John Freymann
- Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD, United States
| | - Keyvan Farahani
- National Cancer Institute, Rockville, MD, United States
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, MD, United States
| | - Jayashree Kaplathy-Cramer
- Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging, MGH/Harvard Medical School, Boston, MA, United States
| | - Clifton D. Fuller
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- Baylor College of Medicine, Houston, TX, United States
- Department of Radiation Physics, Graduate School of Biomedical Sciences, MD Anderson Cancer Center, Houston, TX, United States
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8
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Jensen GL, Yost CM, Mackin DS, Fried DV, Zhou S, Court LE, Gomez DR. Prognostic value of combining a quantitative image feature from positron emission tomography with clinical factors in oligometastatic non-small cell lung cancer. Radiother Oncol 2018; 126:362-367. [DOI: 10.1016/j.radonc.2017.11.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 07/31/2017] [Accepted: 11/13/2017] [Indexed: 01/24/2023]
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9
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Ger RB, Cardenas CE, Anderson BM, Yang J, Mackin DS, Zhang L, Court LE. Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics. J Vis Exp 2018. [PMID: 29364284 DOI: 10.3791/57132] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Imaging Biomarker Explorer (IBEX) is an open-source tool for medical imaging radiomics work. The purpose of this paper is to describe how to use IBEX's graphical user interface (GUI) and to demonstrate how IBEX calculated features have been used in clinical studies. IBEX allows for the import of DICOM images with DICOM radiation therapy structure files or Pinnacle files. Once the images are imported, IBEX has tools within the Data Selection GUI to manipulate the viewing of the images, measure voxel values and distances, and create and edit contours. IBEX comes with 27 preprocessing and 132 feature choices to design feature sets. Each preprocessing and feature category has parameters that can be altered. The output from IBEX is a spreadsheet that contains: 1) each feature from the feature set calculated for each contour in a data set, 2) image information about each contour in a data set, and 3) a summary of the preprocessing and features used with their selected parameters. Features calculated from IBEX have been used in studies to test the variability of features under different imaging conditions and in survival models to improve current clinical models.
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Affiliation(s)
- Rachel B Ger
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center; UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center
| | - Carlos E Cardenas
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center; UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center
| | - Brian M Anderson
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center; UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center; UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center
| | - Dennis S Mackin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center
| | - Lifei Zhang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center; UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center;
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10
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Rubinstein AE, Ingram WS, Anderson BM, Gay SS, Fave XJ, Ger RB, McCarroll RE, Owens CA, Netherton TJ, Kisling KD, Court LE, Yang J, Li Y, Lee J, Mackin DS, Cardenas CE. Cost-effective immobilization for whole brain radiation therapy. J Appl Clin Med Phys 2017; 18:116-122. [PMID: 28585732 PMCID: PMC5874864 DOI: 10.1002/acm2.12101] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Revised: 02/24/2017] [Accepted: 04/05/2017] [Indexed: 11/23/2022] Open
Abstract
To investigate the inter‐ and intra‐fraction motion associated with the use of a low‐cost tape immobilization technique as an alternative to thermoplastic immobilization masks for whole‐brain treatments. The results of this study may be of interest to clinical staff with severely limited resources (e.g., in low‐income countries) and also when treating patients who cannot tolerate standard immobilization masks. Setup reproducibility of eight healthy volunteers was assessed for two different immobilization techniques. (a) One strip of tape was placed across the volunteer's forehead and attached to the sides of the treatment table. (b) A second strip was added to the first, under the chin, and secured to the table above the volunteer's head. After initial positioning, anterior and lateral photographs were acquired. Volunteers were positioned five times with each technique to allow calculation of inter‐fraction reproducibility measurements. To estimate intra‐fraction reproducibility, 5‐minute anterior and lateral videos were taken for each technique per volunteer. An in‐house software was used to analyze the photos and videos to assess setup reproducibility. The maximum intra‐fraction displacement for all volunteers was 2.8 mm. Intra‐fraction motion increased with time on table. The maximum inter‐fraction range of positions for all volunteers was 5.4 mm. The magnitude of inter‐fraction and intra‐fraction motion found using the “1‐strip” and “2‐strip” tape immobilization techniques was comparable to motion restrictions provided by a thermoplastic mask for whole‐brain radiotherapy. The results suggest that tape‐based immobilization techniques represent an economical and useful alternative to the thermoplastic mask.
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Affiliation(s)
- Ashley E Rubinstein
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - W Scott Ingram
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Brian M Anderson
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Skylar S Gay
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xenia J Fave
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Rachel B Ger
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Rachel E McCarroll
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Constance A Owens
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Tucker J Netherton
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Kelly D Kisling
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Yuting Li
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Joonsang Lee
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Dennis S Mackin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Carlos E Cardenas
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
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11
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Polf JC, Avery S, Mackin DS, Beddar S. Reply to Comment on 'Imaging of prompt gamma rays emitted during delivery of clinical proton beams with a Compton camera: feasibility studies for range verification'. Phys Med Biol 2016; 61:8945-8946. [PMID: 27910821 DOI: 10.1088/1361-6560/61/24/8945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
A reply is provided to the points raised in the comment by Dr Sitek (2016 Phys. Med. Biol. 61 8941) on Polf et al (2015 Phys. Med. Biol. 60 7085).
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Affiliation(s)
- Jerimy C Polf
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
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12
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Suzuki K, Palmer MB, Sahoo N, Zhang X, Poenisch F, Mackin DS, Liu AY, Wu R, Zhu XR, Frank SJ, Gillin MT, Lee AK. Quantitative analysis of treatment process time and throughput capacity for spot scanning proton therapy. Med Phys 2016; 43:3975. [DOI: 10.1118/1.4952731] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
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13
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Polf JC, Avery S, Mackin DS, Beddar S. Imaging of prompt gamma rays emitted during delivery of clinical proton beams with a Compton camera: feasibility studies for range verification. Phys Med Biol 2015; 60:7085-99. [DOI: 10.1088/0031-9155/60/18/7085] [Citation(s) in RCA: 91] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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14
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Lee E, Polf JC, Mackin DS, Beddar S, Dolney D, Ainsley C, Kassaee A, Avery S. Study of the Angular Dependence of a Prompt Gamma Detector Response during Proton Radiation Therapy. Int J Part Ther 2014. [DOI: 10.14338/ijpt-14-00012.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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15
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Polf JC, Panthi R, Mackin DS, McCleskey M, Saastamoinen A, Roeder BT, Beddar S. Measurement of characteristic prompt gamma rays emitted from oxygen and carbon in tissue-equivalent samples during proton beam irradiation. Phys Med Biol 2013; 58:5821-31. [PMID: 23920051 DOI: 10.1088/0031-9155/58/17/5821] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The purpose of this work was to characterize how prompt gamma (PG) emission from tissue changes as a function of carbon and oxygen concentration, and to assess the feasibility of determining elemental concentration in tissues irradiated with proton beams. For this study, four tissue-equivalent water-sucrose samples with differing densities and concentrations of carbon, hydrogen, and oxygen were irradiated with a 48 MeV proton pencil beam. The PG spectrum emitted from each sample was measured using a high-purity germanium detector, and the absolute detection efficiency of the detector, average beam current, and delivered dose distribution were also measured. Changes to the total PG emission from (12)C (4.44 MeV) and (16)O (6.13 MeV) per incident proton and per Gray of absorbed dose were characterized as a function of carbon and oxygen concentration in the sample. The intensity of the 4.44 MeV PG emission per incident proton was found to be nearly constant for all samples regardless of their carbon concentration. However, we found that the 6.13 MeV PG emission increased linearly with the total amount (in grams) of oxygen irradiated in the sample. From the measured PG data, we determined that 1.64 × 10(7) oxygen PGs were emitted per gram of oxygen irradiated per Gray of absorbed dose delivered with a 48 MeV proton beam. These results indicate that the 6.13 MeV PG emission from (16)O is proportional to the concentration of oxygen in tissue irradiated with proton beams, showing that it is possible to determine the concentration of oxygen within tissues irradiated with proton beams by measuring (16)O PG emission.
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Affiliation(s)
- Jerimy C Polf
- Department of Physics, Oklahoma State University, Stillwater, OK 74078, USA.
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