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3. Characterization of a commercial plastic scintillator for electron FLASH dosimetry.

Author

Oh, Kyuhak, Hyun, Megan A., Gallagher, Kyle J., Yan, Ying, and Zhou, Sumin

Subjects
IONIZATION chambers, SIGNAL processing, MEDICAL dosimetry, ABSORBED dose, SCINTILLATORS, DOSIMETERS, ELECTRON beams
Abstract

Purpose: This study investigated the potential of a commercially available plastic scintillator, the Exradin W2, as a real‐time dosimeter for ultra‐high‐dose‐rate (UHDR) electron beams. This work aimed to characterize this system's performance under UHDR conditions and addressed limitations inherent to other conventional dosimetry systems. Methods and materials: We assessed the W2's performance as a UHDR electron dosimeter using a 16 MeV UHDR electron beam from the FLASH research extension (FLEX) system. Additionally, the vendor provided a beta firmware upgrade to better handle the processing of the high signal generated in the UHDR environment. We evaluated the W2 regarding dose‐per‐pulse, pulse repetition rate, charge versus distance, and pulse linearity. Absorbed dose measurements were compared against those from a plane‐parallel ionization chamber, optically stimulated luminescent dosimeters and radiochromic film. Results: We observed that the 1 × 1 mm W2 scintillator with the MAX SD was more suitable for UHDR dosimetry compared to the 1 × 3 mm W2 scintillator, capable of matching film measurements within 2% accuracy for dose‐per‐pulse up to 3.6 Gy/pulse. The W2 accurately ascertained the inverse square relationship regarding charge versus virtual source distance with R2 of ∼1.00 for all channels. Pulse linearity was accurately measured with the W2, demonstrating a proportional response to the delivered pulse number. There was no discernible impact on the measured charge of the W2 when switching between the available repetition rates of the FLEX system (18–180 pulses/s), solidifying consistent beam output across pulse frequencies. Conclusions: This study tested a commercial plastic scintillator detector in a UHDR electron beam, paving the way for its potential use as a real‐time, patient‐specific dosimetry tool for future FLASH radiotherapy treatments. Further research is warranted to test and improve the signal processing of the W2 dosimetry system to accurately measure in UHDR environments using exceedingly high dose‐per‐pulse and pulse numbers. [ABSTRACT FROM AUTHOR]

Published
2024
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7. The current state, future supply and demand of therapy physicists ‐ A special report of the 2020 AAPM therapy physicist workforce survey results

Author

Chen, Erli, primary, Mills, Michael D., additional, Antolak, John A., additional, Buzurovic, Ivan M., additional, Dezarn, William, additional, Khan, Farhana R., additional, Kirby, Charles, additional, Parker, Brent, additional, Pawlicki, Todd, additional, Su, Zhong, additional, Swanson, Chrisine M., additional, Tarver, Russell B., additional, Tomadsen, Bruce, additional, Wingreen, Nicholai E., additional, White, Susan, additional, Yang, Ming, additional, and Zhou, Sumin, additional

Published
2022
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16. A Mendelian Randomization Study: Roles of Gut Microbiota in Sepsis – Who is the Angle?

Author

Bian Yeping, Xu Jian, Deng Xiaojing, and Zhou Suming

Subjects
causal relationship, gut microbiota, mendelian randomization, sepsis, Genetics, QH426-470, Microbiology, QR1-502
Abstract

Gut microbiota (GM) is a crucial underlying player during sepsis pathogenesis. However, the causal relationship is unclear and remains to be determined. A two-sample Mendelian randomization study was implemented. The statistical data about sepsis together with GM summarized from genome-wide association studies were evaluated. Instrumental variables were defined as single-nucleotide polymorphisms with prominent correlations with exposure. The inverse-variance-weighted test was employed as a major approach of Mendelian randomization analysis to estimate of causal relationships. The inverse-variance-weighted analysis results demonstrated that at different taxa levels, Actinobacteria and Bifidobacteriaceae influence sepsis. Actinobacteria had negative relationships to sepsis risk at the phylum (β = –0.34, SE = 0.10, p = 0.0008) and class (β = –0.23, SE = 0.07, p = 0.0011) levels in outcome coded ieu-b-69. Actinobacteria at the phylum level (β = –0.22, SE = 0.10, p = 0.027) was also negatively associated with sepsis in outcome coded ieu-b-4980. Bifidobacteriaceae at the order (β = –0.20, SE = 0.06, p = 0.0021), family (β = –0.20, SE = 0.06, p = 0.0021), and genus (β = –0.20, SE = 0.06, p = 0.0007) levels were all negatively correlated with the risk of sepsis in outcome coded ieu-b-69. The results of the Wald ratio model showed that Tyzzerella genus (OR (95%CI) = 0.6902[0.4907,0.9708], p = 0.0331) and Gastranaerophilales order (OR (95%CI) = 0.5907[0.3516,0.9926], p = 0.0468) were negatively connected with sepsis. This study implied at different taxa levels Actinobacteria and Bifidobacteriaceae, Tyzzerella genus, and Gastranaerophilales order have a causal relationship with sepsis, indicating that they are protective factors for the incidence of sepsis.

Published
2024
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29. Predicting radiotherapy-induced cardiac perfusion defects

Author

Das, Shiva K., Baydush, Alan H., Zhou, Sumin, Miften, Moyed, Yu, Xiaoli, Craciunescu, Oana, Oldham, Mark, Light, Kim, Wong, Terence, Blazing, Michael, Borges-Neto, Salvador, Dewhirst, Mark W., and Marks, Lawrence B.

Published
2005

30. Carboplatin/Paclitaxel or Carboplatin/Vinorelbine Followed by Accelerated Hyperfractionated Conformal Radiation Therapy: Report of a Prospective Phase I Dose Escalation Trial From the Carolina Conformal Therapy Consortium

Author

Marks, Lawrence B., Garst, Jennifer, Socinski, Mark A., Sibley, Gregory, Blackstock, A William, Herndon, James E., Zhou, Sumin, Shafman, Timothy, Tisch, Andrea, Clough, Robert, Yu, Xiaoli, Turrisi, Andrew, Anscher, Mitchell, Crawford, Jeffrey, and Rosenman, Julian

Published
2004
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37. A comprehensive dosimetric study on switching from a Type-B to a Type-C dose algorithm for modern lung SBRT

Author

Zhou, Christina, primary, Bennion, Nathan, additional, Ma, Rongtao, additional, Liang, Xiaoying, additional, Wang, Shuo, additional, Zvolanek, Kristina, additional, Hyun, Megan, additional, Li, Xiaobo, additional, Zhou, Sumin, additional, Zhen, Weining, additional, Lin, Chi, additional, Wahl, Andrew, additional, and Zheng, Dandan, additional

Published
2017
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44. Technical Note: Fabricating Cerrobend grids with 3D printing for spatially modulated radiation therapy: A feasibility study

Author

Zhu, Xiaofeng, primary, Driewer, Joseph, additional, Li, Sicong, additional, Verma, Vivek, additional, Lei, Yu, additional, Zhang, Mutian, additional, Zhang, Qinghui, additional, Zheng, Dandan, additional, Cullip, Timothy, additional, Chang, Sha X., additional, Wang, Andrew Z., additional, Zhou, Sumin, additional, and Enke, Charles A., additional

Published
2015
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46. Combining multiple models to generate consensus: Application to radiation-induced pneumonitis prediction

Author

Das, Shiva K., Yin, Fang-Fang, Deasy, Joseph O., Chen, Shifeng, Marks, Lawrence B., and Zhou, Sumin

Subjects
Radiation Therapy Physics, Adult, Aged, 80 and over, Male, Risk, Lung Neoplasms, Databases, Factual, Decision Trees, Radiotherapy Dosage, Middle Aged, Models, Biological, Radiation Pneumonitis, ROC Curve, Humans, Regression Analysis, Female, Neural Networks, Computer, Aged, Probability
Abstract

The fusion of predictions from disparate models has been used in several fields to obtain a more realistic and robust estimate of the “ground truth” by allowing the models to reinforce each other when consensus exists, or, conversely, negate each other when there is no consensus. Fusion has been shown to be most effective when the models have some complementary strengths arising from different approaches. In this work, we fuse the results from four common but methodologically different nonlinear multivariate models (Decision Trees, Neural Networks, Support Vector Machines, Self-Organizing Maps) that were trained to predict radiation-induced pneumonitis risk on a database of 219 lung cancer patients treated with radiotherapy (34 with Grade 2+ postradiotherapy pneumonitis). Each model independently incorporated a small number of features from the available set of dose and nondose patient variables to predict pneumonitis; no two models had all features in common. Fusion was achieved by simple averaging of the predictions for each patient from all four models. Since a model’s prediction for a patient can be dependent on the patient training set used to build the model, the average of several different predictions from each model was used in the fusion (predictions were made by repeatedly testing each patient with a model built from different cross-validation training sets that excluded the patient being tested). The area under the receiver operating characteristics curve for the fused cross-validated results was 0.79, with lower variance than the individual component models. From the fusion, five features were extracted as the consensus among all four models in predicting radiation pneumonitis. Arranged in order of importance, the features are (1) chemotherapy; (2) equivalent uniform dose (EUD) for exponent a=1.2 to 3; (3) EUD for a=0.5 to 1.2, lung volume receiving >20–30 Gy; (4) female sex; and (5) squamous cell histology. To facilitate ease of interpretation and prospective use, the fused outcome results for the patients were fitted to a logistic probability function.

Published
2008

48. Assessing the impact of radiation‐induced changes in soft tissue density/thickness on the study of radiation‐induced perfusion changes in the lung and heart

Author

Lawrence, Michael V., primary, Saynak, Mert, additional, Fried, David V., additional, Bateman, Ted A., additional, Green, Rebecca L., additional, Hubbs, Jessica L., additional, Jaszczak, Ronald J., additional, Wong, Terence Z., additional, Zhou, Sumin, additional, Das, Shiva K., additional, and Marks, Lawrence B., additional

Published
2012
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