Reduction of superficial radiation dose with bolus in passive scattering proton beam therapy.

PURPOSE: In passive scattering proton beam therapy, scattered protons from the snout and aperture increase the superficial dose, however, treatment planning systems (TPSs) based on analytic algorithms (such as proton convolution superposition) are often inaccurate in this aspect. This additional dose can cause permanent alopecia or severe radiation dermatitis. This study aimed to evaluate the effect of bolus on the superficial radiation dose in passive scattering proton beam therapy. METHODS: We drew a clinical target volume (CTV) and a scalp-p (phantom), and created plans using a TPS for a solid water phantom with and without bolus. We calculated the dose distribution in the established plans independently with Monte Carlo (MC) simulation and measured the actual dose distribution with an array of ion chambers and radiochromic films. To assess the clinical impact of bolus on scalp dose, we conducted independent dose verification using MC simulation in a clinical case. RESULTS: In the solid water phantom without bolus, the calculated scalp-p volume receiving 190 cGy was 20% with TPS but 80% with MC simulation when the CTV received 200 cGy. With 2 cm bolus, this decreased from 80% to 10% in MC simulation. With the measurements, average superficial dose to the scalp-p was reduced by 5.2% when 2 cm bolus was applied. In the clinical case, the scalp-c (clinical) volume receiving 3000 cGy decreased from 74% to 63% when 2 cm bolus was applied. CONCLUSION: This study revealed that bolus can reduce radiation dose at the superficial body area and alleviate toxicity in passive scattering proton beam therapy.

Evaluation of the dosimetric effect of scattered protons in clinical practice in passive scattering proton therapy.

The present study verified and evaluated the dosimetric effects of protons scattered from a snout and an aperture in clinical practice, when a range compensator was included. The dose distribution calculated by a treatment planning system (TPS) was compared with the measured dose distribution and the dose distribution calculated by Monte Carlo simulation at several depths. The difference between the measured and calculated results was analyzed using Monte Carlo simulation with filtration of scattering in the snout and aperture. The dependence of the effects of scattered protons on snout size, beam range, and minimum thickness of the range compensator was also investigated using the Monte Carlo simulation. The simulated and measured results showed that the additional dose compared with the results calculated by the TPS at shallow depths was mainly due to protons scattered by the snout and aperture. This additional dose was filtered by the structure of the range compensator so that it was observed under the thin region of the range compensator. The maximum difference was measured at a depth of 16 mm (8.25%), with the difference decreasing with depth. Analysis of protons contributing to the additional dose showed that the contribution of protons scattered from the snout was greater than that of protons scattered from the aperture when a narrow snout was used. In the Monte Carlo simulation, this effect of scattered protons was reduced when wider snouts and longer-range proton beams were used. This effect was also reduced when thicker range compensator bases were used, even with a narrow snout. This study verified the effect of scattered protons even when a range compensator was included and emphasized the importance of snout-scattered protons when a narrow snout is used for small fields. It indicated that this additional dose can be reduced by wider snouts, longer range proton beams, and thicker range compensator bases. These results provide a better understanding of the additional dose from scattered protons in clinical practice.

Facial expression monitoring system for predicting patient's sudden movement during radiotherapy using deep learning.

PURPOSE: Imaging, breath-holding/gating, and fixation devices have been developed to minimize setup errors so that the prescribed dose can be exactly delivered to the target volume in radiotherapy. Despite these efforts, additional patient monitoring devices have been installed in the treatment room to view patients' whole-body movement. We developed a facial expression recognition system using deep learning with a convolutional neural network (CNN) to predict patients' advanced movement, enhancing the stability of the radiation treatment by giving warning signs to radiation therapists. MATERIALS AND METHODS: Convolutional neural network model and extended Cohn-Kanade datasets with 447 facial expressions of source images for training were used. Additionally, a user interface that can be used in the treatment control room was developed to monitor real-time patient's facial expression in the treatment room, and the entire system was constructed by installing a camera in the treatment room. To predict the possibility of patients' sudden movement, we categorized facial expressions into two groups: (a) uncomfortable expressions and (b) comfortable expressions. We assumed that the warning sign about the sudden movement was given when the uncomfortable expression was recognized. RESULTS: We have constructed the facial expression monitoring system, and the training and test accuracy were 100% and 85.6%, respectively. In 10 patients, their emotions were recognized based on their comfortable and uncomfortable expressions with 100% detection rate. The detected various emotions were represented by a heatmap and motion prediction accuracy was analyzed for each patient. CONCLUSION: We developed a system that monitors the patient's facial expressions and predicts patient's advanced movement during the treatment. It was confirmed that our patient monitoring system can be complementarily used with the existing monitoring system. This system will help in maintaining the initial setup and improving the accuracy of radiotherapy for the patients using deep learning in radiotherapy.

FMCW LiDAR System to Reduce Hardware Complexity and Post-Processing Techniques to Improve Distance Resolution.

As the autonomous driving technology develops, research on related sensors is also being actively conducted. One system that is widely used today uses a light source with a wavelength in the 905 nm band for the pulse Light Detection And Ranging (LiDAR) system. This has the disadvantages of being harmful to the human eye and in making digital signal processing difficult at high sampling rates. The Frequency Modulated Continuous Wave (FMCW) LiDAR system has been proposed as an alternative. However, the FMCW LiDAR is formed with a high beat frequency by a method different from that of the FMCW Radar, which causes a hardware burden on the FFT (Fast Fourier Transform) module for interpreting the beat frequency information. In this paper, the FFT module that may occur in the FMCW LiDAR using Digital Down Convert (DDC) technology is extracted at 256 points, which is 25 times smaller than the existing 8192 points, and the beat frequency is 0 to 50 m at 3 cm intervals. As a result of generating and restoring the distance, the performance of 0.03 m Root Mean Square Error (RMSE) compared to the conventional one was confirmed. In this process, the hardware module was directly mounted and verified on the FPGA. In the case of the Simple Threshold-Constant False Alarm Rate (ST-CFAR) provided, the RMSE was measured by generating beat frequencies from 0 to 50 m at 1 cm intervals, and as a result, the result of 0.019 m was confirmed at 0.03 m in the past.

Changes in foot pressure elicited by 3D air balance exercise and pelvic stability exercise for functional leg-length discrepancy in adult women.

[Purpose] This study was conducted to examine the effect of pelvic stabilization exercise and 3D equipment exercise on adult women with Functional Leg-Length Discrepancy (FLLD). [Subjects and Methods] Twenty female students in their 20's having FLLD without Structural Leg Length Discrepancy were selected. Exercise was performed for 50 min per session, three times a week, for six weeks. The Pelvic stabilization exercise (PSE) group performed pelvic stabilization exercises for 50 minutes, and the 3D exercise (3DE) group performed 3D Air Balance exercise for 10 minutes after performing the pelvic stabilization exercise program for 40 minutes. [Results] The PSE group showed statistically significant differences in tape measure method (TMM) and maximum pressure between pre-test and post-test, and 3DE showed statistically significant differences in TMM, the difference in maximum pressure, the difference in average pressure, and the difference in support area. At the end of the 6-week intervention, TMM, difference in maximum pressure, difference in average pressure, and difference in support area showed significantly greater reduction in the 3DE group. [Conclusion] The results show that 3D stabilization exercise was more effective at improving the stabilization of the deep muscles surrounding the pelvis and left-right muscular balance. We consider that 3D exercise should be included in exercise programs for improving pelvic cavity and spinal stability in the future.

Myeloid skewing in murine autoimmune arthritis occurs in hematopoietic stem and primitive progenitor cells.

Skewing toward myeloid cell production is often observed in chronic inflammation and autoimmune diseases. Herein, we determined whether persistent myeloid activation and proinflammatory output occurring in pathologic conditions is at the level of hematopoietic stem and primitive progenitor cells (HSPPCs). By using a mouse arthritis model, we found that even though HSPPCs in arthritis still retained the capacity to differentiate into different lineages, they acquired enhanced in vitro and in vivo propensity in a disease-dependent manner to generate myeloid cells, the key perpetrators of tissue damage in arthritis. This myeloid skewing was cell intrinsic, as arthritic HSPPCs up-regulate myeloid-specific transcripts including S100a8. Exogenous S100a8 promoted myeloid cell output from wild-type HSPPCs, suggesting mechanistic involvement of this gene in the myeloid priming that occurs in arthritic HSPPCs. Therefore, our results indicate that in arthritic mice, HSPPCs adopt a pathologic state that favors disease persistence.

Influence of plasma conditions on the intensity ratio calibration curve during laser induced breakdown spectroscopy analysis.

Quantitative prediction of elemental concentration or concentration ratio of solid samples can be achieved by laser induced breakdown spectroscopy if a calibration curve that is little influenced by plasma conditions could be obtained. This work demonstrates that such a calibration curve is available for copper indium gallium diselenide (CuIn(1-x)Ga(x)Se2) thin film solar cells for properly selected spectral lines. The possible changes of calibration curves based on the selected spectral lines are discussed in consideration of self-absorption in optically thick plasma and the dependency of spectral line properties on plasma temperature.

Feasibility study for the development of multilayered solar cells for proton linear energy transfer depth profile measurement.

BACKGROUND: Previous study proposed a method to measure linear energy transfer (LET) at specific points using the quenching magnitude of thin film solar cells. This study was conducted to propose a more advanced method for measuring the LET distribution. PURPOSE: This study focuses on evaluating the feasibility of estimating the proton LET distribution in proton therapy. The feasibility of measuring the proton LET and dose distribution simultaneously using a single-channel configuration comprising two solar cells with distinct quenching constants is investigated with the objective of paving the way for enhanced proton therapy dosimetry. METHODS: Two solar cells with different quenching constants were used to estimate the proton LET distribution. Detector characteristics (e.g., dose linearity and dose-rate dependency) of the solar cells were evaluated to assess their suitability for dosimetry applications. First, using a reference beam condition, the quenching constants of the two solar cells were determined according to the modified Birks equation. The signal ratios of the two solar cells were then evaluated according to proton LET in relation to the estimated quenching constants. The proton LET distributions of six test beams were obtained by measuring the signal ratios of the two solar cells at each depth, and the ratios were evaluated by comparing them with those calculated by Monte Carlo simulation. RESULTS: The detector characterization of the two solar cells including dose linearity and dose-rate dependence affirmed their suitability for use in dosimetry applications. The maximum difference between the LET measured using the two solar cells and that calculated by Monte Carlo simulation was 2.34 keV/µm. In the case of the dose distribution measured using the method proposed in this study, the maximum difference between range measured using the proposed method and that measured using a multilayered ionization chamber was 0.7 mm. The expected accuracy of simultaneous LET and dose distribution measurement using the method proposed in this study were estimated to be 3.82%. The signal ratios of the two solar cells, which are related to quenching constants, demonstrated the feasibility of measuring LET and dose distribution simultaneously. CONCLUSION: The feasibility of measuring proton LET and dose distribution simultaneously using two solar cells with different quenching constants was demonstrated. Although the method proposed in this study was evaluated using a single channel by varying the measuring depth, the results suggest that the proton LET and dose distribution can be simultaneously measured if the detector is configured in a multichannel form. We believe that the results presented in this study provide the envisioned transition to a multichannel configuration, with the promise of substantially advancing proton therapy's accuracy and efficacy in cancer treatment.

Independence of elemental intensity ratio on plasma property during laser-induced breakdown spectroscopy.

The authors report that the elemental composition ratio of Ga to In in a CuIn(1-x)Ga(x)Se(2) compound semiconductor, a thin-film solar cell material, can be measured with little influence of plasma property changes by laser-induced breakdown spectroscopy (λ = 1064 nm, τ = 5 ns). It is shown that the similarity in excitation energy levels of the selected Ga and In emission lines and the fact that these elements belong to the same group of the periodic table are the critical factors ensuring the independence of intensity ratio on plasma conditions.

Herbal formula PM012 induces neuroprotection in stroke brain.

Stroke is a major cause of long-term disability world-wide. Limited pharmacological therapy has been used in stroke patients. Previous studies indicated that herb formula PM012 is neuroprotective against neurotoxin trimethyltin in rat brain, and improved learning and memory in animal models of Alzheimer's disease. Its action in stroke has not been reported. This study aims to determine PM012-mediated neural protection in cellular and animal models of stroke. Glutamate-mediated neuronal loss and apoptosis were examined in rat primary cortical neuronal cultures. Cultured cells were overexpressed with a Ca++ probe (gCaMP5) by AAV1 and were used to examine Ca++ influx (Ca++i). Adult rats received PM012 before transient middle cerebral artery occlusion (MCAo). Brain tissues were collected for infarction and qRTPCR analysis. In rat primary cortical neuronal cultures, PM012 significantly antagonized glutamate-mediated TUNEL and neuronal loss, as well as NMDA-mediated Ca++i. PM012 significantly reduced brain infarction and improved locomotor activity in stroke rats. PM012 attenuated the expression of IBA1, IL6, and CD86, while upregulated CD206 in the infarcted cortex. ATF6, Bip, CHOP, IRE1, and PERK were significantly down-regulated by PM012. Using HPLC, two potential bioactive molecules, paeoniflorin and 5-hydroxymethylfurfural, were identified in the PM012 extract. Taken together, our data suggest that PM012 is neuroprotective against stroke. The mechanisms of action involve inhibition of Ca++i, inflammation, and apoptosis.

Determination of the proton LET using thin film solar cells coated with scintillating powder.

PURPOSE: The amount of luminescent light detected in a scintillator is reduced with increased proton linear energy transfer (LET) despite receiving the same proton dose, through a phenomenon called quenching. This study evaluated the ability of a solar cell coated with scintillating powder (SC-SP) to measure therapeutic proton LET by measuring the quenching effect of the scintillating powder using a solar cell while simultaneously measuring the dose of the proton beam. METHODS: SC-SP was composed of a flexible thin film solar cell and scintillating powder. The LET and dose of the pristine Bragg peak in the 14 cm range were calculated using a validated Monte Carlo model of a double scattering proton beam nozzle. The SC-SP was evaluated by measuring the proton beam under the same conditions at specific depths using SC-SP and Markus chamber. Finally, the 10 and 20 cm range pristine Bragg peaks and 5 cm spread-out Bragg peak (SOBP) in the 14 cm range were measured using the SC-SP and the Markus chamber. LETs measured using the SC-SP were compared with those calculated using Monte Carlo simulations. RESULTS: The quenching factors of the SC-SP and solar cell alone, which were slopes of linear fit obtained from quenching correction factors according to LET, were 0.027 and 0.070 µm/keV (R2 : 0.974 and 0.975). For pristine Bragg peaks in the 10 and 20 cm ranges, the maximum differences between LETs measured using the SC-SP and calculated using Monte Carlo simulations were 0.5 keV/µm (15.7%) and 1.2 keV/µm (12.0%), respectively. For a 5 cm SOBP proton beam, the LET measured using the SC-SP and calculated using Monte Carlo simulations differed by up to 1.9 keV/µm (18.7%). CONCLUSIONS: Comparisons of LETs for pristine Bragg peaks and SOBP between measured using the SC-SP and calculated using Monte Carlo simulations indicated that the solar cell-based system could simultaneously measure both LET and dose in real-time and is cost-effective.

A Multicenter, Prospective, Observational Study to Evaluate Ethanol-Induced Symptoms in Patients Receiving Docetaxel Chemotherapy.

PURPOSE: Several previous studies and case reports have reported ethanol-induced symptoms in patients receiving anticancer drugs containing ethanol. Most docetaxel formulations contain ethanol as a solvent. However, there are insufficient data on ethanol-induced symptoms when docetaxel-containing ethanol is administered. The primary purpose of this study was to investigate the frequency and pattern of ethanol-induced symptoms during and after docetaxel administration. The secondary purpose was to explore the risk factors for ethanol-induced symptoms. MATERIALS AND METHODS: This was a prospective, multicenter, observational study. The participants filled out ethanol-induced symptom questionnaire on the day of chemotherapy and the following day. RESULTS: Data from 451 patients were analyzed. The overall occurrence rate of ethanol-induced symptoms was 44.3% (200/451 patients). The occurrence rate of facial flushing was highest at 19.7% (89/451 patients), followed by nausea in 18.2% (82/451 patients), and dizziness in 17.5% (79/451 patients). Although infrequent, unsteady walking and impaired balance occurred in 4.2% and 3.3% of patients, respectively. Female sex, presence of underlying disease, younger age, docetaxel dose, and docetaxel-containing ethanol amount were significantly associated with the occurrence of ethanol-induced symptoms. CONCLUSION: The occurrence of ethanol-induced symptoms was not low in patients receiving docetaxel-containing ethanol. Physicians need to pay more attention to the occurrence of ethanol-induced symptoms and prescribe ethanol-free or low-ethanol-containing formulations to high-risk patients.

Serum Antibody Response Comparison and Adverse Reaction Analysis in Healthcare Workers Vaccinated with the BNT162b2 or ChAdOx1 COVID-19 Vaccine.

The COVID-19 pandemic is changing rapidly and requires different strategies to maintain immunization. In Korea, different COVID-19 vaccines are recommended and available for various populations, including healthcare workers (HCWs) at high risk of SARS-CoV-2 infection. We plan to evaluate the adverse events (AEs) and immunogenicity of the BNT162b2 and ChAdOx1 vaccines in HCWs at a single center. This cohort study included HCWs fully vaccinated with either BNT162b2 or ChAdOx1. Blood samples were taken eight weeks after the second vaccination with both COVID-19 vaccines and six months after the second vaccination from participants with the BNT162b2 vaccine. The primary endpoint for immunogenicity was the serum neutralizing antibody responses eight weeks after vaccination. The secondary endpoint was the incidence of various AEs within 28 days of each vaccination. Between 16 March and 23 June 2021, 115 participants were enrolled (65 in the ChAdOx1 group and 50 in the BNT162b2 group). Significantly higher surrogate virus neutralization test (sVNT) inhibition was observed in participants vaccinated with two doses of BNT162b2 (mean (SD) 91.4 (9.68)%) than in those vaccinated with ChAdOx1 (mean (SD) 73.3 (22.57)%). The effectiveness of the BNT162b2 vaccine was maintained across all age and gender categories. At six months after the second dose, serum antibody levels declined significantly in the BNT162b2 group. The main adverse events, including fever, myalgia, fatigue, and headache, were significantly higher in the ChAdOx1 group after the first dose, whereas, after the second dose, those AEs were significantly higher in the BNT162b2 group (p < 0.05). Two doses of either the ChAdOx1 or the BNT162b2 COVID-19 vaccine resulted in very high seropositivity among the HCWs at our center. The quality of the antibody response, measured by sVNT inhibition, was significantly better with the BNT162b2 vaccine than with the ChAdOx1 vaccine. There was no significant association between neutralizing antibody response and AE after each vaccination in our cohort.

Deep learning method for prediction of patient-specific dose distribution in breast cancer.

BACKGROUND: Patient-specific dose prediction improves the efficiency and quality of radiation treatment planning and reduces the time required to find the optimal plan. In this study, a patient-specific dose prediction model was developed for a left-sided breast clinical case using deep learning, and its performance was compared with that of conventional knowledge-based planning using RapidPlan™. METHODS: Patient-specific dose prediction was performed using a contour image of the planning target volume (PTV) and organs at risk (OARs) with a U-net-based modified dose prediction neural network. A database of 50 volumetric modulated arc therapy (VMAT) plans for left-sided breast cancer patients was utilized to produce training and validation datasets. The dose prediction deep neural network (DpNet) feature weights of the previously learned convolution layers were applied to the test on a cohort of 10 test sets. With the same patient data set, dose prediction was performed for the 10 test sets after training in RapidPlan. The 3D dose distribution, absolute dose difference error, dose-volume histogram, 2D gamma index, and iso-dose dice similarity coefficient were used for quantitative evaluation of the dose prediction. RESULTS: The mean absolute error (MAE) and one standard deviation (SD) between the clinical and deep learning dose prediction models were 0.02 ± 0.04%, 0.01 ± 0.83%, 0.16 ± 0.82%, 0.52 ± 0.97, - 0.88 ± 1.83%, - 1.16 ± 2.58%, and - 0.97 ± 1.73% for D95%, Dmean in the PTV, and the OARs of the body, left breast, heart, left lung, and right lung, respectively, and those measured between the clinical and RapidPlan dose prediction models were 0.02 ± 0.14%, 0.87 ± 0.63%, - 0.29 ± 0.98%, 1.30 ± 0.86%, - 0.32 ± 1.10%, 0.12 ± 2.13%, and - 1.74 ± 1.79, respectively. CONCLUSIONS: In this study, a deep learning method for dose prediction was developed and was demonstrated to accurately predict patient-specific doses for left-sided breast cancer. Using the deep learning framework, the efficiency and accuracy of the dose prediction were compared to those of RapidPlan. The doses predicted by deep learning were superior to the results of the RapidPlan-generated VMAT plan.

Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer.

BACKGROUND: Accurate and standardized descriptions of organs at risk (OARs) are essential in radiation therapy for treatment planning and evaluation. Traditionally, physicians have contoured patient images manually, which, is time-consuming and subject to inter-observer variability. This study aims to a) investigate whether customized, deep-learning-based auto-segmentation could overcome the limitations of manual contouring and b) compare its performance against a typical, atlas-based auto-segmentation method organ structures in liver cancer. METHODS: On-contrast computer tomography image sets of 70 liver cancer patients were used, and four OARs (heart, liver, kidney, and stomach) were manually delineated by three experienced physicians as reference structures. Atlas and deep learning auto-segmentations were respectively performed with MIM Maestro 6.5 (MIM Software Inc., Cleveland, OH) and, with a deep convolution neural network (DCNN). The Hausdorff distance (HD) and, dice similarity coefficient (DSC), volume overlap error (VOE), and relative volume difference (RVD) were used to quantitatively evaluate the four different methods in the case of the reference set of the four OAR structures. RESULTS: The atlas-based method yielded the following average DSC and standard deviation values (SD) for the heart, liver, right kidney, left kidney, and stomach: 0.92 ± 0.04 (DSC ± SD), 0.93 ± 0.02, 0.86 ± 0.07, 0.85 ± 0.11, and 0.60 ± 0.13 respectively. The deep-learning-based method yielded corresponding values for the OARs of 0.94 ± 0.01, 0.93 ± 0.01, 0.88 ± 0.03, 0.86 ± 0.03, and 0.73 ± 0.09. The segmentation results show that the deep learning framework is superior to the atlas-based framwork except in the case of the liver. Specifically, in the case of the stomach, the DSC, VOE, and RVD showed a maximum difference of 21.67, 25.11, 28.80% respectively. CONCLUSIONS: In this study, we demonstrated that a deep learning framework could be used more effectively and efficiently compared to atlas-based auto-segmentation for most OARs in human liver cancer. Extended use of the deep-learning-based framework is anticipated for auto-segmentations of other body sites.

The incidence of venous thromboembolism is not lowin Korean patients with advanced pancreatic cancer [corrected]

Background: Pancreatic cancer is among the most common malignancies associated with venous thromboembolism (VTE). Asian patients are known to have a lower incidence of VTE compared to Caucasian patients. However, few studies have investigated the incidence of VTE in Asian patients with pancreatic cancer. Methods: This retrospective review of medical records was performed on 505 patients with histopathologically proven advanced stage pancreatic cancer, from January 2006 to December 2012, at Soonchunhyang University Hospitals. Results: Ninety-four patients (18.6%) had at least one pulmonary embolism (PE), deep vein thrombosis (DVT), or splanchnic vein thrombosis (SVT); 38 patients had isolated SVT; and 56 patients (11.1%) had at least one classic VTE (PE and/or DVT of lower extremities). Patients with more advanced stages of pancreatic cancer (distant metastatic stage, recurrence) or who had received chemotherapy had a higher incidence of classic VTE. Patients who were simultaneously diagnosed with pancreatic cancer and classic VTE had a poorer prognosis than patients with subsequent VTEs. There was a significant difference in overall survival (OS) between the presence and absence of a concurrent classic VTE diagnosis (median: OS, 2.1 mo vs. 10.7 mo; P<0.001). Even when VTE included SVT, the result was similar (P<0.001). Conclusion: In Korean patients with advanced pancreatic cancer, the incidence of VTEs is comparable to that of Caucasian patients. We also found that pancreatic cancer patients with concurrent VTEs had a poor prognosis compared to patients who developed VTEs later.

Successful eradication of relapsed primary effusion lymphoma with high-dose chemotherapy and autologous stem cell transplantation in a patient seronegative for human immunodeficiency virus.

Primary effusion lymphoma (PEL) is a recently recognized disease that occurs most often in immunosuppressed patients, either with human immunodeficiency virus (HIV) or in the posttransplantation setting, and it occasionally occurs in nonimmunosuppressed patients. Patients present with lymphomatous effusions in serous cavities--pleura, pericardium, or peritoneum--without any identifiable tumor mass. PEL rarely responds to systemic chemotherapy, and the prognosis is poor, with a median survival time of less than 6 months for most cohorts. A standard treatment for PEL has not yet been identified. We describe a patient with HIV-seronegative PEL who relapsed after combination chemotherapy and then underwent successful treatment with high-dose chemotherapy (HDC) and autologous stem cell transplantation (ASCT). The treatment was well tolerated, and the patient has been in remission for 12 months after HDC and ASCT.

PLASTIC SCINTILLATOR FOR RADIATION DOSIMETRY.

Inorganic scintillators, composed of high-atomic-number materials such as the CsI(Tl) scintillator, are commonly used in commercially available a silicon diode and a scintillator embedded indirect-type electronic personal dosimeters because the light yield of the inorganic scintillator is higher than that of an organic scintillator. However, when it comes to tissue-equivalent dose measurements, a plastic scintillator such as polyvinyl toluene (PVT) is a more appropriate material than an inorganic scintillator because of the mass energy absorption coefficient. To verify the difference in the absorbed doses for each scintillator, absorbed doses from the energy spectrum and the calculated absorbed dose were compared. From the results, the absorbed dose of the plastic scintillator was almost the same as that of the tissue for the overall photon energy. However, in the case of CsI, it was similar to that of the tissue only for a photon energy from 500 to 4000 keV. Thus, the values and tendency of the mass energy absorption coefficient of the PVT are much more similar to those of human tissue than those of the CsI.

CALCULATION OF GAMMA SPECTRA IN A PLASTIC SCINTILLATOR FOR ENERGY CALIBRATIONAND DOSE COMPUTATION.

Plastic scintillation detectors have practical advantages in the field of dosimetry. Energy calibration of measured gamma spectra is important for dose computation, but it is not simple in the plastic scintillators because of their different characteristics and a finite resolution. In this study, the gamma spectra in a polystyrene scintillator were calculated for the energy calibration and dose computation. Based on the relationship between the energy resolution and estimated energy broadening effect in the calculated spectra, the gamma spectra were simply calculated without many iterations. The calculated spectra were in agreement with the calculation by an existing method and measurements.

Development of an environmental radiation analysis research capability in the UAE.

The UAE has started a nuclear energy program with the aim of having its first four units on-line between 2017 and 2020 and it is important that the country has an environmental radiation analysis capability to support this program. Khalifa University is therefore implementing a research laboratory to support both experimental analysis and radionuclide transport modeling in the aquatic and terrestrial environment. This paper outlines the development of this capability as well as the work in progress and planned for the future.