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A laboratory‐based physical modeling environment has great potential to reproduce the complex physical hydrologic phenomena and understand the interactions of rainfall‐runoff processes in a visual and informative manner. In this study, a three‐layer physical modeling environment was developed to represent the dynamics of runoff production from the urban drainage system. The three‐layer physical modeling environment consists of a rainfall simulator (the 1st layer), a surface drainage network (the 2nd layer) and a subsurface rainwater pipe network (the 3rd layer). The degree of homogeneity of the spatial rainfall distribution produced by the rainfall simulator ranged from 78.6% to 84.0%, which lies within an acceptable range in the rainfall uniformity. The physical catchment model accurately represented the dynamic characteristics of the catchment response in a natural system associated with differing rainfall intensities within a controlled laboratory modeling environment, particularly the magnitude, volume, and shape of the discharge hydrographs. The three‐layer physical modeling setup was implemented to identify the effects of stormwater management facilities such as the rooftop detention storage and the permeable road pavement on the urban rainfall‐runoff responses. The runoff reduction rates for the peak discharge and the total discharge volume showed a strong linearity with the percentage coverages of the stormwater management facilities. Functional relationships between the variables were established to provide intuitive criteria for the runoff reduction rates for a specific coverage percentage of the rooftop detention storage and the permeable road pavement. These results demonstrate the effectiveness of the three‐layer physical setup for modeling rainfall‐runoff processes within the urban drainage network.

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 D 95% , D mean 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.

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.

Background In electric field-based cancer treatment, the intensity of the electric field applied to the tumor depends on the position of the electrode array, directly affecting the efficacy of treatment. The present study evaluated the effects of changing the position of the electrode array on the efficacy of electric field treatment for pancreatic cancer. Methods A 3D model was created based on computed tomography images of 13 pancreatic cancer patients. An electrode array was placed on the surface of the model at various positions, and the electric field was calculated for each. Six treatment plans were created for each patient by rotating each electrode array ± 15⁰, ± 30⁰ in the axial plane, and ± 10⁰ in the sagittal plane relative to the reference plan. The frequency was set at 150 kHz and the current density at 31 mArms/cm 2 for calculation of all treatment plans. The mean electric field, minimum electric field, homogeneity index ( HI ) and coverage index ( CI ) calculated from the six simulated plans were compared with the reference plan to evaluate the effects of each simulated plan on the tumor. Results Comparisons of the simulated plans for each patient with the reference plan showed differences of -2.61 ∼ 11.31% in the mean electric field, -7.03 ∼ 13.87% in the minimum electric field, -64.14 ∼ 13.12% in the HI , and − 24.23 ∼ 11.00% in the CI . Compared with the reference plan, the optimal plans created by changing the electrode position improved the mean electric field 7.41%, the minimum electric field 7.20%, the HI 4.57%, and the CI 8.46%. Conclusions Use of a treatment planning system to determine the optimal placement of the electrode array based on the anatomical characteristics of each patient can improve the intensity of the electric field applied to the tumor.

(1) In this study, we developed a deep learning (DL) model that can be used to predict late bladder toxicity. (2) We collected data obtained from 281 uterine cervical cancer patients who underwent definitive radiation therapy. The DL model was trained using 16 features, including patient, tumor, treatment, and dose parameters, and its performance was compared with that of a multivariable logistic regression model using the following metrics: accuracy, prediction, recall, F1-score, and area under the receiver operating characteristic curve (AUROC). In addition, permutation feature importance was calculated to interpret the DL model for each feature, and the lightweight DL model was designed to focus on the top five important features. (3) The DL model outperformed the multivariable logistic regression model on our dataset. It achieved an F1-score of 0.76 and an AUROC of 0.81, while the corresponding values for the multivariable logistic regression were 0.14 and 0.43, respectively. The DL model identified the doses for the most exposed 2 cc volume of the bladder (BD2cc) as the most important feature, followed by BD5cc and the ICRU bladder point. In the case of the lightweight DL model, the F-score and AUROC were 0.90 and 0.91, respectively. (4) The DL models exhibited superior performance in predicting late bladder toxicity compared with the statistical method. Through the interpretation of the model, it further emphasized its potential for improving patient outcomes and minimizing treatment-related complications with a high level of reliability.

It is difficult to calculate monitor units in the proton treatment planning system due to the complexity of using this system in the double scattering mode of proton therapy. Moreover, the range and spread-out Bragg peak (SOBP) values using the conversion algorithm (CONVALGO) provided by IBA ( C range , C SOBP ) are different from the actual measured range ( M range ) and SOBP ( M SOBP ) values. In this regard, the CONVALGO (FC) value ( FC range , FC SOBP ) should be measured according to the quality assurance (QA) of patient treatment, which requires physical effort and time. This study, therefore, aimed to reduce the time and effort spent on QA. The predictive model was trained using six parameters. Main option, sub-option, M range and M SOBP were used as input values, and FC range and FC SOBP were used as label. The trained model predicted the CONVALGO (PC) values of PC range and PC SOBP . The test dataset has 261 patient data that were not used for training. Difference, mean absolute error (MAE), and root mean square error (RMSE) values were used for comparison. Compared to the FC value, the maximum difference was − 2.2 mm for PC range and − 3.4 mm for C range . The acceptable standard of patient QA in our institute is within 1 mm and the number of data points that met the acceptable standard was 196 for PC range and 191 for C range . For the MAE of PC SOBP , options 1, 2, and 3 showed values within 1 mm. In the MAE of C SOBP , the values were > 1 mm for all options.

Several detectors have been developed to measure radiation doses during radiotherapy. However, most detectors are not flexible. Consequently, the airgaps between the patient surface and detector could reduce the measurement accuracy. Thus, this study proposes a dose measurement system based on a flexible copper indium gallium selenide (CIGS) solar cell. Our system comprises a customized CIGS solar cell (with a size 10 × 10 cm2 and thickness 0.33 mm), voltage amplifier, data acquisition module, and laptop with in-house software. In the study, the dosimetric characteristics, such as dose linearity, dose rate independence, energy independence, and field size output, of the dose measurement system in therapeutic X-ray radiation were quantified. For dose linearity, the slope of the linear fitted curve and the R-square value were 1.00 and 0.9999, respectively. The differences in the measured signals according to changes in the dose rates and photon energies were <2% and <3%, respectively. The field size output measured using our system exhibited a substantial increase as the field size increased, contrary to that measured using the ion chamber/film. Our findings demonstrate that our system has good dosimetric characteristics as a flexible in vivo dosimeter. Furthermore, the size and shape of the solar cell can be easily customized, which is an advantage over other flexible dosimeters based on an a-Si solar cell.

The objective of this study was to establish a method to calibrate a large-scale laboratory rainfall simulator through developing and implementing an automated rainfall collection system to assess the reliability and accuracy of a rainfall simulator. The automated rainfall collection system was designed to overcome the limitations caused by the traditional manual measurement for obtaining the rainfall intensity and the spatial rainfall distribution in a large experimental area. The developed automated rainfall collection system was implemented to calibrate a large-scale laboratory rainfall simulator. The adequacy of average rainfall intensities automatically collected from the miniature tipping bucket rain gauges was assessed by comparison with those based on the volumetric method using the flowmeter. The functional relationships between the system variables of the rainfall simulator and the simulated intensity and uniformity distribution of rainfall (i.e., operation models) were derived based on a multiple regression approach incorporating correlation analysis on linear and logarithm scales, with consideration of a significance level. The operation models exhibited high accuracy with respect to both the rainfall intensity and the uniformity coefficients.

Background The Korean Radiation Oncology Group (KROG) 19 − 09 prospective cohort study aims to determine the effect of regional nodal irradiation on regional recurrence rates in ypN0 breast cancer patients. Dosimetric variations between radiotherapy (RT) plans of participating institutions may affect the clinical outcome of the study. We performed this study to assess inter-institutional dosimetric variations by dummy run. Methods Twelve participating institutions created RT plans for four clinical scenarios using computed tomography images of two dummy cases. Based on a reference structure set, we analyzed dose-volume histograms after collecting the RT plans. Results We found variations in dose distribution between institutions, especially in the regional nodal areas. Whole breast and regional nodal irradiation (WBI + RNI) plans had lower inter-institutional agreement and similarity for 95% isodose lines than WBI plans. Fleiss’s kappa values, which were used to measure inter-institutional agreement for the 95% isodose lines, were 0.830 and 0.767 for the large and medium breast WBI plans, respectively, and 0.731 and 0.679 for the large and medium breast WBI + RNI plans, respectively. There were outliers in minimum dose delivered to 95% of the structure (D95%) of axillary level 1 among WBI plans and in D95% of the interpectoral region and axillary level 4 among WBI + RNI plans. Conclusion We found inter-institutional and inter-case variations in radiation dose delivered to target volumes and organs at risk. As KROG 19 − 09 is a prospective cohort study, we accepted the dosimetric variation among the different institutions. Actual patient RT plan data should be collected to achieve reliable KROG 19 − 09 study results.

Background We performed a prospective phase II study to compare acute toxicity among five different hypofractionated schedules using proton therapy. This study was an exploratory analysis to investigate the secondary end-point of biochemical failure-free survival (BCFFS) of patients with long-term follow-up. Methods Eighty-two patients with T1-3bN0M0 prostate cancer who had not received androgen-deprivation therapy were randomized to one of five arms: Arm 1, 60 cobalt gray equivalent (CGE)/20 fractions/5 weeks; Arm 2, 54 CGE/15 fractions/5 weeks; Arm 3, 47 CGE/10 fractions/5 weeks; Arm 4, 35 CGE/5 fractions/2.5 weeks; and Arm 5, 35 CGE/5 fractions/4 weeks. In the current exploratory analysis, these ardms were categorized into the moderate hypofractionated (MHF) group (52 patients in Arms 1–3) and the extreme hypofractionated (EHF) group (30 patients in Arms 4–5). Results At a median follow-up of 7.5 years (range, 1.3–9.6 years), 7-year BCFFS was 76.2% for the MHF group and 46.2% for the EHF group ( p  = 0.005). The 7-year BCFFS of the MHF and EHF groups were 90.5 and 57.1% in the low-risk group ( p  = 0.154); 83.5 and 42.9% in the intermediate risk group ( p  = 0.018); and 41.7 and 40.0% in the high risk group ( p  = 0.786), respectively. Biochemical failure tended to be a late event with a median time to occurrence of 5 years. Acute GU toxicities were more common in the MHF than the EHF group (85 vs. 57%, p  = 0.009), but late GI and GU toxicities did not differ between groups. Conclusions Our results suggest that the efficacy of EHF is potentially inferior to that of MHF and that further studies are warranted, therefore, to confirm these findings. Trial registration This study is registered at ClinicalTrials.gov, no. NCT01709253 ; registered October 18, 2012; retrospectively registered).