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.

Range shift verification in spot scanning proton therapy using gamma electron vertex imaging.

BACKGROUND: In proton therapy, a highly steep distal dose penumbra can be utilized for dose conformity, given the Bragg peak characteristic of protons. However, the location of the Bragg peak in patients (i.e., the beam range) is very sensitive to range uncertainty. Even a small shift of beam range can produce a significant variation of delivered dose to tumor and normal tissues, thus degrading treatment quality and threatening patient safety. This range uncertainty issue, therefore, is one of the important aspects to be managed in proton therapy. PURPOSE: For better management of range uncertainty, range verification has been widely studied, and prompt gamma imaging (PGI) is considered one of the promising methods in that effort. In this context, a PGI system named the gamma electron vertex imaging (GEVI) system was developed and recently upgraded for application to pencil-beam scanning (PBS) proton therapy. Here, we report the first experimental results using the therapeutic spot scanning proton beams. METHODS: A homogeneous slab phantom and an anthropomorphic phantom were employed. Spherical and cubic planning target volumes (PTVs) were defined. Various range shift scenarios were introduced. Prompt gamma (PG) measurement was synchronized with beam irradiation. The measured PG distributions were aggregated to improve the PG statistics. The range shift was estimated based on the relative change of the centroid in the measured PG distribution. The estimated range shifts were analyzed by range shift mapping, confidence interval (CI) estimation, and statistical hypothesis testing. RESULTS: The range shift mapping results showed an obvious measured range shift tendency following the true shift values. However, some fluctuations were found for spots that had still-low PG statistics after spot aggregation. The 99% CI distributions showed clearly distributed range shift measurement data. The overall accuracy and precision for all investigated scenarios were 0.36 and 0.20 mm, respectively. The results of one-sample t-tests confirmed that every shift scenario could be observed up to 1 mm of shift. The ANOVA results proved that the measured range shift data could be discriminated from one another, except for 16 (of 138) comparison cases having 1-2 mm shift differences. CONCLUSIONS: This study demonstrated the feasibility of the GEVI system for measurement of range shift in spot scanning proton therapy. Our experimental results showed that the proton beam can be measured up to 1 mm of range shift with high accuracy and precision. We believe that the GEVI system is one of the most promising PGI systems for in vivo range verification. Further research for application to more various cases and patient treatments is planned.

Development of a Real-Time Pixel Array-Type Detector for Ultrahigh Dose-Rate Beams.

Although research into ultrahigh dose-rate (UHDR) radiation therapy is ongoing, there is a significant lack of experimental measurements for two-dimensional (2D) dose-rate distributions. Additionally, conventional pixel-type detectors result in significant beam loss. In this study, we developed a pixel array-type detector with adjustable gaps and a data acquisition system to evaluate its effectiveness in measuring UHDR proton beams in real time. We measured a UHDR beam at the Korea Institute of Radiological and Medical Sciences using an MC-50 cyclotron, which produced a 45-MeV energy beam with a current range of 10-70 nA, to confirm the UHDR beam conditions. To minimize beam loss during measurement, we adjusted the gap and high voltage on the detector and determined the collection efficiency of the developed detector through Monte Carlo simulation and experimental measurements of the 2D dose-rate distribution. We also verified the accuracy of the real-time position measurement using the developed detector with a 226.29-MeV PBS beam at the National Cancer Center of the Republic of Korea. Our results indicate that, for a current of 70 nA with an energy beam of 45 MeV generated using the MC-50 cyclotron, the dose rate exceeded 300 Gy/s at the center of the beam, indicating UHDR conditions. Simulation and experimental measurements show that fixing the gap at 2 mm and the high voltage at 1000 V resulted in a less than 1% loss of collection efficiency when measuring UHDR beams. Furthermore, we achieved real-time measurements of the beam position with an accuracy of within 2% at five reference points. In conclusion, our study developed a beam monitoring system that can measure UHDR proton beams and confirmed the accuracy of the beam position and profile through real-time data transmission.

Feature Importance Analysis of a Deep Learning Model for Predicting Late Bladder Toxicity Occurrence in Uterine Cervical Cancer Patients.

(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.

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.

Development of a real-time in vivo dosimetry tool for electron beam therapy using a flexible thin film solar cell coated with scintillator powder.

PURPOSE: A real-time solar cell based in vivo dosimetry system (SC-IVD) was developed using a flexible thin film solar cell and scintillating powder. The present study evaluated the clinical feasibility of the SC-IVD in electron beam therapy. METHODS: A thin film solar cell was coated with 100 mg of scintillating powder using an optical adhesive to enhance the sensitivity of the SC-IVD. Calibration factors were obtained by dividing the dose, measured at a reference depth for 6-20 MeV electron beam energy, by the signal obtained using the SC-IVD. Dosimetric characteristics of SC-IVDs containing variable quantities of scintillating powder (0-500 mg) were evaluated, including energy, dose rate, and beam angle dependencies, as well as dose linearity. To determine the extent to which the SC-IVD affected the dose to the medium, doses at R90 were compared depending on whether the SC-IVD was on the surface. Finally, the accuracy of surface doses measured using the SC-IVD was evaluated by comparison with surface doses measured using a Markus chamber. RESULTS: Charge measured using the SC-IVD increased linearly with dose and was within 1% of the average signal according to the dose rate. The signal generated by the SC-IVD increased as the beam angle increased. The presence of the SC-IVD on the surface of a phantom resulted in a 0.5%-2.2% reduction in dose at R90 for 6-20 MeV electron beams compared with the bare phantom. Surface doses measured using the SC-IVD system and Markus chamber differed by less than 5%. CONCLUSIONS: The dosimetric characteristics of the SC-IVD were evaluated in this study. The results showed that it accurately measured the surface dose without a significant difference of dose in the medium when compared with the Markus chamber. The flexibility of the SC-IVD allows it to be attached to a patient's skin, enabling real-time and cost-effective measurement.

Interobserver Variability Prediction of Primary Gross Tumor in a Patient with Non-Small Cell Lung Cancer.

This research addresses the problem of interobserver variability (IOV), in which different oncologists manually delineate varying primary gross tumor volume (pGTV) contours, adding risk to targeted radiation treatments. Thus, a method of IOV reduction is urgently needed. Hypothesizing that the radiation oncologist's IOV may shrink with the aid of IOV maps, we propose IOV prediction network (IOV-Net), a deep-learning model that uses the fuzzy membership function to produce high-quality maps based on computed tomography (CT) images. To test the prediction accuracy, a ground-truth pGTV IOV map was created using the manual contour delineations of radiation therapy structures provided by five expert oncologists. Then, we tasked IOV-Net with producing a map of its own. The mean squared error (prediction vs. ground truth) and its standard deviation were 0.0038 and 0.0005, respectively. To test the clinical feasibility of our method, CT images were divided into two groups, and oncologists from our institution created manual contours with and without IOV map guidance. The Dice similarity coefficient and Jaccard index increased by ~6 and 7%, respectively, and the Hausdorff distance decreased by 2.5 mm, indicating a statistically significant IOV reduction (p < 0.05). Hence, IOV-net and its resultant IOV maps have the potential to improve radiation therapy efficacy worldwide.

Dose Profile Modulation of Proton Minibeam for Clinical Application.

The feasibility of proton minibeam radiation therapy (pMBRT) using a multislit collimator (MSC) and a scattering device was evaluated for clinical use at a clinical proton therapy facility. We fabricated, through Monte Carlo (MC) simulations, not only an MSC with a high peak-to-valley dose ratio (PVDR) at the entrance of the proton beam, to prevent radiation toxicity, but also a scattering device to modulate the PVDR in depth. The slit width and center-to-center distance of the diverging MSC were 2.5 mm and 5.0 mm at the large end, respectively, and its thickness and available field size were 100 mm and 76 × 77.5 mm2, respectively. Spatially fractionated dose distributions were measured at various depths using radiochromic EBT3 films and also tested on bacterial cells. MC simulation showed that the thicker the MSC, the higher the PVDR at the phantom surface. Dosimetric evaluations showed that lateral dose profiles varied according to the scatterer's thickness, and the depths satisfying PVDR = 1.1 moved toward the surface as their thickness increased. The response of the bacterial cells to the proton minibeams' depth was also established, in a manner similar to the dosimetric pattern. Conclusively, these results strongly suggest that pMBRT can be implemented in clinical centers by using MSC and scatterers.

Active small bowel sparing in intracavitary brachytherapy for cervical cancer.

OBJECTIVE: To propose and evaluate an active method for sparing the small bowel in the treatment field of cervical cancer brachytherapy by prone position procedure. METHODS: The prone position procedure consists of five steps: making bladder empty, prone-positioning a patient on belly board, making the small bowel move to abdomen, filling the bladder with Foley catheter and finally turning the patient into the supine position. The proposed method was applied for the treatment of seven cervical cancer patients. Its effectiveness was evaluated and a correlation between the patient characteristics and the volumetric dose reduction of small bowel was also investigated. Brachytherapy treatment plans were built before and after the proposed method, and their dose-volume histograms were compared for targets and organs-at-risk. In this comparison, all plans were normalized to satisfy the same D90% for high-risk clinical target volume. RESULTS: For the enrolled patients, the average dose of small bowel was significantly reduced from 75.2 ± 4.9 Gy before to 60.2 ± 4.0 Gy after the prone position procedure, while minor dosimetric changes were observed in rectum, sigmoid and bladder. The linear correlation to body mass index, thickness and width of abdominopelvic cavity and bladder volume were 76.2, 69.7, 28.8 and -36.3%, respectively. CONCLUSIONS: The application of prone position procedure could effectively lower the volumetric dose of the small bowel. The dose reduction in the small bowel had a strong correlation with the patient's obesity and abdominal thickness. This means the patients for whom the proposed method would be beneficial can be judiciously selected for safe brachytherapy.

Beam Angle Optimization for Double-Scattering Proton Delivery Technique Using an Eclipse Application Programming Interface and Convolutional Neural Network.

To automatically identify optimal beam angles for proton therapy configured with the double-scattering delivery technique, a beam angle optimization method based on a convolutional neural network (BAODS-Net) is proposed. Fifty liver plans were used for training in BAODS-Net. To generate a sequence of input data, 25 rays on the eye view of the beam were determined per angle. Each ray collects nine features, including the normalized Hounsfield unit and the position information of eight structures per 2° of gantry angle. The outputs are a set of beam angle ranking scores (S beam) ranging from 0° to 359°, with a step size of 1°. Based on these input and output designs, BAODS-Net consists of eight convolution layers and four fully connected layers. To evaluate the plan qualities of deep-learning, equi-spaced, and clinical plans, we compared the performances of three types of loss functions and performed K-fold cross-validation (K = 5). For statistical analysis, the volumes V27Gy and V30Gy as well as the mean, minimum, and maximum doses were calculated for organs-at-risk by using a paired-samples t-test. As a result, smooth-L1 loss showed the best optimization performance. At the end of the training procedure, the mean squared errors between the reference and predicted S beam were 0.031, 0.011, and 0.004 for L1, L2, and smooth-L1 loss, respectively. In terms of the plan quality, statistically, PlanBAO has no significant difference from PlanClinic (P >.05). In our test, a deep-learning based beam angle optimization method for proton double-scattering treatments was developed and verified. Using Eclipse API and BAODS-Net, a plan with clinically acceptable quality was created within 5 min.

Treatment outcomes of passive scattering proton beam therapy for stage I non-small cell lung cancer.

INTRODUCTION: To investigate the treatment outcomes of passive scattering proton beam therapy using stereotactic ablative radiotherapy (SABR) or hypofractionated radiation therapy (RT) for inoperable patients or those who refused surgery for stage I non-small cell lung cancer (NSCLC). METHODS: From January 2016 to December 2019, we retrospectively analyzed 42 patients with stage I NSCLC treated with proton beam therapy. The initially intended dose regimen was 60 cobalt Gray equivalents (CGE) in 4 fractions; however, sequentially modified dose regimens were used when the dose-volume constraints could not be met. The median total dose was 50 CGE (range 50-70 CGE), while the corresponding median biologically effective dose using [Formula: see text]= 10 (BED10) was 112.5 CGE (range 96-150 CGE). RESULTS: The median follow-up time was 40 months (interquartile range 32-48 months). Among the 42 treated patients, 33 had pathologically proven cancers of which most were adenocarcinoma (n = 21, 64%). The 3-year overall survival rate was 71.8%. The estimated rates of local control and progression free survival at 3 years were 91.5% and 66.9%, respectively. Thirteen patients experienced disease progression consisting of three local, six regional, and nine distant failures. No grade 4 or 5 toxicities were observed. CONCLUSION: Passive scattering proton beam therapy for stage I NSCLC using SABR or hypofractionated RT was safe and showed high LC rates.

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.

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.

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.

Long-term results of a phase II study of hypofractionated proton therapy for prostate cancer: moderate versus extreme hypofractionation.

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).

Clinical outcome of proton therapy for patients with chordomas.

PURPOSE: To investigate the clinical outcome of proton therapy (PT) in patients with chordoma. MATERIALS AND METHODS: Fifty-eight patients with chordoma treated with PT between June 2007 and December 2015 at the National Cancer Center, Korea, were retrospectively analyzed. The median total dose was 69.6 cobalt gray equivalent (CGE; range, 64.8 to 79.2 CGE). Local progression-free survival (LPFS), distant metastasis-free survival (DMFS), overall survival (OS), and diseasespecific survival (DSS) rates were calculated by the Kaplan-Meier method. RESULTS: With the median follow-up of 42.8 months (range, 4 to 174 months), the 5-year LPFS, DMFS, OS, and DSS rates were 87.9%, 86.7%, 88.3%, and 92.9%, respectively. The tumor location was associated with the patterns of failure: the LPFS rates were lower for cervical tumors (57.1%) than for non-cervical tumors (93.1%) (p = 0.02), and the DMFS rates were lower for sacral tumors (53.5%) than for non-sacral tumors (100%) (p = 0.001). The total dose was associated with both the LPFS rate and DMFS rate. The initial tumor size was associated with the DMFS rate, but was not associated with the LPFS rate. Three patients had grade 3 late toxicity with none ≥grade 4. CONCLUSION: PT is an effective and safe treatment in patients with chordomas. The tumor location was associated with the patterns of failure: local failure was common in cervical tumors, and distant failure was common in sacral tumors. Further refinement of PT, such as the utilization of intensity modulated PT for cervical tumors, is warranted to improve the outcome.

Toward a novel dosimetry system using acrylic disk radiation sensor for proton pencil beam scanning.

PURPOSE: Fabricate an acrylic disk radiation sensor (ADRS) and characterize the photoluminescence signal generated from the optical device as basis for the development and evaluation of a new dosimetry system for pencil beam proton therapy. METHODS: Based on the characteristics of the proposed optical dosimetry sensor, we established the relation between the photoluminescence output and the applied dose using an ionization chamber. Then, we obtained the relative integral depth dose profiles using the photoluminescence signal generated by pencil beam irradiation at energies of 99.9 and 162.1 MeV, and compared the results with the curve measured using a Bragg peak ionization chamber. RESULTS: The relation between the photoluminescence output and applied dose was linear. In addition, the ADRS was dose independent for beam currents up to 6 Gy/min, and the calibration factor for energy was close to 1. Hence, the energy dependence on the optical device can be disregarded. The integral depth dose profiles obtained for the ADRS suitable agreed with the curve measured in the Bragg peak ionization chamber without requiring correction. CONCLUSIONS: These results suggest that the ADRS is suitable for dosimetry measurements in pencil beam scanning, and it will be employed as a low-cost and versatile dosimetry sensor in upcoming developments.

Independent dose verification system with Monte Carlo simulations using TOPAS for passive scattering proton therapy at the National Cancer Center in Korea.

For the independent validation of treatment plans, we developed a fully automated Monte Carlo (MC)-based patient dose calculation system with the tool for particle simulation (TOPAS) and proton therapy machine installed at the National Cancer Center in Korea to enable routine and automatic dose recalculation for each patient. The proton beam nozzle was modeled with TOPAS to simulate the therapeutic beam, and MC commissioning was performed by comparing percent depth dose with the measurement. The beam set-up based on the prescribed beam range and modulation width was automated by modifying the vendor-specific method. The CT phantom was modeled based on the DICOM CT files with TOPAS-built-in function, and an in-house-developed C++ code directly imports the CT files for positioning the CT phantom, RT-plan file for simulating the treatment plan, and RT-structure file for applying the Hounsfield unit (HU) assignment, respectively. The developed system was validated by comparing the dose distributions with those calculated by the treatment planning system (TPS) for a lung phantom and two patient cases of abdomen and internal mammary node. The results of the beam commissioning were in good agreement of up to 0.8 mm2 [Formula: see text] for B8 option in both of the beam range and the modulation width of the spread-out Bragg peaks. The beam set-up technique can predict the range and modulation width with an accuracy of 0.06% and 0.51%, respectively, with respect to the prescribed range and modulation in arbitrary points of B5 option (128.3, 132.0, and 141.2 mm2 [Formula: see text] of range). The dose distributions showed higher than 99% passing rate for the 3D gamma index (3 mm distance to agreement and 3% dose difference) between the MC simulations and the clinical TPS in the target volume. However, in the normal tissues, less favorable agreements were obtained for the radiation treatment planning with the lung phantom and internal mammary node cases. The discrepancies might come from the limitations of the clinical TPS, which is the inaccurate dose calculation algorithm for the scattering effect, in the range compensator and inhomogeneous material. Moreover, the steep slope of the compensator, conversion of the HU values to the human phantom, and the dose calculation algorithm for the HU assignment also could be reasons of the discrepancies. The current study could be used for the independent dose validation of treatment plans including high inhomogeneities, the steep compensator, and riskiness such as lung, head & neck cases. According to the treatment policy, the dose discrepancies predicted with MC could be used for the acceptance decision of the original treatment plan.

Two-dimensional in vivo rectal dosimetry using an endorectal balloon with unfoldable radiochromic film during prostate cancer radiotherapy.

BACKGROUND AND PURPOSE: The present study aims to investigate the feasibility of two-dimensional (2D) in vivo rectal dosimetry using an endorectal balloon for the radiotherapy of prostate cancer. MATERIALS AND METHODS: The endorectal balloon was equipped with an unfoldable radiochromic film. The film was unrolled as the balloon was inflated, and rolled as it was deflated. Its mechanical and imaging properties were tested, and the dosimetric effectiveness was evaluated in clinical photon and proton beams. RESULTS: The size of the endorectal balloon including the film was linearly proportional to the volume of water filled in the balloon, and its position could be identified by X-ray radiography. The loss of dose information due to film cutting was within ±1mm from the cutting line. Applying linear interpolation on cut film, the gamma passing rate was more than 95% for 2%/2mm criteria. The measured dose profiles agreed with the plan within 3% and 4% for the photon and proton beams, respectively. A dose-volume histogram of the anterior rectal wall could be obtained from the measured dose distribution in the balloon, which also agreed well with the plan. CONCLUSIONS: 2D in vivo rectal dosimetry is feasible using the endorectal balloon with a radiochromic film in the radiotherapy of prostate cancer.