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.
This study aimed to investigate the performance of a deep learning-based survival-prediction model, which predicts the overall survival (OS) time of glioblastoma patients who have received surgery followed by concurrent chemoradiotherapy (CCRT). The medical records of glioblastoma patients who had received surgery and CCRT between January 2011 and December 2017 were retrospectively reviewed. Based on our inclusion criteria, 118 patients were selected and semi-randomly allocated to training and test datasets (3:1 ratio, respectively). A convolutional neural network–based deep learning model was trained with magnetic resonance imaging (MRI) data and clinical profiles to predict OS. The MRI was reconstructed by using four pulse sequences (22 slices) and nine images were selected based on the longest slice of glioblastoma by a physician for each pulse sequence. The clinical profiles consist of personal, genetic, and treatment factors. The concordance index (C-index) and integrated area under the curve (iAUC) of the time-dependent area-under-the-curve curves of each model were calculated to evaluate the performance of the survival-prediction models. The model that incorporated clinical and radiomic features showed a higher C-index (0.768 (95% confidence interval (CI): 0.759, 0.776)) and iAUC (0.790 (95% CI: 0.783, 0.797)) than the model using clinical features alone (C-index = 0.693 (95% CI: 0.685, 0.701); iAUC = 0.723 (95% CI: 0.716, 0.731)) and the model using radiomic features alone (C-index = 0.590 (95% CI: 0.579, 0.600); iAUC = 0.614 (95% CI: 0.607, 0.621)). These improvements to the C-indexes and iAUCs were validated using the 1000-times bootstrapping method; all were statistically significant (p < 0.001). This study suggests the synergistic benefits of using both clinical and radiomic parameters. Furthermore, it indicates the potential of multi-parametric deep learning models for the survival prediction of glioblastoma patients.
Purpose To evaluate the dosimetric characteristics and applications of a dosimetry system composed of a flexible amorphous silicon thin‐film solar cell and scintillator screen (STFSC‐SS) for therapeutic X‐rays. Methods The real‐time dosimetry system was composed of a flexible a‐Si thin‐film solar cell (0.2‐mm thick), a scintillator screen to increase its efficiency, and an electrometer to measure the generated charge. The dosimetric characteristics of the developed system were evaluated including its energy dependence, dose linearity, and angular dependence. Calibration factors for the signal measured by the system and absorbed dose‐to‐water were obtained by setting reference conditions. The application and correction accuracy of the developed system were evaluated by comparing the absorbed dose‐to‐water measured using a patient treatment beam with that measured using the ion chamber. Results The responses of STFSC‐SS to energy, field size, depth, and source‐to‐surface distance (SSD) were more dependent on measurement conditions than were the responses of the ion chamber, although the former dependence was due to the scintillator screen, not the solar cell. The signals of STFSC‐SS were also dependent on dose rate, while the responses of solar cell alone and scintillator screen were not dependent on dose rate. The scintillator screen reduced the output of solar cell at 6 and 15 MV by 0.60 and 0.55%, respectively. The different absorbed dose‐to‐water measured using STFSC‐SS for patient treatment beam differed by 0.4% compared to those measured using the ionization chamber. The uncertainties of the developed system for 6 and 15 MV photon beams were 1.8 and 1.7%, respectively, confirming the accuracy and applicability of this system. Conclusions The thin‐film solar cell‐based detector developed in this study can accurately measure absorbed dose‐to‐water. The increased signal resulting from the use of the scintillator screen is advantageous for measuring low doses and stable signal output. In addition, this system is flexible, making it applicable to curved surfaces, such as a patient's body, and is cost‐effective.
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