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Abstract Background and purpose Treatment record contains most of information related to treatment plan delivery in radiation therapy. Reviewing treatment record is an important quality assurance (QA) task for safety and quality of patient treatments. This task is usually performed by senior medical physicists. However, it is time-consuming, tedious, and error-prone. To assist this process, a treatment record review system (TRRS) is developed to automatically review items related to treatment delivery record. Methods The treatment record is firstly extracted from oncology information system (OIS). Based on the daily patient treatment information, the original plan from the treatment planning system is identified. Then the original plan and the delivered plan are correlated. Eight review categories (parameter consistency, treatment completeness, treatment progression, image guidance, override, treatment couch, documentation, and treatment mode) are created. Tailored rules are designed for various review items to automate the review process. As a result, for each daily treatment record, a reviewed flag (pass, failure, warning, and N/A) is assigned by the TRRS. Finally, this system is evaluated by 6 months patient treatment records collected in our institute and compared to the manual process on the same data. Results TRRS processed a total of 76,651 treatment fractions from 4230 patients with an average of 574 treatments per day. The percentage of the detected anomalies among the total records was 0.76%. The average processing time was 3.9 s and 282 s per treatment record for the automatic and manual processes, respectively. Comparing with the manual process, the time efficiency of TRRS is improved by a factor of 72. The average numbers of anomalies detected by the automatic and manual processes are 21 and 13 per day, respectively. TRRS detects 61.5% more anomalies than those of the manual process. Conclusion TRRS is not only efficient in processing a large amount of treatment records on a daily basis but also effective in finding more anomalies than those of physics weekly check. The application of the TRRS could significantly reduce the workload of the review physicists and let them focus on more important works related to patient safety.

Abstract Background Volumetric modulated arc therapy (VMAT) planning optimization involves iterative adjustment of numerous parameters, and hence requires repeatedly dose recalculation. In this study, we used the deep learning method to develop a fast and accurate dose calculation method for VMAT. Methods The classical 3D UNet was adopted and trained to learn the physics principle of dose calculation. The inputs included the projected fluence map (FM), computed tomography (CT) images, the radiological depth and the source-to-voxel distance (SVD). The projected FM was generated by projecting the accumulated FM between two consecutive control points (CPs) onto the patient’s anatomy. The accumulated FM was calculated by simulating the movement of the multi-leaf collimator (MLC) from one CP to the next. The dose, calculated by the treatment planning system (TPS), was used as ground truth. 51 head and neck VMAT plans were used, with 43, 1 and 7 cases as training, validation, and testing datasets, respectively. Correspondingly, 7182, 180 and 1260 CP samples were included in the training, validation, and testing datasets. Results This presented method was evaluated by comparing the derived dose distribution to the TPS calculated dose distribution. The dose profiles coincided for both the single CP and the entire plan (summation of all CPs). But the network derived dose was smoother than the TPS calculated dose. Gamma analysis was performed between the network derived dose and the TPS calculated dose. The average gamma pass rate was 96.56%, 98.75%, 98.03% and 99.30% under the criteria of 2% (tolerance) -2 mm (distance to agreement, DTA). 2%-3 mm, 3%-2 mm and 3%-3 mm. No significant difference was observed on the critical indices including the max, mean dose, and the relative volume covered by the 2000 cGy, 4000 cGy and the prescription dose. For one CP, the average computational time of the network and TPS was 0.09s and 0.53s. And for one patient, the average time was 16.51s and 95.60s. Conclusion The dose distribution derived by the network showed good agreement with the TPS calculated dose distribution. The computational time was reduced to approximate one-sixth of its original duration. Therefore the presented deep learning-based dose calculation method has the potential to be used for planning optimization.

PurposeVolumetric modulated arc therapy (VMAT) is a new treatment modality in modern radiotherapy. To ensure the quality of the radiotherapy plan, a physics plan review is routinely conducted by senior clinicians; however, this process is less efficient and less accurate. In this study, a multi-task AutoEncoder (AE) is proposed to automate anomaly detection of VMAT plans for lung cancer patients.MethodsThe feature maps are first extracted from a VMAT plan. Then, a multi-task AE is trained based on the input of a feature map, and its output is the two targets (beam aperture and prescribed dose). Based on the distribution of reconstruction errors on the training set, a detection threshold value is obtained. For a testing sample, its reconstruction error is calculated using the AE model and compared with the threshold value to determine its classes (anomaly or regular). The proposed multi-task AE model is compared to the other existing AE models, including Vanilla AE, Contractive AE, and Variational AE. The area under the receiver operating characteristic curve (AUC) and the other statistics are used to evaluate the performance of these models.ResultsAmong the four tested AE models, the proposed multi-task AE model achieves the highest values in AUC (0.964), accuracy (0.821), precision (0.471), and F1 score (0.632), and the lowest value in FPR (0.206).ConclusionThe proposed multi-task AE model using two-dimensional (2D) feature maps can effectively detect anomalies in radiotherapy plans for lung cancer patients. Compared to the other existing AE models, the multi-task AE is more accurate and efficient. The proposed model provides a feasible way to carry out automated anomaly detection of VMAT plans in radiotherapy.

Purpose This study aims to investigate the influence of the magnetic field on treatment plan quality using typical phantom test cases, which encompass a circle target test case, AAPM TG119 test cases (prostate, head-and-neck, C-shape, multi-target test cases), and a lung test case. Materials and Methods For the typical phantom test cases, two plans were formulated. The first plan underwent optimization in the presence of a 1.5 Tesla magnetic field (1.5 T plan). The second plan was re-optimized without a magnetic field (0 T plan), utilizing the same optimization conditions as the first plan. The two plans were compared based on various parameters, including con­formity index (CI), homogeneity index (HI), fit index (FI) and dose coverage of the planning target volume (PTV), dose delivered to organs at risk (OARs) and normal tissue (NT), monitor unit (MU). A plan-quality metric (PQM) scoring procedure was employed. For the 1.5 T plans, dose verifications were performed using an MR-compatible ArcCHECK phantom. Results A smaller dose influence of the magnetic field was found for the circle target, prostate, head-and-neck, and C-shape test cases, compared with the multi-target and lung test cases. In the multi-target test case, the significant dose influence was on the inferior PTV, followed by the superior PTV. There was a relatively large dose influence on the PTV and OARs for lung test case. No statistically significant differences in PQM and MUs were observed. For the 1.5 T plans, gamma passing rates were all higher than 95% with criteria of 2 mm/3% and 2 mm/2%. Conclusion The presence of a 1.5 T magnetic field had a relatively large impact on dose parameters in the multi-target and lung test cases compared with other test cases. However, there were no significant influences on the plan-quality metric, MU and dose accuracy for all test cases.

PurposeDifficulties remain in dose optimization and evaluation of cervical cancer radiotherapy that combines external beam radiotherapy (EBRT) and brachytherapy (BT). This study estimates and improves the accumulated dose distribution of EBRT and BT with deep learning–based dose prediction.Materials and methodsA total of 30 patients treated with combined cervical cancer radiotherapy were enrolled in this study. The dose distributions of EBRT and BT plans were accumulated using commercial deformable image registration. A ResNet-101–based deep learning model was trained to predict pixel-wise dose distributions. To test the role of the predicted accumulated dose in clinic, each EBRT plan was designed using conventional method and then redesigned referencing the predicted accumulated dose distribution. Bladder and rectum dosimetric parameters and normal tissue complication probability (NTCP) values were calculated and compared between the conventional and redesigned accumulated doses.ResultsThe redesigned accumulated doses showed a decrease in mean values of V50, V60, and D2cc for the bladder (−3.02%, −1.71%, and −1.19 Gy, respectively) and rectum (−4.82%, −1.97%, and −4.13 Gy, respectively). The mean NTCP values for the bladder and rectum were also decreased by 0.02‰ and 0.98%, respectively. All values had statistically significant differences (p < 0.01), except for the bladder D2cc (p = 0.112).ConclusionThis study realized accumulated dose prediction for combined cervical cancer radiotherapy without knowing the BT dose. The predicted dose served as a reference for EBRT treatment planning, leading to a superior accumulated dose distribution and lower NTCP values.

Background and purposeCurrent studies have substantiated the sparing effect of ultra-high dose rate irradiation (FLASH) in various organs including the brain, lungs, and intestines. Whether this sparing effect extends to esophageal tissue remains unexplored. This study aims to compare the different responses of esophageal tissue in histological and protein expression levels following conventional dose rate irradiation (CONV) and FLASH irradiation to ascertain the presence of a sparing effect.Methods and materialsC57 female mice were randomly divided into three groups: control, CONV, and FLASH groups. The chest region of the mice in the radiation groups was exposed to a prescribed dose of 20 Gy using a modified electron linear accelerator. The CONV group received an average dose rate of 0.1 Gy/s, while the FLASH group received an average dose rate of 125 Gy/s. On the 10th day after irradiation, the mice were euthanized and their esophagi were collected for histopathological analysis. Subsequently, label-free proteomic quantification analysis was performed on esophageal tissue. The validation process involved analyzing transmission electron microscopy images and utilizing the parallel reaction monitoring method.ResultsHistopathology results indicated a significantly lower extent of esophageal tissue damage in the FLASH group compared to the CONV group (p < 0.05). Label-free quantitative proteomic analysis revealed that the sparing effect observed in the FLASH group may be attributed to a reduction in radiation-induced protein damage associated with mitochondrial functions, including proteins involved in the tricarboxylic acid cycle and oxidative phosphorylation, as well as a decrease in acute inflammatory responses.ConclusionsCompared with CONV irradiation, a sparing effect on esophageal tissue can be observed after FLASH irradiation. This sparing effect is associated with alleviated mitochondria damage and acute inflammation.

Abstract Background Although magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis studies based on deep learning have significantly progressed, the similarity between synthetic CT (sCT) and real CT (rCT) has only been evaluated in image quality metrics (IQMs). To evaluate the similarity between synthetic CT (sCT) and real CT (rCT) comprehensively, we comprehensively evaluated IQMs and radiomic features for the first time. Methods This study enrolled 127 patients with nasopharyngeal carcinoma who underwent CT and MRI scans. Supervised-learning (Unet) and unsupervised-learning (CycleGAN) methods were applied to build MRI-to-CT synthesis models. The regions of interest (ROIs) included nasopharynx gross tumor volume (GTVnx), brainstem, parotid glands, and temporal lobes. The peak signal-to-noise ratio (PSNR), mean absolute error (MAE), root mean square error (RMSE), and structural similarity (SSIM) were used to evaluate image quality. Additionally, 837 radiomic features were extracted for each ROI, and the correlation was evaluated using the concordance correlation coefficient (CCC). Results The MAE, RMSE, SSIM, and PSNR of the body were 91.99, 187.12, 0.97, and 51.15 for Unet and 108.30, 211.63, 0.96, and 49.84 for CycleGAN. For the metrics, Unet was superior to CycleGAN (P

Abstract Background Radiotherapy‐induced esophagitis (RE) diminishes the quality of life and interrupts treatment in patients with non‐small cell lung cancer (NSCLC) undergoing postoperative radiotherapy. Dosimetric models showed limited capability in predicting RE. We aimed to develop dosiomic models to predict RE. Methods Models were trained with a real‐world cohort and validated with PORT‐C randomized controlled trial cohort. Patients with NSCLC undergoing resection followed by postoperative radiotherapy between 2004 and 2015 were enrolled. The endpoint was grade ≥2 RE. Esophageal three‐dimensional dose distribution features were extracted using handcrafted and convolutional neural network (CNN) methods, screened using an entropy‐based method, and selected using minimum redundancy and maximum relevance. Prediction models were built using logistic regression. The areas under the receiver operating characteristic curve (AUC) and precision‐recall curve were used to evaluate prediction model performance. A dosimetric model was built for comparison. Results A total of 190 and 103 patients were enrolled in the training and validation sets, respectively. Using handcrafted and CNN methods, 107 and 4096 features were derived, respectively. Three handcrafted, four CNN‐extracted and three dosimetric features were selected. AUCs of training and validation sets were 0.737 and 0.655 for the dosimetric features, 0.730 and 0.724 for handcrafted features, and 0.812 and 0.785 for CNN‐extracted features, respectively. Precision‐recall curves revealed that CNN‐extracted features outperformed dosimetric and handcrafted features. Conclusions Prediction models may identify patients at high risk of developing RE. Dosiomic models outperformed the dosimetric‐feature model in predicting RE. CNN‐extracted features were more predictive but less interpretable than handcrafted features.

Abstract Background Accurate delineation of clinical target volume of tumor bed (CTV-TB) is important but it is also challenging due to surgical effects and soft tissue contrast. Recently a few auto-segmentation methods were developed to improve the process. However, those methods had comparatively low segmentation accuracy. In this study the prior information was introduced to aid auto-segmentation of CTV-TB based on a deep-learning model. Methods To aid the delineation of CTV-TB, the tumor contour on preoperative CT was transformed onto postoperative CT via deformable image registration. Both original and transformed tumor contours were used for prior information in training an auto-segmentation model. Then, the CTV-TB contour on postoperative CT was predicted by the model. 110 pairs of preoperative and postoperative CT images were used with a 5-fold cross-validation strategy. The predicted contour was compared with the clinically approved contour for accuracy evaluation using dice similarity coefficient (DSC) and Hausdorff distance. Results The average DSC of the deep-learning model with prior information was improved than the one without prior information (0.808 vs. 0.734, P

Abstract Purpose This study was to improve image quality for high-speed MR imaging using a deep learning method for online adaptive radiotherapy in prostate cancer. We then evaluated its benefits on image registration. Methods Sixty pairs of 1.5 T MR images acquired with an MR-linac were enrolled. The data included low-speed, high-quality (LSHQ), and high-speed low-quality (HSLQ) MR images. We proposed a CycleGAN, which is based on the data augmentation technique, to learn the mapping between the HSLQ and LSHQ images and then generate synthetic LSHQ (synLSHQ) images from the HSLQ images. Five-fold cross-validation was employed to test the CycleGAN model. The normalized mean absolute error (nMAE), peak signal-to-noise ratio (PSNR), structural similarity index measurement (SSIM), and edge keeping index (EKI) were calculated to determine image quality. The Jacobian determinant value (JDV), Dice similarity coefficient (DSC), and mean distance to agreement (MDA) were used to analyze deformable registration. Results Compared with the LSHQ, the proposed synLSHQ achieved comparable image quality and reduced imaging time by ~ 66%. Compared with the HSLQ, the synLSHQ had better image quality with improvement of 57%, 3.4%, 26.9%, and 3.6% for nMAE, SSIM, PSNR, and EKI, respectively. Furthermore, the synLSHQ enhanced registration accuracy with a superior mean JDV (6%) and preferable DSC and MDA values compared with HSLQ. Conclusion The proposed method can generate high-quality images from high-speed scanning sequences. As a result, it shows potential to shorten the scan time while ensuring the accuracy of radiotherapy.

Purpose: Convolutional neural networks (CNNs) offer a promising approach to automating organ segmentation in radiotherapy. However, variations of segmentation protocols made by different medical centers may induce a well-trained CNN model in one center and may not perform well in other centers. In this study, we proposed a transfer learning method to improve the performance of deep-learning based segmentation models among different medical centers using nasopharyngeal cancer (NPC) data.Methods: The NPC data included 300 cases (S_Train) from one institution (the source center) and 60 cases from another (the target center), divided into a training set of 50 cases (T_Train) and a test set of 10 target cases (T_Test). A ResNet CNN architecture was developed with 103 layers. We first trained Model_S and Model_T from scratch with the datasets S_Train and T_train, respectively. Transfer learning was then used to train Model_ST by fine-tuning the last 10 layers of Model_S with images from T_Train. We also investigated the effect of the numbers of re-trained layers on the performance. The performance of each model was evaluated using the dice similarity coefficient, and it was used as the evaluation metrics. We compared the dice similarity coefficient value using the three different models (Model_S, Model_T, and Model_ST).Results: When Model_S, Model_T, and Model_ST were applied to the T_Test dataset, the transfer learning (Model_ST) had the best performance. Compared with Model_S, the p-values of all organs at risk were less than 0.05. Compared with Model_T, the p-values of most organs at risk were less than 0.05, but there was no significant statistical difference in Model_ST for the brain stem (p = 0.071), mandible (p = 0.177), left temporal lobes (p = 0.084), and right temporal lobes (p = 0.068). Although there was no statistical difference for these organs, the mean accuracy of Model_ST was higher than that of Model_T. The proposed transfer learning can reduce the training time by up to 33%.Conclusion: Transfer learning can improve organ segmentation for NPC by adapting a previously trained CNN model to a new image domain, reducing the training time and saving physicians from labeling a large number of contours.

Abstract Purpose This study implemented a piecewise volumetric modulated arc therapy (P-VMAT) for realizing whole-brain radiation therapy (WBRT) with simultaneous integrated boost (SIB) for multiple brain metastases (> 40 metastases) with a conventional C-arm linear accelerator. Materials and methods This study retrospectively analyzed 10 patients with multiple brain metastases (40–120 metastases, median 76), who underwent WBRT and SIB using helical tomotherapy (HT). The prescribed doses were 40 Gy/20 f and 60 Gy/20 f for WBRT and SIB, respectively. Corresponding new HT plans were designed with P-VMAT using 7 arcs. For each arc, the collimator was rotated to 45°, and the field width was limited to 2.5 cm with 0.5 cm overlap with adjacent arcs. Thus, each arc covered only one section of the brain target volume. A conventional dual arc VMAT (DA-VMAT) plan was also designed. HT, P-VMAT, and DA-VMAT plans were compared using dose distribution reviews and dosimetric parameters. ArcCHECK phantom measurements were performed for verification of P-VMAT plans. Results No significant differences in the mean coverage of the whole-brain target and metastases were observed between HT and P-VMAT (p > 0.05). The conformity index for the whole-brain target improved with P-VMAT compared with HT (p

Abstract Background Robotic linac is ideally suited to deliver hypo-fractionated radiotherapy due to its compact head and flexible positioning. The non-coplanar treatment space improves the delivery versatility but the complexity also leads to prolonged optimization and treatment time. Methods In this study, we attempted to use the deep learning (pytorch) framework for the plan optimization of circular cone based robotic radiotherapy. The optimization problem was topologized into a simple feedforward neural network, thus the treatment plan optimization was transformed into network training. With this transformation, the pytorch toolkit with high-efficiency automatic differentiation (AD) for gradient calculation was used as the optimization solver. To improve the treatment efficiency, plans with fewer nodes and beams were sought. The least absolute shrinkage and selection operator (lasso) and the group lasso were employed to address the “sparsity” issue. Results The AD-S (AD sparse) approach was validated on 6 brain and 6 liver cancer cases and the results were compared with the commercial MultiPlan (MLP) system. It was found that the AD-S plans achieved rapid dose fall-off and satisfactory sparing of organs at risk (OARs). Treatment efficiency was improved by the reduction in the number of nodes (28%) and beams (18%), and monitor unit (MU, 24%), respectively. The computational time was shortened to 47.3 s on average. Conclusions In summary, this first attempt of applying deep learning framework to the robotic radiotherapy plan optimization is promising and has the potential to be used clinically.

Background The incorporation of noncoplanar beam arrangements has been proposed in liver radiotherapy modalities, which can reduce the dose in normal tissues compared to coplanar techniques. Noncoplanar radiotherapy techniques for hepatocellular carcinoma treatment based on the Linac design have a limited effective arc angle to avoid collisions. Purpose To propose a novel noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system and investigate its performance in hepatocellular carcinoma patients. Methods The computed tomography was deflected 90° to meet the structure of a cage-like radiotherapy system and design the noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system plan in the Pinnacle3 planning system. An noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system plan was customized for each of 10 included hepatocellular carcinoma patients, with 6 dual arcs ranging from −30° to 30°. Six couch angles were set with an interval of 36° and distributed along with the longest diameter of planning target volume. The dosimetric parameters of noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system plan were compared with the noncoplanar volumetric modulated arc therapy and volumetric modulated arc therapy plan. Results The 3 radiotherapy techniques regarding planning target volume were statistically different for D98%, D2%, conformity index, and homogeneity index with χ 2 = 9.692, 14.600, 8.600, and 12.600, and P = .008, .001, .014, and .002, respectively. Further multiple comparisons revealed that noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system significantly reduced the mean dose ( P = .005) and V5 ( P = .005) of the normal liver, the mean dose ( P = .005) of the stomach, and V30 ( P = .028) of the lung compared to noncoplanar volumetric modulated arc therapy. Noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system significantly reduced the mean dose ( P = .005) and V5 ( P = .005) of the normal liver, the mean dose ( P = .017) of the spinal cord, V50 ( P = .043) of the duodenum, the maximum dose ( P = .007) of the esophagus, and V30 ( P = .047) of the whole lung compared to volumetric modulated arc therapy. The results indicate that noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system protects the normal liver, stomach, and lung better than noncoplanar volumetric modulated arc therapy and protects the normal liver, spinal cord, duodenum, esophagus, and lung better than volumetric modulated arc therapy. Conclusions The noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system technique with the arrangement of noncoplanar arcs provided optimal dosimetric gains compared with noncoplanar volumetric modulated arc therapy and volumetric modulated arc therapy, except for the heart. Noncoplanar volumetric modulated arc therapy technique based on a cage-like radiotherapy system should be considered in more clinically challenging cases.

PurposeTreatment plans are used for patients under radiotherapy in clinics. Before execution, these plans are checked for safety and quality by human experts. A few of them were identified with flaws and needed further improvement. To automate this checking process, an unsupervised learning method based on an autoencoder was proposed.MethodsFirst, features were extracted from the treatment plan by human experts. Then, these features were assembled and used for model learning. After network optimization, a reconstruction error between the predicted and target signals was obtained. Finally, the questionable plans were identified based on the value of the reconstruction error. A large value of the reconstruction error indicates a longer distance from the standard distribution of normal plans. A total of 576 treatment plans for breast cancer patients were used for the test. Among them, 19 were questionable plans identified by human experts. To evaluate the performance of the autoencoder, it was compared with four baseline detection algorithms, namely, local outlier factor (LOF), hierarchical density-based spatial clustering of applications with noise (HDBSCAN), one-class support vector machine (OC-SVM), and principal component analysis (PCA).ResultsThe results showed that the autoencoder achieved the best performance than the other four baseline algorithms. The AUC value of the autoencoder was 0.9985, while the second one was 0.9535 (LOF). While maintaining 100% recall, the average accuracy and precision of the results by the autoencoder were 0.9658 and 0.5143, respectively. While maintaining 100% recall, the average accuracy and precision of the results by LOF were 0.8090 and 0.1472, respectively.ConclusionThe autoencoder can effectively identify questionable plans from a large group of normal plans. There is no need to label the data and prepare the training data for model learning. The autoencoder provides an effective way to carry out an automatic plan checking in radiotherapy.

PurposeDeep-learning effectively predicts dose distributions in knowledge-based radiotherapy planning. Using anatomical information that includes a structure map and computed tomography (CT) data as input has been proven to work well. The minimum distance from each voxel in normal structures to planning target volume (DPTV) closely affects each voxel’s dose. In this study, we combined DPTV and anatomical information as input for a deep-learning–based dose-prediction network to improve performance.Materials and methodsOne hundred patients who underwent volumetric-modulated arc therapy for nasopharyngeal cancer were selected in this study. The prediction model based on a residual network had DPTV maps, structure maps, and CT as inputs and the corresponding dose distribution maps as outputs. The performances of the combined distance and anatomical information (COM) model and the traditional anatomical (ANAT) model with two-channel inputs (structure maps and CT) were compared. A 10-fold cross validation was performed to separately train and test the COM and ANAT models. The voxel-based mean error (ME), mean absolute error (MAE), dosimetric parameters, and dice similarity coefficient (DSC) of isodose volumes were used for modeling evaluation.ResultsThe mean MAE of the body volume of the COM model were 4.89 ± 1.35%, highly significantly lower than those for the ANAT model of 5.07 ± 1.37% (p0.05). The mean DSC values of the isodose volumes in the range of 60 Gy were all better in the COM model (p

PurposeThis study proposes a method to identify plans that failed patient-specific quality assurance (QA) and attempts to establish a criterion to prescreen treatment plans for patient-specific QA measurements with independent Monte Carlo dose calculations.Materials and methodsPatient-specific QA results measured with an ArcCHECK diode array of 207 patients (head and neck: 25; thorax: 61; abdomen: 121) were retrospectively analyzed. All patients were treated with the volumetric modulated arc therapy (VMAT) technique and plans were optimized with a Pinnacle v16.2 treatment planning system using an analytical algorithm-based dose engine. Afterwards, phantom verification plans were designed and recalculated by an independent GPU-accelerated Monte Carlo (MC) dose engine, ArcherQA. Moreover, sensitivity and specificity analyzes of gamma passing rates between measurements and MC calculations were carried out to show the ability of MC to monitor failing plans (ArcCHECK 3%/3 mm,0.9) showed excellent classification accuracy for monitoring failing plans with independent MC calculations.

PurposeIn principle, intensity-modulated electron therapy (IMET) can be delivered through mechanical scanning, with a robotic arm mounting a linac.Materials and methodsHere is a scanning algorithm to identify the back-and-forth, top-to-bottom (zigzag) pattern scan sequence. The algorithm includes generating beam positions with a uniform resolution according to the applicator size; adopting discrete energies to achieve the depth of 90% dose by compositing energies; selecting energy by locating the target’s distal edge; and employing the energy-by-energy scan strategy for step-and-shoot discrete scanning. After a zigzag scan sequence is obtained, the delivery order of the scan spots is optimized by fast simulated annealing (FSA) to minimize the path length. For algorithm evaluation, scan sequences were generated using the computed tomography data of 10 patients with pancreatic cancer undergoing intraoperative radiotherapy, and the results were compared between the zigzag path and an optimized path. A simple calculation of the treatment delivery time, which comprises the irradiation time, the total robotic arm moving time, the time for energy switch, and the time to stop and restart the beam, was also made.ResultsIn these clinical cases, FSA optimization shortened the path lengths by 12%–43%. Assuming the prescribed dose was 15 Gy, machine dose rate was 15 Gy/s, energy switch time was 2 s, stop and restart beam time was 20 ms, and robotic arm move speed was 50 mm/s, the average delivery time was 124±38 s. The largest reduction in path length yielded an approximately 10% reduction in the delivery time, which can be further reduced by increasing the machine dose rate and the robotic arm speed, decreasing the time for energy switch, and/or developing more efficient algorithms.ConclusionMechanically scanning IMET is potentially feasible and worthy of further exploration.

PurposeThe challenge of cone-beam computed tomography (CBCT) is its low image quality, which limits its application for adaptive radiotherapy (ART). Despite recent substantial improvement in CBCT imaging using the deep learning method, the image quality still needs to be improved for effective ART application. Spurred by the advantages of transformers, which employs multi-head attention mechanisms to capture long-range contextual relations between image pixels, we proposed a novel transformer-based network (called TransCBCT) to generate synthetic CT (sCT) from CBCT. This study aimed to further improve the accuracy and efficiency of ART.Materials and methodsIn this study, 91 patients diagnosed with prostate cancer were enrolled. We constructed a transformer-based hierarchical encoder–decoder structure with skip connection, called TransCBCT. The network also employed several convolutional layers to capture local context. The proposed TransCBCT was trained and validated on 6,144 paired CBCT/deformed CT images from 76 patients and tested on 1,026 paired images from 15 patients. The performance of the proposed TransCBCT was compared with a widely recognized style transferring deep learning method, the cycle-consistent adversarial network (CycleGAN). We evaluated the image quality and clinical value (application in auto-segmentation and dose calculation) for ART need.ResultsTransCBCT had superior performance in generating sCT from CBCT. The mean absolute error of TransCBCT was 28.8 ± 16.7 HU, compared to 66.5 ± 13.2 for raw CBCT, and 34.3 ± 17.3 for CycleGAN. It can preserve the structure of raw CBCT and reduce artifacts. When applied in auto-segmentation, the Dice similarity coefficients of bladder and rectum between auto-segmentation and oncologist manual contours were 0.92 and 0.84 for TransCBCT, respectively, compared to 0.90 and 0.83 for CycleGAN. When applied in dose calculation, the gamma passing rate (1%/1 mm criterion) was 97.5% ± 1.1% for TransCBCT, compared to 96.9% ± 1.8% for CycleGAN.ConclusionsThe proposed TransCBCT can effectively generate sCT for CBCT. It has the potential to improve radiotherapy accuracy.

PurposeThe current algorithms for measuring ventilation images from 4D cone-beam computed tomography (CBCT) are affected by the accuracy of deformable image registration (DIR). This study proposes a new deep learning (DL) method that does not rely on DIR to derive ventilation images from 4D-CBCT (CBCT-VI), which was validated with the gold-standard single-photon emission-computed tomography ventilation image (SPECT-VI).Materials and MethodsThis study consists of 4D-CBCT and 99mTc-Technegas SPECT/CT scans of 28 esophagus or lung cancer patients. The scans were rigidly registered for each patient. Using these data, CBCT-VI was derived using a deep learning-based model. Two types of model input data are studied, namely, (a) 10 phases of 4D-CBCT and (b) two phases of peak-exhalation and peak-inhalation of 4D-CBCT. A sevenfold cross-validation was applied to train and evaluate the model. The DIR-dependent methods (density-change-based and Jacobian-based methods) were used to measure the CBCT-VIs for comparison. The correlation was calculated between each CBCT-VI and SPECT-VI using voxel-wise Spearman’s correlation. The ventilation images were divided into high, medium, and low functional lung regions. The similarity of different functional lung regions between SPECT-VI and each CBCT-VI was evaluated using the dice similarity coefficient (DSC). One-factor ANONA model was used for statistical analysis of the averaged DSC for the different methods of generating ventilation images.ResultsThe correlation values were 0.02 ± 0.10, 0.02 ± 0.09, and 0.65 ± 0.13/0.65 ± 0.15, and the averaged DSC values were 0.34 ± 0.04, 0.34 ± 0.03, and 0.59 ± 0.08/0.58 ± 0.09 for the density change, Jacobian, and deep learning methods, respectively. The strongest correlation and the highest similarity with SPECT-VI were observed for the deep learning method compared to the density change and Jacobian methods.ConclusionThe results showed that the deep learning method improved the accuracy of correlation and similarity significantly, and the derived CBCT-VIs have the potential to monitor the lung function dynamic changes during radiotherapy.

Abstract Background To analyze and present the China’s national survey on patient-specific IMRT quality assurance (QA). Methods A national survey was conducted in all radiotherapy centers in China to collect comprehensive information on status of IMRT QA practice, including machine, technique, equipment, issues and suggestions. Results Four hundred and three centers responded to this survey, accounting for 56.92% of all the centers implementing IMRT in China. The total number of medical physicists and the total number of patients treated with IMRT annually in these centers was 1599 and 305,000 respectively. All centers implemented measurement-based verification. Point dose verification and 2D dose verification was implemented in 331 and 399 centers, respectively. Three hundred forty-eight centers had 2D arrays, and 52 centers had detector devices designed to measure VMAT beams. EPID and film were used in 78 and 70 centers, respectively. Seventeen and 20 centers used log file and 3D DVH analysis, respectively. One hundred sixty-eight centers performed measurement-based verification not for each patient based on different selection criteria. The techniques and methods varied significantly in both point dose and dose distribution verification, from evaluation metrics, criteria, tolerance limit, and steps to check failed IMRT QA plans. Major issues identified in this survey were the limited resources of physicists, QA devices, and linacs. Conclusions IMRT QA was implemented in all the surveyed centers. The practice of IMRT QA varied significantly between centers. An increase in personnel, QA devices and linacs is highly desired. National standard, guideline, regulation and training programs are urgently needed in China for consistent and effective implementation of IMRT QA.

(1) Background: There was a need for 3D image reconstruction from a series of 2D projections in medical applications. However, additional exposure to X-ray projections may harm human health. To alleviate it, minimizing the projection numbers is a solution to reduce X-ray exposure, but this would cause significant image noise and artifacts. (2) Purpose: In this study, a method was proposed for the reconstruction of a 3D image from a minimal set of 2D X-ray projections using a B-spline-based deformable transformation. (3) Methods: The inputs of this method were a 3D image which was acquired in previous treatment and used as a prior image and a minimal set of 2D projections which were acquired during the current treatment. The goal was to reconstruct a new 3D image in current treatment from the two inputs. The new 3D image was deformed from the prior image via the displacement matrixes that were interpolated by the B-spline coefficients. The B-spline coefficients were solved with the objective function, which was defined as the mean square error between the reconstructed and the ground-truth projections. In the optimization process the gradient of the objective function was calculated, and the B-spline coefficients were then updated. For the acceleration purpose, the computation of the 2D and 3D image reconstructions and B-spline interpolation were implemented on a graphics processing unit (GPU). (4) Results: When the scan angles were more than 60°, the image quality was significantly improved, and the reconstructed image was comparable to that of the ground-truth image. As the scan angles were less than 30°, the image quality was significantly degraded. The influence of the scan orientation on the image quality was minor. With the application of GPU acceleration, the reconstruction efficiency was improved by hundred times compared to that of the conventional CPU. (5) Conclusions: The proposed method was able to generate a high-quality 3D image using a few 2D projections, which amount to ~ 20% of the total projections required for a standard image. The introduction of the B-spline-interpolated displacement matrix was effective in the suppressing noise in the reconstructed image. This method could significantly reduce the imaging time and the radiation exposure of patients under treatment.

BackgroundRadical radiotherapy is the main treatment modality for early and locally advanced nasopharyngeal carcinoma (NPC). Magnetic resonance imaging (MRI) has the advantages of no ionizing radiation and high soft-tissue resolution compared to computed tomography (CT), but it does not provide electron density (ED) information for radiotherapy planning. Therefore, in this study, we developed a pseudo-CT (pCT) generation method to provide necessary ED information for MRI-only planning in NPC radiotherapy.MethodsTwenty patients with early-stage NPC who received radiotherapy in our hospital were investigated. First, 1433 sets of paired T1 weighted magnetic resonance (MR) simulation images and CT simulation images were rigidly registered and preprocessed. A 16-layer U-Net was used to train the pCT generative model and a “pix2pix” generative adversarial network (GAN) was also trained to compare with the pure U-Net regrading pCT quality. Second, the contours of all target volumes and organs at risk in the original CT were transferred to the pCT for planning, and the beams were copied back to the original CT for reference dose calculation. Finally, the dose distribution calculated on the pCT was compared with the reference dose distribution through gamma analysis and dose-volume indices.ResultsThe average time for pCT generation for each patient was 7.90 ± 0.47 seconds. The average mean (absolute) error was −9.3 ± 16.9 HU (102.6 ± 11.4 HU), and the mean-root-square error was 209.8 ± 22.6 HU. There was no significant difference between the pCT quality of pix2pix GAN and that of pure U-Net (p > 0.05). The dose distribution on the pCT was highly consistent with that on the original CT. The mean gamma pass rate (2 mm/3%, 10% low dose threshold) was 99.1% ± 0.3%, and the mean absolute difference of nasopharyngeal PGTV D99% and PTV V95% were 0.4% ± 0.2% and 0.1% ± 0.1%.ConclusionThe proposed deep learning model can accurately predict CT from MRI, and the generated pCT can be employed in precise dose calculations. It is of great significance to realize MRI-only planning in NPC radiotherapy, which can improve structure delineation and considerably reduce additional imaging dose, especially when an MR-guided linear accelerator is adopted for treatment.

The snail Oncomelania hupensis is the only intermediate host of the highly invasive parasite Schistosoma japonicum. Molluscicide is often used to curb transmission of S. japonicum. Niclosamide, the only World Health Organization (WHO) recognized molluscicide, presents major drawbacks, including high cost and toxicity towards aquatic animals. In the present study, a number of aryl pyrrole derivatives (ADs) were synthesized to serve as potential molluscicides and were tested on O. hupensis. To uncover the underlying mechanisms, adenosine triphosphate (ATP) and adenosine diphosphate (ADP) levels were assessed in the soft body of ADs-exposed O. hupensis, using high performance liquid chromatography (HPLC). The effect of C6 on key points of energy metabolism (the activities of complexes I, III, IV and the membrane potential) was determined. We demonstrated that the Compound 6 (C6, 4-bromo-1-(bromomethyl)-2-(4-chlorophenyl)-5-(trifluoromethyl)-1H-pyrrole-3-carbonitrile) exerted the strongest molluscicidal activity against adult O. hupensis at LC50 of 0.27, 0.19, and 0.13 mg/L for 24, 48, and 72 h respectively. Moreover, we found that the bromide on the pyrrole ring of C6 was essential for molluscicidal activity. Furthermore, the ATP content reduced from 194.46 to 139.75 μg/g after exposure to 1/2 LC50, and reduced to 93.06 μg/g after exposure to LC50. ADP, on the other hand, remained the same level before and after C6 exposure. We found that C6, at 1/2 LC50, reduced the membrane potential of O. hupensis, while no significant changes were observed in the activities of complexes I, III, and IV. C6 was identified with excellent activities on O. hupensis. The obtained structure−activity relationship and action mechanism study results should be useful for further compound design and development.

BackgroundThe dynamic tracking of tumors with radiation beams in radiation therapy requires the prediction of real-time target locations prior to beam delivery, as treatment involving radiation beams and gating tracking results in time latency. ObjectiveIn this study, a deep learning model that was based on a temporal convolutional neural network was developed to predict internal target locations by using multiple external markers. MethodsRespiratory signals from 69 treatment fractions of 21 patients with cancer who were treated with the CyberKnife Synchrony device (Accuray Incorporated) were used to train and test the model. The reported model’s performance was evaluated by comparing the model to a long short-term memory model in terms of the root mean square errors (RMSEs) of real and predicted respiratory signals. The effect of the number of external markers was also investigated. ResultsThe average RMSEs of predicted (ahead time=400 ms) respiratory motion in the superior-inferior, anterior-posterior, and left-right directions and in 3D space were 0.49 mm, 0.28 mm, 0.25 mm, and 0.67 mm, respectively. ConclusionsThe experiment results demonstrated that the temporal convolutional neural network–based respiratory prediction model could predict respiratory signals with submillimeter accuracy.

PurposeTreatment of multiple brain metastases with single-isocenter volumetric modulated arc therapy causes unnecessary exposure to normal brain tissue. In this study, a longitudinal grouping method was developed to reduce such unnecessary exposure.Materials and MethodsThis method has two main aspects: grouping brain lesions longitudinally according to their longitudinal projection positions in beam’s eye view, and rotating the collimator to 90° to make the multiple leaf collimator leaves conform to the targets longitudinally group by group. For 11 patients with multiple (5–30) brain metastases, two single-isocenter volumetric modulated arc therapy plans were generated using a longitudinal grouping strategy (LGS) and the conventional strategy (CVS). The prescription dose was 52 Gy for 13 fractions. Dose normalization to 100% of the prescription dose in 95% of the planning target volume was adopted. For plan quality comparison, Paddick conformity and the gradient index of the planning target volume, and the mean dose, the V100%, V50%, V25%, and V10% volumes of normal brain tissue were calculated.ResultsThere were no significant differences between the LGS and CVS plans in Paddick conformity (p = 0.374) and the gradient index (p = 0.182) of the combined planning target volumes or for V100% (p = 0.266) and V50% (p = 0.155) of the normal brain. However, the V25% and V10% of the normal brain which represented the low-dose region were significantly reduced in the LGS plans (p = 0.004 and p = 0.003, respectively). Consistently, the mean dose of the entire normal brain was 12.04 and 11.17 Gy in the CVS and LGS plans, respectively, a significant reduction in the LGS plans (p = 0.003).ConclusionsThe longitudinal grouping method can decrease unnecessary exposure and reduces the low-dose range in normal brain tissue.

Pomacea canaliculata, one of the 100 most destructive invasive species in the world, and it is an important intermediate host of Angiostrongylus cantonensis. The molluscicides in current use are an effective method for controlling snails. However, most molluscicides have no slow-release effect and are toxic to nontarget organisms. Thus, these molluscicides cannot be used on a large scale to effectively act on snails. In this study, gelatin, a safe and nontoxic substance, was combined with sustained-release molluscicide and was found to reduce the toxicity of niclosamide to nontarget organisms. We assessed the effects of gelatin and molluscicide in controlling P. canaliculata snails and eggs. The results demonstrated that the niclosamide retention time with 1.0% and 1.5% gelatin sustained-release agents reached 20 days. Additionally, the mortality rate of P. canaliculata and their eggs increased as the concentration of the niclosamide sustained-release agents increased. The adult mortality rate of P. canaliculata reached 50% after the snails were exposed to gelatin with 0.1 mg/L niclosamide for 48 h. The hatching rate of P. canaliculata was only 28.5% of the normal group after the treatment was applied. The sustained-release molluscicide at this concentration was less toxic to zebrafish, which means that this molluscicide can increase the safety of niclosamide to control P. canaliculata in aquatic environments. In this study, we explored the safety of using niclosamide sustained-release agents with gelatin against P. canaliculata. The results suggest that gelatin is an ideal sustained-release agent that can provide a foundation for subsequent improvements in control of P. canaliculata.

PurposeThe aim of this work is to introduce the 2019 International Planning Competition and to analyze its results.Methods and materialsA locally advanced non-small cell lung cancer (LA-NSCLC) case using the simultaneous integrated boost approach was selected. The plan quality was evaluated by using a ranking system in accordance with practice guidelines. Planners used their clinical Treatment Planning System (TPS) to generate the best possible plan along with a survey, designed to obtain medical physics aspects information. We investigated the quality of the large population of plans designed by worldwide planners using different planning and delivery systems. The correlations of plan quality with relevant planner characteristics (work experience, department scale, and competition experience) and with technological parameters (TPS and modality) were examined.ResultsThe number of the qualified plans was 287 with a wide range of scores (38.61–97.99). The scores showed statistically significant differences by the following factors: 1) department scale: the mean score (89.76 ± 8.36) for planners from the departments treating >2,000 patients annually was the highest of all; 2) competition experience: the mean score for the 107 planners with previous competition experience was 88.92 ± 9.59, statistically significantly from first-time participants (p = .001); 3) techniques: the mean scores for planners using VMAT (89.18 ± 6.43) and TOMO (90.62 ± 7.60) were higher than those using IMRT (82.28 ± 12.47), with statistical differences (p

Background and Purpose: Automatic segmentation model is proven to be efficient in delineation of organs at risk (OARs) in radiotherapy; its performance is usually evaluated with geometric differences between automatic and manual delineations. However, dosimetric differences attract more interests than geometric differences in the clinic. Therefore, this study aimed to evaluate the performance of automatic segmentation with dosimetric metrics for volumetric modulated arc therapy of esophageal cancer patients.Methods: Nineteen esophageal cancer cases were included in this study. Clinicians manually delineated the target volumes and the OARs for each case. Another set of OARs was automatically generated using convolutional neural network models. The radiotherapy plans were optimized with the manually delineated targets and the automatically delineated OARs separately. Segmentation accuracy was evaluated by Dice similarity coefficient (DSC) and mean distance to agreement (MDA). Dosimetric metrics of manually and automatically delineated OARs were obtained and compared. The clinically acceptable dose difference and volume difference of OARs between manual and automatic delineations are supposed to be within 1 Gy and 1%, respectively.Results: Average DSC values were greater than 0.92 except for the spinal cord (0.82), and average MDA values were 0.05). Although there were significant differences (P < 0.05) for the spinal cord (D2%), left lung (V10, V20, V30, and mean dose), and bilateral lung (V10, V20, V30, and mean dose), their absolute differences were small and acceptable for the clinic. The maximum dosimetric metrics differences of OARs between manual and automatic delineations were ΔD2% = 0.35 Gy for the spinal cord and ΔV30 = 0.4% for the bilateral lung, which were within the clinical criteria in this study.Conclusion: Dosimetric metrics were proposed to evaluate the automatic delineation in radiotherapy planning of esophageal cancer. Consequently, the automatic delineation could substitute the manual delineation for esophageal cancer radiotherapy planning based on the dosimetric evaluation in this study.

PurposeThis study investigates the impact of the magnetic field on plan quality and dose at the tissue–air interface in MR-guided radiotherapy of head and neck cancer.Materials and MethodsThe charts of 10 patients with hypopharyngeal carcinoma who were treated with conventional fractionated radiotherapy were collected and reviewed. The skin and tissues containing air cavities were contoured. Three plans using 9 fields of intensity-modulated radiation therapy were generated for each patient in the Monaco treatment planning system of an Elekta Unity MR-linac. The first plan was optimized without the magnetic field (plan0T). The second plan was recalculated in the presence of a 1.5-T magnetic field (plan1.5T_reCal) using the same segment shape and monitor units as the first plan. The third plan was reoptimized in the presence of a 1.5-T magnetic field (plan1.5T_reOpt) using the same cost function as the first plan. The dose to the skin and tissues containing air cavities were compared across the three types of plans. A plan-quality metric method was used to evaluate the plan quality according to the clinical requirements.ResultsThe skin dose was increased in the presence of the 1.5-T magnetic field, and the amplitude increase of plan1.5T_reOpt (ΔDmean 1.30 ± 0.42 Gy, ΔDmax 1.68 ± 1.36 Gy) was smaller than that of plan1.5T_reCal (ΔDmean 1.81 ± 0.79 Gy, ΔDmax 5.43 ± 2.26 Gy). There were no significant differences in terms of the metrics of interfaces of tissues containing air cavities except for an increased maximum dose to the larynx and trachea. The plan quality of plan1.5T_reCal (68.0 ± 9.2) was significantly worse than that of plan0T (82.2 ± 7.0), and the plan quality of plan1.5T_reOpt (80.0 ± 7.0) was similar to that of plan0T.ConclusionThe presence of a 1.5-T magnetic field had an apparent impact on the dose distribution, in particular, a significant increase in the skin dose. The plan quality of plan1.5T_reOpt was similar to that of the original plan0T when the same cost function was used.

Purpose: More and more automatic segmentation tools are being introduced in routine clinical practice. However, physicians need to spend a considerable amount of time in examining the generated contours slice by slice. This greatly reduces the benefit of the tool's automaticity. In order to overcome this shortcoming, we developed an automatic quality assurance (QA) method for automatic segmentation using convolutional neural networks (CNNs).Materials and Methods: The study cohort comprised 680 patients with early-stage breast cancer who received whole breast radiation. The overall architecture of the automatic QA method for deep learning-based segmentation included the following two main parts: a segmentation CNN model and a QA network that was established based on ResNet-101. The inputs were from computed tomography, segmentation probability maps, and uncertainty maps. Two kinds of Dice similarity coefficient (DSC) outputs were tested. One predicted the DSC quality level of each slice ([0.95, 1] for “good,” [0.8, 0.95] for “medium,” and [0, 0.8] for “bad” quality), and the other predicted the DSC value of each slice directly. The performances of the method to predict the quality levels were evaluated with quantitative metrics: balanced accuracy, F score, and the area under the receiving operator characteristic curve (AUC). The mean absolute error (MAE) was used to evaluate the DSC value outputs.Results: The proposed methods involved two types of output, both of which achieved promising accuracy in terms of predicting the quality level. For the good, medium, and bad quality level prediction, the balanced accuracy was 0.97, 0.94, and 0.89, respectively; the F score was 0.98, 0.91, and 0.81, respectively; and the AUC was 0.96, 0.93, and 0.88, respectively. For the DSC value prediction, the MAE was 0.06 ± 0.19. The prediction time was approximately 2 s per patient.Conclusions: Our method could predict the segmentation quality automatically. It can provide useful information for physicians regarding further verification and revision of automatic contours. The integration of our method into current automatic segmentation pipelines can improve the efficiency of radiotherapy contouring.

Radiation pneumonitis (RP) is one of the major side effects of thoracic radiotherapy. The aim of this study is to build a dose distribution based prediction model, and investigate the correlation of RP incidence and high-order features of dose distribution. A convolution 3D (C3D) neural network was used to construct the prediction model. The C3D network was pre-trained for action recognition. The dose distribution was used as input of the prediction model. With the C3D network, the convolution operation was performed in 3D space. The guided gradient-weighted class activation map (grad-CAM) was utilized to locate the regions of dose distribution which were strongly correlated with grade≥2 and grade

Purpose: To investigate whether a deep learning-assisted contour (DLAC) could provide greater accuracy, inter-observer consistency, and efficiency compared with a manual contour (MC) of the clinical target volume (CTV) for non-small cell lung cancer (NSCLC) receiving postoperative radiotherapy (PORT).Materials and Methods: A deep dilated residual network was used to achieve the effective automatic contour of the CTV. Eleven junior physicians contoured CTVs on 19 patients by using both MC and DLAC methods independently. Compared with the ground truth, the accuracy of the contour was evaluated by using the Dice coefficient and mean distance to agreement (MDTA). The coefficient of variation (CV) and standard distance deviation (SDD) were rendered to measure the inter-observer variability or consistency. The time consumed for each of the two contouring methods was also compared.Results: A total of 418 CTV sets were generated. DLAC improved contour accuracy when compared with MC and was associated with a larger Dice coefficient (mean ± SD: 0.75 ± 0.06 vs. 0.72 ± 0.07, p < 0.001) and smaller MDTA (mean ± SD: 2.97 ± 0.91 mm vs. 3.07 ± 0.98 mm, p < 0.001). The DLAC was also associated with decreased inter-observer variability, with a smaller CV (mean ± SD: 0.129 ± 0.040 vs. 0.183 ± 0.043, p < 0.001) and SDD (mean ± SD: 0.47 ± 0.22 mm vs. 0.72 ± 0.41 mm, p < 0.001). In addition, a value of 35% of time saving was provided by the DLAC (median: 14.81 min vs. 9.59 min, p < 0.001).Conclusions: Compared with MC, the DLAC is a promising strategy to obtain superior accuracy, consistency, and efficiency for the PORT-CTV in NSCLC.

The purpose of this study was to evaluate the outcomes and toxicity experienced by cervical cancer patients with positive lymph nodes (LNs) who were treated with intensity-modulated radiation therapy (IMRT) and intracavitary brachytherapy (ICBT) plus concurrent chemotherapy. We retrospectively evaluated 108 cervical cancer patients with computed tomography (CT)-based positive LNs treated with IMRT and ICBT plus concurrent chemotherapy between 2009 and 2011. IMRT plans were designed to deliver 50 Gy to 95% of the planning target volume (PTV; cervical tumor, pelvis, and parametrium), with daily doses of 1.6–1.8 and 60–70 Gy to 95% of the planning gross tumor volume (PGTV)-LN (pelvic or para-aortic LNs), with daily doses of 2.0–2.2 Gy. Overall survival (OS) and progression-free survival (PFS) Kaplan–Meier curves were plotted. Acute and late toxicities were evaluated according to the Radiation Therapy Oncology Group and European Organization for Research and Treatment of Cancer toxicity criteria. Of the 108 cases, 45 were stage IIB and 63 were stage IIIB. The median follow-up was 65 months (range 2–83). Overall, the 5 year cumulative incidences of pelvic failure alone, distant failure alone, and synchronous pelvic and distant failure were 8.3, 12.9, and 8.3%, respectively. The 5 year OS rate was 67.6%, and the 5 year PFS rate was 53.7%. The 5 year cumulative incidence was 9.2% for late gastrointestinal and genitourinary toxicities of Grade ≥3 and 51.8% for acute leukopenia of Grade ≥3. The clinical results suggest that IMRT and ICBT with concurrent chemotherapy is an effective treatment, with acceptable toxicity, for advanced cervical cancer involving positive LNs.

Radiation pneumonitis (RP) is one of the major toxicities of thoracic radiation therapy. RP incidence has been proven to be closely associated with the dosimetric factors and normal tissue control possibility (NTCP) factors. However, because these factors only utilize limited information of the dose distribution, the prediction abilities of these factors are modest. We adopted the dosiomics method for RP prediction. The dosiomics method first extracts spatial features of the dose distribution within ipsilateral, contralateral, and total lungs, and then uses these extracted features to construct prediction model via univariate and multivariate logistic regression (LR). The dosiomics method is validated using 70 non-small cell lung cancer (NSCLC) patients treated with volumetric modulated arc therapy (VMAT) radiotherapy. Dosimetric and NTCP factors based prediction models are also constructed to compare with the dosiomics features based prediction model. For the dosimetric, NTCP and dosiomics factors/features, the most significant single factors/features are the mean dose, parallel/serial (PS) NTCP and gray level co-occurrence matrix (GLCM) contrast of ipsilateral lung, respectively. And the area under curve (AUC) of univariate LR is 0.665, 0.710 and 0.709, respectively. The second significant factors are V5 of contralateral lung, equivalent uniform dose (EUD) derived from PS NTCP of contralateral lung and the low gray level run emphasis of gray level run length matrix (GLRLM) of total lungs. The AUC of multivariate LR is improved to 0.676, 0.744, and 0.782, respectively. The results demonstrate that the univariate LR of dosiomics features has approximate predictive ability with NTCP factors, and the multivariate LR outperforms both the dosimetric and NTCP factors. In conclusion, the spatial features of dose distribution extracted by the dosiomics method effectively improves the prediction ability.

Taxol is a widely used chemotherapy drug used clinically for ovarian cancer, although the response to Taxol among individuals varies due to the heterogeneity among ovarian cancer patients. In this work, we analyzed differences in the prognostic effect of gene expression and Taxol usage in the Cancer Genome Atlas (TCGA) dataset and identified specific genes associated with the Taxol effect. Using the Cox regression model, a risk model (Taxol score) was developed to assess the outcome of ovarian cancer patients who underwent chemotherapy with Taxol. According to the results, survival was significantly associated with the Taxol score. Moreover, the patients in the high and low Taxol score group had different responses to Taxol. This result was further validated in another two independent datasets. The correlation between clinicopathological indicators was also analyzed, and we determined that the Taxol score is not associated with age, pathological stage, or Taxol treatment, while there was significant correlation with tumor size and grade. Gene Set Enrichment Analysis (GSEA) showed that various signaling pathways including ECM receptor, drug metabolism and ascorbate metabolism pathways were significantly enriched in the high Taxol score group. Collectively, these results indicate that the model is robust for predicting the effectiveness of Taxol by reflecting the various cell statuses of serous ovarian carcinoma.

BackgroundRadiotherapy is one of the main treatment methods for nasopharyngeal carcinoma (NPC). It requires exact delineation of the nasopharynx gross tumor volume (GTVnx), the metastatic lymph node gross tumor volume (GTVnd), the clinical target volume (CTV), and organs at risk in the planning computed tomography images. However, this task is time-consuming and operator dependent. In the present study, we developed an end-to-end deep deconvolutional neural network (DDNN) for segmentation of these targets.MethodsThe proposed DDNN is an end-to-end architecture enabling fast training and testing. It consists of two important components: an encoder network and a decoder network. The encoder network was used to extract the visual features of a medical image and the decoder network was used to recover the original resolution by deploying deconvolution. A total of 230 patients diagnosed with NPC stage I or stage II were included in this study. Data from 184 patients were chosen randomly as a training set to adjust the parameters of DDNN, and the remaining 46 patients were the test set to assess the performance of the model. The Dice similarity coefficient (DSC) was used to quantify the segmentation results of the GTVnx, GTVnd, and CTV. In addition, the performance of DDNN was compared with the VGG-16 model.ResultsThe proposed DDNN method outperformed the VGG-16 in all the segmentation. The mean DSC values of DDNN were 80.9% for GTVnx, 62.3% for the GTVnd, and 82.6% for CTV, whereas VGG-16 obtained 72.3, 33.7, and 73.7% for the DSC values, respectively.ConclusionDDNN can be used to segment the GTVnx and CTV accurately. The accuracy for the GTVnd segmentation was relatively low due to the considerable differences in its shape, volume, and location among patients. The accuracy is expected to increase with more training data and combination of MR images. In conclusion, DDNN has the potential to improve the consistency of contouring and streamline radiotherapy workflows, but careful human review and a considerable amount of editing will be required.

Purpose: To explore the dosimetric effects of flattening filter-free (FFF) beams in volumetric modulated arc therapy (VMAT) of nasopharyngeal carcinoma via a retrospective planning study. Materials and Methods: A linear accelerator (LINAC) was prepared to operate in FFF mode and the beam data were collected and used to build a model in TPS. For 10 nasopharyngeal carcinoma (NPC) cases, VMAT plans of FFF beams and normal flattened (FF) beams were designed. Differences of plan quality and delivery efficiency between FFF-VMAT plans and filter filtered VMAT (FF-VMAT) plans were analyzed using two-tailed paired t-tests. Results: Removal of the flattening filter increased the dose rate. Averaged beam on time (BOT) of FFF-VMAT plans was decreased by 24.2%. Differences of target dose coverage between plans with flattened and unflattened beams were statistically insignificant. For dose to normal organs, up to 4.9% decrease in V35 of parotid grand and 4.5% decrease in averaged normal tissue (NT) dose was observed. Conclusions: The TPS used in our study was able to handle FFF beams. The FFF beam prone to improve the normal tissue sparing while achieving similar target dose distribution. Decreasing of BOT in NPC cases was valuable in terms of patient′s comfort.

Schistosomiasis japonica is a zoonotic parasitic disease; developing transmission blocking veterinary vaccines are urgently needed for the prevention and control of schistosomiasis in China. Heterologous prime-boost strategy, a novel vaccination approach, is more effective in enhancing vaccine efficacy against multiple pathogens. In the present study, we established a novel heterologous prime-boost vaccination strategy, the rAdV-SjTPI.opt intramuscular priming and rSjTPI subcutaneous boosting strategy, and evaluated its protective efficacy against Schistosoma japonicum in mice.Adenoviral vectored vaccine (rAdV-SjTPI.opt) and recombinant protein vaccine (rSjTPI) were prepared and used in different combinations as vaccines in a mouse model. The specific immune responses and protective efficacies were evaluated. Furthermore, the longevity of protective efficacy was also determined. Results showed that the rAdV-SjTPI.opt priming-rSjTPI boosting strategy elicited higher levels of specific IgG responses and broad-spectrum specific cellular immune responses. The protective efficacy could reach up to nearly 70% and 50% of protection could be observed at 10 weeks after the last immunization in mice.The rAdV-SjTPI.opt intramuscular priming-rSjTPI subcutaneous boosting vaccination strategy is a novel, highly efficient, and stable approach to developing vaccines against Schistosoma japonicum infections in China.

BACKGROUND: Schistosomiasis japonica remains a major public-health concern in China. Praziquantel-based chemotherapy effectively reduces both infections and intensity; however, it can not prevent re-infection. Furthermore, there is an increasing concern about praziquantel resistance following long-term repeated use of the drug in endemic areas. Therefore, development of a schistosomiasis vaccine, as a strategy to prevent and control schistosomiasis japonica, has been given high priority. The present study was conducted to develop PAMAM dendrimers as a novel vaccine delivery vector for a schistosomiasis japonica DNA vaccine and evaluate its ability to enhance protective effects against Schistosoma japonicum infection. METHODOLOGY/PRINCIPAL FINDINGS: Lysine was used to modify 4.0G PAMAM, and the modified product PAMAM-Lys was synthesized. PAMAM-Lys showed both high transfection and low cytotocity for gene delivery in vitro. DNA vaccines combined with PAMAM-Lys produced higher level of protection compare with naked DNA vaccines against S. japonicum infection in a mouse model. Futhermore,antibodies from mice immunized with PAMAM-Lys combined DNA vaccines were significantly higher than those of mice immunized with the naked DNA vaccines. The PAMAM-Lys vector elicited a predominantly IgG2a antibody response and a tremendously increase in the production of IL-2 and IFN-γ. CONCLUSION/SIGNIFICANCE: Lysine-modified PAMAM-Lys is an excellent vector. PAMAM-Lys may enhance the immunoreactivity of DNA vaccine and increase the protective effect of the SjC23 DNA vaccine against S. japonicum infection.