Hybrid Immuno-RT for Bulky Tumors: Standard Fractionation with Partial Tumor SBRT

PURPOSE/OBJECTIVE(S)

Bulky tumors remain challenging to be treated. Stereotactic body radiation therapy (SBRT) is effective against radioresistant tumor cells and can induce immunogenic cell death (ICD) that leads to T-cell-mediated antitumor effects. Low-dose radiation (LDRT) can inflame the tumor microenvironment (TME) by recruiting T cells. We designed a novel radiotherapy technique (RT, ERT) whose dose distribution map resembles the "eclipse" by concurrently delivering LDRT to the whole tumor, meanwhile SBRT to only a part of the same tumor. This study examined the safety and efficacy of ERT to bulky lesions with PD-1 inhibitors in mice and patients.

MATERIALS/METHODS

In mice with CT26 colon or LLC1 lung bulky tumors (400 - 500 cm3), the whole tumor was irradiated by LDRT (2 Gy x 3), meanwhile the tumor center was irradiated by SBRT (10 Gy x 3); αPD-1 was given weekly. The dependence of therapeutic effects on CD8+ T cells was determined using depleting antibodies. Frequencies of CD8+ T cells and M1 macrophages (Mφ) were determined by flow cytometry. Multiplex Immunohistochemistry (mIHC) was applied to analyze the number and the location of CD8+ T cells and their subpopulations, as well as the phospho-eIF2α level (the ICD marker) of tumor cells in TME. Patients with advanced lung or liver bulky tumors who failed standard treatment or with oncologic emergencies were treated. Kaplan-Meier method was applied to estimate patients' progression-free survival (PFS) and overall survival (OS).

RESULTS

ERT/αPD-1 is superior to SBRT/αPD-1 or LDRT/αPD-1 in controlling bulky tumors in both mouse models in a CD8+ T-cell dependent manner. In the CT26 model, ERT/αPD-1 resulted in complete tumor regression in 3/11 mice and induced more CD8+ T cells and M1 Mφ in TME compared to other groups. mIHC analysis showed that ERT/αPD-1 induced higher bulk, stem-like (TCF1+ TIM3- PD-1+), and more differentiated (TCF1- TIM3+ PD-1+) CD8+ T cells infiltration into the tumor center and periphery compared to other groups. Compared to untreated or LDRT-treated tumor centers, tumor centers irradiated with ERT or SBRT showed elevated phospho-eIF2α accompanied by higher dendritic cell infiltration. In total, 39 advanced cancer patients were treated with ERT/αPD-1 or plus chemotherapy. Radiation-induced pneumonitis occurred in 1 of 26 patients receiving thoracic ERT. There were two cases of grade III toxicity associated with PD-1 inhibitors. No toxicity above grade III was observed. The objective response rate was 38.5%. The median PFS was 5.6 months and median OS was not reached at a median follow-up of 11.7 months.

CONCLUSION

ERT/αPD-1 showed superior efficacy in controlling bulky tumor in two mouse models. The hybrid immuno-RT (ERT) combing PD-1 inhibitors was safe and effective in patients with bulky tumors. Further clinical trials in combination with bioimaging to identify the optimal SBRT target region for the bulky tumor are warranted.

Neural signals-based respiratory motion tracking: a proof-of-concept study

Objective.Respiratory motion tracking techniques can provide optimal treatment accuracy for thoracoabdominal radiotherapy and robotic surgery. However, conventional imaging-based respiratory motion tracking techniques are time-lagged owing to the system latency of medical linear accelerators and surgical robots. This study aims to investigate the precursor time of respiratory-related neural signals and analyze the potential of neural signals-based respiratory motion tracking.Approach.The neural signals and respiratory motion from eighteen healthy volunteers were acquired simultaneously using a 256-channel scalp electroencephalography (EEG) system. The neural signals were preprocessed using the MNE python package to extract respiratory-related EEG neural signals. Cross-correlation analysis was performed to assess the precursor time and cross-correlation coefficient between respiratory-related EEG neural signals and respiratory motion.Main results.Respiratory-related neural signals that precede the emergence of respiratory motion are detectable via non-invasive EEG. On average, the precursor time of respiratory-related EEG neural signals was 0.68 s. The representative cross-correlation coefficients between EEG neural signals and respiratory motion of the eighteen healthy subjects varied from 0.22 to 0.87.Significance.Our findings suggest that neural signals have the potential to compensate for the system latency of medical linear accelerators and surgical robots. This indicates that neural signals-based respiratory motion tracking is a potential promising solution to respiratory motion and could be useful in thoracoabdominal radiotherapy and robotic surgery.

Clinical evaluation on automatic segmentation results of convolutional neural networks in rectal cancer radiotherapy

Purpose

Image segmentation can be time-consuming and lacks consistency between different oncologists, which is essential in conformal radiotherapy techniques. We aimed to evaluate automatic delineation results generated by convolutional neural networks (CNNs) from geometry and dosimetry perspectives and explore the reliability of these segmentation tools in rectal cancer.

Methods

Forty-seven rectal cancer cases treated from February 2018 to April 2019 were randomly collected retrospectively in our cancer center. The oncologists delineated regions of interest (ROIs) on planning CT images as the ground truth, including clinical target volume (CTV), bladder, small intestine, and femoral heads. The corresponding automatic segmentation results were generated by DeepLabv3+ and ResUNet, and we also used Atlas-Based Autosegmentation (ABAS) software for comparison. The geometry evaluation was carried out using the volumetric Dice similarity coefficient (DSC) and surface DSC, and critical dose parameters were assessed based on replanning optimized by clinically approved or automatically generated CTVs and organs at risk (OARs), i.e., the Planref and Plantest. Pearson test was used to explore the correlation between geometric metrics and dose parameters.

Results

In geometric evaluation, DeepLabv3+ performed better in DCS metrics for the CTV (volumetric DSC, mean = 0.96, P< 0.01; surface DSC, mean = 0.78, P< 0.01) and small intestine (volumetric DSC, mean = 0.91, P< 0.01; surface DSC, mean = 0.62, P< 0.01), ResUNet had advantages in volumetric DSC of the bladder (mean = 0.97, P< 0.05). For critical dose parameters analysis between Planref and Plantest, there was a significant difference for target volumes (P< 0.01), and no significant difference was found for the ResUNet-generated small intestine (P > 0.05). For the correlation test, a negative correlation was found between DSC metrics (volumetric, surface DSC) and dosimetric parameters (δD95, δD95, HI, CI) for target volumes (P< 0.05), and no significant correlation was found for most tests of OARs (P > 0.05).

Conclusions

CNNs show remarkable repeatability and time-saving in automatic segmentation, and their accuracy also has a certain potential in clinical practice. Meanwhile, clinical aspects, such as dose distribution, may need to be considered when comparing the performance of auto-segmentation methods.

The association between mammographic density and breast cancer molecular subtypes: a systematic review and meta-analysis

AIM

To conduct a systematic review and meta-analysis to evaluate the whether high mammographic density (MD) is differentially associated with all subtypes of breast cancer.

MATERIALS AND METHODS

The PubMed, Cochrane Library, and Embase databases were searched systematically in October 2022 to include all studies that investigated the association between MD and breast cancer subtype. Aggregate data of 17,193 breast cancer cases from 23 studies were selected, including five cohort/case-control and 18 case-only studies. The relative risk (RR) of MD were combined using random/fixed effects models for case-control studies, and for case-only studies, relative risk ratios (RRRs) were a combination of luminal A, luminal B, and HER2-positive versus triple-negative tumours.

RESULTS

Women in the highest density category in case-control/cohort studies had a 2.24-fold (95% confidence interval [CI] 1.53, 3.28), 1.81-fold (95% CI 1.15, 2.85), 1.44-fold (95% CI 1.14, 1.81), and 1.59-fold (95% CI 0.89, 2.85) higher risk of triple-negative, HER-2 (human epidermal growth factor receptor 2) positive, luminal A, and luminal B breast cancer compared to women in the lowest density category. RRRs for breast tumours being luminal A, luminal B, and HER-2 positive versus triple-negative in case-only studies were 1.62 (95% CI 1.14, 2.31), 1.81 (95% CI 1.22, 2.71) and 2.58 (95% CI 1.63, 4.08), respectively, for BIRADS 4 versus BIRADS 1.

CONCLUSION

The evidence indicates MD is a potent risk factor for the majority of breast cancer subtypes to different degrees. Increased MD is more strongly linked to HER-2-positive cancers compared to other breast cancer subtypes. The application of MD as a subtype-specific risk marker may facilitate the creation of personalised risk prediction models and screening procedures.

Young oncologists benefit more than experts from deep learning-based organs-at-risk contouring modeling in nasopharyngeal carcinoma radiotherapy: A multi-institution clinical study exploring working experience and institute group style factor

Background

To comprehensively investigate the behaviors of oncologists with different working experiences and institute group styles in deep learning-based organs-at-risk (OAR) contouring.

Methods

A deep learning-based contouring system (DLCS) was modeled from 188 CT datasets of patients with nasopharyngeal carcinoma (NPC) in institute A. Three institute oncology groups, A, B, and C, were included; each contained a beginner and an expert. For each of the 28 OARs, two trials were performed with manual contouring first and post-DLCS edition later, for ten test cases. Contouring performance and group consistency were quantified by volumetric and surface Dice coefficients. A volume-based and a surface-based oncologist satisfaction rate (VOSR and SOSR) were defined to evaluate the oncologists' acceptance of DLCS.

Results

Based on DLCS, experience inconsistency was eliminated. Intra-institute consistency was eliminated for group C but still existed for group A and group B. Group C benefits most from DLCS with the highest number of improved OARs (8 for volumetric Dice and 10 for surface Dice), followed by group B. Beginners obtained more numbers of improved OARs than experts (7 v.s. 4 in volumetric Dice and 5 v.s. 4 in surface Dice). VOSR and SOSR varied for institute groups, but the rates of beginners were all significantly higher than those of experts for OARs with experience group significance. A remarkable positive linear relationship was found between VOSR and post-DLCS edition volumetric Dice with a coefficient of 0.78.

Conclusions

The DLCS was effective for various institutes and the beginners benefited more than the experts.

Effects of Rehabilitation Therapy at Different Intervention Times on Daily Living Activity and Motor Function in Patients with Traumatic Spinal Cord Injury

Objectives: We investigated the impact of traumatic spinal cord injury (TSCI) on daily living activities and motor function of TSCI patients. Methods: A total of 88 TSCI patients were randomly divided into Group A (N=44) and Group B (N=44). Group A received rehabilitation treatment 7 days after the stabilization of vital signs, and Group B received rehabilitation treatment 30 days after hospitalization. Results: The compliance rate of Group A (93.18%) was higher than that of Group B (72.73%) (χ 2 =6.510, p<.05); The scores of American Spinal Injury Association (ASIA) and Activities of Daily Living (ADL) in Group A were higher than those in Group B. The self-rating score of anxiety and depression was lower than that of Group B (p<.05). Conclusion: For the rehabilitation treatment of TSCI patients, it is better to choose the intervention after the vital signs are stable to improve patients' ability for daily living activities and motor function.

Guaranteed performance of individual control chart used in gamma passing rate-based patient-specific quality assurance

PURPOSE

To assess the effect of sampling variability on the performance of individual charts (I-charts) for PSQA and provide a robust and reliable method for unknown PSQA processes.

MATERIALS AND METHODS

A total of 1327 pretreatment PSQAs were analyzed. Different datasets with samples in the range of 20-1000 were used to estimate the lower control limit (LCL). Based on the iterative "Identify-Eliminate-Recalculate" and direct calculation without any outlier filtering procedures, five I-charts methods, namely the Shewhart, quantile, scaled weighted variance (SWV), weighted standard deviation (WSD), and skewness correction (SC) method, were used to compute the LCL. The average run length (ARL₀) and false alarm rate (FAR₀) were calculated to evaluate the performance of LCL.

RESULTS

The ground truth of the values of LCL, FAR₀, and ARL₀ obtained via in-control PSQAs were 92.31%, 0.135%, and 740.7, respectively. Further, for in-control PSQAs, the width of the 95% confidence interval of LCL values for all methods tended to decrease with the increase in sample size. In all sample ranges of in-control PSQAs, only the median LCL and ARL₀ values obtained via WSD and SWV methods were close to the ground truth. For the actual unknown PSQAs, based on the "Identify-Eliminate-Recalculate" procedure, only the median LCL values obtained by the WSD method were closest to the ground truth.

CONCLUSIONS

Sampling variability seriously affected the I-chart performance in PSQA processes, particularly for small samples. For unknown PSQAs, the WSD method based on the implementation of the iterative "Identify-Eliminate-Recalculate" procedure exhibited sufficient robustness and reliability.

A Script-Based Automatic Intensity Modulated Radiation Therapy Planning Method With Robust Optimization for Craniospinal Irradiation

This report describes a script-based automatic planning method with robust optimization for craniospinal irradiation (CSI) to reduce sensitivity to field matching errors and increase planning efficiency. The data of 10 CSI patients with planning target volume (PTV) lengths between 49.8 and 85.0 cm were retrospectively studied. Robust intensity modulated radiation therapy plans with ±5-mm longitudinal position uncertainty were generated by the automatic planning script. A simple dose prediction model and a self-adjusting method were implied in the automatic plans. The plans' robustness against setup errors was evaluated by deliberately shifting the middle beamset ±5 mm in the superior-inferior direction. Manual and nonrobust plans were also created to evaluate the automatic robust plans' quality, efficiency, and robustness. There were no significant differences between the manual and automatic plans in terms of homogeneity index; conformity index; D1%, D2%, and D98% of PTV; and average doses of organs at risk. However, the D99% of the PTV in the automatic plans was slightly inferior to that in the manual plans. Compared with the manual plans, the automatic plans greatly increased efficiency, with a reduction in planning time of approximately 48%. When ±5-mm superior-inferior errors were introduced, the average deviations of the maximum dose D1% and minimum dose D99% to the spinal cord were 4.9% (±1.1%) and -3.4% (±1.3%), respectively. However, the corresponding values of the nonrobust plans were 20.0% (±5.4%) and -21.2 (±6.3%), respectively. The script-based automatic CSI planning method, combining robust optimization and a dose prediction model, efficiently created a good-quality plan that was robust to setup errors.

Machine learning for predicting accuracy of lung and liver tumor motion tracking using radiomic features

Background

Internal tumor motion is commonly predicted using external respiratory signals. However, the internal/external correlation is complex and patient-specific. The purpose of this study was to develop various models based on the radiomic features of computed tomography (CT) images to predict the accuracy of tumor motion tracking using external surrogates and to find accurate and reliable tracking algorithms.

Methods

Images obtained from a total of 108 and 71 patients pathologically diagnosed with lung and liver cancers, respectively, were examined. Real-time position monitoring motion was fitted to tumor motion, and samples with fitting errors greater than 2 mm were considered positive. Radiomic features were extracted from internal target volumes of average intensity projections, and cross-validation least absolute shrinkage and selection operator (LassoCV) was used to conduct feature selection. Based on the radiomic features, a total of 26 separate models (13 for the lung and 13 for the liver) were trained and tested. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to assess performance. Relative standard deviation was used to assess stability.

Results

Thirty-three and 22 radiomic features were selected for the lung and liver, respectively. For the lung, the AUC varied from 0.848 (decision tree) to 0.941 [support vector classifier (SVC), logistic regression]; sensitivity varied from 0.723 (extreme gradient boosting) to 0.848 [linear support vector classifier (linearSVC)]; specificity varied from 0.834 (gaussian naive bayes) to 0.936 [multilayer perceptron (MLP), wide and deep (W&D)]; and MLP and W&D had better performance and stability than the median. For the liver, the AUC varied from 0.677 [light gradient boosting machine (Light)] to 0.892 (logistic regression); sensitivity varied from 0.717 (W&D) to 0.862 (MLP); specificity varied from 0.566 (Light) to 0.829 (linearSVC); and logistic regression, MLP, and SVC had better performance and stability than the median.

Conclusions

Respiratory-sensitive radiomic features extracted from CT images of lung and liver tumors were proved to contain sufficient information to establish an external/internal motion relationship. We developed a rapid and accurate method based on radiomics to classify the accuracy of monitoring a patient's external surface for lung and liver tumor tracking. Several machine learning algorithms-in particular, MLP-demonstrated excellent classification performance and stability.

[Analysis on the status quo of the awareness rate of core knowledge of cancer prevention and treatment and its influencing factors among residents in Liaoning Province in 2021]

Objective: To analyze the status quo of the knowledge and related factors of cancer prevention and treatment among residents in Liaoning Province in 2021. Methods: From August to November 2021, through network sampling method, 17 474 permanent residents aged 15-69 years in Liaoning Province were surveyed. The WeChat public account was used to collect information such as demographic characteristics and core knowledge of cancer prevention and treatment. The Chi-square test was used to compare the difference of the level of the cancer prevention and treatment knowledge among different groups. The multivariate logistic regression model was used to analyze the related factors. Results: Among the 17 474 subjects, 43.1% (7 528) were male and 58.7% (10 262) were urban residents. The overall awareness rate was 72.3%, and the awareness rate of cancer cognition, prevention, early diagnosis and treatment, cancer management and rehabilitation were 71.4%, 67.6%, 72.7%, 83.4% and 63.5%, respectively. The multivariate logistic regression model showed that the residents who were man (OR: 0.850, 95%CI: 0.781-0.925), in rural areas (OR: 0.753, 95%CI: 0.694-0.817), 55-59 years old (OR: 0.851, 95%CI: 0.751-0.963), quitters (OR: 0.721, 95%CI: 0.640-0.813) and smoker (OR: 0.724, 95%CI: 0.654-0.801) had lower awareness rates, while the residents who were 35-54 years old (OR: 1.312, 95%CI: 1.202-1.432), with an educational level of junior high school/senior high school/college degree or above (OR: 1.834-5.130, 95%CI: 1.575-6.047), technical personnel (OR: 1.592, 95%CI: 1.367-1.854), civil servant/institution staff (OR: 1.282, 95%CI: 1.094-1.503), enterprise/business/service staff (OR: 1.218, 95%CI: 1.071-1.385), retired (OR: 1.324, 95%CI: 1.114-1.573) and with family history of cancer (OR: 1.369, 95%CI: 1.266-1.481) had higher awareness rates. Conclusion: The level of the awareness of core knowledge of cancer prevention and treatment among residents in Liaoning Province has met the requirements of the Healthy China Action. Region, gender, education level, age, family history of cancer and smoking are relevant factors.

Predictive value of magnetic resonance imaging radiomics-based machine learning for disease progression in patients with high-grade glioma

Background

Accurately predicting the prognosis of patients with high-grade glioma (HGG) is potentially important for treatment. However, the predictive value of images of various magnetic resonance imaging (MRI) sequences for prognosis at different time points is unknown. We established predictive machine learning models of HGG disease progression and recurrence using MRI radiomics and explored the factors influencing prediction accuracy.

Methods

Radiomics features were extracted from T1-weighted (T1WI), contrast-enhanced T1-weighted (CE-T1WI), T2-weighted (T2WI), and fluid-attenuated inversion recovery (FLAIR) images (postoperative radiotherapy planning MRI images) obtained from 162 patients with HGG. The Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection. Machine learning models were used to build prediction models to estimate disease progression or recurrence. The influence of different MRI sequences, regions of interest (ROIs), and prediction time points was also explored. The receiver operating characteristic (ROC) curve was used to evaluate the discriminative performance of each model, and the DeLong test was employed to compare the ROC curves.

Results

Radiomics features from T2WI and FLAIR demonstrated greater predictive value for disease progression compared with T1WI or CE-TIWI. The best predictive models, with areas under the ROC curves (AUCs) of 0.70, 0.68, 0.78, 0.78, and 0.78 for predicting disease progression at the 6th, 9th, 12th, 15th, and 18th month after radiotherapy, respectively, were obtained by combining clinical features with gross tumor volume (GTV) and clinical target volume (CTV) features extracted from T2WI and FLAIR.

Conclusions

Structural MRI obtained before radiotherapy can be used to predict the disease progression or posttreatment recurrence of HGG. When using MRI radiomics to predict long-term outcomes as opposed to short-term outcomes, better predictive results may be obtained.

Machine Learning Radiomics Model for External and Internal Respiratory Motion Correlation Prediction in Lung Tumor

Objectives: The complexity and specificity of lung tumor motion render it necessary to determine the external and internal correlation individually before applying indirect tumor tracking. However, the correlation cannot be determined from patient respiratory and tumor clinical characteristics before treatment. The purpose of this study is to present a machine learning model for an external/internal correlation prediction that is based on computed tomography (CT) radiomic features. Methods: 4-dimensional computed tomography (4DCT) images of 67 patients were collected retrospectively, and the external/internal correlation of lung tumor was calculated based on Spearman's rank correlation coefficient. Radiomic features were extracted from average intensity projection and the light gradient boosting machine (LightGBM)-based cross-validation (the recursive elimination method) was used for feature selection. The LightGBM framework forecasting models with classification thresholds 0.7, 0.8, and 0.9 are established using stratified 5-fold cross-validation. Model performance was assessed using receiver operating characteristics, sensitivity, and specificity. Results: There were 16, 18, and 13 features selected for models 0.7, 0.8, and 0.9, respectively. Texture features are of great importance in external/internal correlation prediction compared to other features in all models. The sensitivities of the predictions in models 0.7, 0.8, and 0.9 were 0.800 ± 0.126, 0.829 ± 0.140, and 0.864 ± 0.086, respectively. The specificities were 0.771 ± 0.114, 0.936 ± 0.0581, and 0.839 ± 0.101, whereas the area under the curve (AUC) was 0.837, 0.946, and 0.877, respectively. Conclusions: Our findings indicate that radiomics is an effective tool for respiratory motion correlation prediction, which can extract tumor motion characteristics. We proposed a machine learning framework for correlation prediction in the motion management strategy for lung tumor patients.

[Automatic Delineation of Clinical Target Volume and Organ at Risk by Deep Learning for Prostate Cancer Adaptive Radiotherapy]

Adaptive radiotherapy can modify the treatment plan online based on the clinical target volume (CTV) and organ at risk (OAR) contours on the cone-beam CT (CBCT) before treatment, improving the accuracy of radiotherapy. However, manual delineation of CTV and OAR on CBCT is time-consuming. In this study, a deep neural network-based method based on U-Net was purposed. CBCT images and corresponding mask were used for model training and validation, showing superior performance in terms of the segmentation accuracy. The proposed method could be used in the clinic to support rapid CTV and OAR contouring for prostate adaptive radiotherapy.

CDC6 is a prognostic biomarker and correlated with immune infiltrates in glioma

BACKGROUND

Cell division cycle 6 (CDC6) has been proven to be associated with the initiation and progression of human multiple tumors. However, it's role in glioma, which is ranked as one of the common primary malignant tumor in the central nervous system and is associated with high morbidity and mortality, is unclear.

METHODS

In this study, we explored CDC6 gene expression level in pan-cancer. Furthermore, we focused on the relationships between CDC6 expression, its prognostic value, potential biological functions, and immune infiltrates in glioma patients. We also performed vitro experiments to assess the effect of CDC6 expression on proliferative, apoptotic, migrant and invasive abilities of glioma cells.

RESULTS

As a result, CDC6 expression was upregulated in multiple types of cancer, including glioma. Moreover, high expression of CDC6 was significantly associated with age, IDH status, 1p/19q codeletion status, WHO grade and histological type in glioma (all p < 0.05). Meanwhile, high CDC6 expression was associated with poor overall survival (OS) in glioma patients, especially in different clinical subgroups. Furthermore, a univariate Cox analysis showed that high CDC6 expression was correlated with poor OS in glioma patients. Functional enrichment analysis indicated that CDC6 was mainly involved in pathways related to DNA transcription and cytokine activity, and Gene Set Enrichment Analysis (GSEA) revealed that MAPK pathway, P53 pathway and NF-κB pathway in cancer were differentially enriched in glioma patients with high CDC6 expression. Single-sample gene set enrichment analysis (ssGSEA) showed CDC6 expression in glioma was positively correlated with Th2 cells, Macrophages and Eosinophils, and negative correlations with plasmacytoid dendritic cells, CD8 T cells and NK CD56bright cells, suggesting its role in regulating tumor immunity. Finally, CCK8 assay, flow cytometry and transwell assays showed that silencing CDC6 could significantly inhibit proliferation, migration, invasion, and promoted apoptosis of U87 cells and U251 cells (p < 0.05).

CONCLUSION

In conclusion, high CDC6 expression may serve as a promising biomarker for prognosis and correlated with immune infiltrates, presenting to be a potential immune therapy target in glioma.

Automated Segmentation of the Clinical Target Volume in the Planning CT for Breast Cancer Using Deep Neural Networks

3-D radiotherapy is an effective treatment modality for breast cancer. In 3-D radiotherapy, delineation of the clinical target volume (CTV) is an essential step in the establishment of treatment plans. However, manual delineation is subjective and time consuming. In this study, we propose an automated segmentation model based on deep neural networks for the breast cancer CTV in planning computed tomography (CT). Our model is composed of three stages that work in a cascade manner, making it applicable to real-world scenarios. The first stage determines which slices contain CTVs, as not all CT slices include breast lesions. The second stage detects the region of the human body in an entire CT slice, eliminating boundary areas, which may have side effects for the segmentation of the CTV. The third stage delineates the CTV. To permit the network to focus on the breast mass in the slice, a novel dynamically strided convolution operation, which shows better performance than standard convolution, is proposed. To train and evaluate the model, a large dataset containing 455 cases and 50 425 CT slices is constructed. The proposed model achieves an average dice similarity coefficient (DSC) of 0.802 and 0.801 for right-0 and left-sided breast, respectively. Our method shows superior performance to that of previous state-of-the-art approaches.

Correlation of Optical Surface Respiratory Motion Signal and Internal Lung and Liver Tumor Motion: A Retrospective Single-Center Observational Study

Purpose: Surface-guided radiation therapy (SGRT) application has limitations. This study aimed to explore the relationship between patient characteristics and their external/internal correlation to qualitatively assess the external/internal correlation in a particular patient. Methods: Liver and lung cancer patients treated with radiotherapy in our institution were retrospectively analyzed. The external/internal correlation were calculated with Spearman correlation coefficient (SCC) and SCC after support vector regression (SVR) fitting (SCC_svr). The relationship between the external/internal correlation and magnitudes of motion of the tumor and external marker (A_i, A_e), tumor volume V_t, patient age, gender, and tumor location were explored. Results: The external/internal motions of liver and lung cancer patients were strongly correlated in the S-I direction, with mean SCC_svr values of 0.913 and 0.813. The correlation coefficients between the external/internal correlations and the patients’ characteristics (A_i, A_e, V_t, and age) were all smaller than 0.5; A_i, A_e and liver tumor volumes were positively correlated with the strength of the external/internal correlation, while lung tumor volumes and patient age were negative. The external/internal correlations in males and females were roughly equal, and the external/internal correlations in patients with peripheral lung cancers were stronger than those in patients with central lung cancers. Conclusion: The external/internal correlation shows great individual differences. The effects of A_i, A_e, V_t, and age are weakly to moderately correlated. Our results suggest the necessity of individualized assessment of patient's external/internal motion correlation prior to the application of SGRT technique for breath motion monitoring.

A robust approach to establish tolerance limits for the gamma passing rate-based patient-specific quality assurance using the heuristic control charts

PURPOSE

Establishing the tolerance limits of patient-specific quality assurance (PSQA) processes based on the gamma passing rate (GPR) by using normal statistical process control (SPC) methods involves certain problems. The aim of this study was threefold: (a) to show that the heuristic SPC method can replace the quantile method for establishing tolerance limits in PSQA processes and is more robust, (b) to introduce an iterative procedure of "Identify-Eliminate-Recalculate" for establishing the tolerance limits in PSQA processes with unknown states based on retrospective GPRs, and (c) to recommend a workflow to define tolerance limits based on actual clinical retrospective GPRs.

MATERIALS AND METHODS

A total of 1671 volumetric-modulated arc therapy (VMAT) pretreatment plans were measured on four linear accelerators (linacs) and analyzed by treatment sites using the GPRs under the 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria. Normality testing was performed using the Anderson-Darling (AD) statistic and the optimal distributions of GPRs were determined using the Fitter Python package. The iterative "Identify-Eliminate-Recalculate" procedure was used to identify the PSQA outliers. The tolerance limits of the initial PSQAs, remaining PSQAs after elimination, and in-control PSQAs after correction were calculated using the conventional Shewhart method, two transformation methods, three heuristic methods, and two quantile methods. The tolerance limits of PSQA processes with different states for the respective methods, linacs, and treatment sites were comprehensively compared and analyzed.

RESULTS

It was found that 75% of the initial PSQA processes and 63% of the in-control processes were non-normal (AD test, p < 0.05). The optimal distributions of GPRs for the initial and in-control PSQAs varied with different linacs and treatment sites. In the implementation of the "Identify-Eliminate-Recalculate" procedure, the quantile methods could not identify the out-of-control PSQAs effectively due to the influence of outliers. The tolerance limits of the in-control PSQAs, calculated using the quantile of optimal fitting distributions, represented the ground truth. The tolerance limits of the in-control PSQAs and remaining PSQAs after elimination calculated using the heuristic methods were considerably close to the ground truth (the maximum average absolute deviations were 0.50 and 1.03%, respectively). Some transformation failures occurred under both transformation methods. For the in-control PSQAs at 3%/2 mm gamma criteria, the maximum differences in the tolerance limits for four linacs and different treatment sites were 3.10 and 5.02%, respectively.

CONCLUSIONS

The GPR distributions of PSQA processes vary with different linacs and treatment sites but most are skewed. In applying SPC methodologies to PSQA processes, heuristic methods are robust. For in-control PSQA processes, the tolerance limits calculated by heuristic methods are in good agreement with the ground truth. For unknown PSQA processes, the tolerance limits calculated by the heuristic methods after the iterative "Identify-Eliminate-Recalculate" procedure are closest to the ground truth. Setting linac- and treatment site-specific tolerance limits for PSQA processes is necessary for clinical applications.

Analysis of EPID Transmission Fluence Maps Using Machine Learning Models and CNN for Identifying Position Errors in the Treatment of GO Patients

Purpose

To find a suitable method for analyzing electronic portal imaging device (EPID) transmission fluence maps for the identification of position errors in the in vivo dose monitoring of patients with Graves' ophthalmopathy (GO).

Methods

Position errors combining 0-, 2-, and 4-mm errors in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions in the delivery of 40 GO patient radiotherapy plans to a human head phantom were simulated and EPID transmission fluence maps were acquired. Dose difference (DD) and structural similarity (SSIM) maps were calculated to quantify changes in the fluence maps. Three types of machine learning (ML) models that utilize radiomics features of the DD maps (ML 1 models), features of the SSIM maps (ML 2 models), and features of both DD and SSIM maps (ML 3 models) as inputs were used to perform three types of position error classification, namely a binary classification of the isocenter error (type 1), three binary classifications of LR, SI, and AP direction errors (type 2), and an eight-element classification of the combined LR, SI, and AP direction errors (type 3). Convolutional neural network (CNN) was also used to classify position errors using the DD and SSIM maps as input.

Results

The best-performing ML 1 model was XGBoost, which achieved accuracies of 0.889, 0.755, 0.778, 0.833, and 0.532 in the type 1, type 2-LR, type 2-AP, type 2-SI, and type 3 classification, respectively. The best ML 2 model was XGBoost, which achieved accuracies of 0.856, 0.731, 0.736, 0.949, and 0.491, respectively. The best ML 3 model was linear discriminant classifier (LDC), which achieved accuracies of 0.903, 0.792, 0.870, 0.931, and 0.671, respectively. The CNN achieved classification accuracies of 0.925, 0.833, 0.875, 0.949, and 0.689, respectively.

Conclusion

ML models and CNN using combined DD and SSIM maps can analyze EPID transmission fluence maps to identify position errors in the treatment of GO patients. Further studies with large sample sizes are needed to improve the accuracy of CNN.

[Application of Virtual Monochromatic Images Reconstructed by Dual-energy Computed Tomography in Radiotherapy Treatment Planning System]

Virtual monochromatic images (VMI) that reconstructed on dual-energy computed tomography (DECT) have further application prospects in radiotherapy, and there is still a lack of clinical dose verification. In this study, GE Revolution CT scanner was used to perform conventional imaging and gemstone spectral imaging on the simulated head and body phantom. The CT images were imported to radiotherapy treatment planning system (TPS), and the same treatment plans were transplanted to compare the CT value and the dose distribution. The results show that the VMI can be imported into TPS for CT value-relative electron density conversion and dose calculation. Compared to conventional images, the VMI varies from 70 to 140 keV, has little difference in dose distribution of 6 MV photon treatment plan.

Radiomics analysis of EPID measurements for patient positioning error detection in thyroid associated ophthalmopathy radiotherapy

PURPOSE

Electronic portal imaging detector (EPID)-based patient positioning verification is an important component of safe radiotherapy treatment delivery. In computer simulation studies, learning-based approaches have proven to be superior to conventional gamma analysis in the detection of positioning errors. To approximate a clinical scenario, the detectability of positioning errors via EPID measurements was assessed using radiomics analysis for patients with thyroid-associated ophthalmopathy.

METHODS

Treatment plans of 40 patients with thyroid-associated ophthalmopathy were delivered to a solid anthropomorphic head phantom. To simulate positioning errors, combinations of 0-, 2-, and 4-mm translation errors in the left-right (LR), superior-inferior (SI), and anterior-posterior (AP) directions were introduced to the phantom. The positioning errors-induced dose differences between measured portal dose images were used to predict the magnitude and direction of positioning errors. The detectability of positioning errors was assessed via radiomics analysis of the dose differences. Three classification models-support vector machine (SVM), k-nearest neighbors (KNN), and XGBoost-were used for the detection of positioning errors (positioning errors larger or smaller than 3 mm in an arbitrary direction) and direction classification (positioning errors larger or smaller than 3 mm in a specific direction). The receiver operating characteristic curve and the area under the ROC curve (AUC) were used to evaluate the performance of classification models.

RESULTS

For the detection of positioning errors, the AUC values of SVM, KNN, and XGBoost models were all above 0.90. For LR, SI, and AP direction classification, the highest AUC values were 0.76, 0.91, and 0.80, respectively.

CONCLUSIONS

Combined radiomics and machine learning approaches are capable of detecting the magnitude and direction of positioning errors from EPID measurements. This study is a further step toward machine learning-based positioning error detection during treatment delivery with EPID measurements.

Clinically Oriented Target Contour Evaluation Using Geometric and Dosimetric Indices Based on Simple Geometric Transformations

Purpose:

In radiotherapy, geometric indices are often used to evaluate the accuracy of contouring. However, the ability of geometric indices to identify the error of contouring results is limited primarily because they do not consider the clinical background. The purpose of this study is to investigate the relationship between geometric and clinical dosimetric indices.

Methods:

Four different types of targets were selected (C-shaped target, oropharyngeal cancer, metastatic spine cancer, and prostate cancer), and the translation, scaling, rotation, and sine function transformation were performed with the software Python to introduce systematic and random errors. The transformed contours were regarded as reference contours. Dosimetric indices were obtained from the original dose distribution of the radiotherapy plan. The correlations between geometric and dosimetric indices were quantified by linear regression.

Results:

The correlations between the geometric and dosimetric indices were inconsistent. For systematic errors, and with the exception of the sine function transformation (R²: 0.023-0.04, P > 0.05), the geometric transformations of the C-shaped target were correlated with the D98% and D_mean (R²: 0.689-0.988), 80% of which were P < 0.001. For the random errors, the correlations obtained by the all targets were R² > 0.384, P < 0.05. The Wilcoxon signed-rank test was used to compare the spatial direction resolution capability of geometric indices in different directions of the C-shaped target (with systematic errors), and the results showed only the volumetric geometric indices with P < 0.05.

Conclusions:

Clinically, an assessment of the contour accuracy of the region-of-interest is not feasible based on geometric indices alone. Dosimetric indices should be added to the evaluations of the accuracy of the delineation results, which can be helpful for explaining the clinical dose response relationship of delineation more comprehensively and accurately.

[Immunogenicity and safety of a boost dose of measles, mumps, and rubella combined vaccine for 4-6 years old children]

Objective: To investigate the immunogenicity and safety of a boost dose of measles, mumps, and rubella combined vaccine (MMR) for children 4 to 6 years old. Methods: Children, aged 4 to 6 years old, had vaccinated with 1 dose of measles and rubella combined vaccine(MR) at the age of 8 months and 1 dose of MMR vaccine at 18-months, were recruited in Shanxi, Inner Mongolia, and Beijing, respectively. All children were assigned into 4, 5 and 6-year-old group. The children who met inclusion and exclusion criteria were vaccinated with 1 dose MMR vaccine, and were collected blood samples before vaccination and 35 to 42 d after the vaccination. During the study period, adverse events were collected at 30 min, 1 d, 2 d, 3 d, 4-12 d, and 13 to 42 days after vaccination. Serum was tested for IgG antibodies against measles, mumps and rubella. Geometric mean concentrations (GMC) of measles, mumps, and rubella antibodies were compared among groups by analysis of variance or non-parametric test. Seropositive rates and adverse event rates were compared among groups by Chi-square test or Fisher exact test. Results: A total of 500 children were included in immunogenicity analysis and 535 children were included in safety analysis. The overall adverse event rate was 20.37%, the most of severity for adverse events was mild. The rates of local and systemic adverse events were 0.37% and 20.00%, respectively. Symptoms of local adverse events were redness. The main systemic adverse events were fever, followed by cough, rash and runny nose. Received a dose of MMR vaccine for booster immunization, the seropositive rates of measles antibody, mumps antibody and rubella antibody were above 99% for all 3 age groups, and there was no significant difference between groups. There were significant differences in mumps antibody GMC among groups (P=0.042), but no significant differences in measles and rubella antibodies GMC. Conclusion: The immunogenicity and safety of a boosted MMR vaccintion in children aged 4, 5 and 6 years were all similar good.

[Application of New Posture Fixation Device Compatible with Magnetic Resonance Simulation Positioning in Head and Neck Radiotherapy]

OBJECTIVE

Whether the developed new type of radiotherapy auxiliary fixation device compatible with the head and neck joint coil can improve the quality of magnetic resonance images in radiotherapy and verify whether it can be applied to clinical treatment.

METHODS

The clinical trial selected patients with brain metastases and nasopharyngeal cancer patients, using thermoplastic film and head and shoulder molds for posture fixation, and treatment on the ELekta Versa accelerator. SPSS 20.0 statistical software was used to analyze the data. The measurement data were expressed by X±S, and the t test was used. P<0.05 was considered as statistically significant.

RESULTS

Considering the influence of the outer contour of the device, the target dose meets the clinical requirements. The setting error is less than 2 mm in the three translation directions, and the rotation error is less than 2^o in the three rotation directions.

CONCLUSIONS

There is no statistical difference between the treatment results of patients using the new type of fixation device and the conventional method. The target area threatens the organ dose, and the positioning error meets the treatment requirements.

Analysis of local setup errors of sub-regions in cone-beam CT-guided post-mastectomy radiation therapy

The purpose of the study was to quantify local setup errors and evaluate the planning target volume (PTV) margins for sub-regions in cone-beam computed tomography (CBCT)-guided post-mastectomy radiation therapy (PMRT). The local setup errors of 20 patients undergoing CBCT-guided PMRT were analysed retrospectively. Image registration between CBCT and planning CT was performed using four sub-regions of interest (ROIs): the supraclavicular area (S_ROI), ipsilateral chest wall region (C_ROI), ipsilateral chest wall plus supraclavicular region (S_ROI + C_ROI) and vertebral region (T_ROI). Bland–Altman analysis, correlation, local setup errors and PTV margins among these ROIs were evaluated. There was no significant consistency or correlation for registration results between the T_ROI and the C_ROI or S_ROI regions on any translational axis. When using the S_ROI + C_ROI as the ROI, the systematic error (Σ) and random error (σ) of the local setup errors for the C_ROI region were 1.81, 1.19 and 1.76 mm and 1.84, 2.64 and 3.00 mm along the medial–lateral (ML), superior–inferior (SI) and anterior–posterior (AP) directions, respectively. The PTV margins for the C_ROI region were 5.80, 4.82 and 6.50 mm. The Σ and σ of the local setup errors for the S_ROI region were 1.29, 1.15 and 0.77 mm and 1.96, 2.65 and 2.2 mm, respectively, and the PTV margins were 4.59, 4.73 and 3.47 mm. Large setup errors and local setup errors occur in PMRT. The vertebral body should not be a position surrogate for the supraclavicular region or chest wall. To compensate for the local setup errors, different PTV margins are required, even with CBCT guidance.

[Improved Design and Acceptance Test of Localization Couch for New Type of CT Simulator]

Accurate CT simulation is the key link of precision radiotherapy, and the performance of the localization couch of CT simulator directly affects the accuracy of radiotherapy. With the rapid development of precision radiotherapy, conventional large aperture radiotherapy special CT simulator is difficult to meet the needs of precision radiotherapy localization, so most radiotherapy centers choose high-end diagnostic CT machines equipped with a flat tabletop for radiotherapy localization. In clinical work, the performance testing of the CT simulator localization couch is easy to be ignored. In addition, there are some problems such as insufficient precision in transforming the cradle-shaped couch top of diagnostic CT into a special flat couch top for radiotherapy. This paper provided an in-depth description of the improved design and performance test of the localization couch of the first special GE Revolution CT simulator for radiotherapy introduced by West China Hospital of Sichuan University. After the improvement, all the acceptance tests of the localization couch are in line with the standard, and the performance meets the high-precision radiotherapy localization needs of patients with different body weight in the center.

Three-dimensional morphological analysis of neocondyle bone growth after fibula free flap reconstruction

The aim of this retrospective study was to verify the three-dimensional morphological change in neocondyle bone growth after fibula free flap (FFF) reconstruction. The independent variables were age, sex, and diagnosis. Outcome variables included the direction and volume of neocondyle bone growth, and the time to a stable neocondyle following bone growth. The outcome variables were measured on postoperative computed tomography scans using iPlan 3.0. Of the 35 patients included, 25 showed neocondyle bone growth. The direction of neocondyle bone growth included the direction of lateral pterygoid traction (DLPT) and the direction towards the glenoid fossa (DGF). The bone growth of the neocondyle showed three patterns: only DLPT (eight patients), only DGF (two patients), and a combination of DLPT and DGF (15 patients). The average volume of bone growth in the 25 patients was 0.479 ± 0.380 cm³. The average volume of neocondyle bone growth was significantly greater in patients aged <18 years (0.746 ± 0.346 cm³) than in patients aged >18 years (0.219 ± 0.191 cm³) (P < 0.001). The time to a stable neocondyle following bone growth was 5.6 months postoperatively. In conclusion, neocondyle bone growth after FFF reconstruction occurred in two different directions, DLPT and DGF. Osteogenesis of the lateral pterygoid muscle affects neocondyle growth with DLPT. Neocondyle bone growth is more marked in paediatric patients than in adults.

Clinical target volume segmentation for stomach cancer by stochastic width deep neural network

PURPOSE

Precise segmentation of clinical target volume (CTV) is the key to stomach cancer radiotherapy. We proposed a novel stochastic width-deep neural network (SW-DNN) for better automatically contouring stomach CTV.

METHODS

Stochastic width-deep neural network was an end-to-end approach, of which the core component was a novel SW mechanism that employed shortcut connections between the encoder and decoder in a random manner, and thus the width of the SW-DNN was stochastically adjustable to obtain improved segmentation results. In total, 150 stomach cancer patient computed tomography (CT) cases with the corresponding CTV labels were collected and used to train and evaluate the SW-DNN. Three common quantitative measures: true positive volume fraction (TPVF), positive predictive value (PPV), and Dice similarity coefficient (DSC) were used to evaluate the segmentation accuracy.

RESULTS

Clinical target volumes calculated by SW-DNN had significant quantitative advantages over three state-of-the-art methods. The average DSC value of SW-DNN was 2.1%, 2.8%, and 3.6% higher than that of three state-of-the-art methods. The average DSC, TPVF, and PPV values of SW-DNN were 2.1%, 4.0%, and 0.3% higher than that of the corresponding constant width DNN.

CONCLUSIONS

Stochastic width-deep neural network provided better performance for contouring stomach cancer CTV accurately and efficiently. It is a promising solution in clinical radiotherapy planning for stomach cancer.

[Application of Statistical Process Control in Evaluation of Performance for Beam-matched Medical Electron Linacs]

Clinically, beam matching can greatly improve the flexibility and efficiency of treating patients between different medical electron linacs. However, in addition to the regular quality assurance (QA) test of the machine performance of linacs, there is still a lack of comprehensive evaluation of the clinical radiotherapy performance of beam-matched linacs. In this paper, the performance of volumetric modulated arc therapy (VMAT) between three closely matched linacs was evaluated by statistical process control (SPC) technology. It was found that the average and median γ passing rates of the VMAT QA processes of the three linacs had little difference, but the process capability levels were at three different levels. The results show that SPC technology can effectively evaluate the performance of beam matching for medical electron linacs, improve the patient-specific VMAT QA processes, and guide clinical decision-making.

Real-time liver tracking algorithm based on LSTM and SVR networks for use in surface-guided radiation therapy

Background

Surface-guided radiation therapy can be used to continuously monitor a patient’s surface motions during radiotherapy by a non-irradiating, noninvasive optical surface imaging technique. In this study, machine learning methods were applied to predict external respiratory motion signals and predict internal liver motion in this therapeutic context.

Methods

Seven groups of interrelated external/internal respiratory liver motion samples lasting from 5 to 6 min collected simultaneously were used as a dataset, D_v. Long short-term memory (LSTM) and support vector regression (SVR) networks were then used to establish external respiratory signal prediction models (LSTMpred/SVRpred) and external/internal respiratory motion correlation models (LSTMcorr/SVRcorr). These external prediction and external/internal correlation models were then combined into an integrated model. Finally, the LSTMcorr model was used to perform five groups of model updating experiments to confirm the necessity of continuously updating the external/internal correlation model. The root-mean-square error (RMSE), mean absolute error (MAE), and maximum absolute error (MAX_AE) were used to evaluate the performance of each model.

Results

The models established using the LSTM neural network performed better than those established using the SVR network in the tasks of predicting external respiratory signals for latency-compensation (RMSE < 0.5 mm at a latency of 450 ms) and predicting internal liver motion using external signals (RMSE < 0.6 mm). The prediction errors of the integrated model (RMSE ≤ 1.0 mm) were slightly higher than those of the external prediction and external/internal correlation models. The RMSE/MAE of the fifth model update was approximately ten times smaller than that of the first model update.

Conclusions

The LSTM networks outperform SVR networks at predicting external respiratory signals and internal liver motion because of LSTM’s strong ability to deal with time-dependencies. The LSTM-based integrated model performs well at predicting liver motion from external respiratory signals with system latencies of up to 450 ms. It is necessary to update the external/internal correlation model continuously.

Description and evaluation of a new volumetric-modulated arc therapy plan complexity metric

This study describes a new plan complexity metric for volumetric-modulated arc therapy (VMAT) and evaluates the relationship of this metric with the VMAT dosimetric accuracy. The new modulation complexity score for VMAT (NMCSv) that is based on the aperture shape and multi-leaf collimator (MLC) leaf travel is described. Its performance is evaluated through correlation and receiver operating characteristic (ROC) analyses with patient-specific gamma passing rates using 2 3-dimensional diode arrays. For comparison, the following metrics are evaluated using the same correlation analyses: average field width, average leaf travel, modulation complexity score, and leaf travel modulation complexity score. Spearman's rank correlation analysis is performed to examine any relationships between the complexity metrics and the patient-specific gamma passing rates. ROC curves are used to assess the performance of the plan metrics using a gamma passing rate of 3%/3 mm criterion with a 95% tolerance level. In both the diode arrays, the gamma passing rates (3%/3 mm and 2%/2 mm) for patient-specific dosimetric verification of VMAT plans are moderately or weakly correlated to all the complexity metrics. NMCSv demonstrates the highest correlation with the passing rates (r = 0.652, p < 0.001 for Delta4 and r = 0.499, p < 0.001 for ArcCheck) and the highest area under the curve value (0.809, p < 0.01 for Delta4 and 0.734, p < 0.01 for ArcCheck). While using the Delta4 system, NMCSv exhibits an excellent classification performance with area under the curves of 0.926 (sensitivity: 0.913; specificity: 0.860; p < 0.01) and 0.918 (sensitivity: 0.943; specificity: 0.720; p < 0.01) for rectal and cervical cancer plans, respectively. NMCSv as a novel potential clinical plan complexity metric is moderately correlated with the gamma passing rate. It demonstrates the best performance with respect to distinguishing the dosimetric accuracy of VMAT plans among the evaluated metrics. The classification performance of complexity metrics can be affected by various dosimetry verification devices and treatment sites.

A Special Report on 2019 International Planning Competition and a Comprehensive Analysis of Its Results

Purpose

The aim of this work is to introduce the 2019 International Planning Competition and to analyze its results.

Methods and materials

A 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.

Results

The 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 <.001). The plan scores were negligibly correlated with the planner’s years of work experience or the type of TPS used. Regression analysis demonstrated that plan score was associated with dosimetric objectives that were difficult to achieve, which is generally consistent with a clinical practice evaluation. However, 51.2% of the planners abandoned the difficult component of total lung receiving a dose of 5 Gy in their plan design to achieve the optimal plan.

Conclusion

The 2019 international planning competition was carried out successfully, and its results were analyzed. Plan quality was not correlated with work experiences or the TPS used, but it was correlated with department scale, modality, and competition experience. These findings differed from those reported in previous studies.

[Accuracy and Influence Analysis of 4D CBCT Automatic Registration Algorithm under the Guidance of Chest Tumor Image]

OBJECTIVE

In order to provide guidance for clinical use of four-dimensional cone-beam CT (4D CBCT), the accuracy of image registration and its influencing factors were analyzed using the automatic registration method when 4D CBCT was used as an image guidance strategy for patients with chest tumors.

METHODS

The respiratory motion model and two kinds of lung plug-ins were used to simulate two types of tumors and their movements in the chest. 4D CT was scanned for each kind of simulated tumor, and 4D CBCT was scanned under various artificial positioning errors. For the registration of 4D CBCT, the manual and automatic registration methods were used for each group.

RESULTS

There were more obvious mismatches in the intrapulmonary adhesion tumor group. When the masks were created based on the size of the target area or expanding the target area by 0.5 cm, the results between the automatic registration and manual registration were statistically different. There were no significant mismatches in the isolated lung tumor group, and there was no statistical difference between the results of automatic registration and manual registration.

CONCLUSIONS

When 4D CBCT is used as an image guidance strategy for patients with chest tumors, the automatic registration procedure should not be used for tumors adhering to chest wall and mediastinum. For solitary lung tumors, the automatic registration method and the manual registration method have similar registration accuracy, but significant mismatches need to be excluded.

Application of 3D-print silica bolus for nasal NK/T-cell lymphoma radiation therapy

The aim of the study was to evaluate the clinical feasibility of a 3D-print silica bolus for nasal NK/T-cell lymphoma radiation therapy. Intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) plans were designed using an anthropomorphic head phantom with a 3D-print silica bolus and other kinds of bolus used clinically, and the surface dose was measured by a metal oxide semiconductor field-effect transistor (MOSFET) dosimeter. Four nasal NK/T patients with or without 3D-print silica bolus were treated and the nose surface dose was measured using a MOSFET dosimeter during the first treatment. Plans for the anthropomorphic head phantom with 3D-print bolus have more uniform dose and higher conformity of the planning target volume (PTV) compared to other boluses; the homogeneity index (HI) and conformity index (CI) of the VMAT plan were 0.0589 and 0.7022, respectively, and the HI and CI of the IMRT plan were 0.0550 and 0.7324, respectively. The MOSFET measurement results showed that the surface dose of the phantom with 3D-print bolus was >180 cGy, and that of patients with 3D-print bolus was higher than patients without bolus. The air gap volume between the 3D-print bolus and the surface of patients was <0.3 cc. The 3D-print silica bolus fitted well on the patient’s skin, effectively reducing air gaps between bolus and patient surface. Meanwhile, the 3D-print silica bolus provided patients with higher individuation, and improved the conformity and uniformity of the PTV compared to other kinds of boluses.

Accuracy of real-time respiratory motion tracking and time delay of gating radiotherapy based on optical surface imaging technique

Background

Surface-guided radiation therapy (SGRT) employs a non-invasive real-time optical surface imaging (OSI) technique for patient surface motion monitoring during radiotherapy. The main purpose of this study is to verify the real-time tracking accuracy of SGRT for respiratory motion and provide a fitting method to detect the time delay of gating.

Methods

A respiratory motion phantom was utilized to simulate respiratory motion using 17 cosine breathing pattern curves with various periods and amplitudes. The motion tracking of the phantom was performed by the Catalyst™ system. The tracking accuracy of the system (with period and amplitude variations) was evaluated by analyzing the adjusted coefficient of determination (A_R²) and root mean square error (RMSE). Furthermore, 13 actual respiratory curves, which were categorized into regular and irregular patterns, were selected and then simulated by the phantom. The Fourier transform was applied to the respiratory curves, and tracking accuracy was compared through the quantitative analyses of curve similarity using the Pearson correlation coefficient (PCC). In addition, the time delay of amplitude-based respiratory-gating radiotherapy based on the OSI system with various beam hold times was tested using film dosimetry for the Elekta Versa-HD and Varian Edge linacs. A dose convolution-fitting method was provided to accurately measure the beam-on and beam-off time delays.

Results

A_R² and RMSE for the cosine curves were 0.9990–0.9996 and 0.110–0.241 mm for periods ranging from 1 s to 10 s and 0.9990–0.9994 and 0.059–0.175 mm for amplitudes ranging from 3 mm to 15 mm. The PCC for the actual respiratory curves ranged from 0.9955 to 0.9994, which was not significantly affected by breathing patterns. For gating radiotherapy, the average beam-on and beam-off time delays were 1664 ± 72 and 25 ± 30 ms for Versa-HD and 303 ± 45 and 34 ± 25 ms for Edge, respectively. The time delay was relatively stable as the beam hold time increased.

Conclusions

The OSI technique provides high accuracy for respiratory motion tracking. The proposed dose convolution-fitting method can accurately measure the time delay of respiratory-gating radiotherapy. When the OSI technique is used for respiratory-gating radiotherapy, the time delay for the beam-on is considerably longer than the beam-off.

TaqMan real-time PCR for detecting bovine viral diarrhea virus

The present study was aimed to establish a novel TaqMan real-time PCR (RTm-PCR) for detecting and typing bovine viral diarrhea virus (BVDV), and also to develop a diagnostic protocol which simplifies sample collection and processing. Universal primers and TaqMan-MGB probes were designed from the known sequences of conserved 5' - and 3'-untranslated regions (5'UTR, 3'UTR) of the NADL strain of BVDV. Prior to optimizing the assay, cDNAs were transcribed in vitro to make standard curves. The sensitivity, specificity and stability (reproducibility) were evaluated. The RTm-PCR was tested on the 312 feces specimens collected from persistently infected (PI) calves. The results showed the optimum conditions for RTm-PCR were 17.0 μmol/L primer, 7.5 μmol/L probe and 51.4°C annealing temperature. The established TaqMan RTm-PCR assay could specially detect BVDV without detecting any other viruses. Its detection limit was 1.55×100 copies/μL for viral RNA. It was 10000-fold higher than conventional PCR with excellent specificity and reproducibility. 312 samples were tested using this method and universal PCR from six dairy farms, respectively. Positive detections were found in 49 and 44 feces samples, respectively. The occurrence rate was 89.80%. In conclusion, the established TaqMan RTm-PCR could rapidly detect BVDV and effectively identify PI cattle. The detection limit of RTm-PCR was 1.55 copies/μL. It will be beneficial for enhancing diagnosis and therapy efficacy and reduce losses in cattle farms.

A systematic review and computational modelling analysis of unilateral montages in electroconvulsive therapy

OBJECTIVE

To examine the clinical outcomes of ECT unilateral placements compared in prior studies and apply insights from computational modelling to understand differences between placements.

METHODS

PubMed, Embase, Scopus and PsycINFO and reference lists were systematically searched for studies of depressed patients where two unilateral placements were compared and clinical outcomes were reported. Computational modelling was done to generate electric field maps for each unilateral placement identified in the systematic review.

RESULTS

A total of 29 studies met criteria for inclusion. Eight studies reported efficacy outcomes and 23 studies reported cognitive outcomes. Most studies found no significant difference in efficacy between right unilateral (RUL) and left unilateral (LUL) ECT, and no difference was found between temporo-parietal and fronto-temporal ECT. For the majority of studies, RUL placements had better verbal anterograde memory outcomes compared with the LUL placements. There was some evidence suggestive of cognitive advantages for fronto-frontal and fronto-parietal placements relative to temporo-parietal ECT.

CONCLUSIONS

For efficacy, studies mainly focused on the comparison of right vs. left hemispheric stimulation, with the available evidence suggesting no substantive difference. RUL placements tended to have better verbal anterograde memory outcomes relative to LUL placements, though limited differences were found between the RUL placements.

[Feasibility Research of the New Fixation Device Compatible with Head and Neck Coil of MRI for Radiotherapy]

MRI simulation images quality of head and neck coil scanning is better than that of radiotherapy surface coil, but currently the head and neck coil is not compatible with radiotherapy positioning devices. In this paper, a new fixation device is developed based on computer reverse engineering technology, which can be used in combination with head and neck coil. This article focuses on discussing the feasibility of the new device in radiotherapy. The obtained ACR phantom and Cat phantom 504 images were used to analyze MR and CT images quality assurance indicators. The dose attenuation of 6 MV photons was measured using the ionization chamber. The results showed each index met the clinical application requirements of intracranial tumor radiotherapy, thereby it can be used in intracranial tumor radiotherapy.