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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.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.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.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.

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

PurposeInterrupting work continuity provides a way to improve some project performance, but unexpected and harmful interruptions may impede the implementation. This paper aims to mitigate the negative impact caused by work continuity uncertainty based on the notion of robustness.Design/methodology/approachThis paper develops a float-based robustness measurement method for the work continuity uncertainty in repetitive projects. A multi-objective optimization model is formulated to generate a schedule that achieves a balance between crew numbers and robustness. This model is solved using two modules: optimization module and decision-making module. The Monte Carlo simulation is designed to validate the effectiveness of the generated schedule.FindingsThe results confirmed that it is necessary to consider the robustness as an essential factor when scheduling a repetitive project with uncertainty. Project managers may develop a schedule that is subject to delays if they only make decisions according to the results of the deadline satisfaction problem. The Monte Carlo simulation validated that an appropriate way to measure robustness is conducive to generating a schedule that can avoid unnecessary delay, compared to the schedule generated by the traditional model.Originality/valueAvailable studies assume that the work continuity is constant, but it cannot always be maintained when affected by uncertainty. This paper regards the work continuity as a new type of uncertainty factor and investigates how to mitigate its negative effects. The proposed float-based robustness measurement can measure the ability of a schedule to absorb unpredictable and harmful interruptions, and the proposed multi-objective scheduling model provides a way to incorporate the uncertainty into a schedule.

Identifying position errors for Graves' ophthalmopathy (GO) patients using electronic portal imaging device (EPID) transmission fluence maps is helpful in monitoring treatment. However, most of the existing models only extract features from dose difference maps computed from EPID images, which do not fully characterize all information of the positional errors. In addition, the position error has a three-dimensional spatial nature, which has never been explored in previous work. To address the above problems, a deep neural network (DNN) model with structural similarity difference and orientation-based loss is proposed in this paper, which consists of a feature extraction network and a feature enhancement network. To capture more information, three types of Structural SIMilarity (SSIM) sub-index maps are computed to enhance the luminance, contrast, and structural features of EPID images, respectively. These maps and the dose difference maps are fed into different networks to extract radiomic features. To acquire spatial features of the position errors, an orientation-based loss function is proposed for optimal training. It makes the data distribution more consistent with the realistic 3D space by integrating the error deviations of the predicted values in the left-right, superior-inferior, anterior-posterior directions. Experimental results on a constructed dataset demonstrate the effectiveness of the proposed model, compared with other related models and existing state-of-the-art methods.

Background: Prostate alignment is subject to interobserver variability in cone-beam CT (CBCT)-based soft-tissue matching. This study aims to analyze the impact of interobserver variability in CBCT-based soft-tissue matching for prostate cancer radiotherapy.Methods: Retrospective data, consisting of 156 CBCT images from twelve patients with intermediate- or high-risk prostate cancer were analyzed in this study. To simulate interobserver variability, couch shifts of 2 mm relative to the resulting patient position of prostate alignment were assumed as potential patient positions (27 possibilities). For each CBCT, the doses of the potential patient positions were recalculated using deformable image registration-based synthetic CT. The impact of the simulated interobserver variability was evaluated using tumor control probabilities (TCPs) and normal tissue complication probabilities (NTCPs).Results: No significant differences in TCPs were found between prostate alignment and potential patient positions (0.944 ± 0.003 vs 0.945 ± 0.003, P = 0.117). The average NTCPs of the rectum ranged from 5.16 (%) to 7.29 (%) among the potential patient positions and were highly influenced by the couch shift in anterior-posterior direction. In contrast, the average NTCPs of the bladder ranged from 0.75 (%) to 1.12 (%) among the potential patient positions and were relatively negligible.Conclusions: The NTCPs of the rectum, rather than the TCPs of the target, were highly influenced by the interobserver variability in CBCT-based soft-tissue matching. This study provides a theoretical explanation for daily CBCT-based image guidance and the prostate-rectum interface matching procedure.Trial registration: Not applicable.

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.

Purpose To assess the effectiveness of SGRT in clinical applications through statistical process control (SPC). Methods Taking the patients' positioning through optical surface imaging (OSI) as a process, the average level of process execution was defined as the process mean. Setup errors detected by cone-beam computed tomography (CBCT) and OSI were extracted for head-and-neck cancer (HNC) and breast cancer patients. These data were used to construct individual and exponentially weighted moving average (EWMA) control charts to analyze outlier fractions and small process shifts from the process mean. Using the control charts and process capability indices derived from this process, the patient positioning-related OSI performance and setup error were analyzed for each patient. Results Outlier fractions and small shifts from the process mean that are indicative of setup errors were found to be widely prevalent, with the outliers randomly distributed between fractions. A systematic error of up to 1.6 mm between the OSI and CBCT results was observed in all directions, indicating a significantly degraded OSI performance. Adjusting this systematic error for each patient using setup errors of the first five fractions could effectively mitigate these effects. Process capability analysis following adjustment for systematic error indicated that OSI performance was acceptable (process capability index Cpk = 1.0) for HNC patients but unacceptable (Cpk Conclusion SPC is a powerful tool for detecting the outlier fractions and process changes. Our application of SPC to patient-specific evaluations validated the suitability of OSI in clinical applications involving patient positioning.

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.

Patient-specific volumetric modulated arc therapy (VMAT) quality assurance (QA) process is an important component of the implementation process of clinical radiotherapy. The tolerance limit and action limit of discrepancies between the calculated dose and the delivered radiation dose are the key parts of the VMAT QA processes as recognized by the AAPM TG-218 report, however, there is no unified standard for these two values among radiotherapy centers. In this study, based on the operational recommendations given in the AAPM TG-218 report, treatment site-specific tolerance limits and action limits of gamma pass rate in VMAT QA processes when using ArcCHECK for dose verification were established by statistical process control (SPC) methodology. The tolerance limit and action limit were calculated based on the first 25 in-control VMAT QA for each site. The individual control charts were drawn to continuously monitor the VMAT QA process with 287 VMAT plans and analyze the causes of VMAT QA out of control. The tolerance limits for brain, head and neck, abdomen and pelvic VMAT QA processes were 94.56%, 94.68%, 94.34%, and 92.97%, respectively, and the action limits were 93.82%, 92.54%, 93.23%, and 90.29%, respectively. Except for pelvic, the tolerance limits for the brain, head and neck, and abdomen were close to the universal tolerance limit of TG-218 (95%), and the action limits for all sites were higher than the universal action limit of TG-218 (90%). The out-of-control VMAT QAs were detected by the individual control chart, including one case of head and neck, two of the abdomen and two of the pelvic site. Four of them were affected by the setup error, and one was affected by the calibration of ArcCHECK. The results show that the SPC methodology can effectively monitor the IMRT/VMAT QA processes. Setting treatment site-specific tolerance limits is helpful to investigate the cause of out-of-control VMAT QA.患者个体化容积旋转调强放疗（VMAT）质量保证（QA）过程是临床调强放疗实施流程中重要的一部分。计算剂量与实施剂量之间差异的容差限值和干预限值是 AAPM TG-218 报告认为的 VMAT QA 过程的关键部分，然而这两个值在各放疗中心之间尚无统一标准。本研究基于 AAPM TG-218 报告，应用统计过程控制（SPC）技术建立了采用 ArcCHECK 进行剂量验证下不同部位 VMAT 计划 γ 通过率的容差限值和干预限值。根据每个部位前 25 例处于受控状态的 VMAT QA 结果计算相应部位的容差限值和干预限值，绘制单值控制图持续监测了 287 例 VMAT 计划的调强剂量验证过程，并分析 QA 失控原因。脑部、头颈部、腹部和盆腔部 VMAT QA 过程的容差限值分别为 94.56%、94.68%、94.34% 和 92.97%，干预限值分别为 93.82%、92.54%、93.23% 和 90.29%。除盆腔部外，脑部、头颈部和腹部容差限值均接近 TG-218 通用容差限值（95%），所有部位干预限值均高于 TG-218 通用干预限值（90%）。通过单值图监测发现头颈部失控的 VMAT QA 为 1 例，腹部和盆腔部各 2 例，其中 4 例受摆位误差影响，1 例受测量设备校准错误影响。结果显示 SPC 技术能有效监测 IMRT/VMAT QA 过程，根据不同部位设置相应的容差限值有助于探查 QA 失控原因。.

Coordinating the charging and discharging behaviors of Plug-in electric vehicles (PEVs) to explore the dynamic economic dispatch (DED) of PEVs in power system has received widespread attention, but how to reasonably compensate for the battery degradation cost resulting from battery depletion during discharge is still in the research gap stage. This study reconstructs DED model and establishes a dynamic mechanism for compensating battery degradation cost with carbon trading revenue. Through 3 case simulations, the rationality of the model and solution algorithm in this study is first verified. Then, the negative impacts of battery degradation cost on dispatch are quantitatively verified from aspects of users' enthusiasm, users’ cost and grid load fluctuation. Finally, by setting 6 carbon prices, it is verified that the carbon trading revenue when carbon trading price is $0.07 and $0.08 achieves full compensation for battery degradation cost and eliminates their negative impacts. The further analysis discusses the possibility of this compensation method becoming a new subsidy policy for electric vehicles. The proposed strategy is an effective way to compensate for battery degradation cost and can significantly increase the motivation of vehicle owners to participate in microgrid dispatch, which will maximize the energy-saving and emission-reduction benefits of PEVs.