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To assess the planned dose, in vivo dosimetry, acute skin toxicity, pain, and distress using Thermoplastic Elastomer (TPE) bolus for postmastectomy radiotherapy (PMRT). Thirty-two PMRT patients with TPE bolus (17 patients for 25 fractions, 15 patients for the first 20 fractions) were selected for the study. The acute skin toxicity, pain, and psychological distress were assessed from the first treatment week to the fourth week after the end of treatment. At the first treatment, the MOSFET was used in vivo dosimetry measurement. In vivo dosimetry with the bolus, the dose deviation ranged from −6.22% to −1.56% for 5 points. The presence of grade 1 and 2 skin toxicity reached its peak (70.0% and 13.3%) in the sixth week. Two patients (6.6%) with 25 fractions bolus experienced moist desquamation in the fifth and seventh week, with pain score 2 and 3, and interruptions of 3 and 5 days, respectively. The incidence of pain score 1, 2, and 3 peaked in the fifth (33.3%), fourth (33.3%), and seventh (10.0%) week. No patients experienced grade 3 skin toxicity and severe pain. One patient had significant anxiety, and two patients had significant depression. The TPE bolus can accurately fit skin and improve the surface dose to more than 90%. Twenty fractions with TPE bolus had similar skin toxicity and pain to those without bolus and did not increase patients' distress and clinical workload, compared with the literature's data, which is an alternative to the 3D printing bolus for PMRT. • First reported the soft and self-viscosity TPE bolus in PMRT. • Evaluated the planning and in vivo dose in PMRT using TPE bolus. • TPE bolus had similar skin toxicity to those without bolus compared with the literature's data. • Using TPE bolus did not increase patients' psychological distress and clinical workload compared with the literature's data. [ABSTRACT FROM AUTHOR]

• The 4D CT failed to represent the motion characteristic during dose delivery. • Quantify the amplitude changes of respiratory motion based on intra-fractional CBCT. • Quantify the baseline drift of respiratory motion based on intra-fractional CBCT. • The ITV to PTV margin should be 4.0 mm (LR), 7.0 mm (SI), and 4.0 mm (AP) to compensate for the baseline drifts. • The recalculated ITV to PTV margin was clinically acceptable based on dosimetric parameters. Using intra-fractional cone-beam CT (CBCT) to evaluate the amplitude changes and baseline shifts of respiratory motion in liver stereotactic body radiation therapy (SBRT). The amplitude changes and baseline shifts of respiratory motion for 24 liver patients were evaluated by the four-dimensional (4D) CT, inter- and intra-fractional CBCT. The difference of the average liver position errors among all treatment fractions and the 4D CT representthe baseline shifts. According to the baseline shifts, the ITV to PTV margin was recalculated and the plan was re-designed to compare the dosimetric variation. The systematic and random errors of the baseline shifts for intra-fractional CBCT in the left–right (LR), superior-inferior (SI), and anterior-posterior (AP) directions were 0.99/1.60 mm, 2.03/2.46 mm, and 1.02/2.07 mm, respectively. The new ITV to PTV margins should be 4.0 mm, 7.0 mm, and 4.0 mm, respectively. The amplitude change of motion between the 4D CT and the intra-fractional CBCT was 1.03 ± 4.35 mm, with 31% of fractions exceeding 5 mm. To achieve the same dose coverage of the new PTV, the D mean , V50, V40, V30, V25 of normal liver and maximum dose of the duodenum were significantly different. Significant amplitude changes and baseline shifts of motion occurred during dose delivery compared with those in 4D CT. Using the ITV to PTV margin of 4.0 mm (LR), 7.0 mm (SI), and 4.0 mm (AP) can ensure the target dose coverage and keep the dose constrain of normal tissues at an acceptable level. [ABSTRACT FROM AUTHOR]

• Radiomics analysis of EPID measurements can classify the magnitude and directions of positioning errors. • AUCs of the detection of positioning errors and directions classification were above 0.90 and 0.76, respectively. • Positioning errors are detectable from EPID measurements with multifactorial error sources. • High performance is achievable using a small-size sample for patients with thyroid-associated ophthalmopathy. • This study is a further step toward machine learning-based positioning error detection via EPID measurements. 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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

• The sampling variability seriously affects the performance of PSQA tolerance limits. • The smaller the sample, the higher the risk of tolerance limits for clinical use. • Out-of-control values must be identified and eliminated before calculating the limits. • The WSD method exhibits sufficient robustness and reliability for unknown PSQAs. 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. 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 (ARL 0) and false alarm rate (FAR 0) were calculated to evaluate the performance of LCL. The ground truth of the values of LCL, FAR 0 , and ARL 0 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 ARL 0 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. 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. [ABSTRACT FROM AUTHOR]