甲状腺相关性眼病的研究及治疗进展

甲状腺相关性眼病(TAO)是具有一系列体征和症状的多因素自身免疫性疾病。糖胺聚糖(GAG)的过量沉积、炎性浸润以及细胞因子的过度产生是甲状腺相关性眼病的主要特征。甲状腺相关性眼病的临床表现多种多样,可以从轻度的眼睑肿胀、上睑退缩、结膜充血、眼球突出乃至重度的威胁视力的暴露性角膜溃疡和压迫性视神经病变。通常,根据病史和查体是可以直接诊断甲状腺相关性眼病。实验室检查和影像学检查对于诊断甲状腺相关性眼病也具有一定的作用。目前可以根据"NO SPECS"法、临床活动评分(CAS)和VISA分类这三种方法对TAO病情情况进行分类。甲状腺相关性眼部的治疗包括保守治疗、药物治疗、眼眶放射治疗和手术治疗等,需根据患者的病情来决定其治疗方案。本文的目的是帮助眼科医生了解甲状腺相关性眼病的分期(轻度,中度至重度和视力威胁)的重要性和相关的可用治疗方式。     

Machine learning helps identifying volume-confounding effects in radiomics

PurposeHighlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based methods to identify the presence of volume-confounding effects in radiomics features.Methods841 radiomics features were extracted from two retrospective publicly available datasets of lung and head neck cancers using open source software. Unsupervised hierarchical clustering and principal component analysis (PCA) identified relations between radiomics and clinical outcomes (overall survival). Bootstrapping techniques with logistic regression verified features’ prognostic power and robustness.ResultsOver 80% of the features had large pairwise correlations. Nearly 30% of the features presented strong correlations with tumor volume. Using volume-independent features for clustering and PCA did not allow risk stratification of patients. Clinical predictors outperformed radiomics features in bootstrapping and logistic regression.ConclusionsThe adoption of safeguards in radiomics is imperative to improve the quality of radiomics studies. We proposed machine learning (ML) – based methods for robust radiomics signatures development.     

MRI-based radiomics signature for tumor grading of rectal carcinoma using random forest model

The present study aimed to construct prospective models for tumor grading of rectal carcinoma by using magnetic resonance (MR)-based radiomics features. A set of 118 patients with rectal carcinoma was analyzed. After imbalance-adjustments of the data using Synthetic Minority Oversampling Technique (SMOTE), the final data set was randomized into the training set and validation set at the ratio of 3:1. The radiomics features were captured from manually segmented lesion of magnetic resonance imaging (MRI). The most related radiomics features were selected using the random forest model by calculating the Gini importance of initial extracted characteristics. A random forest classifier model was constructed using the top important features. The classifier model performance was evaluated via receive operator characteristic curve and area under the curve (AUC). A total of 1,131 radiomics features were extracted from segmented lesion. The top 50 most important features were selected to construct a random forest classifier model. The AUC values of grade 1, 2, 3, and 4 for training set were 0.918, 0.822, 0.775, and 1.000, respectively, and the corresponding AUC values for testing set were 0.717, 0.683, 0.690, and 0.827 separately. The developed feature selection method and machine learning-based prediction models using radiomics features of MRI show a relatively acceptable performance in tumor grading of rectal carcinoma and could distinguish the tumor subjects from the healthy ones, which is important for the prognosis of cancer patients.     

QA of dynamic MLC based on EPID portal dosimetry

PurposeDynamic delivery of intensity modulated beams (dIMRT) requires not only accurate verification of leaf positioning but also a control on the speed of motion. The latter is a parameter that has a major impact on the dose delivered to the patient. Time consumed in quality assurance (QA) procedures is an issue of relevance in any radiotherapy department. Electronic portal imaging dosimetry (EPID) can be very efficient for routine tests. The purpose of this work is to investigate the ability of our EPID for detecting small errors in leaf positioning, and to present our daily QA procedures for dIMRT based on EPID.Methods and materialsA Varian 2100 CD Clinac equipped with an 80 leaf Millennium MLC and with amorphous silicon based EPID (aS500, Varian) is used. The daily QA program consists in performing: Stability check of the EPID signal, Garden fence test, Sweeping slit test, and Leaf speed test.Results and discussionThe EPID system exhibits good long term reproducibility. The mean portal dose at the centre of a 10 × 10 cm² static field was 1.002 ± 0.004 (range 1.013–0.995) for the period evaluated of 47 weeks. Garden fence test shows that leaf position errors of up to 0.2 mm can be detected. With the Sweeping slit test we are able to detect small deviations on the gap width and errors of individual leaves of 0.5 and 0.2 mm. With the Leaf speed test problems due to motor fatigue or friction between leaves can be detected.ConclusionsThis set of tests takes no longer than 5 min in the linac treatment room. With EPID dosimetry, a consistent daily QA program can be applied, giving complete information about positioning/speed MLC.     

Gamma index comparison of three VMAT QA systems and evaluation of their sensitivity to delivery errors

PurposeTo compare detectors for dosimetric verification before VMAT treatments and evaluate their sensitivity to errors.Methods and materialsMeasurements using three detectors (ArcCheck, 2d array 729 and EPID) were used to validate the dosimetric accuracy of the VMAT delivery. Firstly, performance of the three devices was studied. Secondly, to assess the reliability of the detectors, 59 VMAT treatment plans from a variety of clinical sites were considered. Thirdly, systematic variations in collimator, couch and gantry angle plus MLC positioning were applied to four clinical treatments (two prostate, two head and neck cases) in order to establish the detection sensitivity of the three devices. Measurements were compared with TPS computed doses via gamma analysis (3%/3 mm and 2%/2 mm) with an agreement of at least 95% and 90% respectively in all pixels. Effect of the errors on the dose distributions was analyzed.ResultsRepeatability and reproducibility were excellent for the three devices. The average pass rate for the 59 cases was superior to 98% for all devices with 3%/3 mm criteria. It was found that for the plans delivered with errors, the sensitivity was quite similar for all devices. Devices were able to detect a 2 mm opened or closed MLC error with 3%/3 mm tolerance level. An error of 3° in collimator, gantry or couch rotation was detected by the three devices using 2%/2 mm criteria.ConclusionsAll three devices have the potential to detect errors with more or less the same threshold. Nevertheless, there is no guarantee that pretreatment QA will catch delivery errors.     

Machine learning algorithms to predict the 1 year unfavourable prognosis for advanced schistosomiasis

Short-term prognosis of advanced schistosomiasis has not been well studied. We aimed to construct prognostic models using machine learning algorithms and to identify the most important predictors by utilising routinely available data under the government medical assistance programme. An established database of advanced schistosomiasis in Hunan, China was utilised for analysis. A total of 9541 patients for the period from January 2008 to December 2018 were enrolled in this study. Candidate predictors were selected from demographics, clinical features, medical examinations and test results. We applied five machine learning algorithms to construct 1 year prognostic models: logistic regression (LR), decision tree (DT), random forest (RF), artificial neural network (ANN) and extreme gradient boosting (XGBoost). An area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. The important predictors of the optimal model for unfavourable prognosis within 1 year were identified and ranked. There were 1249 (13.1%) cases having unfavourable prognoses within 1 year of discharge. The mean age of all participants was 61.94 years, of whom 70.9% were male. In general, XGBoost showed the best predictive performance with the highest AUC (0.846; 95% confidence interval (CI): 0.821, 0.871), compared with LR (0.798; 95% CI: 0.770, 0.827), DT (0.766; 95% CI: 0.733, 0.800), RF (0.823; 95% CI: 0.796, 0.851), and ANN (0.806; 95% CI: 0.778, 0.835). Five most important predictors identified by XGBoost were ascitic fluid volume, haemoglobin (HB), total bilirubin (TB), albumin (ALB), and platelets (PT). We proposed XGBoost as the best algorithm for the evaluation of a 1 year prognosis of advanced schistosomiasis. It is considered to be a simple and useful tool for the short-term prediction of an unfavourable prognosis for advanced schistosomiasis in clinical settings.     

Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography

ObjectivesThe subtype classification of lung adenocarcinoma is important for treatment decision. This study aimed to investigate the deep learning and radiomics networks for predicting histologic subtype classification and survival of lung adenocarcinoma diagnosed through computed tomography (CT) images.MethodsA dataset of 1222 patients with lung adenocarcinoma were retrospectively enrolled from three medical institutions. The anonymised preoperative CT images and pathological labels of atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, invasive adenocarcinoma (IAC) with five predominant components were obtained. These pathological labels were divided into 2-category classification (IAC; non-IAC), 3-category and 8-category. We modeled the classification task of histological subtypes based on modified ResNet-34 deep learning network, radiomics strategies and deep radiomics combined algorithm. Then we established the prognostic models in lung adenocarcinoma patients with survival outcomes. The accuracy (ACC), area under ROC curves (AUCs) and C-index were primarily performed to evaluate the algorithms.ResultsThis study included a training set (n = 802) and two validation cohorts (internal, n = 196; external, n = 224). The ACC of deep radiomics algorithm in internal validation achieved 0.8776, 0.8061 in the 2-category, 3-category classification, respectively. Even in 8 classifications, the AUC ranged from 0.739 to 0.940 in internal set. Further, we constructed a prognosis model that C-index was 0.892(95% CI: 0.846–0.937) in internal validation set.ConclusionsThe automated deep radiomics based triage system has achieved the great performance in the subtype classification and survival predictability in patients with CT-detected lung adenocarcinoma nodules, providing the clinical guide for treatment strategies.     

Machine learning-based in-hospital mortality prediction of HIV/AIDS patients with Talaromyces marneffei infection in Guangxi,China

ObjectiveTalaromycosis is a serious regional disease endemic in Southeast Asia. In China, Talaromyces marneffei (T. marneffei) infections is mainly concentrated in the southern region, especially in Guangxi, and cause considerable in-hospital mortality in HIV-infected individuals. Currently, the factors that influence in-hospital death of HIV/AIDS patients with T. marneffei infection are not completely clear. Existing machine learning techniques can be used to develop a predictive model to identify relevant prognostic factors to predict death and appears to be essential to reducing in-hospital mortality.MethodsWe prospectively enrolled HIV/AIDS patients with talaromycosis in the Fourth People’s Hospital of Nanning, Guangxi, from January 2012 to June 2019. Clinical features were selected and used to train four different machine learning models (logistic regression, XGBoost, KNN, and SVM) to predict the treatment outcome of hospitalized patients, and 30% internal validation was used to evaluate the performance of models. Machine learning model performance was assessed according to a range of learning metrics, including area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) tool was used to explain the model.ResultsA total of 1927 HIV/AIDS patients with T. marneffei infection were included. The average in-hospital mortality rate was 13.3% (256/1927) from 2012 to 2019. The most common complications/coinfections were pneumonia (68.9%), followed by oral candida (47.5%), and tuberculosis (40.6%). Deceased patients showed higher CD4/CD8 ratios, aspartate aminotransferase (AST) levels, creatinine levels, urea levels, uric acid (UA) levels, lactate dehydrogenase (LDH) levels, total bilirubin levels, creatine kinase levels, white blood-cell counts (WBC) counts, neutrophil counts, procaicltonin levels and C-reactive protein (CRP) levels and lower CD3⁺ T-cell count, CD8⁺ T-cell count, and lymphocyte counts, platelet (PLT), high-density lipoprotein cholesterol (HDL), hemoglobin (Hb) levels than those of surviving patients. The predictive XGBoost model exhibited 0.71 sensitivity, 0.99 specificity, and 0.97 AUC in the training dataset, and our outcome prediction model provided robust discrimination in the testing dataset, showing an AUC of 0.90 with 0.69 sensitivity and 0.96 specificity. The other three models were ruled out due to poor performance. Septic shock and respiratory failure were the most important predictive features, followed by uric acid, urea, platelets, and the AST/ALT ratios.ConclusionThe XGBoost machine learning model is a good predictor in the hospitalization outcome of HIV/AIDS patients with T. marneffei infection. The model may have potential application in mortality prediction and high-risk factor identification in the talaromycosis population.     

Dosiomics-based prediction of radiation-induced hypothyroidism in nasopharyngeal carcinoma patients

PurposeTo predict the incidence of radiation-induced hypothyroidism (RHT) in nasopharyngeal carcinoma (NPC) patients, dosiomics features based prediction models were established.Materials and methodsA total of 145 NPC patients treated with radiotherapy from January 2012 to January 2015 were included. Dosiomics features of the dose distribution within thyroid gland were extracted. The minimal-redundancy-maximal-relevance (mRMR) criterion was used to rank the extracted features and selected the most relevant features. Machine learning (ML) algorithms including logistic regression (LR), support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN) were utilized to establish prediction models, respectively. Nested sampling and hyper-tuning methods were adopted to train and validate the prediction models. The dosiomics-based (DO) prediction models were evaluated through comparing with the dose-volume factor-based (DV) models in terms of the area under the receiver operating characteristic (ROC) curve (AUC). The demographics factors (age and gender) were included in both DO model and DV model.ResultsAge, V45 and 37 dosiomics features exhibited significant correlations with RHT in univariate analysis. For prediction performance, DO prediction models exhibited better results with the best AUC value 0.7 while DV prediction models 0.61. In DO prediction models, the AUC values displayed a trend from ascending to descending with the increasing of selected features. The highest AUC value was achieved when the number of selected features was 3. In DV prediction model, similar trend was not observed.ConclusionThis study established a prediction model based on the dosiomics features with better performance than conventional dose-volume factors, leading to early predict the possible RHT among NPC patients who had received radiotherapy and take precaution measures for NPC patients.     

Intravoxel incoherent motion radiomics nomogram for predicting tumor treatment responses in nasopharyngeal carcinoma

BackgroundIntravoxel incoherent motion (IVIM) plays an important role in predicting treatment responses in patient with nasopharyngeal carcinoma (NPC). The goal of this study was to develop and validate a radiomics nomogram based on IVIM parametric maps and clinical data for the prediction of treatment responses in NPC patients.MethodsEighty patients with biopsy-proven NPC were enrolled in this study. Sixty-two patients had complete responses and 18 patients had incomplete responses to treatment. Each patient received a multiple b-value diffusion-weighted imaging (DWI) examination before treatment. Radiomics features were extracted from IVIM parametric maps derived from DWI image. Feature selection was performed by the least absolute shrinkage and selection operator method. Radiomics signature was generated by support vector machine based on the selected features. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) values were used to evaluate the diagnostic performance of radiomics signature. A radiomics nomogram was established by integrating the radiomics signature and clinical data.ResultsThe radiomics signature showed good prognostic performance to predict treatment response in both training (AUC = 0.906, P<0.001) and testing (AUC = 0.850, P<0.001) cohorts. The radiomic nomogram established by integrating the radiomic signature with clinical data significantly outperformed clinical data alone (C-index, 0.929 vs 0.724; P<0.0001).ConclusionsThe IVIM-based radiomics nomogram provided high prognostic ability to treatment responses in patients with NPC. The IVIM-based radiomics signature has the potential to be a new biomarker in prediction of the treatment responses and may affect treatment strategies in patients with NPC.     

Evaluation of the ability of three commercially available dosimeters to detect systematic delivery errors in step-and-shoot IMRT plans

BackgroundThere is limited data on error detectability for step-and-shoot intensity modulated radiotherapy (sIMRT) plans, despite significant work on dynamic methods. However, sIMRT treatments have an ongoing role in clinical practice. This study aimed to evaluate variations in the sensitivity of three patient-specific quality assurance (QA) devices to systematic delivery errors in sIMRT plans.Materials and methodsFour clinical sIMRT plans (prostate and head and neck) were edited to introduce errors in: Multi-Leaf Collimator (MLC) position (increasing field size, leaf pairs offset (1–3 mm) in opposite directions; and field shift, all leaves offset (1–3 mm) in one direction); collimator rotation (1–3 degrees) and gantry rotation (0.5–2 degrees). The total dose for each plan was measured using an ArcCHECK diode array. Each field, excluding those with gantry offsets, was also measured using an Electronic Portal Imager and a MatriXX Evolution 2D ionisation chamber array. 132 plans (858 fields) were delivered, producing 572 measured dose distributions. Measured doses were compared to calculated doses for the no-error plan using Gamma analysis with 3%/3 mm, 3%/2 mm, and 2%/2 mm criteria (1716 analyses).ResultsGenerally, pass rates decreased with increasing errors and/or stricter gamma criteria. Pass rate variations with detector and plan type were also observed. For a 3%/3 mm gamma criteria, none of the devices could reliably detect 1 mm MLC position errors or 1 degree collimator rotation errors.ConclusionsThis work has highlighted the need to adapt QA based on treatment plan type and the need for detector specific assessment criteria to detect clinically significant errors.     

Prediction of minimal hepatic encephalopathy by using an radiomics nomogram in chronic hepatic schistosomiasis patients

ObjectiveTo construct an MR-radiomics nomogram to predict minimal hepatic encephalopathy (MHE) in patients with chronic hepatic schistosomiasis (CHS).MethodsFrom July 2017 to July 2020, 236 CHS patients with non-HE (n = 140) and MHE (n = 96) were retrospective collected and randomly divided into training group and testing group. Radiomics features were extracted from substantia nigra-striatum system of a brain diffusion weighted images (DWI) and combined with clinical predictors to build a radiomics nomogram for predicting MHE in CHS patients. The ROC curve was used to evaluate the predicting performance in training group and testing group. The clinical decisive curve (CDC) was used to assess the clinical net benefit of using radiomics nomogram in predicting MHE.ResultsLow seralbumin (P < 0.05), low platelet count (P < 0.05) and high plasma ammonia (P < 0.05) was the significant clinical predictors for MHE in CHS patients. The AUC, specificity and sensitivity of the radiomics nomogram were 0.89, 0.90 and 0.86 in the training group, and were 0.83, 0.85 and 0.75 in the training group. The CDC analysis showed clinical net benefits for the radiomics nomogram in predicting MHE.ConclusionsThe radiomics nomogram combining DWI radiomics features and clinical predictors could be useful tool to predict MHE in CHS patients.     

CT radiomics combined with clinical variables for predicting the overall survival of hepatocellular carcinoma patients after hepatectomy

PurposeTo establish a model for assessing the overall survival (OS) of the hepatocellular carcinoma (HCC) patients after hepatectomy based on the clinical and radiomics features.MethodsThis study recruited a total of 267 patients with HCC, which were randomly divided into the training (N = 188) and validation (N = 79) cohorts. In the training cohort, radiomic features were selected with the intra-reader and inter-reader correlation coefficient (ICC), Spearman's correlation coefficient, and the least absolute shrinkage and selection operator (LASSO). The radiomics signatures were built by COX regression analysis and compared the predictive potential in the different phases (arterial, portal, and double-phase) and regions of interest (tumor, peritumor 3 mm, peritumor 5 mm). A clinical-radiomics model (CR model) was established by combining the radiomics signatures and clinical risk factors. The validation cohort was used to validate the proposed models.ResultsA total of 267 patients 86 (45.74%) and 37 (46.84%) patients died in the training and validation cohorts, respectively. Among all the radiomics signatures, those based on the tumor and peritumor (5 mm) (AP-TP5-Signature) showed the best prognostic potential (training cohort 1–3 years AUC:0.774–0.837; validation cohort 1–3 years AUC:0.754–0.810). The CR model showed better discrimination, calibration, and clinical applicability as compared to the clinical model and radiomics features. In addition, the CR model could perform risk-stratification and also allowed for significant discrimination between the Kaplan-Meier curves in most of the subgroups.ConclusionsThe CR model could predict the OS of the HCC patients after hepatectomy.     

Virtual patient 3D dose reconstruction using in air EPID measurements and a back-projection algorithm for IMRT and VMAT treatments

PurposeAt our institute, a transit back-projection algorithm is used clinically to reconstruct in vivo patient and in phantom 3D dose distributions using EPID measurements behind a patient or a polystyrene slab phantom, respectively. In this study, an extension to this algorithm is presented whereby in air EPID measurements are used in combination with CT data to reconstruct ‘virtual’ 3D dose distributions. By combining virtual and in vivo patient verification data for the same treatment, patient-related errors can be separated from machine, planning and model errors.Methods and materialsThe virtual back-projection algorithm is described and verified against the transit algorithm with measurements made behind a slab phantom, against dose measurements made with an ionization chamber and with the OCTAVIUS 4D system, as well as against TPS patient data. Virtual and in vivo patient dose verification results are also compared.ResultsVirtual dose reconstructions agree within 1% with ionization chamber measurements. The average γ-pass rate values (3% global dose/3 mm) in the 3D dose comparison with the OCTAVIUS 4D system and the TPS patient data are 98.5 ± 1.9%(1SD) and 97.1 ± 2.9%(1SD), respectively. For virtual patient dose reconstructions, the differences with the TPS in median dose to the PTV remain within 4%.ConclusionsVirtual patient dose reconstruction makes pre-treatment verification based on deviations of DVH parameters feasible and eliminates the need for phantom positioning and re-planning. Virtual patient dose reconstructions have additional value in the inspection of in vivo deviations, particularly in situations where CBCT data is not available (or not conclusive).     

A Deep Learning-based correction to EPID dosimetry for attenuation and scatter in the Unity MR-Linac system

PurposeEPID dosimetry in the Unity MR-Linac system allows for reconstruction of absolute dose distributions within the patient geometry. Dose reconstruction is accurate for the parts of the beam arriving at the EPID through the MRI central unattenuated region, free of gradient coils, resulting in a maximum field size of ~10 × 22 cm² at isocentre. The purpose of this study is to develop a Deep Learning-based method to improve the accuracy of 2D EPID reconstructed dose distributions outside this central region, accounting for the effects of the extra attenuation and scatter.MethodsA U-Net was trained to correct EPID dose images calculated at the isocenter inside a cylindrical phantom using the corresponding TPS dose images as ground truth for training. The model was evaluated using a 5-fold cross validation procedure. The clinical validity of the U-Net corrected dose images (the so-called DEEPID dose images) was assessed with in vivo verification data of 45 large rectum IMRT fields. The sensitivity of DEEPID to leaf bank position errors (±1.5 mm) and ±5% MU delivery errors was also tested.ResultsCompared to the TPS, in vivo 2D DEEPID dose images showed an average γ-pass rate of 90.2% (72.6%–99.4%) outside the central unattenuated region. Without DEEPID correction, this number was 44.5% (4.0%–78.4%). DEEPID correctly detected the introduced delivery errors.ConclusionsDEEPID allows for accurate dose reconstruction using the entire EPID image, thus enabling dosimetric verification for field sizes up to ~19 × 22 cm² at isocentre. The method can be used to detect clinically relevant errors.     

Anti-eye muscle antibodies and hypothyroid Graves' disease: a case report.

We report the case of a 70-year-old man who developed hypothyroidism associated with TSH receptor antibodies and severe ophthalmopathy during lithium therapy. He had received lithium therapy for more than 20 years for manic depression, when ophthalmopathy (class VI of the American Thyroid Association classification) and mild hypothyroidism developed. Orbital magnetic resonance imaging indicated marked enlargement of the superior, medial and inferior rectus muscles in the left eye. He had anti-eye muscle antibodies in his serum, detected by Western blotting and quantified by chromatoscanning, as well as anti-TSH receptor antibodies. He was treated with supplementation of levothyroxine and four cycles of methylprednisolone pulse therapy. After the pulse therapy, both anti-eye muscle antibodies and anti-TSH receptor antibodies decreased and disappeared in parallel with the improvement in eye symptoms and signs. These observations suggest the importance of anti-eye muscle antibodies as clinical markers in the development of thyroid-associated ophthalmopathy.     

Comparison of three-dimensional patient-specific dosimetry systems with delivery errors: Toward a new synchronous measurement method

PurposeThis study proposed a synchronous measurement method for patient-specific dosimetry using two three-dimensional dose verification systems with delivery errors.MethodsTwenty hypofractionated radiotherapy treatment plans for patients with lung cancer were retrospectively reviewed. Monitor unit (MU) changes, leaf in-position errors, and angles of deviation of the collimator were intentionally introduced to investigate the detection sensitivity of the EDose + EPID (EE) and Dolphin + Compass (DC) systems.ResultsBoth systems accurately detected the MU modifications and had a similar ability to detect leaf in-position errors. The detection of multi-leaf collimator (MLC) errors was difficult for the whole body using different gamma criteria. When the introduced MLC error was 1.0 mm, the numbers of errors detected in the clinical target volume (CTV) by the EE system were 20, 20, and 20 and the numbers of errors detected by the DC system were 18, 19, and 20, at 3%/2 mm, 2%/2 mm, and 1%/1 mm, respectively. The average dose deviation of all DVH parameters exceeded 3%. The gamma and DVH evaluation results remained unchanged for the DC system when different collimator angle errors were introduced. The number of errors detected by the EE system was <11 for each anatomical structure for all gamma criteria. The mean dose deviation of the CTV was not distinguished.ConclusionsThis synchronous measurement approach can effectively eliminate the influence of random errors during treatment. The EE and DC systems reconstruct the three-dimensional dose distribution accurately and are convenient and reliable for dose verification.     

A comparative study of prostate PTV margins for patients using hydrogel spacer or rectal balloon in proton therapy

PurposeTo compare the planning target volume (PTV) margins needed for prostate patients who have used hydrogel spacer or rectal balloon during proton treatments.MethodTotal of 190 prostate patients treated with proton therapy during 2017 were selected for this study. Of these patients, 96 had hydrogel spacer injection and 94 patients had only rectal balloons insertion. All patients had implanted gold markers inside the prostate for daily target alignment. Post-treatment radigraphs were obtained to evaluate prostate intrafraction motion. The systematic and random components of patient setup residual error and prostate intrafraction motion error were obtained. PTV margins were calculated using the van Herk formula for both patient groups.ResultsFor setup residual error, the mean values in the superior-inferior (SI) direction and the variances in the left–right (LR) direction were statistically different between the two groups. For intrafraction motion, there were significant differences of the mean values in the SI direction and of the variances in both LR and anterior-posterior (AP) directions. The population PTV margins for hydrogel spacer group were 2.6 mm, 3.3 mm, and 1.6 mm in LR, SI, AP directions, respectively. For the rectal balloon group, the PTV margins were 2.1 mm, 3.1 mm, and 2.0 mm in LR, SI, AP directions, respectively.ConclusionStatistically significant differences were observed in the patient setup and prostate intrafraction motion errors of the two patient groups. However, under the current protocol of bladder preparation and daily marker-based x-ray image-guidance, population PTV margins were comparable between the two patient groups.