Feasibility of intensity-modulated radiotherapy to treat gastric cancer

AimTo present a proposed gastric cancer intensity-modulated radiotherapy (IMRT) treatment planning protocol for an institution that have not introduced volumetric modulated arc therapy in clinical practice. A secondary aim was to determine the impact of 2DkV set-up corrections on target coverage and organ at risk (OAR).Methods and MaterialsTwenty consecutive patients were treated with a specially-designed non-coplanar 7-field IMRT technique. The isocenter-shift method was used to estimate the impact of 2DkV-based set-up corrections on the original base plan (BP) coverage. An alternative plan was simulated (SP) by taking into account isocenter shifts. The SP and BP were compared using dose-volume histogram (DVH) plots calculated for the internal target volume (ITV) and OARs.ResultsBoth plans delivered a similar mean dose to the ITV (100.32 vs. 100.40%), with no significant differences between the plans in internal target coverage (5.37 vs. 4.96%). Similarly, no significant differences were observed between the maximal dose to the spinal cord (67.70 and 67.09%, respectively) and volume received 50% of the prescribed dose of: the liver (62.11 vs. 59.84%), the right (17.62 vs. 18.58%) and left kidney (29.40 vs. 30.48%). Set-up margins (SM) were computed as 7.80 mm, 10.17 mm and 6.71 mm in the left-right, cranio-caudal and anterior-posterior directions, respectively.ConclusionPresented IMRT protocol (OAR dose constraints with selected SM verified by 2DkV verification) for stomach treatment provided optimal dose distribution for the target and the critical organs. Comparison of DVH for the base and the modified plan (which considered set-up uncertainties) showed no significant differences.     

A patient immobilization device for prone breast radiotherapy: Dosimetric effects and inclusion in the treatment planning system

PurposeTo assess the dosimetric impact of a patient positioning device for prone breast radiotherapy and assess the accuracy of a treatment planning system (TPS) in predicting this impact.MethodsBeam attenuation and build-up dose perturbations, quantified by ionization chamber and radiochromic film dosimetry, were evaluated for 3 components of the patient positioning device: the carbon fiber baseplate, the support cushions and the support wedge for the contralateral breast. Dose calculations were performed using the XVMC dose engine implemented in the Monaco TPS. All components were included during planning CT acquisition.ResultsBeam attenuation amounted to 7.57% (6 MV) and 5.33% (15 MV) for beams obliquely intersecting the couchtop–baseplate combination. Beams traversing large sections of the support wedge were attenuated by 12.28% (6 MV) and 9.37% (15 MV). For the support cushion foam, beam attenuation remained limited to 0.11% (6 MV) and 0.08% (15 MV) per centimeter thickness. A substantial loss of dose build-up was detected when irradiating through any of the investigated components. TPS dose calculations accurately predicted beam attenuation by the baseplate and support wedge. A manual density overwrite was needed to model attenuation by the support cushion foam. TPS dose calculations in build-up regions differed considerably from measurements for both open beams and beams traversing the device components.ConclusionsIrradiating through the components of the positioning device resulted in a considerable degradation of skin sparing. Inclusion of the device components in the treatment planning CT allowed to accurately model the most important attenuation effect, but failed to accurately predict build-up doses.     

Repeated probit regression when covariates are measured with error

This paper develops a model for repeated binary regression when a covariate is measured with error. The model allows for estimating the effect of the true value of the covariate on a repeated binary response. The choice of a probit link for the effect of the error-free covariate, coupled with normal measurement error for the error-free covariate, results in a probit model after integrating over the measurement error distribution. We propose a two-stage estimation procedure where, in the first stage, a linear mixed model is used to fit the repeated covariate. In the second stage, a model for the correlated binary responses conditional on the linear mixed model estimates is fit to the repeated binary data using generalized estimating equations. The approach is demonstrated using nutrient safety data from the Diet Intervention of School Age Children (DISC) study.     

Detection of setup uncertainties with 3D surface registration system for conformal radiotherapy of breast cancer

Aim

To investigate the clinical application of a technique for patient set-up verification in breast cancer radiotherapy based on a 3D surface image registration system.

Background

Accurate and reproducible patient set-up is a prerequisite to correctly deliver fractionated radiotherapy. Various approaches are available to verify and correct patient setup for 3D image acquisition in a radiation treatment room.

Materials and methods

The study analyzed the setup reproducibility of 15 patients affected by breast cancer and candidates for conformal radiotherapy by using the AlignRT system (VisionRT, London, UK). At the initial setup, electronic portal imaging device (EPID) images were compared with Digitally Reconstructed Radiographs (DRRs) and a reference three-dimensional (3D) surface image was obtained by AlignRT. Surface images were acquired prior to every subsequent setup procedure. The systematic and random errors along longitudinal and vertical directions were measured and compared for the two systems.

Results

The procedure for surface registration, image acquisition and comparison with the reference image took less than 1 min on average. The T test for systematic error showed no significant difference between the 2 verification systems along the longitudinal (p = 0.69) and vertical (p = 0.67) axes. The T-test for random error showed a significant difference between the 2 systems along the vertical axis (p = 0.05).

Conclusion

AlignRT is fast, simple, non-invasive and seems to be reliable in detecting patient setup errors. Our results suggest that it could be used to assess the setup reproducibility for breast cancer patients.     

Latent model for correlated binary data with diagnostic error

We propose a methodology for modeling correlated binary data measured with diagnostic error. A shared random effect is used to induce correlations in repeated true latent binary outcomes and in observed responses and to link the probability of a true positive outcome with the probability of having a diagnosis error. We evaluate the performance of our proposed approach through simulations and compare it with an ad hoc approach. The methodology is illustrated with data from a study that assessed the probability of corneal arcus in patients with familial hypercholesterolemia.     

Shared uncertainty in measurement error problems, with application to Nevada Test Site fallout data

In radiation epidemiology, it is often necessary to use mathematical models in the absence of direct measurements of individual doses. When complex models are used as surrogates for direct measurements to estimate individual doses that occurred almost 50 years ago, dose estimates will be associated with considerable error, this error being a mixture of (a) classical measurement error due to individual data such as diet histories and (b) Berkson measurement error associated with various aspects of the dosimetry system. In the Nevada Test Site(NTS) Thyroid Disease Study, the Berkson measurement errors are correlated within strata. This article concerns the development of statistical methods for inference about risk of radiation dose on thyroid disease, methods that account for the complex error structure inherence in the problem. Bayesian methods using Markov chain Monte Carlo and Monte-Carlo expectation-maximization methods are described, with both sharing a key Metropolis-Hastings step. Regression calibration is also considered, but we show that regression calibration does not use the correlation structure of the Berkson errors. Our methods are applied to the NTS Study, where we find a strong dose-response relationship between dose and thyroiditis. We conclude that full consideration of mixtures of Berkson and classical uncertainties in reconstructed individual doses are important for quantifying the dose response and its credibility/confidence interval. Using regression calibration and expectation values for individual doses can lead to a substantial underestimation of the excess relative risk per gray and its 95% confidence intervals.     

数字图像处理法确定林带疏透度随机误差研究

在分析以数字图像处理为测定法确定林带疏透度误差来源的基础上,对其中的随机误差进行研究,结果表明:林带整体疏透度的机误方差小于其冠部和干部的较大者;所研究的各类型林带各部位疏透度随机误差均遵从正态分布;林带整体疏透度随机误差的分布与树种和带内配置无关;北京杨、双阳快杨和其它类杂交杨的矩形或品字形配置林带各自冠部与干部疏透度机误方差之间无显著差异,而乡土杨林带干部的显著大于冠部的。本文还分别各类型林带的各部位确定了由林带疏透度测定值估计其总体实际值的随机误差限,并讨论了在测定林带疏透度过程中据该误差限计算样本量和划定测定范围的应用意义。最后总结提出:以增加测定同一林带不同样段像片数限定随机误差,通过模型计算订正疏透度测定值中的投影误差和影缩误差确定林带疏透度是建立完善的“数字图像处理法确定林带疏透度”新方法的可行途径。     

Bias in Ellenberg indicator values — problems with detection of the effect of vegetation type

A recent analysis published in this journal found different relationships between mean Ellenberg indicator values and environmental measurements in different vegetation types. The cause was stated as bias in mean Ellenberg values between relevés which in turn suggested to reflect a bias in individual Ellenberg values. We discuss two phenomena that could explain these results without the need to invoke bias in either individual or mean Ellenberg values. Firstly, slopes of linear regression lines underestimate true relationships when analyses involve explanatory variables measured with error. Secondly, syntaxon‐specific distributions of Ellenberg values follow from the floristic definition of phytosociological units. Mean Ellenberg values per relevé therefore carry the stamp of their associated syntaxon even though associated abiotic conditions may vary between relevés. This will lead to variation in slopes and intercepts between vegetation types not because of bias in individual Ellenberg values but because of prescribed bias in the distribution of Ellenberg values between syntaxa. The residual variation in calibrations carried out across vegetation types is undoubtedly reduced by introducing vegetation type as a factor. However users should note that this is unlikely to reflect bias in individual Ellenberg values but is more likely to reflect error in environmental measurements as well the constraint imposed by phytosociological classification.     

Comparison of three differently shaped ROIs in free breathing breast radiotherapy setup using surface guidance with AlignRT®

BackgroundSetup accuracy within adjuvant radiotherapy of breast cancer treated in free breathing is well studied, but a comparison of the typical regions of interest (ROI) used in surface guided radiation therapy (SGRT) does not exist. The aim of this study was to estimate the setup accuracy obtained with differently shaped ROIs in SGRT.Materials and methodsA total of 573 orthogonal image pairs were analyzed from free breathing breast patients in two groups: positioning using AlignRT^® surface guidance system (Group A, n = 20), and setup using conventional laser and tattoo setup (Group L, n = 20). For SGRT, three different setup ROIs were used: a Breast-shaped, O-shaped and T-shaped (B-O and T-ROI). We evaluated the isocenter-, rotation-, pitch and arm position accuracy and residual errors for the chest wall and shoulder joint in kV orthogonal and tangential setup images with laser- or SGRT-based setup.ResultsLess isocenter variance was found in Group A than in Group L. Rotations and posture errors were larger in group L than in Group A (p ≤ 0.05). Rotation error was smaller with T-shaped ROI than with O- or B-shape (p = 0.01–0.04).ConclusionSetup with AlignRT^® improves reproducibility compared to laser setup. Between the different ROI shapes only small differences were found in the patient posture or the isocenter position in the images. The T-ROI is recommended to set up the chest wall bony structure and an additional B-ROI may be used to fine-tune the soft tissue accuracy.     

Approximate nonparametric corrected-score method for joint modeling of survival and longitudinal data measured with error

We consider the problem of jointly modeling survival time and longitudinal data subject to measurement error. The survival times are modeled through the proportional hazards model and a random effects model is assumed for the longitudinal covariate process. Under this framework, we propose an approximate nonparametric corrected-score estimator for the parameter, which describes the association between the time-to-event and the longitudinal covariate. The term nonparametric refers to the fact that assumptions regarding the distribution of the random effects and that of the measurement error are unnecessary. The finite sample size performance of the approximate nonparametric corrected-score estimator is examined through simulation studies and its asymptotic properties are also developed. Furthermore, the proposed estimator and some existing estimators are applied to real data from an AIDS clinical trial.     

Product limit estimation for infectious disease data when the diagnostic test for the outcome is measured with uncertainty

Low sensitivity and/or specificity of a diagnostic test for outcome results in biased estimates of the time to first event using product limit estimation. For example, if a test has low specificity, estimates of the cumulative distribution function (cdf) are biased towards time zero, while estimates of the cdf are biased away from time zero if a test has low sensitivity. In the context of discrete time survival analysis for infectious disease data, we develop self-consistent algorithms to obtain unbiased estimates of the time to first event when the sensitivity and/or specificity of the diagnostic test for the outcome is less than 100%. Two examples are presented. The first involves estimating time to first detection of HIV-1 infection in infants in a randomized clinical trial, and the second involves estimating time to first Neisseria gonorrhoeae infection in a cohort of Kenyan prostitutes.     

Joint modeling of progression of HIV resistance mutations measured with uncertainty and failure time data

Development of HIV resistance mutations is a major cause for failure of antiretroviral treatment. This article proposes a method for jointly modeling the processes of viral genetic changes and treatment failure. Because the viral genome is measured with uncertainty, a hidden Markov model is used to fit the viral genetic process. The uncertain viral genotype is included as a time-dependent covariate in a Cox model for failure time, and an expectation-maximization algorithm is used to estimate the model parameters. This model allows simultaneous evaluation of the sequencing uncertainty and the effect of resistance mutation on the risk of virological and immunological failures. Various model checking tests are provided to assess the appropriateness of the model. Simulation studies are performed to investigate the finite-sample properties of the proposed methods, which are then applied to data collected in three phase II clinical trials testing antiretroviral treatments containing the drug efavirenz.     

Random effects logistic regression analysis with auxiliary covariates

We study a semiparametric estimation method for the random effects logistic regression when there is auxiliary covariate information about the main exposure variable. We extend the semiparametric estimator of Pepe and Fleming (1991, Journal of the American Statistical Association 86, 108-113) to the random effects model using the best linear unbiased prediction approach of Henderson (1975, Biometrics 31, 423-448). The method can be used to handle the missing covariate or mismeasured covariate data problems in a variety of real applications. Simulation study results show that the proposed method outperforms the existing methods. We analyzed a data set from the Collaborative Perinatal Project using the proposed method and found that the use of DDT increases the risk of preterm births among U.S. children.     

Efficient Estimation for Rank‐Based Regression with Clustered Data

Summary Rank‐based inference is widely used because of its robustness. This article provides optimal rank‐based estimating functions in analysis of clustered data with random cluster effects. The extensive simulation studies carried out to evaluate the performance of the proposed method demonstrate that it is robust to outliers and is highly efficient given the existence of strong cluster correlations. The performance of the proposed method is satisfactory even when the correlation structure is misspecified, or when heteroscedasticity in variance is present. Finally, a real dataset is analyzed for illustration.     

Postural taping applied to the low back influences kinematics and EMG activity during patient transfer in physical therapists with chronic low back pain

We examined the influence of the application of postural taping on the kinematics of the lumbo–pelvic–hip complex, electromyographic (EMG) activity of back extensor muscles, and the rating of perceived exertion (RPE) in the low back during patient transfer. In total, 19 male physical therapists with chronic low back pain performed patient transfers with and without the application of postural taping on the low back. The kinematics of the lumbo–pelvic–hip complex and EMG activity of the erector spinae were recorded using a synchronized 3-D motion capture system and surface EMG. RPE was measured using Borg’s CR-10 scale. Differences in kinematic data, EMG activity, and RPE between the two conditions were analyzed using a paired t-test. Peak angle and range of motion (ROM) of lumbar flexion, EMG activity of the erector spinae, and RPE decreased significantly, while peak angle and ROM of pelvic anterior tilt and hip flexion increased significantly during patient transfer under the postural taping condition versus no taping (p < 0.05). These findings suggest that postural taping can change back extensor muscle activity and RPE as well as the kinematics of the lumbo–pelvic–hip complex in physical therapists with chronic low back pain during patient transfer.     

Simultaneous Inference and Bias Analysis for Longitudinal Data with Covariate Measurement Error and Missing Responses

Summary Longitudinal data arise frequently in medical studies and it is common practice to analyze such data with generalized linear mixed models. Such models enable us to account for various types of heterogeneity, including between‐ and within‐subjects ones. Inferential procedures complicate dramatically when missing observations or measurement error arise. In the literature, there has been considerable interest in accommodating either incompleteness or covariate measurement error under random effects models. However, there is relatively little work concerning both features simultaneously. There is a need to fill up this gap as longitudinal data do often have both characteristics. In this article, our objectives are to study simultaneous impact of missingness and covariate measurement error on inferential procedures and to develop a valid method that is both computationally feasible and theoretically valid. Simulation studies are conducted to assess the performance of the proposed method, and a real example is analyzed with the proposed method.     

Shrinkage estimation for functional principal component scores with application to the population kinetics of plasma folate

We present the application of a nonparametric method to performing functional principal component analysis for functional curve data that consist of measurements of a random trajectory for a sample of subjects. This design typically consists of an irregular grid of time points on which repeated measurements are taken for a number of subjects. We introduce shrinkage estimates for the functional principal component scores that serve as the random effects in the model. Scatterplot smoothing methods are used to estimate the mean function and covariance surface of this model. We propose improved estimation in the neighborhood of and at the diagonal of the covariance surface, where the measurement errors are reflected. The presence of additive measurement errors motivates shrinkage estimates for the functional principal component scores. Shrinkage estimates are developed through best linear prediction and in a generalized version, aiming at minimizing one-curve-leave-out prediction error. The estimation of individual trajectories combines data obtained from that individual as well as all other individuals. We apply our methods to new data regarding the analysis of the level of 14C-folate in plasma as a function of time since dosing of healthy adults with a small tracer dose of 14C-folic acid. A time transformation was incorporated to handle design irregularity concerning the time points on which the measurements were taken. The proposed methodology, incorporating shrinkage and data-adaptive features, is seen to be well suited for describing population kinetics of 14C-folate-specific activity and random effects, and can also be applied to other functional data analysis problems.     

Gut microbial dysbiosis is associated with development and progression of radiation enteritis during pelvic radiotherapy

Radiation enteritis (RE) is the most common complication of radiotherapy for pelvic irradiation receivers. Herein we investigated the alterations in gut microbial profiles and their association with enteritis in patients undergoing pelvic radiotherapy. Faecal samples were collected from 18 cervical cancer patients during radiotherapy. Microbiota profiles were characterized based on 16S rRNA sequencing using the Illumina HiSeq platform. Epithelial inflammatory response was evaluated using bacterial‐epithelial co‐cultures. Dysbiosis was observed among patients with RE, which was characterized by significantly reduced α‐diversity but increased β‐diversity, relative higher abundance of Proteobacteria and Gammaproteobacteria and lower abundance of Bacteroides. Coprococcus was clearly enriched prior to radiotherapy in patients who later developed RE. Metastat analysis further revealed unique grade‐related microbial features, such as more abundant Virgibacillus and Alcanivorax in patients with mild enteritis. Additionally, using bacterial‐epithelial co‐cultures, RE patient‐derived microbiota induced epithelial inflammation and barrier dysfunction, enhanced TNF‐α and IL‐1β expression compared with control microbiota. Taken together, we define the overall picture of gut microbiota in patients with RE. Our results suggest that dysbiosis of gut microbiota may contribute to development and progression of RE. Gut microbiota can offer a set of biomarkers for prediction, disease activity evaluation and treatment selection in RE.     

Definition of the margin of major coronary artery bifurcations during radiotherapy with electrocardiograph-gated 4D-CT

PurposeThe aim was to measure the cardiac motion-induced displacements of major coronary artery bifurcations utilizing electrocardiography (ECG)-gated four-dimensional computed tomography (4D-CT) and to determine the margin of coronary artery bifurcations.MethodsThirty-seven female patients who underwent retrospective ECG-gated 4D-CT in inspiratory breath hold (IBH) were enrolled. The left main coronary artery bifurcation (LM), the obtuse marginal branch bifurcation (OM), the first diagonal branch bifurcation (D₁), the second diagonal branch bifurcation (D₂), the caudal portion of the left anterior descending branch (APX), the first right ventricular artery bifurcation (V) and the acute marginal branch bifurcation (AM) were contoured. The center of the contour of the coronary arterial bifurcations at end systole was defined as the standard, and the margin were then calculated.ResultsThe margin in the left–right (LR), cranio-caudal (CC), and anterior-posterior (AP) coordinates were as follows: LM 3, 3, and 3 mm; D₁ 6, 3, and 3 mm; D₂ 3, 3, and 3 mm; APX 4, 4, and 4 mm; OM 4, 6, and 5 mm; V 6, 8, and 7 mm; and AM 6, 8, and 7 mm, respectively.ConclusionCoronary artery bifurcations should be considered a separate organ at risk (OAR), and different margin should be provided due to the differences resulting from motion displacement. The maximum margin in the LR, CC, and AP coordinates of left coronary artery bifurcations were 6, 6, and 5 mm, and those of the right coronary artery bifurcations were 6, 8, and 7 mm, respectively.