Outlier identification in radiation therapy knowledge-based planning: A study of pelvic cases.

PURPOSE: The purpose of this study was to apply statistical metrics to identify outliers and to investigate the impact of outliers on knowledge-based planning in radiation therapy of pelvic cases. We also aimed to develop a systematic workflow for identifying and analyzing geometric and dosimetric outliers. METHODS: Four groups (G1-G4) of pelvic plans were sampled in this study. These include the following three groups of clinical IMRT cases: G1 (37 prostate cases), G2 (37 prostate plus lymph node cases) and G3 (37 prostate bed cases). Cases in G4 were planned in accordance with dynamic-arc radiation therapy procedure and include 10 prostate cases in addition to those from G1. The workflow was separated into two parts: 1. identifying geometric outliers, assessing outlier impact, and outlier cleaning; 2. identifying dosimetric outliers, assessing outlier impact, and outlier cleaning. G2 and G3 were used to analyze the effects of geometric outliers (first experiment outlined below) while G1 and G4 were used to analyze the effects of dosimetric outliers (second experiment outlined below). A baseline model was trained by regarding all G2 cases as inliers. G3 cases were then individually added to the baseline model as geometric outliers. The impact on the model was assessed by comparing leverages of inliers (G2) and outliers (G3). A receiver-operating-characteristic (ROC) analysis was performed to determine the optimal threshold. The experiment was repeated by training the baseline model with all G3 cases as inliers and perturbing the model with G2 cases as outliers. A separate baseline model was trained with 32 G1 cases. Each G4 case (dosimetric outlier) was subsequently added to perturb the model. Predictions of dose-volume histograms (DVHs) were made using these perturbed models for the remaining 5 G1 cases. A Weighted Sum of Absolute Residuals (WSAR) was used to evaluate the impact of the dosimetric outliers. RESULTS: The leverage of inliers and outliers was significantly different. The Area-Under-Curve (AUC) for differentiating G2 (outliers) from G3 (inliers) was 0.98 (threshold: 0.27) for the bladder and 0.81 (threshold: 0.11) for the rectum. For differentiating G3 (outlier) from G2 (inlier), the AUC (threshold) was 0.86 (0.11) for the bladder and 0.71 (0.11) for the rectum. Significant increase in WSAR was observed in the model with 3 dosimetric outliers for the bladder (P \u3c 0.005 with Bonferroni correction), and in the model with only 1 dosimetric outlier for the rectum (P \u3c 0.005). CONCLUSIONS: We established a systematic workflow for identifying and analyzing geometric and dosimetric outliers, and investigated statistical metrics for outlier detection. Results validated the necessity for outlier detection and clean-up to enhance model quality in clinical practice

Nonparametric Covariate Adjustment for Receiver Operating Characteristic Curves

The accuracy of a diagnostic test is typically characterised using the receiver operating characteristic (ROC) curve. Summarising indexes such as the area under the ROC curve (AUC) are used to compare different tests as well as to measure the difference between two populations. Often additional information is available on some of the covariates which are known to influence the accuracy of such measures. We propose nonparametric methods for covariate adjustment of the AUC. Models with normal errors and non-normal errors are discussed and analysed separately. Nonparametric regression is used for estimating mean and variance functions in both scenarios. In the general noise case we propose a covariate-adjusted Mann-Whitney estimator for AUC estimation which effectively uses available data to construct working samples at any covariate value of interest and is computationally efficient for implementation. This provides a generalisation of the Mann-Whitney approach for comparing two populations by taking covariate effects into account. We derive asymptotic properties for the AUC estimators in both settings, including asymptotic normality, optimal strong uniform convergence rates and MSE consistency. The usefulness of the proposed methods is demonstrated through simulated and real data examples

Designing a sampling scheme to reveal correlations between weeds and soil properties at multiple spatial scales

Weeds tend to aggregate in patches within fields and there is evidence that this is partly owing to variation in soil properties. Because the processes driving soil heterogeneity operate at different scales, the strength of the relationships between soil properties and weed density would also be expected to be scale-dependent. Quantifying these effects of scale on weed patch dynamics is essential to guide the design of discrete sampling protocols for mapping weed distribution. We have developed a general method that uses novel within-field nested sampling and residual maximum likelihood (REML) estimation to explore scale-dependent relationships between weeds and soil properties. We have validated the method using a case study of Alopecurus myosuroides in winter wheat. Using REML, we partitioned the variance and covariance into scale-specific components and estimated the correlations between the weed counts and soil properties at each scale. We used variograms to quantify the spatial structure in the data and to map variables by kriging. Our methodology successfully captured the effect of scale on a number of edaphic drivers of weed patchiness. The overall Pearson correlations between A. myosuroides and soil organic matter and clay content were weak and masked the stronger correlations at >50 m. Knowing how the variance was partitioned across the spatial scales we optimized the sampling design to focus sampling effort at those scales that contributed most to the total variance. The methods have the potential to guide patch spraying of weeds by identifying areas of the field that are vulnerable to weed establishment

Photorefractive planar waveguides in BaTiO₃ fabricated by ion-beam implantation

For the first time to our knowledge, photorefractive properties have been observed in planar waveguides fabricated by the technique of ion-beam implantation in BaTiO3 single crystals. The implantation was carried out by using 1.5 MeV H+ ions at a dose of 10-16 ions/cm2. For a given input power, a decrease in the effective photo-refractive two-beam coupling response time of ≥102 has been observed, owing to a combination of optical confinement within the waveguide and possible modification of charge-transport properties induced through implantation. Experiments carried out on the two-beam coupling gain show that the gain direction has been reversed in the waveguide compared with that of the bulk crystal

Landmark Papers no. 8: Burgess, T.M. & Webster, R. 1980. Optimal interpolation and isarithmic mapping of soil properties. I. The semi-variogram and punctual kriging. Journal of Soil Science, 31, 315-331: Commentary on the impact of Burgess & Webster (1980a)

This landmark paper by Burgess & Webster (1980a) signalled a new era in the spatial mapping of the soil. The emergence of pedometrics as a distinct subdiscipline of soil science was a gradual process, and had its roots in earlier studies than this one, but if one publication is to mark the start of pedometrics, then this is it

Waveguide mutually pumped phase conjugators

The operation of the Bridge Mutually Pumped Phase Conjugator is reported in a planar waveguide structure in photorefractive BaTiO3. The waveguide was fabricated by the technique of ion implantation. using 1.5 MeV H+ at a dose of 1016 ions/cm2. An order of magnitude decrease in response time is observed in the waveguide as compared to typical values obtained in bulk crystals, probably resulting from a combination of the optical confinement within the waveguide, and possibly modification of the charge transport properties induced by the implantation process

A note on Youden's J and its cost ratio

<p>Abstract</p> <p>Background</p> <p>The Youden index, the sum of sensitivity and specificity minus one, is an index used for setting optimal thresholds on medical tests.</p> <p>Discussion</p> <p>When using this index, one implicitly uses decision theory with a ratio of misclassification costs which is equal to one minus the prevalence proportion of the disease. It is doubtful whether this cost ratio truly represents the decision maker's preferences. Moreover, in populations with a different prevalence, a selected threshold is optimal with reference to a different cost ratio.</p> <p>Summary</p> <p>The Youden index is not a truly optimal decision rule for setting thresholds because its cost ratio varies with prevalence. Researchers should look into their cost ratio and employ it in a decision theoretic framework to obtain genuinely optimal thresholds.</p

Total quality: its origins and its future

This article discusses how an efficient organization is characterized by its knowledge and learning capability. It examines the learning ability of the human animal, the logic of continuous, never-ending improvement, the catalysis of learning by scientific method, and Grosseteste's Inductive-Deductive iteration related to the Shewhart Cycle. Total Quality is seen as the democratization and comprehensive diffusion of Scientific Method and involves extrapolating knowledge from experiment to reality which is the essence of the idea of robustness. Finally, barriers to progress are discussed and the question of how these can be tackled is considered