Valen E. Johnson

A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets.

Expert landmark correspondences are widely reported for evaluating deformable image registration (DIR) spatial accuracy. In this report, we present a framework for objective evaluation of DIR spatial accuracy using large sets of expert-determined landmark point pairs. Large samples (>1100) of pulmonary landmark point pairs were manually generated for five cases. Estimates of inter- and intra-observer variation were determined from repeated registration. Comparative evaluation of DIR spatial accuracy was performed for two algorithms, a gradient-based optical flow algorithm and a landmark-based moving least-squares algorithm. The uncertainty of spatial error estimates was found to be inversely proportional to the square root of the number of landmark point pairs and directly proportional to the standard deviation of the spatial errors. Using the statistical properties of this data, we performed sample size calculations to estimate the average spatial accuracy of each algorithm with 95% confidence intervals within a 0.5 mm range. For the optical flow and moving least-squares algorithms, the required sample sizes were 1050 and 36, respectively. Comparative evaluation based on fewer than the required validation landmarks results in misrepresentation of the relative spatial accuracy. This study demonstrates that landmark pairs can be used to assess DIR spatial accuracy within a narrow uncertainty range.

Revised standards for statistical evidence

Significance The lack of reproducibility of scientific research undermines public confidence in science and leads to the misuse of resources when researchers attempt to replicate and extend fallacious research findings. Using recent developments in Bayesian hypothesis testing, a root cause of nonreproducibility is traced to the conduct of significance tests at inappropriately high levels of significance. Modifications of common standards of evidence are proposed to reduce the rate of nonreproducibility of scientific research by a factor of 5 or greater. Recent advances in Bayesian hypothesis testing have led to the development of uniformly most powerful Bayesian tests, which represent an objective, default class of Bayesian hypothesis tests that have the same rejection regions as classical significance tests. Based on the correspondence between these two classes of tests, it is possible to equate the size of classical hypothesis tests with evidence thresholds in Bayesian tests, and to equate P values with Bayes factors. An examination of these connections suggest that recent concerns over the lack of reproducibility of scientific studies can be attributed largely to the conduct of significance tests at unjustifiably high levels of significance. To correct this problem, evidence thresholds required for the declaration of a significant finding should be increased to 25–50:1, and to 100–200:1 for the declaration of a highly significant finding. In terms of classical hypothesis tests, these evidence standards mandate the conduct of tests at the 0.005 or 0.001 level of significance.

Segmentation, registration, and measurement of shape variation via image object shape

A model of object shape by nets of medial and boundary primitives is justified as richly capturing multiple aspects of shape and yet requiring representation space and image analysis work proportional to the number of primitives. Metrics are described that compute an object representations prior probability of local geometry by reflecting variabilities in the nets node and link parameter values, and that compute a likelihood function measuring the degree of match of an image to that object representation. A paradigm for image analysis of deforming such a model to optimize a posteriori probability is described, and this paradigm is shown to be usable as a uniform approach for object definition, object-based registration between images of the same or different imaging modalities, and measurement of shape variation of an abnormal anatomical object, compared with a normal anatomical object. Examples of applications of these methods in radiotherapy, surgery, and psychiatry are given.

Bayesian reconstruction and use of anatomical a priori information for emission tomography

A Bayesian method is presented for simultaneously segmenting and reconstructing emission computed tomography (ECT) images and for incorporating high-resolution, anatomical information into those reconstructions. The anatomical information is often available from other imaging modalities such as computed tomography (CT) or magnetic resonance imaging (MRI). The Bayesian procedure models the ECT radiopharmaceutical distribution as consisting of regions, such that radiopharmaceutical activity is similar throughout each region. It estimates the number of regions, the mean activity of each region, and the region classification and mean activity of each voxel. Anatomical information is incorporated by assigning higher prior probabilities to ECT segmentations in which each ECT region stays within a single anatomical region. This approach is effective because anatomical tissue type often strongly influences radiopharmaceutical uptake. The Bayesian procedure is evaluated using physically acquired single-photon emission computed tomography (SPECT) projection data and MRI for the three-dimensional (3-D) Hoffman brain phantom. A clinically realistic count level is used. A cold lesion within the brain phantom is created during the SPECT scan but not during the MRI to demonstrate that the estimation procedure can detect ECT structure that is not present anatomically.

Specific lymphocyte subsets predict response to adoptive cell therapy using expanded autologous tumor-infiltrating lymphocytes in metastatic melanoma patients.

Purpose: Adoptive cell therapy (ACT) using autologous tumor-infiltrating lymphocytes (TIL) is a promising treatment for metastatic melanoma unresponsive to conventional therapies. We report here on the results of an ongoing phase II clinical trial testing the efficacy of ACT using TIL in patients with metastatic melanoma and the association of specific patient clinical characteristics and the phenotypic attributes of the infused TIL with clinical response. Experimental Design: Altogether, 31 transiently lymphodepleted patients were treated with their expanded TIL, followed by two cycles of high-dose interleukin (IL)-2 therapy. The effects of patient clinical features and the phenotypes of the T cells infused on the clinical response were determined. Results: Overall, 15 of 31 (48.4%) patients had an objective clinical response using immune-related response criteria (irRC) with 2 patients (6.5%) having a complete response. Progression-free survival of more than 12 months was observed for 9 of 15 (60%) of the responding patients. Factors significantly associated with the objective tumor regression included a higher number of TIL infused, a higher proportion of CD8+ T cells in the infusion product, a more differentiated effector phenotype of the CD8+ population, and a higher frequency of CD8+ T cells coexpressing the negative costimulation molecule “B- and T-lymphocyte attenuator” (BTLA). No significant difference in the telomere lengths of TIL between responders and nonresponders was identified. Conclusion: These results indicate that the immunotherapy with expanded autologous TIL is capable of achieving durable clinical responses in patients with metastatic melanoma and that CD8+ T cells in the infused TIL, particularly differentiated effectors cells and cells expressing BTLA, are associated with tumor regression. Clin Cancer Res; 18(24); 6758–70. ©2012 AACR.

Redefine Statistical Significance

We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of new discoveries.

Do some taxa have better domain-general cognition than others? A meta- analysis of nonhuman primate studies

Although much recent attention has focused on identifying domain-specific taxonomic differences in cognition, little effort has been directed towards investigating whether domain-general differences also exist. We therefore conducted a meta-analysis of published nonhuman primate cognition studies, testing the prediction that some taxa outperform others across a range of testing situations. First, within each of nine experimental paradigms with interspecific variation, we grouped studies by their procedures and the characteristics of their study subjects. Then, using Bayesian latent variable methods, we tested whether taxonomic differences consistently held within or across paradigms. No genus performed especially well within particular paradigms, but genera differed significantly in overall performance. In addition, there was evidence of variation at higher taxonomic levels; most notably, great apes significantly outperformed other lineages. These results cannot be readily explained by perceptual biases or any other contextual confound and instead suggest that primate taxa differ in some kind of domain-general ability.

Image restoration using Gibbs priors: boundary modeling, treatment of blurring, and selection of hyperparameter

The authors propose a Bayesian model for the restoration of images based on counts of emitted photons. The model treats blurring within the context of an incomplete data problem and utilizes a Gibbs prior to model the spatial correlation of neighboring regions. The Gibbs prior includes line sites to account for boundaries between regions, and the line sites are assigned continuous values to permit efficient estimation using a method called iterative conditional averages. In addition, the effect of blurring in masking differences between images and the effects of misspecifying the amount of blurring are discussed. >

Fully Bayesian estimation of Gibbs hyperparameters for emission computed tomography data

In recent years, many investigators have proposed Gibbs prior models to regularize images reconstructed from emission computed tomography data. Unfortunately, hyperparameters used to specify Gibbs priors can greatly influence the degree of regularity imposed by such priors and, as a result, numerous procedures have been proposed to estimate hyperparameter values, from observed image data. Many of these, procedures attempt to maximize the joint posterior distribution on the image scene. To implement these methods, approximations to the joint posterior densities are required, because the dependence of the Gibbs partition function on the hyperparameter values is unknown. Here, the authors use recent results in Markov chain Monte Carlo (MCMC) sampling to estimate the relative values of Gibbs partition functions and using these values, sample from joint posterior distributions on image scenes. This allows for a fully Bayesian procedure which does not fix the hyperparameters at some estimated or specified value, but enables uncertainty about these values to be propagated through to the estimated intensities. The authors utilize realizations from the posterior distribution for determining credible regions for the intensity of the emission source. The authors consider two different Markov random field (MRF) models-the power model and a line-site model. As applications they estimate the posterior distribution of source intensities from computer simulated data as well as data collected from a physical single photon emission computed tomography (SPECT) phantom.

Videofluoroscopic assessment of dysphagia in children with severe spastic cerebral palsy

Very little has been published about the characteristics and sequelae of dysphagia in children with neurological impairment. The swallowing difficulties encountered by children with spastic cerebral palsy are particularly debilitating and potentially lethal. However, aggressive evaluation and management of their feeding is typically deferred until they are medically or nutritionally compromised. Reports of the use of videofluoroscopy to analyze the swallowing patterns and presence or absence of aspiration in such children are rare. This paper describes the histories and analyzes the videofluorographic swallow studies of 22 patients with the primary diagnosis of severe spastic cerebral palsy. The ages of the subjects ranged from 7 months to 19 years. All had severe dysphagia and were slow, inefficient eaters. Fifteen patients (68.2%) demonstrated significant silent aspiration during their swallow study. Analysis of specific features of their swallowing patterns indicated that decreased or poorly coordinated pharyngeal motility was predictive of silent aspiration. Moderately to severely impaired oral-motor coordination was indicative of severity of feeding complications. Our data suggeest that early diagnostic workup, including baseline and comparative videofluoroscopic swallow studies, could be helpful in managing the feeding difficulties in these children and preventing chronic aspiration, malnutrition, and unpleasant lengthy mealtimes.