The publication data currently available has been vetted by Vanderbilt faculty, staff, administrators and trainees. The data itself is retrieved directly from NCBI's PubMed and is automatically updated on a weekly basis to ensure accuracy and completeness.
If you have any questions or comments, please contact us.
HYPOTHESIS - Image-guided cochlear implant (CI) programming can improve hearing outcomes for pediatric CI recipients.
BACKGROUND - CIs have been highly successful for children with severe-to-profound hearing loss, offering potential for mainstreamed education and auditory-oral communication. Despite this, a significant number of recipients still experience poor speech understanding, language delay, and, even among the best performers, restoration to normal auditory fidelity is rare. Although significant research efforts have been devoted to improving stimulation strategies, few developments have led to significant hearing improvement over the past two decades. Recently introduced techniques for image-guided CI programming (IGCIP) permit creating patient-customized CI programs by making it possible, for the first time, to estimate the position of implanted CI electrodes relative to the nerves they stimulate using CT images. This approach permits identification of electrodes with high levels of stimulation overlap and to deactivate them from a patient's map. Previous studies have shown that IGCIP can significantly improve hearing outcomes for adults with CIs.
METHODS - The IGCIP technique was tested for 21 ears of 18 pediatric CI recipients. Participants had long-term experience with their CI (5 mo to 13 yr) and ranged in age from 5 to 17 years old. Speech understanding was assessed after approximately 4 weeks of experience with the IGCIP map.
RESULTS - Using a two-tailed Wilcoxon signed-rank test, statistically significant improvement (p < 0.05) was observed for word and sentence recognition in quiet and noise, as well as pediatric self-reported quality-of-life (QOL) measures.
CONCLUSION - Our results indicate that image guidance significantly improves hearing and QOL outcomes for pediatric CI recipients.
RATIONALE - Screening for lung cancer using low-dose computed tomography (CT) reduces lung cancer mortality. However, in addition to a high rate of benign nodules, lung cancer screening detects a large number of indolent cancers that generally belong to the adenocarcinoma spectrum. Individualized management of screen-detected adenocarcinomas would be facilitated by noninvasive risk stratification.
OBJECTIVES - To validate that Computer-Aided Nodule Assessment and Risk Yield (CANARY), a novel image analysis software, successfully risk stratifies screen-detected lung adenocarcinomas based on clinical disease outcomes.
METHODS - We identified retrospective 294 eligible patients diagnosed with lung adenocarcinoma spectrum lesions in the low-dose CT arm of the National Lung Screening Trial. The last low-dose CT scan before the diagnosis of lung adenocarcinoma was analyzed using CANARY blinded to clinical data. Based on their parametric CANARY signatures, all the lung adenocarcinoma nodules were risk stratified into three groups. CANARY risk groups were compared using survival analysis for progression-free survival.
MEASUREMENTS AND MAIN RESULTS - A total of 294 patients were included in the analysis. Kaplan-Meier analysis of all the 294 adenocarcinoma nodules stratified into the Good, Intermediate, and Poor CANARY risk groups yielded distinct progression-free survival curves (P < 0.0001). This observation was confirmed in the unadjusted and adjusted (age, sex, race, and smoking status) progression-free survival analysis of all stage I cases.
CONCLUSIONS - CANARY allows the noninvasive risk stratification of lung adenocarcinomas into three groups with distinct post-treatment progression-free survival. Our results suggest that CANARY could ultimately facilitate individualized management of incidentally or screen-detected lung adenocarcinomas.
Abdominal segmentation on clinically acquired computed tomography (CT) has been a challenging problem given the inter-subject variance of human abdomens and complex 3-D relationships among organs. Multi-atlas segmentation (MAS) provides a potentially robust solution by leveraging label atlases via image registration and statistical fusion. We posit that the efficiency of atlas selection requires further exploration in the context of substantial registration errors. The selective and iterative method for performance level estimation (SIMPLE) method is a MAS technique integrating atlas selection and label fusion that has proven effective for prostate radiotherapy planning. Herein, we revisit atlas selection and fusion techniques for segmenting 12 abdominal structures using clinically acquired CT. Using a re-derived SIMPLE algorithm, we show that performance on multi-organ classification can be improved by accounting for exogenous information through Bayesian priors (so called context learning). These innovations are integrated with the joint label fusion (JLF) approach to reduce the impact of correlated errors among selected atlases for each organ, and a graph cut technique is used to regularize the combined segmentation. In a study of 100 subjects, the proposed method outperformed other comparable MAS approaches, including majority vote, SIMPLE, JLF, and the Wolz locally weighted vote technique. The proposed technique provides consistent improvement over state-of-the-art approaches (median improvement of 7.0% and 16.2% in DSC over JLF and Wolz, respectively) and moves toward efficient segmentation of large-scale clinically acquired CT data for biomarker screening, surgical navigation, and data mining.
Copyright © 2015 Elsevier B.V. All rights reserved.
Selective and iterative method for performance level estimation (SIMPLE) is a multi-atlas segmentation technique that integrates atlas selection and label fusion that has proven effective for radiotherapy planning. Herein, we revisit atlas selection and fusion techniques in the context of segmenting the spleen in metastatic liver cancer patients with possible splenomegaly using clinically acquired computed tomography (CT). We re-derive the SIMPLE algorithm in the context of the statistical literature, and show that the atlas selection criteria rest on newly presented principled likelihood models. We show that SIMPLE performance can be improved by accounting for exogenous information through Bayesian priors (so called context learning). These innovations are integrated with the joint label fusion approach to reduce the impact of correlated errors among selected atlases. In a study of 65 subjects, the spleen was segmented with median Dice similarity coefficient of 0.93 and a mean surface distance error of 2.2 mm.
Cochlear Implants (CI) are surgically implanted neural prosthetic devices used to treat severe-to-profound hearing loss. Recent studies have suggested that hearing outcomes with CIs are correlated with the location where individual electrodes in the implanted electrode array are placed, but techniques proposed for determining electrode location have been too coarse and labor intensive to permit detailed analysis on large numbers of datasets. In this paper, we present a fully automatic snake-based method for accurately localizing CI electrodes in clinical post-implantation CTs. Our results show that average electrode localization errors with the method are 0.21 millimeters. These results indicate that our method could be used in future large scale studies to analyze the relationship between electrode position and hearing outcome, which potentially could lead to technological advances that improve hearing outcomes with CIs.
INTRODUCTION - Lung cancer remains the leading cause of cancer-related deaths in the United States and worldwide. Adenocarcinoma is the most common type of lung cancer and encompasses lesions with widely variable clinical outcomes. In the absence of noninvasive risk stratification, individualized patient management remains challenging. Consequently a subgroup of pulmonary nodules of the lung adenocarcinoma spectrum is likely treated more aggressively than necessary.
METHODS - Consecutive patients with surgically resected pulmonary nodules of the lung adenocarcinoma spectrum (lesion size ≤3 cm, 2006-2009) and available presurgical high-resolution computed tomography (HRCT) imaging were identified at Mayo Clinic Rochester. All cases were classified using an unbiased Computer-Aided Nodule Assessment and Risk Yield (CANARY) approach based on the quantification of presurgical HRCT characteristics. CANARY-based classification was independently correlated to postsurgical progression-free survival.
RESULTS - CANARY analysis of 264 consecutive patients identified three distinct subgroups. Independent comparisons of 5-year disease-free survival (DFS) between these subgroups demonstrated statistically significant differences in 5-year DFS, 100%, 72.7%, and 51.4%, respectively (p = 0.0005).
CONCLUSIONS - Noninvasive CANARY-based risk stratification identifies subgroups of patients with pulmonary nodules of the adenocarcinoma spectrum characterized by distinct clinical outcomes. This technique may ultimately improve the current expert opinion-based approach to the management of these lesions by facilitating individualized patient management.
Label fusion is a critical step in many image segmentation frameworks (e.g., multi-atlas segmentation) as it provides a mechanism for generalizing a collection of labeled examples into a single estimate of the underlying segmentation. In the multi-label case, typical label fusion algorithms treat all labels equally - fully neglecting the known, yet complex, anatomical relationships exhibited in the data. To address this problem, we propose a generalized statistical fusion framework using hierarchical models of rater performance. Building on the seminal work in statistical fusion, we reformulate the traditional rater performance model from a multi-tiered hierarchical perspective. The proposed approach provides a natural framework for leveraging known anatomical relationships and accurately modeling the types of errors that raters (or atlases) make within a hierarchically consistent formulation. Herein, the primary contributions of this manuscript are: (1) we provide a theoretical advancement to the statistical fusion framework that enables the simultaneous estimation of multiple (hierarchical) confusion matrices for each rater, (2) we highlight the amenability of the proposed hierarchical formulation to many of the state-of-the-art advancements to the statistical fusion framework, and (3) we demonstrate statistically significant improvement on both simulated and empirical data. Specifically, both theoretically and empirically, we show that the proposed hierarchical performance model provides substantial and significant accuracy benefits when applied to two disparate multi-atlas segmentation tasks: (1) 133 label whole-brain anatomy on structural MR, and (2) orbital anatomy on CT.
Copyright © 2014 Elsevier B.V. All rights reserved.
A cochlear implant (CI) is a neural prosthetic device that restores hearing by directly stimulating the auditory nerve using an electrode array that is implanted in the cochlea. In CI surgery, the surgeon accesses the cochlea and makes an opening where he/she inserts the electrode array blind to internal structures of the cochlea. Because of this, the final position of the electrode array relative to intra-cochlear anatomy is generally unknown. We have recently developed an approach for determining electrode array position relative to intra-cochlear anatomy using a pre- and a post-implantation CT. The approach is to segment the intra-cochlear anatomy in the pre-implantation CT, localize the electrodes in the post-implantation CT, and register the two CTs to determine relative electrode array position information. Currently, we are using this approach to develop a CI programming technique that uses patient-specific spatial information to create patient-customized sound processing strategies. However, this technique cannot be used for many CI users because it requires a pre-implantation CT that is not always acquired prior to implantation. In this study, we propose a method for automatic segmentation of intra-cochlear anatomy in post-implantation CT of unilateral recipients, thus eliminating the need for pre-implantation CTs in this population. The method is to segment the intra-cochlear anatomy in the implanted ear using information extracted from the normal contralateral ear and to exploit the intra-subject symmetry in cochlear anatomy across ears. To validate our method, we performed experiments on 30 ears for which both a pre- and a post-implantation CT are available. The mean and the maximum segmentation errors are 0.224 and 0.734mm, respectively. These results indicate that our automatic segmentation method is accurate enough for developing patient-customized CI sound processing strategies for unilateral CI recipients using a post-implantation CT alone.
Copyright © 2014 Elsevier B.V. All rights reserved.
PURPOSE - High-resolution chest computed tomography (HRCT) is essential in the characterization of interstitial lung disease. The HRCT features of some diseases can be diagnostic. Longitudinal monitoring with HRCT can assess progression of interstitial lung disease; however, subtle changes in the volume and character of abnormalities can be difficult to assess. Accuracy of diagnosis can be dependent on expertise and experience of the radiologist, pathologist, or clinician. Quantitative analysis of thoracic HRCT has the potential to determine the extent of disease reproducibly, classify the types of abnormalities, and automate the diagnostic process.
MATERIALS AND METHODS - Novel software that utilizes histogram signatures to characterize pulmonary parenchyma was used to analyze chest HRCT data, including retrospective processing of clinical CT scans and research data from the Lung Tissue Research Consortium. Additional information including physiological, pathologic, and semiquantitative radiologist assessment was available to allow comparison of quantitative results, with visual estimates of the disease, physiological parameters, and measures of disease outcome.
RESULTS - Quantitative analysis results were provided in regional volumetric quantities for statistical analysis and a graphical representation. These results suggest that quantitative HRCT analysis can serve as a biomarker with physiological, pathologic, and prognostic significance.
CONCLUSIONS - It is likely that quantitative analysis of HRCT can be used in clinical practice as a means to aid in identifying a probable diagnosis, stratifying prognosis in early disease, and consistently determining progression of the disease or response to therapy. Further optimization of quantitative techniques and longitudinal analysis of well-characterized subjects would be helpful in validating these methods.
INTRODUCTION - Pulmonary nodules of the adenocarcinoma spectrum are characterized by distinctive morphological and radiologic features and variable prognosis. Noninvasive high-resolution computed tomography-based risk stratification tools are needed to individualize their management.
METHODS - Radiologic measurements of histopathologic tissue invasion were developed in a training set of 54 pulmonary nodules of the adenocarcinoma spectrum and validated in 86 consecutively resected nodules. Nodules were isolated and characterized by computer-aided analysis, and data were analyzed by Spearman correlation, sensitivity, and specificity and the positive and negative predictive values.
RESULTS - Computer-aided nodule assessment and risk yield (CANARY) can noninvasively characterize pulmonary nodules of the adenocarcinoma spectrum. Unsupervised clustering analysis of high-resolution computed tomography data identified nine unique exemplars representing the basic radiologic building blocks of these lesions. The exemplar distribution within each nodule correlated well with the proportion of histologic tissue invasion, Spearman R = 0.87, p < 0.0001 and 0.89 and p < 0.0001 for the training and the validation set, respectively. Clustering of the exemplars in three-dimensional space corresponding to tissue invasion and lepidic growth was used to develop a CANARY decision algorithm that successfully categorized these pulmonary nodules as "aggressive" (invasive adenocarcinoma) or "indolent" (adenocarcinoma in situ and minimally invasive adenocarcinoma). Sensitivity, specificity, positive predictive value, and negative predictive value of this approach for the detection of aggressive lesions were 95.4, 96.8, 95.4, and 96.8%, respectively, in the training set and 98.7, 63.6, 94.9, and 87.5%, respectively, in the validation set.
CONCLUSION - CANARY represents a promising tool to noninvasively risk stratify pulmonary nodules of the adenocarcinoma spectrum.