The Influence of Radiographic Phenotype and Smoking Status on Peripheral Blood Biomarker Patterns in Chronic Obstructive Pulmonary Disease

Background: Chronic obstructive pulmonary disease (COPD) is characterized by both airway remodeling and parenchymal destruction. The identification of unique biomarker patterns associated with airway dominant versus parenchymal dominant patterns would support the existence of unique phenotypes representing independent biologic processes. A cross-sectional study was performed to examine the association of serum biomarkers with radiographic airway and parenchymal phenotypes of COPD. Methodology/Principal Findings: Serum from 234 subjects enrolled in a CT screening cohort was analyzed for 33 cytokines and growth factors using a multiplex protein array. The association of serum markers with forced expiratory volume in one second percent predicted (FEV1%) and quantitative CT measurements of airway thickening and emphysema was assessed with and without stratification for current smoking status. Significant associations were found with several serum inflammatory proteins and measurements of FEV1%, airway thickening, and parenchymal emphysema independent of smoking status. The association of select analytes with airway thickening and emphysema was independent of FEV1%. Furthermore, the relationship between other inflammatory markers and measurements of physiologic obstruction or airway thickening was dependent on current smoking status. Conclusions/Significance: Airway and parenchymal phenotypes of COPD are associated with unique systemic serum biomarker profiles. Serum biomarker patterns may provide a more precise classification of the COPD syndrome, provide insights into disease pathogenesis and identify targets for novel patient-specific biological therapies. © 2009 Bon et al

Treatment Planning and Volumetric Response Assessment for Yttrium-90 Radioembolization: Semiautomated Determination of Liver Volume and Volume of Tumor Necrosis in Patients with Hepatic Malignancy

PurposeThe primary purpose of this study was to demonstrate intraobserver/interobserver reproducibility for novel semiautomated measurements of hepatic volume used for Yttrium-90 dose calculations as well as whole-liver and necrotic-liver (hypodense/nonenhancing) tumor volume after radioembolization. The secondary aim was to provide initial comparisons of tumor volumetric measurements with linear measurements, as defined by Response Evaluation Criteria in Solid Tumors criteria, and survival outcomes.MethodsBetween 2006 and 2009, 23 consecutive radioembolization procedures were performed for 14 cases of hepatocellular carcinoma and 9 cases of hepatic metastases. Baseline and follow-up computed tomography obtained 1 month after treatment were retrospectively analyzed. Three observers measured liver, whole-tumor, and tumor-necrosis volumes twice using semiautomated software.ResultsGood intraobserver/interobserver reproducibility was demonstrated (intraclass correlation [ICC] > 0.9) for tumor and liver volumes. Semiautomated measurements of liver volumes were statistically similar to those obtained with manual tracing (ICC = 0.868), but they required significantly less time to perform (p < 0.0001, ICC = 0.088). There was a positive association between change in linear tumor measurements and whole-tumor volume (p < 0.0001). However, linear measurements did not correlate with volume of necrosis (p > 0.05). Dose, change in tumor diameters, tumor volume, and necrotic volume did not correlate with survival (p > 0.05 in all instances). However, Kaplan-Meier curves suggest that a >10% increase in necrotic volume correlated with survival (p = 0.0472).ConclusionSemiautomated volumetric analysis of liver, whole-tumor, and tumor-necrosis volume can be performed with good intraobserver/interobserver reproducibility. In this small retrospective study, measurements of tumor necrosis were suggested to correlate with survival

Image Texture Characterization Using the Discrete Orthonormal S-Transform

We present a new efficient approach for characterizing image texture based on a recently published discrete, orthonormal space-frequency transform known as the DOST. We develop a frequency-domain implementation of the DOST in two dimensions for the case of dyadic frequency sampling. Then, we describe a rapid and efficient approach to obtain local spatial frequency information for an image and show that this information can be used to characterize the horizontal and vertical frequency patterns in synthetic images. Finally, we demonstrate that DOST components can be combined to obtain a rotationally invariant set of texture features that can accurately classify a series of texture patterns. The DOST provides the computational efficiency and multi-scale information of wavelet transforms, while providing texture features in terms of Fourier frequencies. It outperforms leading wavelet-based texture analysis methods

Erratum: Semiâ automated pulmonary nodule interval segmentation using the NLST data

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144604/1/mp12905_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144604/2/mp12905.pd