Article

Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression.

1] Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA. [2] Center for Molecular Imaging, University of Michigan, Ann Arbor, Michigan, USA.
Nature medicine (Impact Factor: 28.05). 10/2012; DOI: 10.1038/nm.2971
Source: PubMed

ABSTRACT Chronic obstructive pulmonary disease (COPD) is increasingly being recognized as a highly heterogeneous disorder, composed of varying pathobiology. Accurate detection of COPD subtypes by image biomarkers is urgently needed to enable individualized treatment, thus improving patient outcome. We adapted the parametric response map (PRM), a voxel-wise image analysis technique, for assessing COPD phenotype. We analyzed whole-lung computed tomography (CT) scans acquired at inspiration and expiration of 194 individuals with COPD from the COPDGene study. PRM identified the extent of functional small airways disease (fSAD) and emphysema as well as provided CT-based evidence that supports the concept that fSAD precedes emphysema with increasing COPD severity. PRM is a versatile imaging biomarker capable of diagnosing disease extent and phenotype while providing detailed spatial information of disease distribution and location. PRM's ability to differentiate between specific COPD phenotypes will allow for more accurate diagnosis of individual patients, complementing standard clinical techniques.

Download full-text

Full-text

Available from: Meilan K Han, Jun 20, 2015
0 Followers
 · 
130 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This work is motivated by the modelling of ventilation and deformation in the lung for understanding the biomechanics of respiratory diseases. The main contribution is the derivation and implementation of a lung model that tightly couples a poroelastic model of lung parenchyma to an airway fluid network. The poroelastic model approximates the porous structure of lung parenchyma using a continuum model that allows us to naturally model changes in physiology by spatially varying material parameters, whilst conserving mass and momentum within the tissue. The proposed model will also take advantage of realistic deformation boundary conditions obtained from image registration, to drive the simulation. A finite element method is presented to discretize the equations in a monolithic way to ensure convergence of the nonlinear problem. To demonstrate the coupling between the poroelastic medium and the network flow model numerical simulations on a realistic lung geometry are presented. These numerical simulations are able to reproduce global physiological realistic measurements. We also investigate the effect of airway constriction and tissue weakening on the ventilation, tissue stress and alveolar pressure distribution and highlight the interdependence of ventilation and deformation.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The extent of pulmonary emphysema is commonly estimated from CT images by computing the proportional area of voxels below a predefined attenuation threshold. However, the reliability of this approach is limited by several factors that affect the CT intensity distributions in the lung. This work presents a novel method for emphysema quantification, based on parametric modeling of intensity distributions in the lung and a hidden Markov measure field model to segment emphysematous regions. The framework adapts to the characteristics of an image to ensure a robust quantification of emphysema under varying CT imaging protocols and differences in parenchymal intensity distributions due to factors such as inspiration level. Compared to standard approaches, the present model involves a larger number of parameters, most of which can be estimated from data, to handle the variability encountered in lung CT scans. The method was used to quantify emphysema on a cohort of 87 subjects, with repeated CT scans acquired over a time period of 8 years using different imaging protocols. The scans were acquired approximately annually, and the data set included a total of 365 scans. The results show that the emphysema estimates produced by the proposed method have very high intrasubject correlation values. By reducing sensitivity to changes in imaging protocol, the method provides a more robust estimate than standard approaches. In addition, the generated emphysema delineations promise great advantages for regional analysis of emphysema extent and progression, possibly advancing disease subtyping.
    04/2014; 33(7). DOI:10.1109/TMI.2014.2317520
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: To evaluate diffusion weighted MRI (DW-MR) as a response metric for assessment of neoadjuvant chemotherapy (NAC) in patients with primary breast cancer using prospective multi-center trials which provided MR scans along with clinical outcome information. A total of 39 patients with locally advanced breast cancer accrued from three different prospective clinical trials underwent DW-MR examination prior to and at 3-7 days (Hull University), 8-11 days (University of Michigan) and 35 days (NeoCOMICE) post-treatment initiation. Thirteen patients, 12 of which participated in treatment response study, from UM underwent short interval (<1hr) MRI examinations, referred to as "test-retest" for examination of repeatability. To further evaluate stability in ADC measurements, a thermally controlled diffusion phantom was used to assess repeatability of diffusion measurements. MRI sequences included contrast-enhanced T1-weighted, when appropriate, and DW images acquired at b-values of 0 and 800 s/mm2. Histogram analysis and a voxel-based analytical technique, the Parametric Response Map (PRM), were used to derive diffusion response metrics for assessment of treatment response prediction. Mean tumor apparent diffusion coefficient (ADC) values generated from patient test-retest examinations were found to be very reproducible (|ΔADC|<0.1x10-3mm2/s). This data was used to calculate the 95% CI from the linear fit of tumor voxel ADC pairs of co-registered examinations (±0.45x10-3mm2/s) for PRM analysis of treatment response. Receiver operating characteristic analysis identified the PRM metric to be predictive of outcome at the 8-11 (AUC = 0.964, p = 0.01) and 35 day (AUC = 0.770, p = 0.05) time points (p<.05) while whole-tumor ADC changes where significant at the later 35 day time interval (AUC = 0.825, p = 0.02). This study demonstrates the feasibility of performing a prospective analysis of DW-MRI as a predictive biomarker of NAC in breast cancer patients. In addition, we provide experimental evidence supporting the use of sensitive analytical tools, such as PRM, for evaluating ADC measurements.
    PLoS ONE 03/2015; 10(3):e0122151. DOI:10.1371/journal.pone.0122151 · 3.53 Impact Factor