Biomedical subjects

Chris Fraley

Publications and source records attributed to Chris Fraley.

3 recordsLinked to original sources

PubMed2006

Idiopathic pulmonary fibrosis: prognostic value of changes in physiology and six-minute-walk test.

RATIONALE AND HYPOTHESIS: Idiopathic pulmonary fibrosis is a fatal disease with a variable rate of progression. We hypothesized that changes in distance walked and quantity of desaturation during a six-minute-walk test (6MWT) would add prognostic information to changes in FVC or diffusing capacity for carbon monoxide. METHODS: One hundred ninety-seven patients with idiopathic pulmonary fibrosis were evaluated. Desaturation during the 6MWT was associated with increased mortality even if a threshold of 88% was not reached. Baseline walk distance predicted subsequent walk distance but was not a reliable predictor of subsequent mortality in multivariate survival models. The predictive ability of serial changes in physiology varied when patients were stratified by the presence/absence of desaturation < or = 88% during a baseline 6MWT. For patients with a baseline saturation < or = 88% during a 6MWT, the strongest observed predictor of mortality was serial change in diffusing capacity for carbon monoxide. For patients with saturation > 88% during their baseline walk test, serial decreases in FVC and increases in desaturation area significantly predicted subsequent mortality, whereas decreases in walk distance and in diffusing capacity for carbon monoxide displayed less consistent statistical evidence of increasing mortality in our patients. CONCLUSION: These data highlight the importance of stratifying patients by degree of desaturation during a 6MWT before attributing prognostic value to serial changes in other physiologic variables.

Aged↗

PubMed2005

Donuts, scratches and blanks: robust model-based segmentation of microarray images.

MOTIVATION: Inner holes, artifacts and blank spots are common in microarray images, but current image analysis methods do not pay them enough attention. We propose a new robust model-based method for processing microarray images so as to estimate foreground and background intensities. The method starts with a very simple but effective automatic gridding method, and then proceeds in two steps. The first step applies model-based clustering to the distribution of pixel intensities, using the Bayesian Information Criterion (BIC) to choose the number of groups up to a maximum of three. The second step is spatial, finding the large spatially connected components in each cluster of pixels. The method thus combines the strengths of the histogram-based and spatial approaches. It deals effectively with inner holes in spots and with artifacts. It also provides a formal inferential basis for deciding when the spot is blank, namely when the BIC favors one group over two or three. RESULTS: We apply our methods for gridding and segmentation to cDNA microarray images from an HIV infection experiment. In these experiments, our method had better stability across replicates than a fixed-circle segmentation method or the seeded region growing method in the SPOT software, without introducing noticeable bias when estimating the intensities of differentially expressed genes. AVAILABILITY: spotSegmentation, an R language package implementing both the gridding and segmentation methods is available through the Bioconductor project (http://www.bioconductor.org). The segmentation method requires the contributed R package MCLUST for model-based clustering (http://cran.us.r-project.org). CONTACT: fraley@stat.washington.edu.

Algorithms↗

PubMedDate not supplied

Model-based region-of-interest selection in dynamic breast MRI.

Magnetic resonance imaging (MRI) is emerging as a powerful tool for the diagnosis of breast abnormalities. Dynamic analysis of the temporal pattern of contrast uptake has been applied in differential diagnosis of benign and malignant lesions to improve specificity. Selecting a region of interest (ROI) is an almost universal step in the process of examining the contrast uptake characteristics of a breast lesion. We propose an ROI selection method that combines model-based clustering of the pixels with Bayesian morphology, a new statistical image segmentation method. We then investigate tools for subsequent analysis of signal intensity time course data in the selected region. Results on a database of 19 patients indicate that the method provides informative segmentations and good detection rates.

Bayes Theorem↗