Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Adult (1)
- Aged (1)
- Bioinformatics (1)
- Biomechanical Phenomena (1)
- Breath Holding (1)
-
- Breath-holds (1)
- Cardiovascular magnetic resonance (1)
- DENSE (1)
- Data analysis (1)
- Female (1)
- Generalized Linear Model (1)
- Graphs (1)
- Healthy Volunteers (1)
- Heart Ventricles (1)
- Humans (1)
- Image Interpretation, Computer-Assisted (1)
- Integrative p-value (1)
- Left ventricular torsion (1)
- Magnetic Resonance Imaging, Cine (1)
- Male (1)
- MicroRNA (1)
- Middle Aged (1)
- Mixed Model (1)
- PSA (1)
- Pathway analysis (1)
- Predicted Duration (1)
- Predictive Value of Tests (1)
- Reproducibility of Results (1)
- Respiratory Mechanics (1)
- Respiratory navigator gating (1)
- Publication
- Publication Type
Articles 1 - 3 of 3
Full-Text Articles in Biostatistics
Using A Respiratory Navigator Significantly Reduces Variability When Quantifying Left Ventricular Torsion With Cardiovascular Magnetic Resonance, Sean M. Hamlet, Christopher M. Haggerty, Jonathan D. Suever, Gregory J. Wehner, Kristin N. Andres, David K. Powell, Richard J. Charnigo, Brandon K. Fornwalt
Using A Respiratory Navigator Significantly Reduces Variability When Quantifying Left Ventricular Torsion With Cardiovascular Magnetic Resonance, Sean M. Hamlet, Christopher M. Haggerty, Jonathan D. Suever, Gregory J. Wehner, Kristin N. Andres, David K. Powell, Richard J. Charnigo, Brandon K. Fornwalt
Electrical and Computer Engineering Faculty Publications
Background: Left ventricular (LV) torsion is an important indicator of cardiac function that is limited by high inter-test variability (50% of the mean value). We hypothesized that this high inter-test variability is partly due to inconsistent breath-hold positions during serial image acquisitions, which could be significantly improved by using a respiratory navigator for cardiovascular magnetic resonance (CMR) based quantification of LV torsion.
Methods: We assessed respiratory-related variability in measured LV torsion with two distinct experimental protocols. First, 17 volunteers were recruited for CMR with cine displacement encoding with stimulated echoes (DENSE) in which a respiratory navigator was used to measure …
Integrative Pathway Analysis Pipeline For Mirna And Mrna Data, Diana Mabel Diaz Herrera
Integrative Pathway Analysis Pipeline For Mirna And Mrna Data, Diana Mabel Diaz Herrera
Wayne State University Theses
The identification of pathways that are involved in a particular phenotype helps us understand the underlying biological processes. Traditional pathway analysis techniques aim to infer the impact on individual pathways using only mRNA levels. However, recent studies showed that gene expression alone is unable to capture the whole picture of biological phenomena. At the same time, MicroRNAs (miRNAs) are newly discovered gene regulators that have shown to play an important role in diagnosis, and prognosis for different types of diseases. Current pathway analysis techniques do not take miRNAs into consideration. In this project, we investigate the effect of integrating miRNA …
Studying The Optimal Scheduling For Controlling Prostate Cancer Under Intermittent Androgen Suppression, Sunil K. Dhar, Hans R. Chaudhry, Bruce G. Bukiet, Zhiming Ji, Nan Gao, Thomas W. Findley
Studying The Optimal Scheduling For Controlling Prostate Cancer Under Intermittent Androgen Suppression, Sunil K. Dhar, Hans R. Chaudhry, Bruce G. Bukiet, Zhiming Ji, Nan Gao, Thomas W. Findley
Harvard University Biostatistics Working Paper Series
This retrospective study shows that the majority of patients’ correlations between PSA and Testosterone during the on-treatment period is at least 0.90. Model-based duration calculations to control PSA levels during off-treatment are provided. There are two pairs of models. In one pair, the Generalized Linear Model and Mixed Model are both used to analyze the variability of PSA at the individual patient level by using the variable “Patient ID” as a repeated measure. In the second pair, Patient ID is not used as a repeated measure but additional baseline variables are included to analyze the variability of PSA.