Existing studies suggest that tissue elasticity is possibly correlated with the aggressiveness of cancers. Based on results from deformable image registration, the proposed project investigates the possibility of tracking the organs movement subject to external forces and geometric constraints, thereby deducing patient-specific tissue elasticity parameters for proactive health monitoring. The objectives of this exploratory research project are (1) to develop a computational framework based on extensive studies of a large cohort of cancer patients, in order to accurately estimate patent-specific tissu elasticity using a coupled biomechanical simulation-optimization framework on a pair of medical images (possibly from ultrasound, mammography, computed tomography scan, magnetic resonance imaging, or other imaging technologies);(2) to examine potential association between tissue elasticity in different regions with aggressiveness of know/diagnosed cancer in the corresponding regions;(3) to derive predictive models for cancer staging/grading based on recovered patient-specific tissue elasticity and other explanatory variables;(4) to design a health monitoring system based on individualized analysis of tissue elasticity for 'at-risk'groups who are more likely to develop cancers. This proposal describes a truly ambitious effort and a bold vision that is built upon the investigators'prior scientific accomplishments and strong credentials to potentially transform existing practice to more proactive, preventive, evidence-based health monitoring for individuals at risk of developing cancers. This research is expected to make several major scientific advances. These include new algorithms for non-invasive, image-based techniques for automatic extraction of tissue elasticity parameters without force applications and/or force sensing devices, novel regression models and inference procedures for survival analysis, new force sensing devices, novel regression models and inference procedures for survival analysis, new predictive models for cancer staging and grading based on patient-specific tissue elasticity parameters, and a health monitoring system for at-risk groups based on individual tissue elasticity along with other variables.
Other than health monitoring, the patient-specific tissue parameters can be incorporated into medical simulators to perform patient-specific surgical planning, compute desired force-feedback for tele-surgery, design and prototype medical devices, and conduct virtual surgical training. The statistical inference techniques developed can be applicable to genetic epidemiology, health economics, and bioinformatics.
|Cornea, Emil; Zhu, Hongtu; Kim, Peter et al. (2017) Regression Models on Riemannian Symmetric Spaces. J R Stat Soc Series B Stat Methodol 79:463-482|
|Li, Jialiang; Huang, Chao; Zhu, Hongtu (2017) A Functional Varying-Coefficient Single-Index Model for Functional Response Data. J Am Stat Assoc 112:1169-1181|
|Hyun, Jung Won; Li, Yimei; Huang, Chao et al. (2016) STGP: Spatio-temporal Gaussian process models for longitudinal neuroimaging data. Neuroimage 134:550-562|
|Rao, Shangbang; Ibrahim, Joseph G; Cheng, Jian et al. (2016) SR-HARDI: Spatially Regularizing High Angular Resolution Diffusion Imaging. J Comput Graph Stat 25:1195-1211|
|Luo, Xinchao; Zhu, Lixing; Zhu, Hongtu (2016) Single-index varying coefficient model for functional responses. Biometrics 72:1275-1284|
|Shen, Dan; Shen, Haipeng; Zhu, Hongtu et al. (2016) The Statistics and Mathematics of High Dimension Low Sample Size Asymptotics. Stat Sin 26:1747-1770|
|Yang, Shan; Lin, Ming C (2016) MaterialCloning: Acquiring Elasticity Parameters from Images for Medical Applications. IEEE Trans Vis Comput Graph 22:2122-35|
|Shen, Dan; Zhu, Hongtu (2015) Spatially Weighted Principal Component Regression for High-Dimensional Prediction. Inf Process Med Imaging 24:758-69|
|Lu, Zhao-Hua; Chow, Sy-Miin; Sherwood, Andrew et al. (2015) Bayesian Analysis of Ambulatory Blood Pressure Dynamics with Application to Irregularly Spaced Sparse Data. Ann Appl Stat 9:1601-1620|
|Lee, Eunjee; Zhu, Hongtu; Kong, Dehan et al. (2015) BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER'S DISEASE. Ann Appl Stat 9:2153-2178|
Showing the most recent 10 out of 18 publications