SCH: Proactive Health Monitoring Using Individualized Analysis of Tissue Elastic*

描述（由申请人提供）：现有研究表明，组织弹性可能与癌症的侵袭性相关。基于变形图像配准的结果，该项目研究了跟踪受外力和几何约束的器官运动的可能性，从而推导出患者特定的组织弹性参数，用于主动健康监测。该探索性研究项目的目标是（1）基于对大量癌症患者的广泛研究开发计算框架，以便在一对医学图像上使用耦合的生物力学模拟优化框架来准确估计患者特定组织弹性（可能来自超声波、乳房X线照相术、计算机断层扫描、磁共振成像或其他成像技术）;（2）检查不同区域中的组织弹性与相应区域中已知/诊断的癌症的侵袭性之间的潜在关联;（3）基于恢复的患者特异性组织弹性和其他解释变量导出癌症分期/分级的预测模型;（4）为“高危”人群设计基于个体化组织弹性分析的健康监测系统 更容易患上癌症该提案描述了一项真正雄心勃勃的努力和一个大胆的愿景，该愿景建立在研究人员先前的科学成就和强大的证书基础上，可能将现有的实践转变为对有癌症风险的个人进行更积极，预防性，循证的健康监测。这项研究有望取得几项重大科学进展。这些包括用于在没有力施加和/或力感测设备的情况下自动提取组织弹性参数的非侵入性的基于图像的技术的新算法、用于生存分析的新型回归模型和推断程序、新型力感测设备、用于生存分析的新型回归模型和推断程序、基于患者特异性组织弹性参数的癌症分期和分级的新型预测模型、以及基于个体组织弹性沿着其它变量的用于危险群体的健康监测系统。

DESCRIPTION (provided by applicant): 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.