Tensor-based Dictionary Learning for Imaging Biomarkers

用于成像生物标志物的基于张量的字典学习

基本信息

批准号：
9143765
负责人：
Hengyong Yu
金额：
$ 23.32万
依托单位：
UNIVERSITY OF MASSACHUSETTS LOWELL
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-11 至 2019-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9143765
关键词：
Algorithms Area Big Data Biological Markers Chile Clinical Clinical Trials Computer software Data Data Set Databases Development Diagnostic tests Dictionary Disease Dose Epigenetic Process Frequencies Goals Grant Health Individual Learning Link Lung CAT Scan Machine Learning Malignant Neoplasms Malignant neoplasm of lung Medical Imaging Methodology Methods Mining Monitor Patients Physiological Pilot Projects Process Research Screening Result Sensitivity and Specificity Site Speed Staging System Techniques Testing X-Ray Computed Tomography analog anticancer research base clinical application data mining genome sequencing genome wide association study imaging biomarker imaging informatics improved in vivo imaging innovation learning strategy low-dose spiral CT lung cancer screening novel phenome phenotypic biomarker predictive modeling radiologist reconstruction screening software development treatment response tumor tumor progression

项目摘要

DESCRIPTION (provided by applicant): As a central concept in systems biomedicine, biomarkers are multi-scale, diverse, and inter-connected indicators of physiological and pathological states and activities. Over the past decade, the research in this area has been active and exciting, including imaging informatics based on imaging biomarkers. In this context, the genome-wide association studies are being performed to establish fundamental links between genotypic and phenotypic biomarkers but a prime challenge is that progress along this direction has been far from what was widely expected. A critical observation is that while data are exploding from genome sequencing and epigenetic analysis, in most cases medical image features are still subjective or only defined in classic fashions, which seems an unreasonable imbalance between genotypic and phenotypic worlds. Lung cancer screening is an emerging CT application and an opportunity to identify imaging biomarkers. Like other cancers, lung cancer is not one but many diseases. It is different in each patient and even in each tumor site with overwhelming nonlinearity and dynamics. It is crystal-clear that comprehensive, adaptive and individualized therapies are needed to win the battle against lung cancer. Being consistent to this big picture, research on sophisticated, instead of simplistic, biomarkers is not only helpful but also necessary in cancer research, and imaging informatics must perform exclusive and intelligent mining through rich in vivo imaging data for biomarkers so that correlative and predictive models could be established. The general hypothesis behind this R21 project is that new phenotypic information can be unlocked in tomographic data to improve sensitivity and specificity significantly in lung cancer CT screening. The overall goal of this project is to develp a tensor-based dictionary learning approach for extraction of CT imaging biomarkers, and optimize a tensor-based locally linear embedding to use these biomarkers for differentiation between CT lung screening results. The major innovation of this project is to synergistically integrate tensor decomposition, dictionary learning, compressive sensing, low-dose reconstruction, machine learning, locally linear embedding, super-computing and big data mining into a brand-new imaging informatics approach, which can be viewed as "phenome sequencing" in analog of genome sequencing. Upon the successful completion of this project, the identified imaging biomarkers will have been demonstrated instrumental in reducing the false positive rate significantly for lung CT scans while the false negative rate is kept constant.It will also help accurately stage lung cancers and non-invasively monitor cancer progression and therapeutic response. Equally important is the technical significance of this project. If it is established, a lasting impact will be generated on the field of imaging informatics at large.

描述（由应用程序提供）：作为系统生物医学的中心概念，生物标志物是物理和病理状态和活动的多尺度，潜水员和相互联系的指标。在过去的十年中，该领域的研究一直很活跃和令人兴奋，包括基于成像生物标志物的成像信息。在这种情况下，正在进行全基因组关联研究以在基因型和表型生物标志物之间建立基本联系，但是一个主要的挑战是，沿着这一方向的进步远非广泛预期。一个关键的观察结果是，尽管数据正在从基因组测序和表观遗传分析中爆炸，但在大多数情况下，医学图像特征仍然是主观的或仅在经典时尚中定义的，这似乎是基因型和表型世界之间不合理的失衡。肺癌筛查是一种新兴的CT应用，也是识别成像生物标志物的机会。像其他癌症一样，肺癌不是一种，而是许多疾病。在每个患者中，甚至在每个肿瘤部位都具有压倒性的非线性和动力学。清楚的是，需要全面，适应性和个性化的疗法来赢得与肺癌的斗争。与这一大局一致，对复杂的研究，而不是简单的生物标志物不仅有用，而且在癌症研究中也是必要的，并且成像信息的信息必须通过丰富的生物标志物的体内成像数据进行独家和智能采矿，以便可以确定正确和预测的模型。该R21项目背后的一般假设是，可以在层析成像数据中解锁新的表型信息，以显着提高肺癌CT筛查中的敏感性和特异性。该项目的总体目标是开发基于张量的字典学习方法来提取CT成像生物标志物，并优化基于张量的本地线性嵌入以使用这些生物标志物来区分CT肺筛查结果。该项目的主要创新是针对共同集成的张量分解，字典学习，压缩传感器，低剂量重建，机器学习，本地线性嵌入，超级计算和大数据挖掘和大型数据挖掘中的大型成像信息方法，可以将其视为“基因组序列类似物”中的“现象测序”。成功完成该项目后，已确定的成像生物标志物将有助于降低肺CT扫描的假阳性率，而假阴性阴性率保持恒定。它还将有助于准确地上场肺癌，并非侵入性地监测癌症的进展和治疗反应。同样重要的是该项目的技术意义。如果建立，将对整个成像信息领域产生持久的影响。