Tensor-based Dictionary Learning for Imaging Biomarkers

 描述（由申请人提供）：作为系统生物医学的中心概念，生物标志物是生理和病理状态和活动的多尺度、多样性和相互关联的指标。在过去的十年中，这一领域的研究一直是活跃和令人兴奋的，包括基于成像生物标志物的成像信息学。在此背景下，正在进行全基因组关联研究，以建立基因型和表型生物标志物之间的基本联系，但主要挑战是，沿着这一方向取得的进展远远没有达到人们的广泛预期。一个关键的观察是，虽然基因组测序和表观遗传分析的数据激增，但在大多数情况下，医学图像特征仍然是主观的，或者仅以经典方式定义，这似乎是基因型和表型世界之间不合理的不平衡。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，肺癌不是一种而是多种疾病。它在每个患者甚至每个肿瘤部位都是不同的，具有压倒性的非线性和动力学。很明显，需要全面，适应性和个性化的治疗来赢得与肺癌的斗争。与此相一致的是，复杂的，而不是简单的，生物标志物的研究不仅是有益的，而且在癌症研究中是必要的，成像信息学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的挖掘，以便建立相关和预测模型。R21项目背后的一般假设是，可以在断层扫描数据中解锁新的表型信息，以显着提高肺癌CT筛查的灵敏度和特异性。该项目的总体目标是开发一种基于张量的字典学习方法，用于提取CT成像生物标志物，并优化基于张量的局部线性嵌入，以使用这些生物标志物区分CT肺筛查结果。该项目的主要创新之处在于将张量分解、字典学习、压缩感知、低剂量重建、机器学习、局部线性嵌入、超级计算和大数据挖掘等技术协同集成为一种全新的成像信息学方法，可以看作是基因组测序中的“表型组测序”。该项目成功完成后，将证明所确定的成像生物标志物有助于显著降低肺部CT扫描的假阳性率，同时保持假阴性率不变，还将有助于准确地对肺癌进行分期，并非侵入性地监测癌症进展和治疗反应。同样重要的是这个项目的技术意义。如果建立，将产生持久的影响，在整个成像信息学领域。

 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.