Predicting the Presence of Clinically Significant Thyroid Cancer using Ultrasound Imaging

项目总结/摘要 在创建利用计算机视觉算法实现医疗自动化的工具方面， 图像分析这些算法大多是针对自然图像而开发的，通常是单静态的 可以单独处理的图像。然而，医学图像通常是研究的一部分， 在进行诊断时与其他临床数据一起考虑的各种视图和方向。 三维卷积神经网络（CNN）可以在图像均匀分布时部分解决这个问题。 但是许多医学成像模态，诸如超声（US）、荧光透视和活检成像， 不同的方向和不规则的间距。图卷积网络（GCN）具有解决 这个问题，因为他们概括了CNN的假设工作在任意结构的图形。 超声（US）中的自动甲状腺结节检测是这种基于图形的方法可以 有很大的影响。甲状腺癌的发病率在过去的三十年里增加了两倍，估计成本 2019年将达到180 - 210亿美元。US是首选的成像模式，其由不同的2D图像组成。 位置和方向。美国的读数往往是模糊和主观的，这导致了稳定的 在过去20年中进行的活组织检查数量增加。据估计，大约三分之一的 在美国进行的甲状腺活检手术在医学上是不必要的，导致未满足的需求 用于非侵入性诊断测试，可以可靠地确定哪些结节需要活检。 R21的研究目标是开发一种新的基于图形的方法来利用空间信息 包含在成像研究中，将与生物标志物和其他已知的风险因素相结合。我们图 该模型将能够更全面地检测甲状腺癌，并预测未来的癌症。 侵略，两者都有空间定位的解释。GCN特征将用于预测体素级别的癌症 怀疑，从而使一种新的方法，用于执行"成像活检"。最后，体素级怀疑图 将被聚合成患者水平的定量成像生物标志物，并与临床数据相结合， 用于进行风险分层的多峰列线图。

PROJECT SUMMARY/ABSTRACT There has been significant work in creating tools that leverage computer vision algorithms to automate medical image analysis. Most of these algorithms have been developed for natural images, which are usually single static images that can be treated individually. However, medical images are usually part of a study that may include various views and orientations that are considered together with other clinical data when making a diagnosis. Three dimensional convolution neural networks (CNN) can address this issue in part when images are evenly spaced, but many medical imaging modalities such as ultrasound (US), fluoroscopy, and biopsy imaging have variable orientations and irregular spacing. Graph convolutional networks (GCN) have the potential to address this issue as they generalize the assumptions of CNNs to work on arbitrarily structured graphs. Automatic thyroid nodule detection in ultrasound (US) is one application that such a graph-based approach could have a large impact. The thyroid cancer incidence rate has tripled in the past thirty years, with an estimated cost of $18-21 billon in 2019. US is the imaging modality of choice, which consists of multiple 2D images of different locations and orientations. US readings are often vague and subjective in nature, which has resulted in a steady increase in the number of biopsies performed over the past 20 years. It is estimated that about one-third of all thyroid biopsy procedures performed in the United States are medically unnecessary, leading to the unmet need for noninvasive diagnostic tests that can reliably identify which nodules require a biopsy. The research objective of this R21 is to develop a new graph-based approach to leverage spatial information contained within imaging studies that will be combined with biomarkers and other known risk factors. Our graph model will enable more complete detection of thyroid cancer, as well as the prediction of future cancer aggression, both with spatially localized explanations. GCN features will be used to predict voxel-level cancer suspicion, thereby enabling a novel method for performing “imaging biopsy.” Finally, voxel-level suspicion maps will be aggregated into patient-level quantitative imaging biomarkers and combined with clinical data to create a multimodal nomogram for performing risk stratification.