Predicting the Presence of Clinically Significant Thyroid Cancer using Ultrasound Imaging

使用超声成像预测临床上显着的甲状腺癌的存在

• 批准号: 10604367
• 负责人: William F Speier
• 金额: $ 17.55万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-04 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10604367
• 关键词: 3-Dimensional; Address; Aggressive behavior; Algorithms; Architecture; Attention; Biological Markers; Biopsy; Cancer Detection; Cancer Patient; Classification; Clinical; Clinical Data; Complex; Computer Vision Systems; Data; Data Set; Detection; Diagnosis; Diagnostic Procedure; Diagnostic tests; Disease; Electronic Health Record; Evaluation; Fluoroscopy; Functional disorder; Future; Goals; Graph; Image; Image Analysis; Incidence; Individual; Indolent; Location; Machine Learning; Malignant Neoplasms; Malignant neoplasm of thyroid; Manuals; Maps; Medical; Medical Imaging; Methods; Modeling; Morbidity - disease rate; Nature; Nodule; Nomograms; Pathology; Patient risk; Patients; Pattern; Physiological; Play; Probability; Procedures; Protocols documentation; Research; Risk; Risk Factors; Role; Series; Signal Transduction; Structure; Techniques; Thyroid Gland; Thyroid Nodule; Training; Ultrasonography; United States; Work; body system; cancer imaging; cancer risk; clinical imaging; clinically significant; computer aided detection; convolutional neural network; cost estimate; deep learning; detection platform; functional outcomes; graph neural network; image registration; imaging biomarker; imaging modality; imaging study; innovation; mortality; multimodality; network models; noninvasive diagnosis; novel; patient stratification; predictive modeling; prevent; quantitative imaging; radiologist; risk stratification; tool; treatment planning; ultrasound; unnecessary treatment

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.

项目摘要/摘要 在创建利用计算机视觉算法的工具方面已经做出了重要的工作 图像分析。这些算法中的大多数都是针对自然图像开发的，这些算法通常是单个静态图像 可以单独处理的图像。但是，医学图像通常是可能包括的研究的一部分 进行诊断时，各种视图和方向以及其他临床数据都被考虑。 当图像均匀时，三维卷积神经网络（CNN）可以部分解决此问题 间隔，但是许多医学成像方式，例如超声（US），荧光镜和活检成像 可变方向和不规则间距。图卷积网络（GCN）有潜力 当他们概括了CNN的假设以在任意结构化图上工作时，此问题。 超声（US）中的自动甲状腺结节检测是一种基于图的方法 影响很大。甲状腺癌的事件发生率在过去三十年中增加了两倍，估计成本 2019年的$ 18-21 Billon。美国是选择的成像方式，由多个不同的2D图像组成 位置和方向。美国读物通常是投票和主观的，这导致了稳定的 在过去20年中进行的活检数量增加。据估计，大约三分之一 在美国进行的甲状腺活检程序在医学上是不必要的，导致未满足的需求 对于无创诊断测试，可以可靠地识别哪些结节需要活检。 该R21的研究目标是开发一种新的基于图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危险因素相结合。我们的图 模型将使甲状腺癌以及未来癌症的预测更加完整地检测 侵略性，都具有空间局部的解释。 GCN功能将用于预测体素级癌 可疑，从而实现了一种进行“成像活检”的新方法。最后，体素级可疑地图 将汇总为患者级定量成像生物标志物，并与临床数据相结合以创建一个 进行风险分层的多模式列图。