Improving Radiologist Detection of Lung Nodules with CAD
使用 CAD 改进放射科医生对肺结节的检测
基本信息
- 批准号:7367836
- 负责人:
- 金额:$ 51.77万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2005
- 资助国家:美国
- 起止时间:2005-04-15 至 2011-05-31
- 项目状态:已结题
- 来源:
- 关键词:AgreementAlgorithmsAppearanceBlood VesselsCancer EtiologyCessation of lifeCharacteristicsChestClinicalClinical assessmentsComputer AssistedDataDetectionDevelopmentDiscriminationHumanImageInterobserver VariabilityInvasiveLaboratoriesLungLung noduleMalignant neoplasm of lungMedical centerMethodsNew YorkNodulePatient Care ManagementPatientsPerformanceReaderRecruitment ActivityResolutionRoleSchemeShapesSpecificitySystemTechniquesTestingThickTimeTrainingUnited StatesUniversitiesWorkX-Ray Computed Tomographybasecohortdetectorimprovedlung imagingnovelradiologist
项目摘要
DESCRIPTION (provided by applicant):
Lung cancer is the leading cause of cancer-related deaths in the United States. Both primary and metastatic lung cancer most commonly manifest as pulmonary nodules, which are readily visualized radiographically. CT scanning is currently the most sensitive non-invasive means available for detecting pulmonary nodules, but has suffered from limited sensitivity and high interobserver variability, particularly of smaller nodules. The recent development of multi-detector-row CT (MDCT) allows imaging of the lungs with unprecedented three-dimensional spatial resolution, up to 10 times greater than single-row CT systems within a single less than 10 second breathhold. For radiologists to harness the higher spatial resolution of MDCT data to improve lung nodule detection, they must overcome two key challenges - (1) time efficient interpretation of the 300-600 images that result from high-resolution MDCT scans of the lungs and (2) improve nodule detection sensitivity without losing specificity when examining 1-mm thick CT sections, where lung nodule and blood vessel discrimination is more difficult due to the greater similarity of their appearance when compared to thick-section acquisitions. The focus of this proposal, therefore, is to develop an optimized approach toward the detection of lung cancer with CT. Our specific aims are:
1. To develop an automatic technique for detecting pulmonary nodules from lung CT data.
2. To determine the improvement in radiologist sensitivity and interobserver agreement for the detection of pulmonary nodules in patients suspected of having them when computer-aided detection (CAD) results are considered following initial radiologist assessment of CT images. We will optimize our CAD system by training on CT scans of pulmonary nodules obtained from two medical centers in different regions of the United States, and we will show that CAD can be as effective as a second radiologist in improving a radiologist's ability to detect pulmonary nodules on CT scans without substantially increasing falsely positive detections. Upon completion of this work, we will have enabled radiologists to take better advantage of the improved data available from MDCT scanners and substantially improve their ability to detect pulmonary nodules on CT scans, and thereby contribute to improvements in the management and care of patients with lung cancer.
描述(由申请人提供):
肺癌是美国癌症相关死亡的主要原因。原发性和转移性肺癌最常见的表现为肺结节,这是很容易可视化放射线。CT扫描是目前可用于检测肺结节的最灵敏的非侵入性手段,但其灵敏度有限且观察者间变异性高,特别是对于较小的结节。多探测器行CT(MDCT)的最新发展允许以前所未有的三维空间分辨率对肺部进行成像,在单个不到10秒的屏气内,该分辨率比单排CT系统高10倍。对于放射科医生来说,利用MDCT数据的更高空间分辨率来改善肺结节检测,他们必须克服两个关键挑战-(1)对由肺部的高分辨率MDCT扫描产生的300-600个图像的时间有效解释,以及(2)在检查1 mm厚的CT切片时提高结节检测灵敏度而不丧失特异性,其中,与厚截面采集相比,由于肺结节和血管的外观的更大相似性,所以区分肺结节和血管更加困难。因此,该提案的重点是开发一种优化的方法来检测肺癌的CT。我们的具体目标是:
1.目的:研究一种从肺部CT数据中自动检测肺结节的方法。
2.确定在对CT图像进行初步放射科医师评估后,考虑计算机辅助检测(CAD)结果时,放射科医师检测疑似肺结节患者的灵敏度和观察者间一致性的改善。我们将通过对从美国不同地区的两个医疗中心获得的肺结节CT扫描进行培训来优化我们的CAD系统,我们将证明CAD可以像第二个放射科医生一样有效地提高放射科医生在CT扫描上检测肺结节的能力,而不会大幅增加假阳性检测。这项工作完成后,我们将使放射科医生能够更好地利用MDCT扫描仪提供的改进数据,并大大提高他们在CT扫描中检测肺结节的能力,从而有助于改善肺癌患者的管理和护理。
项目成果
期刊论文数量(13)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Primary interpretation of thoracic MDCT images using coronal reformations.
使用冠状重建对胸部 MDCT 图像的初步解释。
- DOI:10.2214/ajr.04.1335
- 发表时间:2005
- 期刊:
- 影响因子:0
- 作者:Kwan,SharonW;Partik,BernhardL;Zinck,StevenE;Chan,FrandicsP;Kee,StephenT;Leung,AnnN;Voracek,Martin;Rubin,GeoffreyD
- 通讯作者:Rubin,GeoffreyD
Shape "break-and-repair" strategy and its application to automated medical image segmentation.
- DOI:10.1109/tvcg.2010.56
- 发表时间:2011-01
- 期刊:
- 影响因子:5.2
- 作者:Pu J;Paik DS;Meng X;Roos JE;Rubin GD
- 通讯作者:Rubin GD
Fully automated system for three-dimensional bronchial morphology analysis using volumetric multidetector computed tomography of the chest.
使用胸部体积多探测器计算机断层扫描进行三维支气管形态分析的全自动系统。
- DOI:10.1007/s10278-005-9240-0
- 发表时间:2006
- 期刊:
- 影响因子:4.4
- 作者:Venkatraman,Raman;Raman,Raghav;Raman,Bhargav;Moss,RichardB;Rubin,GeoffreyD;Mathers,LawrenceH;Robinson,TerryE
- 通讯作者:Robinson,TerryE
Computer-aided detection (CAD) of lung nodules in CT scans: radiologist performance and reading time with incremental CAD assistance.
- DOI:10.1007/s00330-009-1596-y
- 发表时间:2010-03
- 期刊:
- 影响因子:5.9
- 作者:Roos JE;Paik D;Olsen D;Liu EG;Chow LC;Leung AN;Mindelzun R;Choudhury KR;Naidich DP;Napel S;Rubin GD
- 通讯作者:Rubin GD
Assessing operating characteristics of CAD algorithms in the absence of a gold standard.
在缺乏黄金标准的情况下评估 CAD 算法的操作特性。
- DOI:10.1118/1.3352687
- 发表时间:2010
- 期刊:
- 影响因子:3.8
- 作者:Choudhury,KingshukRoy;Paik,DavidS;Yi,ChinA;Napel,Sandy;Roos,Justus;Rubin,GeoffreyD
- 通讯作者:Rubin,GeoffreyD
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
SANDY A. NAPEL其他文献
SANDY A. NAPEL的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('SANDY A. NAPEL', 18)}}的其他基金
Computing, Optimizing, and Evaluating Quantitative Cancer Imaging Biomarkers
计算、优化和评估定量癌症成像生物标志物
- 批准号:
9753130 - 财政年份:2015
- 资助金额:
$ 51.77万 - 项目类别:
Computing, Optimizing, and Evaluating Quantitative Cancer Imaging Biomarkers
计算、优化和评估定量癌症成像生物标志物
- 批准号:
9324146 - 财政年份:2015
- 资助金额:
$ 51.77万 - 项目类别:
Computing, Optimizing, and Evaluating Quantitative Cancer Imaging Biomarkers
计算、优化和评估定量癌症成像生物标志物
- 批准号:
9132190 - 财政年份:2015
- 资助金额:
$ 51.77万 - 项目类别:
Computing, Optimizing, and Evaluating Quantitative Cancer Imaging Biomarkers
计算、优化和评估定量癌症成像生物标志物
- 批准号:
8960049 - 财政年份:2015
- 资助金额:
$ 51.77万 - 项目类别:
Tools for Linking and Mining image and Genomic Data in Non-Small Cell Lung Cancer
用于链接和挖掘非小细胞肺癌图像和基因组数据的工具
- 批准号:
8889206 - 财政年份:2011
- 资助金额:
$ 51.77万 - 项目类别:
Tools for Linking and Mining image and Genomic Data in Non-Small Cell Lung Cancer
用于链接和挖掘非小细胞肺癌图像和基因组数据的工具
- 批准号:
8693964 - 财政年份:2011
- 资助金额:
$ 51.77万 - 项目类别:
Tools for Linking and Mining image and Genomic Data in Non-Small Cell Lung Cancer
用于链接和挖掘非小细胞肺癌图像和基因组数据的工具
- 批准号:
8332267 - 财政年份:2011
- 资助金额:
$ 51.77万 - 项目类别:
Tools for Linking and Mining image and Genomic Data in Non-Small Cell Lung Cancer
用于链接和挖掘非小细胞肺癌图像和基因组数据的工具
- 批准号:
8513277 - 财政年份:2011
- 资助金额:
$ 51.77万 - 项目类别:
Tools for Linking and Mining image and Genomic Data in Non-Small Cell Lung Cancer
用于链接和挖掘非小细胞肺癌图像和基因组数据的工具
- 批准号:
8153431 - 财政年份:2011
- 资助金额:
$ 51.77万 - 项目类别:
相似海外基金
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
- 批准号:
EP/Y029089/1 - 财政年份:2024
- 资助金额:
$ 51.77万 - 项目类别:
Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
- 批准号:
2337776 - 财政年份:2024
- 资助金额:
$ 51.77万 - 项目类别:
Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
- 批准号:
2338816 - 财政年份:2024
- 资助金额:
$ 51.77万 - 项目类别:
Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
- 批准号:
2338846 - 财政年份:2024
- 资助金额:
$ 51.77万 - 项目类别:
Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
- 批准号:
2348261 - 财政年份:2024
- 资助金额:
$ 51.77万 - 项目类别:
Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
- 批准号:
2348346 - 财政年份:2024
- 资助金额:
$ 51.77万 - 项目类别:
Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
- 批准号:
2348457 - 财政年份:2024
- 资助金额:
$ 51.77万 - 项目类别:
Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
- 批准号:
2404989 - 财政年份:2024
- 资助金额:
$ 51.77万 - 项目类别:
Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
- 批准号:
2339310 - 财政年份:2024
- 资助金额:
$ 51.77万 - 项目类别:
Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
- 批准号:
2339669 - 财政年份:2024
- 资助金额:
$ 51.77万 - 项目类别:
Continuing Grant














{{item.name}}会员




