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):
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
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{{ 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:
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