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

• 批准号: 7367836
• 负责人: SANDY A. NAPEL
• 金额: $ 51.77万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 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: https://reporter.nih.gov/project-details/7367836
• 关键词: Agreement; Algorithms; Appearance; Blood Vessels; Cancer Etiology; Cessation of life; Characteristics; Chest; Clinical; Clinical assessments; Computer Assisted; Data; Detection; Development; Discrimination; Human; Image; Interobserver Variability; Invasive; Laboratories; Lung; Lung nodule; Malignant neoplasm of lung; Medical center; Methods; New York; Nodule; Patient Care Management; Patients; Performance; Reader; Recruitment Activity; Resolution; Role; Scheme; Shapes; Specificity; System; Techniques; Testing; Thick; Time; Training; United States; Universities; Work; X-Ray Computed Tomography; base; cohort; detector; improved; lung imaging; novel; radiologist

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

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: AJR. American journal of roentgenology
• 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: IEEE transactions on visualization and computer graphics
• 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: Journal of digital imaging
• 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: European radiology
• 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: Medical physics
• 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