Virtual True-Color Bronchoscopt to Detect Lung Cancer

描述(申请人提供)：肺癌是美国男性和女性癌症死亡的最常见原因。迫切需要可靠的、可推广的方法来检测支气管镜可见(中央)呼吸道内的早期癌症。这一进展需要与其他已经在进行的识别早期周围型肺癌的努力同时进行。人类呼吸道早期癌症的特征是呼吸道粘膜增厚，粘膜表面颜色改变。在早期阶段，可能会有蛋白质和分子的变化，但到目前为止，这些变化还没有得到很好的描述。然而，人们的期望是，在几年内，将有荧光标记物可用于在早期阶段识别人类呼吸道内的癌症变化。最近，在人体成像硬件和软件方面取得了重大进展。在过去的12个月里，第一台真彩色ccd支气管镜已经商业化推出。数字输出提供了更好的颜色和空间分辨率。通过引入多层螺旋扫描仪，CT扫描的速度和分辨率也有所提高。这些技术的一个问题是，丰富的数字信息进一步加剧了人类观察者在报告异常情况时的相当大的错误率。这项阶段性创新奖申请的目的是利用CCD彩色支气管镜的图像开发对人体呼吸道粘膜的数字分析颜色检测和分析；根据高分辨率/高速体积CT扫描数据开发对呼吸道粘膜的数字分析和显示；并将这两种免费模式集成到单一的可推广的早期癌症检测工具中。在项目完成时，我们将开发这种集成的自动化和分析工具，并在肺癌高危人群中对其进行评估。我们将能够确定正常人类呼吸道的特征，以及吸烟高危肺癌患者的人类呼吸道特征。我们将知道通过CT扫描(增厚和地形图异常)和异常气道颜色单独或共同对照病理金标准确定的气道粘膜病变的阳性和阴性预测值。在未来，我们预计这项技术将易于使用，并普遍可用于在支气管镜下可见的呼吸道内进行有效的肺癌筛查。这种技术将有助于评估分子探针的发展，并在促进图像引导的气道癌治疗。肺癌是美国男性和女性癌症死亡的最常见原因。迫切需要可靠的、可推广的方法来检测支气管镜可见(中央)呼吸道内的早期癌症。这一进展需要与其他已经在进行的识别早期周围型肺癌的努力同时进行。人类呼吸道早期癌症的特征是呼吸道粘膜增厚，粘膜表面颜色改变。在早期阶段，可能会有蛋白质和分子的变化，但到目前为止，这些变化还没有得到很好的描述。然而，人们的期望是，在几年内，将有荧光标记物可用于在早期阶段识别人类呼吸道内的癌症变化。最近，在人体成像硬件和软件方面取得了重大进展。在过去的12个月里，第一台真彩色ccd支气管镜已经商业化推出。数字输出提供了更好的颜色和空间分辨率。通过引入多层螺旋扫描仪，CT扫描的速度和分辨率也有所提高。这些技术的一个问题是，丰富的数字信息进一步加剧了人类观察者在报告异常情况时的相当大的错误率。这项阶段性创新奖申请的目的是利用CCD彩色支气管镜的图像开发对人体呼吸道粘膜的数字分析颜色检测和分析；根据高分辨率/高速体积CT扫描数据开发对呼吸道粘膜的数字分析和显示；并将这两种免费模式集成到单一的可推广的早期癌症检测工具中。在项目完成时，我们将开发这种集成的自动化和分析工具，并在肺癌高危人群中对其进行评估。我们将能够确定正常人类呼吸道的特征，以及吸烟高危肺癌患者的人类呼吸道特征。我们将知道通过CT扫描(增厚和地形图异常)和异常气道颜色单独或共同对照病理金标准确定的气道粘膜病变的阳性和阴性预测值。在未来，我们预计这项技术将易于使用，并普遍可用于在支气管镜下可见的呼吸道内进行有效的肺癌筛查。这种技术将有助于评估分子探针的发展，并在促进图像引导的气道癌治疗。

DESCRIPTION (provided by applicant): Lung cancer is the most common cause of cancer death in both men and women in the United States. Reliable generalizable methods to detect early cancer within the bronchoscopically visible (central) airways are urgently needed. This development needs to occur concurrently with other efforts already underway to identify early peripheral lung cancers. The characteristics of early cancer within the human airway are thickening of the airway mucosa, and a change of color of the mucosal surface. At an earlier stage there will likely be protein and molecular changes, but these are so far poorly described. However, the expectation is that within several years there will be fluorescent markers that could be used to identify cancer changes within the human airway at this early stage. Recently there have been major advances in human imaging hardware and software. The first true color CCD bronchoscopes have been commercially introduced within the last 12 months. The digital output provides better color and spatial resolution. CT scanning has also benefited in speed and resolution through the introduction of multislice spiral scanners. One problem with these technologies is that the richness of the digital information further compounds the substantial human observer error rate in reporting on abnormalities. The purpose of this phased innovation award application is to develop digital analytic color detection and analysis of the human airway mucosa with images from the CCD color bronchoscope; to develop digital analysis and display of the airway mucosa from high resolution/high speed volumetric CT scan data; and to integrate these two complimentary modalities into a single generalizable early cancer detection tool. At the completion of the project we will have developed this integrated automated and analytic tool, and evaluated this in a human population at high risk for lung cancer. We will be able to define the characteristics of the normal human airway, and the human airway in smoking subjects at high risk of lung cancer. We will know the positive and negative predicted values for airway mucosal lesions determined by CT scan (thickening and abnormal topography), and by abnormal airway color, both separately and collectively against a pathologic gold standard. In the future, we expect this technology to be easily used and generally available for undertaking effective screening for lung cancer within the bronchoscopically visible airways. Such technology will be useful for the evaluation of molecular probes as they are developed, and in promoting image guided airway cancer treatment. Lung cancer is the most common cause of cancer death in both men and women in the United States. Reliable generalizable methods to detect early cancer within the bronchoscopically visible (central) airways are urgently needed. This development needs to occur concurrently with other efforts already underway to identify early peripheral lung cancers. The characteristics of early cancer within the human airway are thickening of the airway mucosa, and a change of color of the mucosal surface. At an earlier stage there will likely be protein and molecular changes, but these are so far poorly described. However, the expectation is that within several years there will be fluorescent markers that could be used to identify cancer changes within the human airway at this early stage. Recently there have been major advances in human imaging hardware and software. The first true color CCD bronchoscopes have been commercially introduced within the last 12 months. The digital output provides better color and spatial resolution. CT scanning has also benefited in speed and resolution through the introduction of multislice spiral scanners. One problem with these technologies is that the richness of the digital information further compounds the substantial human observer error rate in reporting on abnormalities. The purpose of this phased innovation award application is to develop digital analytic color detection and analysis of the human airway mucosa with images from the CCD color bronchoscope; to develop digital analysis and display of the airway mucosa from high resolution/high speed volumetric CT scan data; and to integrate these two complimentary modalities into a single generalizable early cancer detection tool. At the completion of the project we will have developed this integrated automated and analytic tool, and evaluated this in a human population at high risk for lung cancer. We will be able to define the characteristics of the normal human airway, and the human airway in smoking subjects at high risk of lung cancer. We will know the positive and negative predicted values for airway mucosal lesions determined by CT scan (thickening and abnormal topography), and by abnormal airway color, both separately and collectively against a pathologic gold standard. In the future, we expect this technology to be easily used and generally available for undertaking effective screening for lung cancer within the bronchoscopically visible airways. Such technology will be useful for the evaluation of molecular probes as they are developed, and in promoting image guided airway cancer treatment.