A Computer Tool for Aiding in Accurate Assessment of Indeterminate Lung Nodules

 描述（由申请人提供）：由于早期缺乏身体症状，肺癌仍然是美国和世界范围内癌症死亡的主要原因。虽然高分辨率计算机断层扫描（CT）已被证明是一种敏感的，非侵入性的方式来可视化小肺结节，这可能是肺癌的早期表现，相当数量的假阳性检测往往会导致。因此，经常需要额外的程序，如侵入性活检/随访扫描，以验证不确定结节的性质。与这些过度诊断程序相关的负面影响，如活检并发症，暴露于额外的辐射，患者焦虑和经济成本，显着限制了CT筛查早期诊断肺癌的疗效。在这个项目中，我们建议开发一个计算机模型，使用纵向数据集定量评估不确定结节的性质。与现有的研究或肺癌风险模型不同，我们将以前所未有的详细方式全面量化结节的各种属性（特征）以及它们随时间的变化，并将它们与患者人口统计信息（例如，年龄、性别、吸烟史）。不仅肺结节的图像特征，而且它们相对于重要的肺界标的空间关系以及其他与烟雾相关的肺异常（例如，肺气肿）将被纳入该模型。该项目的成果，即一种新型的计算机工具，可以帮助临床医生更准确、更有效地评估不确定结节的性质，最终减少对患者和医疗系统的不必要伤害和成本。所有这些都将显著提高CT在早期肺癌筛查中的有效性，保持其高灵敏度，同时减少假阳性结果。在商业潜力方面，开发的工具可以很容易地集成到现有的图像信息系统在医疗机构遵循广泛采用的数字成像和通信医学（DICOM）标准。

 DESCRIPTION (provided by applicant): Primarily due to the lack of physical symptoms in the early stage, lung cancer remains the leading cause of cancer deaths in the United States and worldwide. Although high-resolution computed tomography (CT) has been proved to be a sensitive, non-invasive modality for visualizing small lung nodules, which could be the early manifestation of lung cancer, a considerable number of false positive detections are often resulted. Consequently, additional procedures, such as invasive biopsy / follow-up scans, are frequently needed to verify the nature of the indeterminate nodules. The negative effects associated with these over-diagnosis procedures, such as biopsy complications, exposure to additional radiation, patient anxiety, and economic cost, significantly limits the efficacy of CT screening for early diagnosis of lung cancer. In this project, we propose to develop a computer model to quantitatively assess the nature of indeterminate nodules using a longitudinal dataset. Unlike available investigations or lung cancer risk models, we will comprehensively quantify a wide variety of properties (features) of a nodule in an unprecedented detailed manner as well as their variations over time, and synergize them with patient demographic information (e.g., age, gender, smoke history) using machine learning techniques. Not only the image features of lung nodules but also their spatial relationship with respect to important lung landmarks as well as other smoke related lung abnormalities (e.g., emphysema) will be incorporated into this model. The output of this project, namely a novel computer tool, could aid clinicians to more accurately and efficiently assess the nature of indeterminate nodules, ultimately leading to the reduction of unnecessary harm and costs to patients and the healthcare system. All these will significantly improve the efficacy of CT for early lung cancer screening by maintaining its high sensitivity while reducing false positive findings. In terms of commercial potential, the developed tool could be easily integrated with the available image information systems at medical institutions by following the widely adopted Digital Imaging and Communications in Medicine (DICOM) standard.