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

帮助准确评估不确定肺结节的计算机工具

• 批准号: 9043798
• 负责人: Xin Meng
• 金额: $ 15万
• 依托单位: INTERNATIONAL INTELLIGENT INFOR/SOLU/LAB
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9043798
• 关键词: Adopted; Affect; Age; Anxiety; Bayesian Modeling; Benign; Biopsy; Blood Vessels; Cancer Etiology; Cancer Patient; Categories; Cessation of life; Clinic; Communication; Computer Simulation; Computers; Confidence Intervals; Data Set; Detection; Development; Diagnosis; Disease; Early Diagnosis; Evaluation; Exposure to; Frequencies; Gender; Goals; Healthcare Systems; High Resolution Computed Tomography; Image; Individual; Information Systems; Institution; Investigation; Lead; Lobe; Location; Lung; Lung nodule; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Medical; Medicine; Methods; Modality; Modeling; Motivation; Nature; Needle biopsy procedure; Nodule; Odds Ratio; Outcome Study; Output; Patients; Performance; Phase; Predictive Value; Procedures; Property; Pulmonary Emphysema; Pulmonology; Radiation; Research Design; Resolution; Risk; Scanning; Signal Transduction; Smoke; Smoking History; Staging; Structural defect; Structure; Symptoms; System; Techniques; Testing; Thoracotomy; Time; Trees; United States; Validation; Variant; Work; X-Ray Computed Tomography; airway remodeling; angiogenesis; calcification; cancer risk; clinical practice; computerized tools; cost; digital imaging; economic cost; follow-up; image guided; improved; innovation; interest; lung cancer screening; mortality; novel; predictive modeling; public health relevance; screening; spatial relationship; tool

 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.

 描述(申请人提供)：主要由于早期没有身体症状，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、非侵入性的显示小结节的方法，但它可能是肺癌的早期表现，但经常会导致相当数量的假阳性检测。因此，经常需要更多的程序，如侵入性活组织检查/后续扫描，以核实不明结节的性质。与这些过度诊断程序相关的负面影响，如活检并发症、暴露于额外放射、患者焦虑和经济成本，显著限制了CT筛查早期诊断肺癌的有效性。在这个项目中，我们建议开发一个计算机模型，使用纵向数据集来定量评估不确定结核的性质。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)及其随时间的变化，并使用机器学习技术将它们与患者人口统计信息(例如，年龄、性别、吸烟史)相结合。该模型不仅包含了肺结节的图像特征，而且还包含了肺结节与重要肺部标志的空间关系以及其他与烟雾相关的肺部异常(如肺气肿)。该项目的成果是一种新的计算机工具，可以帮助临床医生更准确和有效地评估不明结节的性质，最终减少对患者和医疗保健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，在保持其高敏感性的同时减少假阳性发现。在商业潜力方面，开发的工具可以很容易地与医疗机构现有的图像信息系统集成，方法是遵循广泛采用的医学数字成像和通信(DICOM)标准。