Leveraging prior knowledge to classify Indeterminate Lung Nodules in CT images using Deep Neural Networks

利用深度神经网络利用先验知识对 CT 图像中的不确定肺结节进行分类

• 批准号: 10389388
• 负责人: Axel Herve Masquelin
• 金额: $ 1.82万
• 依托单位: UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10389388
• 关键词: Address; Adoption; Advisory Committees; Attention; Benign; Biological; Caliber; Cancer Detection; Characteristics; Chronic; Chronic Obstructive Pulmonary Disease; Classification; Clinical; Computer Models; Data; Data Set; Detection; Diagnostic; Early Diagnosis; Environment; Excision; Foundations; Future; Goals; Grant; Growth; Image; Individual; Inflammation; Inflammatory Response; Knowledge; Lead; Learning; Lung; Lung nodule; Machine Learning; Malignant - descriptor; Malignant neoplasm of lung; Masks; Measures; Medical; Methodology; Modality; Morphology; Nodule; Non-Small-Cell Lung Carcinoma; Outcome; Pathogenesis; Patient-Focused Outcomes; Patients; Performance; Precancerous Conditions; Preventive service; Procedures; Pulmonary Emphysema; Pulmonary Inflammation; Quality of life; Recommendation; Research Personnel; Research Proposals; Risk; Risk Factors; Scanning; Sensitivity and Specificity; Severities; Solid; Specific qualifier value; Specificity; Spiculate; Tissues; Training; Translating; United States; Work; X-Ray Computed Tomography; base; career; deep field survey; deep learning; deep neural network; diagnostic biomarker; diagnostic strategy; experimental study; follow-up; high risk; high risk population; idiopathic pulmonary fibrosis; improved; improved outcome; innovation; knowledge base; learning strategy; low dose computed tomography; lung cancer screening; lung development; machine learning method; mortality; neural network; novel; novel strategies; premalignant; screening; standard of care; transfer learning; tumor

PROJECT SUMMARY Management, treatment, and diagnostic approaches for non-small cell lung cancer (NSCLC) have evolved in the last decade from primarily empirical methodologies to objective strategies that rely on clinical characteristics of the patient and morphological features of the nodule1. Recent recommendations by the United States Preventive Service Task Force (USPSTF) recommends that high-risk individuals be screened yearly with low-dose computed tomography (LDCT), as this screening practice provides high sensitivity with acceptable specificity for lung cancer2. However, the introduction of LDCT as the primary screening modality for lung cancer has increased the identification of indeterminate nodules. The increased detection rates caused by this screening practice decreases the overall quality of life for at-risk individuals through repeated follow-up and the frequent need for invasive procedures for what is likely a benign nodule. In this training grant, we aim to improve upon these outcomes by improving the performance of deep neural networks (DNNs) in data-scarce domains, specifically lung cancer. The overall hypothesis of this proposal is that DNN classification accuracy of indeterminate lung nodules will be significantly improved through the use of pre-specified malignant nodule and parenchymal morphological features that would not be readily extractable by a DNN directly from the LDCT scans. We will address this hypothesis and achieve the goals of this proposal by augmenting the National Lung Screening Trial (NLST) dataset to infer important morphological parenchymal features for malignant nodule classification and by using ancillary data from the COPDgene dataset. The experiments proposed in Aim 1 will explore the impact of using augmented morphological parenchymal features on the classification performance of our deep neural networks. Aim 2 will explore the relative contribution of a contextually similar dataset, COPDgene, for classification and parameter tuning. The proposed work will yield improved approaches for classification of indeterminate pulmonary nodules as either malignant or benign via an innovative approach for training DNNs using domain knowledge and contextually related datasets in data-scarce domains. Ultimately, the application of these approaches will improve our understanding of those parenchymal morphological features that are most critical for discriminating pulmonary nodules. In addition, the training grant I will receive in the course of these studies related to generating CT markers, detecting early lung cancer pathogenesis, and computational modeling will serve as a solid foundation for my future career as an independent biomedical investigator.

项目摘要 非小细胞肺癌（NSCLC）的管理、治疗和诊断方法在20世纪已经发展， 在过去的十年里，从主要的经验方法到依赖于临床特征的客观策略， 患者和结节的形态特征1。美国最近的建议 服务工作组（USPSTF）建议，高风险的个人进行筛查，每年低剂量 计算机断层扫描（LDCT），因为这种筛查实践提供了高灵敏度和可接受的特异性， 肺癌2.然而，LDCT作为肺癌的主要筛查方式的引入已经增加， 不确定结节的鉴别。这种筛查做法提高了检出率， 通过重复随访和频繁需要，降低了高危人群的整体生活质量 可能是良性结节的侵入性手术。在这个培训补助金，我们的目标是改善这些 通过提高深度神经网络（DNN）在数据稀缺领域的性能， 肺癌该提案的总体假设是DNN的分类准确性不确定 通过使用预先指定的恶性结节， 实质形态学特征，不容易通过DNN直接从 LDCT扫描。我们将解决这一假设，并通过扩大国家预算来实现这一建议的目标。 肺筛查试验（NLST）数据集，用于推断恶性结节的重要形态实质特征 分类和使用来自COPDgene数据集的辅助数据。目标1中提出的实验将 探索使用增强的形态实质特征对分类性能的影响 我们的深层神经网络。目标2将探索上下文相似数据集的相对贡献， COPDgene，用于分类和参数调整。拟议的工作将产生改进的方法， 通过一种创新的方法将不确定的肺结节分类为恶性或良性， 使用领域知识和数据稀缺领域中的上下文相关数据集训练DNN。最后， 这些方法的应用将提高我们对这些实质形态学特征的理解 这是鉴别肺结节的关键。此外，我将获得的培训补助金 这些研究涉及产生CT标记物、检测早期肺癌发病机制， 计算建模将作为我未来作为一个独立的生物医学专业的职业生涯的坚实基础。 调查员