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

项目总结 非小细胞肺癌(NSCLC)的管理、治疗和诊断方法在 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 结节的患者和形态特征1。美国最近提出的预防性建议 服务工作组(USPSTF)建议每年对高危人群进行低剂量筛查 计算机断层扫描(LDCT)，因为这种筛查实践提供了高敏感性和可接受的特异性 肺癌2.然而，LDCT作为肺癌的主要筛查手段的引入有所增加 不确定结节的识别。这种筛查做法导致的检测率提高 高危人群的整体生活质量因反复跟进和经常需要 可能是良性结节的侵入性手术。在这笔培训补助金中，我们的目标是改进这些 通过改善深度神经网络(DNN)在数据稀缺领域的性能取得的成果，特别是 肺癌。这一建议的总体假设是DNN分类精度不确定 肺结节将通过使用预先指定的恶性结节和 DNN不容易直接从 LDCT扫描。我们将解决这一假设，并通过扩大国家 肺筛查试验(NLST)数据集推断恶性结节的重要形态实质特征 分类，并使用COPDgene数据集中的辅助数据。目标1中提出的实验将 探讨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，以 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与上下文相关的数据集来训练DNN。最终， 这些方法的应用将提高我们对这些实质形态特征的理解 这对区分肺结节最为关键。此外，我将在 这些研究的进程涉及产生CT标记物，检测早期肺癌的发病机制，以及 计算建模将为我未来的独立生物医学职业生涯奠定坚实的基础 调查员。

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.