Improving the Generalizability of Deep Neural Networks by Teaching them Lung Cancer Pathophysiology

项目总结 非小细胞肺癌(NSCLC)的诊断和治疗方法在过去十年中得到了发展 从主要的经验方法到依赖于患者临床特征的客观策略 结节的形态特征。遵循美国预防服务局的建议 特别工作组(USPSTF)每年都会对高危人群进行低剂量计算机断层扫描(LDCT)筛查 因为这为肺癌提供了高敏感性和可接受的特异性。然而，LDCT的引入 作为肺癌的主要筛查手段，提高了不明原因肺癌的发现率 然后需要侵入性调查的结节。这降低了高危人群的生活质量 反复的随访和程序，极大地增加了人们对通常被证明是良性的 结节。在这项提案中，我们的目标是通过确定卷积运算的特征来改进这些结果 神经网络(CNN)用于将肺结节分类为良性或非良性。我们还将确定是否 为CNN提供已知与肺癌相关的预先指定的组织图像特征可改善 他们能够在训练他们的图像集之外概括出新奇的图像。的中心假说 这项建议是增加CNN对LDCT图像特征的关注，这些图像特征被接受为 病理生理学上的相关性将提高其对新图像的泛化能力，从而提高其能力 以准确区分良恶性结节。在这项建议的F99目标中，我们 我将通过使用国家肺部筛查试验(NLST)的LDCT图像来解决这一假设 利用概念激活载体来确定CNN使用哪些实质和肿瘤特异性特征 对肺结节进行分类。在K00的目标中，我们将确定从COPD基因中提取的内表型 LDCT图像集可以用来提高CNN的泛化能力。这些目标的完成将导致增加 肺癌LDCT图像中最常见的形态生物标志物的认识 对于准确的诊断很重要。这将对改进CNN分类具有潜在的应用价值 在其他医疗领域的表现。此外，通过坚持本提案中概述的培训计划， 我将在图像生物标志物、早期癌症发病机制和检测、遗传学方面获得高水平的专业知识 网络和基因组学。这些都将为我未来的职业生涯奠定坚实的基础 独立的生物医学调查员。

PROJECT SUMMARY Diagnostic and treatment approaches for non-small cell lung cancer (NSCLC) have evolved over the last decade from primarily empirical methodologies to objective strategies that rely on clinical characteristics of the patient and morphological features of the nodule. Following recommendations by the United States Preventive Service Task Force (USPSTF), high-risk individuals are screened yearly with low-dose computed tomography (LDCT) as this provides high sensitivity with acceptable specificity for lung cancer. However, the introduction of LDCT as the primary screening modality for lung cancer has increased detection rates of indeterminate pulmonary nodules that then require invasive investigation. This decreases the quality of life for at-risk individuals through repeated follow-ups and procedures, and greatly increases anxiety over what usually turns out to a benign nodule. In this proposal, we aim to improve upon these outcomes by determining the features that convolutional neural networks (CNNs) utilize when classifying lung nodules as either or benign. We will also determine if providing CNNs with pre-specified histologic image features known to be associated with lung cancer improves their ability to generalize to novel images outside the image set used to train them. The central hypothesis of this proposal is that increasing the attention of a CNN on LDCT image features that are accepted as being pathophysiologically relevant will improve its generalizability to novel images and thus its ability to accurately distinguish between malignant versus benign nodules. In the F99 Aim of this proposal, we will address this hypothesis by utilizing LDCT images from the National Lung Screening Trial (NLST) together with concept activation vectors to determine which parenchymal and tumor-specific features are used by CNNs to classify lung nodules. In the K00 aim, we will determine if endophenotypes extracted from the COPDgene LDCT image set can be used to improve CNN generalizability. Completion of these aims will lead to an increased understanding of the morphologic biomarkers of lung cancer inherent in LDCT images of the lung that are most important for accurate diagnosis. This will have potential application to the improvement of CNN classification performance in other medical domains. In addition, by adhering to the training program outlined in this proposal I will gain high levels of expertise in image biomarkers, early cancer pathogenesis and detection, genetic networks, and genomics. These will collectively serve as a solid foundation for my future career as an independent biomedical investigator.