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

通过教授肺癌病理生理学来提高深度神经网络的通用性

基本信息

批准号：
10529498
负责人：
Axel Herve Masquelin
金额：
$ 3.32万
依托单位：
UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2023-08-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10529498
关键词：
Achievement Address Advisory Committees Age-Years Anxiety Attention Benign Biological Biological Markers Caliber Cancer Detection Characteristics Chronic Chronic Obstructive Pulmonary Disease Classification Clinical Data Data Set Detection Diagnostic Early Diagnosis Educational process of instructing Failure Fibrosis Foundations Functional disorder Future Genetic Genomics Goals Growth Guidelines Healthcare Systems Histologic Image Individual Inflammation Investigation Knowledge Learning Left Lung Lung nodule Machine Learning Malignant - descriptor Malignant Neoplasms Malignant neoplasm of lung Measures Medical Methodology Modality Modeling Morphology Network-based Nodule Non-Small-Cell Lung Carcinoma Outcome Pathogenesis Patients Pattern Performance Phenotype Physiological Play Precancerous Conditions Predisposition Preventive service Procedures Process Pulmonary Emphysema Pulmonary Inflammation Quality of life Recommendation Research Personnel Risk Risk Factors Role Scanning Sensitivity and Specificity Smoker Smoking History Solid Specific qualifier value Specificity Spirometry Testing Training Training Programs United States Work accurate diagnosis adjudicate airway obstruction base cancer diagnosis career clinically significant computed tomography screening convolutional neural network cost deep learning deep neural network diagnostic accuracy diagnostic biomarker directed attention endophenotype follow-up genetic variant high risk histological image idiopathic pulmonary fibrosis imaging biomarker improved insight low dose computed tomography lung imaging mortality novel novel strategies radiologist screening trait tumor vector

项目摘要

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.

项目概要非小细胞肺癌 (NSCLC) 的诊断和治疗方法在过去十年中不断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以及结节的形态特征。遵循美国预防服务局的建议特别工作组 (USPSTF)，每年使用低剂量计算机断层扫描 (LDCT) 对高危人群进行筛查因为这为肺癌提供了高灵敏度和可接受的特异性。然而，LDCT的引入作为肺癌的主要筛查方式，增加了不确定性肺癌的检出率然后需要侵入性检查的结节。这降低了高危人群的生活质量重复的随访和手术，大大增加了人们对通常结果是良性的焦虑结核。在这个提案中，我们的目标是通过确定卷积的特征来改进这些结果。神经网络 (CNN) 将肺结节分类为良性或良性。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学图像特征可以改善他们有能力推广到用于训练的图像集之外的新图像。中心假设为这个提议是增加 CNN 对 LDCT 图像特征的关注，这些特征被认为是病理生理学相关性将提高其对新颖图像的概括性，从而提高其能力准确区分恶性结节和良性结节。在本提案的 F99 目标中，我们将通过利用国家肺部筛查试验 (NLST) 的 LDCT 图像来解决这一假设使用概念激活向量来确定 CNN 使用哪些实质和肿瘤特定特征对肺结节进行分类。在 K00 目标中，我们将确定是否从 COPD 基因中提取内表型 LDCT图像集可用于提高CNN的泛化能力。完成这些目标将导致增加了解肺部 LDCT 图像中固有的肺癌形态生物标志物对于准确诊断很重要。这对于 CNN 分类的改进有潜在的应用其他医学领域的表现。此外，通过遵守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、遗传网络和基因组学。这些都将为我未来的职业生涯打下坚实的基础。独立生物医学研究者。