Quantification and automated characterization of mucus plug pathology in asthmatics

哮喘患者粘液栓病理学的量化和自动表征

• 批准号: 10676722
• 负责人: Brendan Huang
• 金额: $ 8.23万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10676722
• 关键词: 3-Dimensional; Acute; Address; Affect; Air Movements; Airway Resistance; Algorithms; Architecture; Area; Artificial Intelligence; Asthma; Automation; Binding; Biological Markers; Blood Vessels; Characteristics; Chest; Chronic; Clinical Trials; Coughing; Data; Data Set; Diagnostic; Diagnostic Procedure; Disease Outcome; Elements; Event; Forced expiratory volume function; Goals; Image; Impairment; Institution; Knowledge; Label; Length; Link; Location; Manuals; Masks; Measures; Modeling; Monitor; Mucolytics; Mucous body substance; Network-based; Pathology; Patients; Performance; Pharmaceutical Preparations; Phenotype; Play; Positioning Attribute; Questionnaires; Research; Resistance; Role; Scanning; Scheme; Severity of illness; Shapes; Slice; Specific qualifier value; Spirometry; Sputum; Symptoms; System; Techniques; Testing; Therapeutic; Time; Training; Trees; Variant; Vital capacity; Width; X-Ray Computed Tomography; airway obstruction; asthmatic; asthmatic patient; automated algorithm; automated segmentation; candidate identification; cohort; convolutional neural network; deep learning model; design; genetic association; image processing; imaging Segmentation; improved; lung imaging; neural network architecture; novel; novel diagnostics; patient subsets; prospective; radiological imaging; radiologist; reconstruction; respiratory; supervised learning; two-dimensional

Project Summary Mucus plugging has long been implicated in acute and fatal respiratory events in severe asthma, but we have recently shown that chronic mucus plugging is common in asthmatic patients and appears mechanistically linked with both impaired airflow and worsening disease severity. In particular, in analyses of baseline computed tomography (CT) lung scans in asthmatic patients, we found that airway mucus plugs are highly prevalent, persist for many years, frequently occur without cough and sputum symptoms, and are strongly associated with airflow obstruction. However, it is unknown what radiographic characteristics of mucus plugging cause severe airflow obstruction, in part because detailed characterization of mucus plugs on CT scan is extremely labor intensive and requires highly trained thoracic radiologists for assessment. In Aim 1 of this application, we propose to test the hypotheses, informed by our preliminary data, that three radiographic features of mucus plugs— mucus plug volume, number of proximal plugs, and fraction of airway tree occluded —all predict worsening airflow obstruction. We additionally propose that the airway tree can be converted into a network of resistive elements in which the effective resistance of the entire tree is computed with and without mucus plugs, and the relative contribution of mucus plugs to airway resistance can be determined. In Aim 2, we aim to substantially lower the barrier to quantification of mucus plugging on CT scans by developing an automated, convolutional neural network-based algorithm for mucus plug segmentation. We believe that our findings will allow the identification of a large subset of patients with chronic severe “mucushigh” asthma and raise possibilities for novel mucus-targeted treatments to improve airflow and other disease outcomes in this subset of patients.

项目摘要 长期以来，在严重哮喘的急性和致命呼吸道事件中，粘液塞一直暗示，但我们有 最近表明，慢性粘液塞在哮喘患者中很常见，并且在机械上看起来 与气流受损和令人担忧的疾病严重程度有关。特别是在基线分析中 哮喘患者中的计算机断层扫描（CT）肺扫描，我们发现气道粘液塞高度 普遍存在，持续多年，经常出现没有咳嗽和痰症状，并且很强烈 与气流异议有关。但是，尚不清楚粘液的影像学特征 堵塞会导致严重的气流异议，部分原因是CT上粘液塞的详细表征 扫描是极度加密的，需要训练有素的胸腔放射科进行评估。在目标1中 我们建议通过我们的初步数据告知该假设，以测试三个放射线学的假设 粘液塞的功能 - 粘液插头量，近端插头数量和气道树的一部分 - 所有人都预测令人担忧的气流异议。我们还建议将气道树转换为 一个电阻元素的网络，其中有和没有有效的整棵树的有效电阻 可以确定粘液塞以及粘液塞对气道电阻的相对贡献。在AIM 2中， 我们的目标是通过开发一个 自动化的，基于卷积神经网络的算法，用于粘液塞进行分割。我们相信我们的 调查结果将允许鉴定大部分慢性严重“粘液”哮喘和 增加了新型粘液靶向治疗的可能性，以改善气流和其他疾病结果 患者子集。