Radiomics Features of Quantitative Interstitial Abnormalities and Early Pulmonary Fibrosis

定量间质异常和早期肺纤维化的放射组学特征

• 批准号: 10603453
• 负责人: Bina Choi
• 金额: $ 9.44万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10603453
• 关键词: Advanced Development; Advisory Committees; Big Data; Characteristics; Chest imaging; Clinical; Communication; Critical Care; Data; Data Science; Detection; Development; Disease; Disease Progression; Disease stratification; Early Intervention; Exercise; Eye; Fibrosis; Future; Genetic; Goals; Grant; Histopathology; Hospitals; Image; Inflammation; Interstitial Lung Diseases; Island; Laboratories; Lung; Lung diseases; Manuscripts; Measurement; Measures; Medical Imaging; Medicine; Mentors; Mentorship; Methods; Oncology; Outcome; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Physicians; Preparation; Process; Prognosis; Pulmonary Fibrosis; Pulmonology; Research; Risk; Scanning; Scientist; Severity of illness; Shapes; Smoker; Smoking; Specificity; Spirometry; Stage at Diagnosis; Statistical Methods; Structure of parenchyma of lung; Teaching Hospitals; Texture; Time; Training; Visit; Visual; Woman; Work; Writing; X-Ray Computed Tomography; advanced disease; adverse event risk; antifibrotic treatment; attenuation; automated image analysis; chest computed tomography; cohort; density; diagnostic value; follow-up; functional outcomes; functional status; high risk; idiopathic pulmonary fibrosis; improved; interest; interstitial; lung health; lung injury; machine learning algorithm; medical schools; microCT; mortality; novel; prevent; prognostic value; progression risk; protein biomarkers; pulmonary function; quantitative imaging; radiomics; skills; smoking-related disease; smoking-related lung disease; statistics; survival outcome; tool; tumor

PROJECT SUMMARY Idiopathic pulmonary fibrosis (IPF) is a smoking-related disease that is end-stage at diagnosis, with a median survival of 3.8 years. Current treatments slow the future progression of IPF but do not reverse the disease. Thus, there is an important need to detect patients who are at risk of developing IPF and may benefit from earlier initiation of anti-fibrotic medications. Recent work has validated changes in the lung parenchyma on chest computed tomography (CT) scans of smokers that represent early pulmonary fibrosis. These parenchymal changes, either detected visually and called interstitial lung abnormalities (ILA), or through an automated image analysis tool developed by Dr. Choi’s lab called quantitative interstitial abnormalities (QIA), are associated with poor lung function, exercise limitations, and increased mortality. However, QIA caught at a point in time likely represents heterogeneous disease, encompassing both the non-progressive and transient processes that are caught on CT, and the clinically meaningful early smoking-related disease that will eventually progress to IPF. Radiomics may enable the characterization of, and increase specificity of, QIA phenotypes associated with IPF. Radiomics analyses use high-throughput computing to measure many features that are already available but not typically measured in CT scans, including measurements and statistics about the textures, shapes, gray levels within regions of interest, and relationships amongst voxels. Radiomics may provide a novel, specific tool to stratify disease severity and predict disease progression of early pulmonary fibrosis. Dr. Choi will use radiomics features to distinguish heterogeneous phenotypes of smoking-related lung injury. In Aim 1, she will characterize the radiomics signatures of smokers with early pulmonary fibrosis (QIA) at risk for worse clinical outcomes. In Aim 2, she will move her focus to identifying the patients at the earliest stage of lung injury. She will characterize the radiomics signatures of smokers with visually normal CTs at risk for progression to early pulmonary fibrosis and worse clinical outcomes. Dr. Choi will perform this work within the Division of Pulmonary and Critical Care Medicine, at Brigham and Women’s Hospital (BWH), a core teaching hospital of the Harvard Medical School, under the mentorship of Dr. George Washko, an expert in quantitative medical imaging analysis and co-director of the Applied Chest Imaging Laboratory at BWH. With her mentors and Scientific Advisory Committee, Dr. Choi has developed a training plan to gain proficiency in big data preparation and analysis, machine learning algorithms, advanced statistical methods, and programming; to maintain and deepen her understanding of pulmonary fibrosis and smoking- related lung disease; and to hone her skills in scientific manuscript preparation, grant-writing, and effective communication. Dr. Choi’s long-term goal is to become a physician-scientist that combines her clinical expertise in pulmonary medicine with advanced technical and research expertise in data science, in order to leverage big data for the improved detection and treatment of lung diseases.

项目摘要 特发性肺纤维化（IPF）是一种与吸烟有关的疾病，诊断时处于终点阶段，中位数 生存3。8年。当前的治疗速度减慢了IPF的未来进展，但不会扭转疾病。那， 检测有发展IPF风险的患者的重要需求，可能会从早期受益 抗纤维化药物的启动。最近的工作已经验证了胸部肺实质的变化 代表早期肺纤维化的吸烟者的计算机断层扫描（CT）扫描。这些副群 更改，可以在视觉上检测到间质肺异常（ILA），或者通过自动图像进行更改 Choi博士实验室开发的分析工具称为定量间质异常（QIA），与 肺功能不良，运动局限性和死亡率增加。但是，Qia可能会在某个时间点抓住 代表异质性疾病，包括非促进和瞬态过程 被CT捕获，以及临床上有意义的早期吸烟相关疾病，最终将发展为IPF。 放射线学可以使与IPF相关的QIA表型的表征和增加。 放射学分析使用高通量计算来衡量许多已经可用但不能的功能 通常在CT扫描中测量，包括有关纹理，形状，灰色水平的测量和统计数据 在感兴趣的地区以及体素之间的关系。放射学可以为 分层疾病的严重程度并预测早期肺纤维化的疾病进展。 Choi博士将使用放射线特征来区分与吸烟相关的肺损伤的异质表型。在 AIM 1，她将表征患有早期肺纤维化（QIA）的吸烟者的放射组特征 临床结果较差。在AIM 2中，她将在肺最早的阶段移动注意力以识别患者 受伤。她将表征具有视觉正常CT的吸烟者的放射线签名，其进展风险 早期肺纤维化和临床结果较差。 Choi博士将在Brigham和 哈佛医学院的核心教学医院妇女医院（BWH）在博士的心态下 乔治·沃斯科（George Washko），定量医学成像分析的专家和应用胸部成像的联合导演 BWH的实验室。 Choi博士与她的导师和科学咨询委员会一起制定了培训计划 为了熟练掌握大数据准备和分析，机器学习算法，高级统计 方法和编程；维持和加深她对肺纤维化和吸烟的理解 相关肺部疾病；并为她的科学手稿准备，赠款写和有效的技巧而蜂蜜 沟通。崔博士的长期目标是成为一个结合她临床专业知识的身体科学家 具有数据科学领域的高级技术和研究专业知识的肺医学专业，以利用大型 改善肺部疾病检测和治疗的数据。