Radiomics Features of Quantitative Interstitial Abnormalities and Early Pulmonary Fibrosis

项目总结 特发性肺纤维化(IPF)是一种与吸烟有关的疾病，诊断为终末期，中位数为 存活3.8年。目前的治疗方法减缓了IPF的未来进展，但并不能逆转这种疾病。因此， 有必要检测出有发生肺间质纤维化风险并可能从早期受益的患者。 开始服用抗纤维化药物。最近的研究证实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)。这些实质 变化，要么是视觉上检测到的，称为间质性肺异常(ILA)，要么是通过自动图像 崔博士的实验室开发的分析工具称为定量间质异常(QIA)，与 肺功能差，运动受限，死亡率增加。然而，卡塔尔投资局可能在某个时间点被抓到 代表异质性疾病，既包括非进行性疾病，也包括暂时性疾病， CT发现，以及临床上有意义的早期吸烟相关疾病，最终将进展为IPF。 放射组学可以表征与IPF相关的QIA表型，并提高其特异性。 放射组学分析使用高通量计算来测量许多已有但不可用的特征 通常在CT扫描中测量，包括关于纹理、形状、灰度级的测量和统计 在感兴趣区域内，以及体素之间的关系。放射组学可能提供一种新的、特定的工具来 对疾病严重程度进行分层，并预测早期肺纤维化的疾病进展。 崔博士将使用放射组学特征来区分与吸烟相关的肺损伤的不同表型。在……里面 目的1，她将描述有早期肺纤维化(QIA)风险的吸烟者的放射组学特征 更糟糕的临床结果。在目标2中，她将把重点转移到在肺的最早阶段识别患者 受伤。她将描述视觉CT正常的吸烟者的放射组学特征，这些吸烟者有进展的风险 导致早期肺纤维化和更差的临床结果。 崔医生将在布里格姆的肺和重症监护医学部内执行这项工作 妇女医院(BWH)是哈佛医学院的核心教学医院，由Dr。 乔治·沃什科，定量医学成像分析专家，应用胸部成像联席主任 BWH的实验室。在她的导师和科学咨询委员会的帮助下，崔博士制定了一项培训计划 熟练掌握大数据准备和分析、机器学习算法、高级统计 方法和程序；保持和加深她对肺纤维化和吸烟的理解- 与肺部疾病相关；并磨练她在科学手稿准备、赠款撰写和有效性方面的技能 沟通。崔医生的长期目标是成为一名结合她的临床专业知识的内科科学家 在肺医学方面拥有先进的数据科学技术和研究专业知识，以便利用大数据 用于改善肺部疾病检测和治疗的数据。

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.