Radiomics Features of Quantitative Interstitial Abnormalities and Early Pulmonary Fibrosis

项目摘要 特发性肺纤维化（IPF）是一种与吸烟相关的疾病，诊断时为终末期， 生存3.8年。目前的治疗减缓了IPF的未来进展，但不能逆转疾病。因此，在本发明中， 非常需要检测有发生IPF风险的患者，并可能从早期治疗中获益， 开始抗纤维化药物治疗。最近的工作证实了胸部肺实质的变化 吸烟者的计算机断层扫描（CT）显示早期肺纤维化。这些实质 变化，通过视觉检测并称为间质性肺异常（ILA），或通过自动图像 Choi博士的实验室开发的一种称为定量间质异常（QIA）的分析工具， 肺功能差、运动受限和死亡率增加。然而，卡塔尔投资局在一个时间点抓住了可能 代表异质性疾病，包括非进行性和短暂性过程， 在CT上发现，以及最终进展为IPF的具有临床意义的早期吸烟相关疾病。 放射组学可以使与IPF相关的QIA表型的表征成为可能，并增加其特异性。 放射组学分析使用高吞吐量计算来测量许多已经可用但尚未 通常在CT扫描中测量，包括关于纹理、形状、灰度 以及体素之间的关系。放射组学可以提供一种新的，具体的工具， 对早期肺纤维化疾病严重程度进行分层并预测疾病进展。 博士Choi将使用放射组学特征来区分吸烟相关肺损伤的异质表型。在 目的1，她将描述具有早期肺纤维化（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，她将把重点转移到识别肺部疾病最早期的患者， 损伤她将描述有进展风险的视觉正常CT的吸烟者的放射组学特征 早期肺纤维化和更差的临床结果。 博士Choi将在布里格姆的肺部和重症监护医学部开展这项工作， 妇女医院（BWH）是哈佛医学院的核心教学医院。 乔治沃什科，定量医学成像分析专家和应用胸部成像联合主任 BWH实验室Choi博士与她的导师和科学咨询委员会一起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 方法和编程;保持和加深她对肺纤维化和吸烟的理解- 相关的肺部疾病;并磨练她在科学手稿准备，赠款写作和有效的技能， 通信崔博士的长期目标是成为一名医生，科学家，结合她的临床专业知识， 在肺部医学与先进的技术和研究专业知识，在数据科学，以利用大 改善肺部疾病的检测和治疗的数据。

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.