Radiomics Features of Quantitative Interstitial Abnormalities and Early Pulmonary Fibrosis

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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.