Lung cancer screening efficacy enhanced through radiomic and epigenetic biomarkers

通过放射组学和表观遗传生物标志物增强肺癌筛查功效

• 批准号: 10663383
• 负责人: Jessica C Sieren
• 金额: $ 34.64万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-11 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10663383
• 关键词: Accounting; Adult; Affect; American; American College of Radiology Imaging Network; American Lung Association; Benign; Biological Assay; Biological Markers; CYP1A1 gene; Cause of Death; Centers for Disease Control and Prevention (U.S.); Cessation of life; Chest; Chronic Bronchitis; Chronic Obstructive Pulmonary Disease; Cities; Clinic; Clinical; Clinical Data; Computational algorithm; Computers; DNA; DNA Methylation; Data; Diagnostic Imaging; Diagnostic tests; Disease Progression; Early Diagnosis; Early treatment; Eligibility Determination; Epigenetic Process; Exposure to; Grant; Heterogeneity; High Resolution Computed Tomography; Image; Lesion; Lobar; Lung; Lung diseases; Lung nodule; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Measurement; Measures; Methods; Methylation; Modeling; Nodule; Obstructive Lung Diseases; Outcome; Patient Self-Report; Performance; Persons; Population; Predictive Value; Predisposition; Pulmonary Emphysema; Radiation exposure; Radiology Specialty; Reporting; Research; Research Subjects; Risk; Risk Factors; Sampling; Screening for cancer; Shapes; Smoking; Smoking Cessation Intervention; Smoking History; Structure; Structure of parenchyma of lung; Testing; Texture; Thoracic Radiography; Tobacco; Tobacco use; United States Department of Veterans Affairs; Universities; Validation; X-Ray Computed Tomography; airway obstruction; automated segmentation; cancer epidemiology; cancer risk; cigarette smoke; cigarette smoking; clinical diagnosis; cohort; computed tomography screening; digital; epigenetic marker; former smoker; image processing; improved; interest; low dose computed tomography; lung cancer screening; machine learning classification; meetings; methylation testing; mortality; predictive modeling; prevent; prospective; radiomics; repository; risk prediction; risk prediction model; screening; shared decision making; smoking exposure; tobacco control; tobacco exposure; trial enrollment; tv watching

ABSTRACT Smoking is the largest risk factor for both lung cancer and obstructive lung disease. The National Lung Screening Trial (NLST) enrolled subjects who reported a cigarette smoking history of at least 30 pack years and showed that annual low-dose computed tomography (LDCT) screening could reduce mortality from lung cancer by approximately 16%, compared to conventional chest x-ray. However, it remains clinically challenging to efficiently distinguish the small number of malignant nodules from the many benign lung nodules detected with screening. In addition, the chest LDCT data captured during screening also has untapped utility in quantitatively evaluating obstructive lung disease. LDCT captures a wealth of information that can be automatically and objectively quantified and extracted from the image data using computer algorithms. We have methods for automated segmentation of structures of interest from the image data and will extract hundreds of radiological biomarkers focused on pulmonary nodules, peri-nodular lung parenchyma, the whole lung, and capture lobar heterogeneity. This study will also incorporate an objective epigenetic biomarker of smoking history via measurement of DNA methylation at cg05575921. Our epigenetic biomarker has been shown to strongly predict smoking intensity by several studies. We will use the objective radiological and epigenetic biomarkers and machine learning approaches to predict both (1) the risk of lung cancer and (2) rapid obstructive lung disease progression in the NLST screening population. We hypothesize that incorporating DNA methylation at cg05575921 will be a valuable addition to both prediction models. Determining the outcome of the hypothesis will guide if this epigenetic biomarker should be incorporated in prospective lung cancer screening studies. This project will have impact as it will result in an improved automatic risk prediction algorithm to guide management in subjects with a lung nodule detected by LDCT screening. This approach can facilitate rapid treatment for those with cancer and prevent complications from invasive diagnostic testing as well as unnecessary radiation exposure from diagnostic imaging in those with benign lesions. Predicting rapid obstructive lung disease progression may be beneficial for clinician/subject shared decision-making discussions and targeted smoking cessation interventions in addition to improving lung cancer prediction.

摘要