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.

抽象的 吸烟是肺癌和阻塞性肺病的最大危险因素。国家肺 筛选试验 (NLST) 纳入了报告有至少 30 包年吸烟史的受试者，并且 研究表明，每年进行低剂量计算机断层扫描 (LDCT) 筛查可以降低肺癌死亡率 与传统胸部 X 光检查相比，减少约 16%。然而，临床上仍然具有挑战性 有效地区分少数恶性结节与检测到的许多良性肺结节 筛选。此外，筛查期间捕获的胸部 LDCT 数据在定量方面也具有尚未开发的效用。 评估阻塞性肺疾病。 LDCT 捕获了大量可以自动、客观地量化和提取的信息 使用计算机算法从图像数据中获得。我们有自动分割结构的方法 从图像数据中提取兴趣，并将提取数百个专注于肺结节的放射生物标志物， 结节周围肺实质、整个肺，并捕获肺叶异质性。这项研究还将纳入 通过测量 cg05575921 的 DNA 甲基化来作为吸烟史的客观表观遗传生物标志物。我们的 多项研究表明表观遗传生物标志物可以强有力地预测吸烟强度。我们将使用 客观的放射学和表观遗传生物标志物以及机器学习方法来预测 (1) 的风险 NLST 筛查人群中肺癌和 (2) 阻塞性肺疾病的快速进展。我们 假设在 cg05575921 处合并 DNA 甲基化将对这两种预测都有价值 模型。确定假设的结果将指导是否应纳入该表观遗传生物标志物 前瞻性肺癌筛查研究。 该项目将会产生影响，因为它将产生改进的自动风险预测算法来指导 对通过 LDCT 筛查检测到肺结节的受试者进行管理。这种方法可以促进快速 治疗癌症患者并预防侵入性诊断测试引起的并发症以及 良性病变患者因诊断成像而遭受不必要的辐射。快速预测 阻塞性肺病进展可能有利于临床医生/受试者共同决策讨论 除了改善肺癌预测之外，还有针对性的戒烟干预措施。