A population-based study of deep learning derived organ and tissue measures for accelerated aging using repurposed abdominal CT images

使用重新调整用途的腹部 CT 图像对深度学习衍生的器官和组织加速衰老措施进行基于人群的研究

• 批准号: 10795414
• 负责人: ANDREW David RULE
• 金额: $ 67.06万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10795414
• 关键词: Abdomen; Acceleration; Adult; Affect; Age; Aging; Aorta; Archives; Artificial Intelligence; Biological; Biological Markers; Biology of Aging; Blood; Body Composition; Bone Density; Calendar; Cardiovascular Diseases; Cardiovascular system; Cessation of life; Chronic; Chronic Kidney Failure; Chronology; Cirrhosis; Classification; Clinical; Cyst; DNA Methylation; Data; Databases; Diagnostic tests; Disease; Early Intervention; End stage renal failure; Epidemiology; Ethnic Population; Event; Fatty acid glycerol esters; Frequencies; General Population; Goals; Health; Hospitalization; Image; International Classification of Disease Codes; Ischemic Bowel Disease; Kidney; Kidney Diseases; Laboratories; Laboratory Finding; Length; Liver; Lower Extremity; Magnetic Resonance Imaging; Measures; Medical Records; Methods; Modeling; Muscle; Older Population; Organ; Outcome; Pancreas; Patients; Peripheral arterial disease; Persons; Physical Function; Population; Population Study; Race; Records; Reference Values; Renal Artery Stenosis; Research; Resolution; Resources; Risk; Sampling; Scanning; Sex Differences; Skeletal Muscle; Symptoms; System; Testing; Tissue Model; Tissues; United States; Variant; X-Ray Computed Tomography; abdominal CT; abdominal fat; age difference; age related; aged; bone; brain magnetic resonance imaging; calcification; clinical diagnosis; clinical practice; cohort; deep learning; deep learning model; density; disease classification; follow-up; high risk; image archival system; improved; living kidney donor; mortality; peer; population based; prognostic; racial difference; sex; telomere; tool; trend

PROJECT SUMMARY There has been a dramatic increase in the number of persons living with reduced physical function and with aging-related chronic conditions. If we compare chronological age (calendar-based age) with biological age (changes at the cellular, tissue, organ, and system levels), we can classify persons as aging faster (accelerated aging) or slower (successful aging) than their peers. Methods have been developed to measure biological age based on DNA methylation, telomere length, and blood biomarkers. However, such measures may not accurately reflect organ- and tissue-level changes from aging. A multi-organ/tissue approach is needed to identify comprehensive age-related structural changes before signs, symptoms, or clinical diagnoses occur. Abdominal computed tomography (CT) has widespread use in the general population (35% of adults ages 20-89 years in an 11-year period). Quantitative measures of the organs and tissues on abdominal CT may predict organ-specific diseases, or in combination, may be used to calculate biological age and predict the more global outcomes of hospitalization and mortality. Therefore, our central hypothesis is that deep learning (DL) models applied to abdominal CTs can quantify structural features of the organs and tissues to identify persons with accelerated aging at high-risk for organ-specific disease, hospitalization, and death. The Rochester Epidemiology Project record-linkage system provides access to a general population archive of images for 423,081 abdominal CTs and to comprehensive medical record data among 181,187 adults (ages 20-89 years) between 2010-2020. Our team has already developed and validated DL tools to measure liver, kidney, aorta, fat, muscle, and bone on abdominal CT images. We will leverage these resources to 1) establish percentiles of abdominal CT biomarkers from both healthy and general population samples; 2) determine the risk of organ-specific clinical disease by abdominal CT biomarkers in the general population; and 3) determine the risk of hospitalization and death associated with abdominal CT measures in the general population. If successful, application of DL tools to abdominal CT images will enrich the characterization of age-related health risks without additional testing burden. Subclinical abdominal CT biomarkers may also inform the biology of aging and early disease, improve disease classification, and provide opportunities for early intervention.

项目概要 身体机能下降和患有疾病的人数急剧增加 与衰老相关的慢性病。如果我们将实际年龄（基于日历的年龄）与生物年龄进行比较 （细胞、组织、器官和系统水平的变化），我们可以将人归类为衰老速度更快 比同龄人（加速衰老）或更慢（成功衰老）。已经开发出测量方法 基于 DNA 甲基化、端粒长度和血液生物标志物的生物年龄。然而，此类措施 可能无法准确反映衰老引起的器官和组织水平的变化。多器官/组织方法是 需要在体征、症状或临床症状之前识别与年龄相关的全面结构变化 发生诊断。腹部计算机断层扫描 (CT) 在普通人群中广泛使用（35% 11 年期间 20-89 岁的成年人）。器官和组织的定量测量 腹部 CT 可以预测器官特异性疾病，或者组合起来可以用来计算生物年龄 并预测住院和死亡率的全球结果。因此，我们的中心假设是 应用于腹部 CT 的深度学习 (DL) 模型可以量化器官和组织的结构特征 识别加速衰老的器官特异性疾病、住院和死亡高风险人群。 罗切斯特流行病学项目记录链接系统提供对一般人群档案的访问 423,081 份腹部 CT 图像以及 181,187 名成年人（年龄 20-89岁）2010-2020年间。我们的团队已经开发并验证了 DL 工具来测量肝脏、 腹部 CT 图像上的肾脏、主动脉、脂肪、肌肉和骨骼。我们将利用这些资源来 1) 建立 健康人群和普通人群样本中腹部 CT 生物标志物的百分位数； 2）确定 一般人群中腹部 CT 生物标志物的器官特异性临床疾病风险； 3) 确定 一般人群中与腹部 CT 测量相关的住院和死亡风险。如果 如果成功的话，深度学习工具在腹部 CT 图像中的应用将丰富与年龄相关的特征 健康风险，无需额外的测试负担。亚临床腹部 CT 生物标志物也可以告知 衰老和早期疾病的生物学，改进疾病分类，并为早期疾病提供机会 干涉。