Translating Molecular and Clinical Data to Population Lung Cancer Risk Assessment

将分子和临床数据转化为人群肺癌风险评估

• 批准号: 9518758
• 负责人: Rayjean J. Hung
• 金额: $ 49.16万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9518758
• 关键词: 3-Dimensional; Address; Administrator; American; Anxiety; Benign; Biological Assay; Biological Markers; Calibration; Canada; Cancer Etiology; Cancer Patient; Cessation of life; Classification; Clinical; Clinical Data; Clinical Protocols; Collaborations; Communities; Data; Data Element; Diagnostic Procedure; Diagnostic radiologic examination; Epidemiology; Equilibrium; Europe; European; General Population; Goals; Health; Individual; International; Joints; Lead; Logistics; Lung; Lung nodule; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Measurement; Modeling; Molecular; Molecular Profiling; Molecular Target; Nodule; Participant; Patient observation; Patients; Performance; Population; Positioning Attribute; Predictive Value; Prevention; Public Health; Questionnaires; Radiology Specialty; Recording of previous events; Reporting; Research; Research Personnel; Respiratory physiology; Risk; Risk Assessment; Risk stratification; Role; Scanning; Statistical Models; System; Translating; Validation; Work; base; cancer risk; cohort; computed tomography screening; data resource; epidemiologic data; genomic biomarker; genomic epidemiology; genomic profiles; high risk; improved; low-dose spiral CT; lung basal segment; lung cancer screening; molecular marker; mortality; programs; radiomics; risk prediction model; screening; screening guidelines; screening program; specific biomarkers; two-dimensional

Project Summary Lung cancer continues to be the most common cancer and reduction of lung cancer related death is a global priority. The National Lung Screening Trial (NLST) reported that the low-dose computed tomography (LDCT) screening reduced the lung cancer mortality by 20%, with a trade-off of more than 95% of false positive results. This underlined our need for a much improved risk prediction model and higher screening efficiency to balance the benefits and potential harms. Although there have been substantial efforts in establishing lung cancer risk prediction models, none have taken all aspects into account and the joint performance of all predictors remains unknown. For those with CT nodules, currently there is a wide range of clinical protocols on how they are managed, from watchful waiting to invasive diagnostic procedures. With the usage of LDCT scans rapidly growing following the NLST report, there is an urgent need to address the issues of (i) who should be recommended for screening and (ii) what to do when a nodule is found. Our research team is in the unique position to conduct this much needed work as we have already established extensive resources for the data elements needed being the lead investigators of the three lung cancer consortia (International Lung Cancer Consortium, Transdisciplinary Research in Cancer of Lung, and Lung Cancer Cohort Consortium), and have established collaborations with the lung cancer CT screening programs in the US, Canada and Europe. The overall goal of this project is to translate the epidemiological, molecular and clinical data into lung cancer risk assessment and to improve nodule assessment. Specifically, we will (i) establish an integrated risk prediction model to identify individuals at high risk of lung cancer, combining personal health and exposure history, targeted molecular and genomic profile and lung function data based lung cancer CT screening populations in US, Canada and Europe based on a total of 950 CT-detected lung cancer patients from cohorts of 46,057 screening individuals; and (ii) establish a comprehensive nodule assessment models for individuals with LDCT-detected non-calcified pulmonary nodules based on both 2 dimensional-based and 3D volume and radiomics-based probability models. We will compare the model performance with the existing classification system such as Lung-RADS and conduct net benefit and decision curve analysis to assess their clinical usefulness. These models will be very valuable for the general public, clinicians, researchers and health administrators. It will increase the efficiency of lung cancer LDCT screening, and reduce unnecessary workup (and patient anxiety) for those who were found to have LDCT-detected pulmonary non-calcified nodules. The impact of this project will be wide-spread in our community.

项目摘要 肺癌仍然是最常见的癌症，减少肺癌相关死亡是最重要的。 全球优先。国家肺筛查试验（NLST）报告说， 断层扫描（LDCT）筛查降低肺癌死亡率20%， 95%以上的假阳性结果。这突出表明我们需要大大改进风险预测 模型和更高的筛选效率，以平衡获益和潜在危害。虽然 在建立肺癌风险预测模型方面已经做了大量的努力，但没有一个人采取了所有 考虑到各个方面，所有预测因素的联合表现仍然未知。者 CT结节，目前有广泛的临床协议，如何管理，从 警惕地等待侵入性诊断程序。随着LDCT扫描的使用快速增长 在NLST报告之后，迫切需要解决以下问题：（i）谁应该 建议进行筛查和（ii）当发现结节时该怎么办。我们的研究团队 在进行这项急需的工作，因为我们已经建立了广泛的 作为三种肺癌的主要研究者， 国际肺癌联合会（International Lung Cancer Consortia） Lung和肺癌队列联盟），并与肺 美国、加拿大和欧洲的癌症CT筛查项目。该项目的总体目标是 将流行病学、分子和临床数据转化为肺癌风险评估， 改进结核评估。具体而言，我们将（i）建立综合风险预测模型 结合个人健康状况和接触史， 基于靶向分子和基因组谱及肺功能数据的肺癌CT筛查 美国、加拿大和欧洲的人群，基于总计950例CT检测的肺癌患者 来自46，057名筛查个体的队列;以及（ii）建立全面的结节 LDCT检测到非钙化肺结节的个体的评估模型， 基于2维和3D体积和基于放射学的概率模型。我们将比较 模型性能与现有的分类系统，如肺RADS和导管网 效益和决策曲线分析，以评估其临床实用性。这些模型将非常 对公众、临床医生、研究人员和卫生管理人员都很有价值。会增加 肺癌LDCT筛查的效率，并减少不必要的检查（和患者焦虑）， LDCT检查发现肺内非钙化结节者。这样做的影响 该项目将在我们的社区广泛传播。