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: 9657412
• 负责人: Rayjean J. Hung
• 金额: $ 49.49万
• 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/9657412
• 关键词: Translating; Molecular; Clinical; Data; Population

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，减少与肺癌相关的死亡 这是全球的优先事项。国家肺部筛查试验(NLST)报告称，低剂量计算机 断层扫描(LDCT)筛查将肺癌死亡率降低了20%，但代价更大 95%以上的假阳性结果。这突显了我们需要更好的风险预测 模型和更高的筛选效率，以平衡好处和潜在的危害。尽管在那里 在建立肺癌风险预测模型方面做出了大量努力，但没有一个人全盘接受 考虑到各方面的因素，所有预测者的联合表现仍不得而知。对于那些拥有 CT结节，目前有各种各样的临床方案来处理它们，从 警惕地等待着侵入性的诊断程序。随着LDCT扫描的使用迅速增长 在NLST报告之后，迫切需要解决以下问题：(1)谁应该 建议进行筛查，以及(2)发现结节时应如何处理。我们的研究团队是 以独特的地位进行这项亟需的工作，因为我们已经建立了广泛的 作为三种肺癌主要调查人员所需的数据元素的资源 国际肺癌联合会，癌症跨学科研究 和肺癌队列联盟)，并与肺组织建立了合作关系 美国、加拿大和欧洲的癌症CT筛查计划。这个项目的总体目标是 将流行病学、分子和临床数据转化为肺癌风险评估，并 改进结节评估。具体地说，我们将(一)建立综合风险预测模型 结合个人健康和接触史，确定肺癌高危人群， 靶向分子和基因组图谱及肺功能数据在肺癌CT筛查中的应用 基于总共950名CT检测的肺癌患者的美国、加拿大和欧洲的人口 从46,057名筛查个人中选出；和(2)建立一个全面的结核 基于LDCT检出非钙化肺结节的个体评估模型 基于二维和三维体积和放射组学的概率模型。我们会比较一下 模型的性能与现有的分类系统，如肺-RADS和行为网 效益和决策曲线分析，以评估其临床有效性。这些模型将非常 对普通公众、临床医生、研究人员和卫生管理人员具有重要价值。这将增加 肺癌LDCT筛查的效率，并减少不必要的工作(和患者焦虑) LDCT检查发现肺内无钙化结节的患者。这件事的影响 该项目将在我们的社区广泛传播。