Evaluating a Risk Prediction Model for Lung Cancer

评估肺癌风险预测模型

• 批准号: 9271910
• 负责人: Lori Sakoda
• 金额: $ 19.27万
• 依托单位: KAISER FOUNDATION RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-15 至 2020-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9271910
• 关键词: Active Learning; Address; Adult; Advisory Committees; Age; Award; Benign; Biometry; Body mass index; California; Cancer Control; Cancer Detection; Cancer Etiology; Cancer Family; Chronic Obstructive Airway Disease; Cigarette; Clinical; Cohort Analysis; Colorectal; Communities; Complex; Custom; Data; Decision Aid; Decision Making; Diagnosis; Discrimination; Doctor of Philosophy; Early Diagnosis; Education; Electronic Health Record; Eligibility Determination; Enrollment; Environment; Family history of; Functional disorder; Genes; Genetic; Genotype; Goals; Guidelines; Health; Health Surveys; Image; Individual; Integrated Delivery of Health Care; Intervention; K-Series Research Career Programs; Knowledge; Lead; Leadership; Lung; Lung nodule; Malignant - descriptor; Malignant neoplasm of lung; Medical; Mentors; Mentorship; Modeling; Ovarian; Performance; Persons; Population; Positioning Attribute; Practice Management; Preventive service; Probability; Professional Organizations; Prostate; Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; Race; Radiation exposure; Recording of previous events; Research; Research Activity; Resources; Risk; Screening for cancer; Skin Carcinoma; Smoker; Smoking; Smoking History; Smoking Status; Subgroup; Surveys; System; Time; Training; Training Activity; Translating; Unnecessary Procedures; Work; aged; arm; base; biomedical informatics; cancer diagnosis; cancer epidemiology; career; clinical practice; clinical predictors; clinically relevant; cohort; cost efficient; ethnic diversity; experience; follow-up; genetic epidemiology; high risk; improved; low-dose spiral CT; lung cancer screening; meetings; member; mortality; predictive modeling; programs; public health relevance; screening; skills; smoking cessation; surveillance strategy; translational scientist; tumor; whole genome

 DESCRIPTION (provided by applicant): This Career Development Award will support Lori Sakoda, PhD, in her transition to independence as a translational researcher in lung cancer. Her long-term career goal is to inform and improve real-world strategies for lung cancer detection and control by leading transdisciplinary research that integrates analysis of complex, large-scale biomedical data. Risk prediction models could be valuably employed to optimize the benefit-to-harm ratio of screening strategies for lung cancer in smokers. To support their use in clinical practice, however, there must be convincing evidence of their predictive ability to identify smokers at highest risk for lung cancer and/or to differentiate those presenting with malignant versus benign lung nodules. Her mentored research will evaluate whether a newly developed, clinically-oriented risk prediction model for lung cancer, as proposed or modified, could aid decision-making in the context of lung cancer screening. The specific aims are to 1) validate the predictive performance of the model; 2) determine the incremental value of adding genetic and other clinical predictors to the model; and 3) examine the predictive performance of the baseline model and the best predictive extended model in persons who meet the U.S. Preventative Services Task Force eligibility criteria for lung cancer screening with low-dose computed tomography. As an exploratory aim, the predictive performance of these same two models will be assessed in the subgroup of screening- eligible persons diagnosed incidentally with lung nodules. These aims will be addressed by integrating survey, whole genome genotyping, and electronic health record (EHR) data on a large, contemporary cohort of smokers in the Kaiser Permanente Northern California (KPNC) Research Program on Genes, Environment, and Health. The proposal builds on the candidate's prior training in cancer epidemiology to fill knowledge gaps in clinical domains (lung pathophysiology, lung cancer detection and management practices, and medical decision-making) and scientific domains pertinent to integrated analysis of EHR and other complex, large-scale data (biostatistics, genetic epidemiology, and biomedical informatics), which will allow her to more effectively generate and translate scientific evidence into clinical practice. Training will be acquired from coursework, seminars, professional society meetings, and experiential learning, under the guidance of a highly qualified team of mentors and scientific advisors. She will also build clinical and scientifc partnerships essential to succeed in her current setting. The KPNC Division of Research is an ideal training environment, given its long history of important contributions to cancer screening guidelines, due to both its scientific leadership and its access to an ethnically diverse and stabl membership (currently over three million adults) for whom EHR data are kept indefinitely. The proposed plan will provide the candidate with preliminary data to develop a competitive R01 proposal, along with specialized knowledge and skills to successfully establish a transdisciplinary research program focused on optimizing strategies for lung cancer detection and control.

 描述（由申请人提供）：这个职业发展奖将支持洛里Sakoda，博士，在她过渡到独立的肺癌翻译研究人员。她的长期职业目标是通过领先的跨学科研究，整合复杂的，大规模的生物医学数据的分析，为肺癌检测和控制提供信息和改善现实世界的策略。风险预测模型可用于优化吸烟者肺癌筛查策略的获益-危害比。然而，为了支持其在临床实践中的使用，必须有令人信服的证据表明其预测能力，以识别吸烟者患肺癌的最高风险和/或区分那些呈现恶性与良性肺结节。她的指导研究将评估一个新开发的，临床导向的肺癌风险预测模型，如建议或修改，是否可以帮助决策的背景下，肺癌筛查。具体目标是：1）验证模型的预测性能; 2）确定将遗传和其他临床预测因子添加到模型中的增量值; 3）检查基线模型和最佳预测扩展模型在符合美国预防服务工作组低剂量计算机断层扫描肺癌筛查资格标准的人群中的预测性能。作为探索性目的，将在偶然诊断为肺结节的符合筛选条件的患者亚组中评估这两种模型的预测性能。这些目标将通过整合调查，全基因组基因分型，电子健康记录（EHR）数据的一个大型，当代队列的吸烟者在凯撒永久北方加州（KPNC）的基因，环境和健康研究计划。该提案建立在候选人先前在癌症流行病学方面的培训基础上，以填补临床领域的知识空白（肺病理生理学、肺癌检测和管理实践以及医疗决策）以及与EHR和其他复杂的大规模数据的综合分析相关的科学领域（生物统计学、遗传流行病学和生物医学信息学），这将使她能够更有效地产生和转化科学证据到临床实践。培训将从课程，研讨会，专业协会会议和体验式学习中获得，在高素质的导师和科学顾问团队的指导下。她还将建立临床和科学伙伴关系，这对她目前的环境取得成功至关重要。KPNC研究部是一个理想的培训环境，鉴于其对癌症筛查指南的重要贡献的悠久历史，由于其科学领导地位以及其获得种族多样性和稳定的成员（目前超过300万成年人），其中EHR数据无限期保存。拟议的计划将为候选人提供初步数据，以制定有竞争力的R 01提案，沿着专业知识和技能，以成功建立一个跨学科的研究计划，重点是优化肺癌检测和控制策略。