PREDICTIVE MODELING IN LUNG CANCER

肺癌的预测模型

• 批准号: 9121525
• 负责人: Varun Puri
• 金额: $ 15.82万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9121525
• 关键词: Address; Adjuvant Therapy; Aftercare; Cancer Etiology; Cancer Patient; Cessation of life; Characteristics; Clinic; Clinical; Clinical Research; Combined Modality Therapy; Communication; Cost Effectiveness Analysis; Data; Databases; Decision Analysis; Decision Modeling; Diagnosis; Disease; Disease-Free Survival; Effectiveness; Electronics; Foundations; Genetic study; Goals; Gold; Health; Intervention; Learning; Life; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of thorax; Master of Science; Medicare; Memorial Sloan-Kettering Cancer Center; Methodology; Methods; Modality; Modeling; Non-Small-Cell Lung Carcinoma; Operative Surgical Procedures; Outcome; Participant; Patient Care; Patients; Performance Status; Policies; Policy Maker; Postdoctoral Fellow; Provider; Publications; Radiation therapy; Research; Research Design; Research Personnel; Research Training; Risk; Sample Size; Societies; Staging; Testing; Thoracic Oncology; Training; Training Support; Treatment Cost; Treatment outcome; United States; Universities; Validation; Washington; abstracting; career; career development; chemoradiation; clinical care; clinical investigation; cost; cost effective; cost effectiveness; design; economic impact; experience; health care delivery; health economics; high risk; improved outcome; markov model; medical schools; mortality; oncology; operation; outcome forecast; patient subsets; predictive modeling; prognostic value; programs; prospective; responsible research conduct; skills; statistics; survival prediction; tool; tumor; user-friendly; web site

DESCRIPTION (provided by applicant): An estimated 201,000 new cases of lung cancer will be diagnosed in the United States in 2012. Lung cancer, the most common cause of cancer-related mortality, causes nearly 150,000 deaths annually. It is estimated that the costs of taking care of patients with lung cancer exceed $40 billion annually. Despite the tremendous impact of lung cancer on society, there are no reliable, clinically applicable methods to predict post-treatment outcomes in lung cancer patients. It has been shown that survival after diagnosis of lung cancer depends on both patient and tumor characteristics. Type of treatment or operation also impacts survival. Previous attempts at creating predictive models for survival after treatment for lung cancer have been severely limited by lack of detailed patient information, methodologic issues, and lack of validation. This career development proposal is designed to provide training and support for the applicant to become an independent clinical researcher focused on evaluating and modeling outcomes in thoracic oncology. The career development goals of this proposal are; 1. Obtain didactic training for a strong foundation in responsible conduct of research, research design, statistics, modeling methodology, decision analysis, and communication of risk to patients and providers. 2. Develop expertise in creating predictive models to assess competing therapies for common thoracic cancers and performing cost-effectiveness analyses. 3. Develop the skills necessary to communicate and disseminate results of the studies, implement research findings in practice, and influence change in policy and healthcare delivery to improve outcomes. The short-term career development goals will be accomplished by completing a Master of Science in Clinical Investigation degree at Washington University. To develop the practical skill set, the applicant will utilize decision analytic modelig to evaluate and predict long-term survival after surgery or radiation therapy for patients with early-stage lung cancer. Similar methods will be used to study the effectiveness and cost- effectiveness of treatment options for locally advanced lung cancer. The clinical objective is to develop and disseminate tools that can predict survival after treatment for lung cancer and to evaluate the cost-effectiveness of treatment options. The models will be made available to clinicians and the public on the Washington University website via an electronic, user-friendly interface. The models will support investigators seeking to assess prognosis for patients. Our results will also serve as baseline for assessing the value of new and emerging tests like genetic studies, which could be compared to and incorporated into the models. The career objective of the proposal is to develop the candidate into an independent investigator, who can implement modeling approaches to assess the impact of interventions on clinical care in oncology.

描述（由申请人提供）：预计 2012 年美国将诊断出 201,000 例新肺癌病例。肺癌是癌症相关死亡的最常见原因，每年导致近 150,000 人死亡。据估计，每年照顾肺癌患者的费用超过 400 亿美元。尽管肺癌对社会产生巨大影响，但目前还没有可靠的、临床适用的方法来预测肺癌患者的治疗后结果。研究表明，肺癌诊断后的生存率取决于患者和肿瘤的特征。治疗或手术的类型也会影响生存。之前创建肺癌治疗后生存预测模型的尝试因缺乏详细的患者信息、方法学问题和缺乏验证而受到严重限制。该职业发展提案旨在为申请人提供培训和支持，使其成为一名专注于胸部肿瘤学结果评估和建模的独立临床研究员。该提案的职业发展目标是： 1. 获得教学培训，为负责任的研究行为、研究设计、统计、建模方法、决策分析以及向患者和提供者传达风险奠定坚实的基础。 2. 发展创建预测模型的专业知识，以评估常见胸部癌症的竞争疗法并进行成本效益分析。 3. 培养沟通和传播研究结果、在实践中实施研究结果以及影响政策和医疗保健服务的变化以改善结果所需的技能。短期职业发展目标将通过完成华盛顿大学临床研究理学硕士学位来实现。为了发展实用技能，申请人将利用决策分析模型来评估和预测早期肺癌患者手术或放射治疗后的长期生存率。类似的方法将用于研究局部晚期肺癌治疗方案的有效性和成本效益。临床目标是开发和传播可以预测肺癌治疗后生存率的工具，并评估治疗方案的成本效益。这些模型将通过用户友好的电子界面在华盛顿大学网站上向临床医生和公众开放。这些模型将支持研究人员评估患者的预后。我们的结果还将作为评估遗传研究等新兴测试价值的基线，可以与模型进行比较并纳入模型中。该提案的职业目标是将候选人培养成为一名独立研究者，能够实施建模方法来评估干预措施对肿瘤学临床护理的影响。