Decreasing Unnecessary Invasive Lung Cancer Diagnostic Procedures

描述(由申请人提供)： 肺癌是癌症死亡的头号原因，退伍军人患这种致命疾病的可能性要高出25%到76%。肺癌研究领域的主要挑战是试图预防导致患者死亡的晚期肺癌，同时将所需的侵入性诊断技术造成的潜在危害降至最低。由于肺癌是如此致命，患者和提供者必须积极寻求诊断，以排除癌症。肺不容易接触，而且这些活检通常需要侵入性的和昂贵的检查。尽管有先进的成像技术和临床判断，但高达40%的疑似肺癌患者的手术结果是良性的。手术切除发现良性疾病的高比率将持续到额外的病人护理。这一职业发展奖允许我在以下方面追求研究技能和研究经验：1)开发和验证基于证据的外科算法，以减少不必要的手术；2)通过不遗漏肺癌病例来提高患者的安全性；3)为可疑肺结节患者实施安全且经济有效的肺结节临床算法。研究一：开发用于诊断外科评估的肺结节处理的循证临床算法。我们假设，在出现可疑肺结节的患者中预测良性疾病的新模型的曲线下ROC面积(AUC)至少为0.85。目前的模型不包括外科医生用来估计手术前癌症或良性疾病的可能性并确定是否对可疑结节进行手术的所有流行病学和成像数据。这一目标将把VA-TVHS患者数据库、Vanderbilt肺结节队列和弗吉尼亚大学的数据库结合成950名患者的肺结节队列。回归模型将从这一队列中开发出来，还将包括对新的肺癌生物标记物的探索性分析。研究二：评价肺结节临床处理算法的普适性，为外科诊断性评价提供参考。我们将使用阿拉巴马大学伯明翰分校(UAB)和已完成的美国外科学会(ACOSOG)Z4031合作试验的现有数据集，对研究一中开发的预测工具进行外部验证。这些数据集将被合并，以形成1500名患者验证队列。生物标志物也将在ACOSOG数据集中从存储的临床样本中进行评估。研究三：在多机构前瞻性队列中评估肺结节临床算法对患者预后的预测影响。预期的686名患者将来自VA-TVHS、VA-Birmingham和VUMC胸外科诊所。这项研究不会在临床实践中实施诊断算法，但为实现两个目标提供了一个安全的港湾。首先，我们将前瞻性地评估这种算法通过不遗漏肺癌病例和避免不必要的手术而潜在受益的患者数量。其次，我们将使用决策分析来对我们的算法在这个队列中进行增量成本效益分析。我们假设，使用该预测工具将使手术切除的肺结节的良性诊断率从40%降至至少30%，总体准确率将超过85%，并且具有成本效益。未来的研究将设计一项前瞻性的多机构VA试点研究，以评估转诊为肺结节外科评估的患者的算法。

DESCRIPTION (provided by applicant): Lung cancer is the number one cause of cancer death and Veterans are 25% to 76% more likely to develop this deadly disease. The main challenge in the field of lung cancer research is trying to prevent advanced lung cancers that kill patients and simultaneously minimize the potential harm caused by required invasive diagnostic techniques. Because lung cancer is so deadly, patients and providers must aggressively pursue a diagnosis to rule out cancer. The lung is not easily accessible and these biopsies often require an invasive and costly. Despite advanced imaging techniques and clinical judgment, up to 40% of the operations on patients with suspected lung cancer result in a benign diagnosis. The high rate of benign disease discovered by operative resection will continue until additional patient care. This career development award permits me to pursue research skills and investigator experience for 1) developing and validating evidence-based surgical algorithms for reducing unnecessary surgery, 2) improving patient safety by not missing cases of lung cancer, 3) implementing a safe and cost effective lung nodule clinical algorithm for patients with suspicious pulmonary nodules. Study One: To develop an evidence-based clinical algorithm for management of lung nodules referred for diagnostic surgical evaluation. We hypothesize that a new model predicting benign disease among patients presenting with suspicious pulmonary nodules will have a ROC area under the curve (AUC) of at least 0.85. Current models do not include all the epidemiological and imaging data used by surgeons to estimate the pre- surgical likelihood of cancer or benign disease and determine whether to operate on a suspicious nodule. This aim will combine the VA-TVHS patient database, Vanderbilt Lung Nodule Cohort, and the University of Virginia database into a 950 patient Lung Nodule Cohort. A regression model will be developed from this cohort and will also include an exploratory analysis of new lung cancer biomarkers. Study Two: To evaluate the generalizability of the lung nodule clinical algorithm for management of lung nodules referred for diagnostic surgical evaluation. We will externally validate the prediction tool developed in Study One with existing datasets from the University of Alabama, Birmingham (UAB) and the completed American College of Surgeons (ACOSOG) Z4031 cooperative trial. These datasets will be combined to form a 1500 patient validation cohort. Biomarkers will also be assessed in the ACOSOG dataset from stored clinical samples. Study Three: To evaluate the predicted impact of the lung nodule clinical algorithm on patient outcomes in a multi-institutional prospective cohort. The prospective 686-patient cohort will be from VA-TVHS, VA- Birmingham and VUMC thoracic surgery clinics. This study will NOT implement the diagnostic algorithm in clinical practice but provide a safe harbor to accomplish two aims. First, we will prospectively evaluate the number of patients potentially benefiting from such algorithm by not missing cases of lung cancer and avoiding unnecessary operations. Second, we will use decision analysis to perform an incremental cost-effectiveness analysis of our algorithm in this cohort. We hypothesize that use of the prediction tool will reduce the benign diagnosis rate in surgically resected pulmonary nodules from 40% to at least 30%, the overall accuracy will be over 85% and it will be cost effective. Future studies will design a prospective multi-institutional VA pilot study to evaluate the algorithm for patients referred for surgical evaluation of pulmonary nodules.