Decreasing Unnecessary Invasive Lung Cancer Diagnostic Procedures

减少不必要的侵袭性肺癌诊断程序

• 批准号: 8201844
• 负责人: Eric L Grogan
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8201844
• 关键词: Affect; Alabama; Algorithms; American College of Surgeons; American College of Surgeons Oncology Group; Area Under Curve; Benign; Biological Markers; Biopsy; Cancer Diagnostics; Cancer Etiology; Cessation of life; Chest; Clinic; Clinical; Clinical Data; Cost Effectiveness Analysis; Data; Data Element; Data Set; Databases; Death Rate; Decision Analysis; Diagnosis; Diagnostic; Diagnostic Procedure; Disease; Dose; Epidemiology; Evaluation; Excision; Future; Image; Imaging Techniques; Individual; Judgment; K-Series Research Career Programs; Lesion; Lung; Lung nodule; Malignant Neoplasms; Malignant neoplasm of lung; Modeling; Morbidity - disease rate; Nodule; Operative Surgical Procedures; Outcome; Patient Care; Patients; Pilot Projects; Population; Provider; Research; Research Personnel; Resected; Safety; Sampling; Scanning; Surgeon; Techniques; Testing; Therapeutic; Thoracic Surgeon; Thoracic Surgical Procedures; Universities; Unnecessary Surgery; Validation; Veterans; Virginia; Work; X-Ray Computed Tomography; anticancer research; clinical practice; cohort; cost effective; cost effectiveness; design; evidence base; experience; high risk; improved; killings; mortality; operation; patient safety; predictive modeling; prevent; prospective; screening; skills; tool

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.

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高出 25% 至 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，同时最大限度地减少所需侵入性诊断技术造成的潜在危害。由于肺癌是如此致命，患者和医疗服务提供者必须积极寻求诊断以排除癌症。肺部不易进入，这些活检通常需要侵入性且昂贵。尽管有先进的成像技术和临床判断，对疑似肺癌患者进行的手术仍有高达 40% 的结果是良性诊断。手术切除发现的良性疾病的高发生率将持续到额外的患者护理为止。该职业发展奖使我能够追求研究技能和研究者经验：1）开发和验证基于证据的手术算法，以减少不必要的手术，2）通过不错过肺癌病例来提高患者安全性，3）为可疑肺结节患者实施安全且具有成本效益的肺结节临床算法。 研究一：开发一种基于证据的临床算法，用于管理转诊诊断性手术评估的肺结节。我们假设，预测可疑肺部结节患者良性疾病的新模型的 ROC 曲线下面积 (AUC) 至少为 0.85。目前的模型不包括外科医生用来估计癌症或良性疾病的术前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影像学数据。该目标将把 VA-TVHS 患者数据库、范德比尔特肺结节队列和弗吉尼亚大学数据库合并为一个包含 950 名患者的肺结节队列。将从该队列中开发回归模型，并且还将包括对新肺癌生物标志物的探索性分析。 研究二：评估肺结节临床算法用于诊断性手术评估的肺结节管理的普遍性。我们将使用阿拉巴马大学伯明翰分校 (UAB) 的现有数据集和已完成的美国外科医生学会 (ACOSOG) Z4031 合作试验对研究一中开发的预测工具进行外部验证。这些数据集将合并形成一个包含 1500 名患者的验证队列。还将在 ACOSOG 数据集中从存储的临床样本中评估生物标志物。 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结果的预测影响。前瞻性的 686 名患者队列将来自 VA-TVHS、VA-伯明翰和 VUMC 胸外科诊所。这项研究不会在临床实践中实施诊断算法，而是为实现两个目标提供一个安全港。首先，我们将前瞻性地评估可能从这种算法中受益的患者数量，避免遗漏肺癌病例并避免不必要的手术。其次，我们将使用决策分析对该队列中的算法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% 降低到至少 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多机构 VA 试点研究，以评估转诊进行肺结节手术评估的患者的算法。