Decreasing Unnecessary Invasive Lung Cancer Diagnostic Procedures (CDA 10-024)
Eric L Grogan MD MPH
Tennessee Valley Healthcare System Nashville Campus, Nashville, TN
Nashville, TN
Funding Period: October 2011 - September 2016

BACKGROUND/RATIONALE:
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 operation. 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 tools are provided.

OBJECTIVE(S):
The three objectives of this study are: 1) to develop an evidence-based clinical algorithm for management of lung nodules referred for diagnostic surgical evaluation, 2) to evaluate the generalizability of the lung nodule clinical algorithm for management of lung nodules referred for diagnostic surgical evaluation, and 3) to evaluate the predicted impact of the lung nodule clinical algorithm on surgical outcomes in a multi-institutional prospective cohort.

METHODS:
To achieve the first two objectives we will develop a model to predict benign disease among patients presenting with suspicious pulmonary nodules. This aim will combine the Vanderbilt and VA-TVHS patient databases an external dataset. A regression model will be developed from this cohort and will also include an exploratory analysis of new lung cancer biomarkers. In the second objective, we will externally validate the prediction tool in a completed national cooperative trial (ACOSOG) and an additional dataset. Finally we will prospectively evaluate the impact of the model on patient outcomes. This study will NOT implement the diagnostic algorithm in clinical practice but provide a safe harbor to evaluate the benefit and potential harm. We will then use decision analysis to perform an incremental cost-effectiveness analysis of our algorithm in this cohort.

FINDINGS/RESULTS:
From the Veteran database, we have evaluated the survival of patient with stage 1 lung cancer after lung resection, presented at a national meeting and published these results. We have also evaluated the impact of FDG-PET scans to diagnose lung cancer in the ACOSOG dataset and these results have been presented at a national meeting. In this study, there were 51 enrolling sites in 39 cities and FDG-PET results were available for 682 participants. FDG-PET sensitivity was 82%, and specificity was 31%. Positive and negative predictive values were 85% and 26%, respectively and accuracy improved with lesion size. Sensitivity differed between the 8 sites with >25 patients (p<0.01). In a national surgical population with clinical stage I NSCLC, FDG-PET to diagnose lung cancer performed poorly compared to published studies.

IMPACT:
Our recent presentation at ASCO had a large impact on lung cancer diagnostics. This presentation was selected 'Best of ASCO' and represented at 3 cities over the summer. In addition, it was highlighted on the front page of the American College of Chest Physician News in the August 2012 edition.

PUBLICATIONS:
None at this time.

DRA: Cancer, Lung Disorders
DRE: Diagnosis, Prevention
Keywords: Career Development
MeSH Terms: none