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 were: 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 developed a model to predict benign disease among patients presenting with suspicious pulmonary nodules. This aim combined the Vanderbilt and VA-TVHS patient databases. A regression model was developed from this cohort and this aim also included an exploratory analysis of new lung cancer biomarkers and FDG-PET utility. In the second objective, we externally validated the prediction tool in a completed national cooperative trial (ACOSOG) and have examined the model in a multi-institutional retrospective cohort to expand model generalizability. We planned to prospectively evaluate the impact of the model on patient outcomes but discovered during external validation that additional testing and evaluation of model calibration was necessary to permit model generalizability.

FINDINGS/RESULTS:
Not yet available.

IMPACT:
Our findings resulting from this CDA have had a significant impact in the non-invasive diagnosis of lung cancer. The TREAT model has better diagnostic accuracy than the Mayo Clinic model in preoperative assessment of suspicious lung lesions in a population being evaluated for lung resection. Ongoing studies are refining and evaluating the TREAT model for dissemination. Our findings showing poor performance of FDG-PET in regions with endemic infectious lung disease do not support use of FDG-PET to diagnose lung cancer in endemic areas unless an institution achieves test performance accuracy similar to that found in nonendemic regions. These regional differences will impact a national prediction model and these findings are helping to revise clinical guidelines on the use of FDG-PET to diagnose lung cancer in regions with infectious lung diseases.

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External Links for this Project

NIH Reporter

Grant Number: IK2HX000758-01
Link: https://reporter.nih.gov/project-details/8201844

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PUBLICATIONS:

None at this time.

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