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 judgement, 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:
We developed and internally validated a clinical prediction model for lung cancer in a prospective cohort evaluated at our institution. The TREAT model was internally validated in a retrospectively collected Veteran Affairs population. The discrimination and calibration of the model were estimated and compared to the Mayo Clinic model in both populations. The validated predictive model has a ROC AUC of 0.87 and better diagnostic accuracy than the Mayo Clinic model in preoperative assessment of suspicious lung lesions in a population being evaluated for lung resection. A web-based calculator that allows for input of data into the TREAT model has been developed.

We expanded our TREAT model into a robust, generalizable prediction model for lung cancer using data from 1500 patients from seven sources. The new TREAT 2.0 model is more accurate and better calibrated than the Mayo Clinic model in patients presenting with lesions at high risk for lung cancer in pulmonary nodule clinics, surgical clinics and in post-resection databases. Model calibration with population cancer prevalence grouping led to improved generalizability across types of clinical practice.

In building a prediction model, we also examined the utility of FDG-PET to diagnose lung cancer. We examined data for 682 participants enrolled at 51 sites in 39 cities in the ACOSOG Z4031 trial, a national surgical population with clinical stage I NSCLC. We found that using FDG-PET to diagnose lung cancer performed poorly compared to published studies.

When looking at the cost-effectiveness of initial diagnostic strategies for pulmonary nodules presenting to thoracic surgeons, we found that both NB and CT-FNA diagnostic strategies are more cost-effective than either VATS biopsy or FDG-PET scan to diagnose lung cancer in moderate- to high-risk nodules and resulted in fewer nontherapeutic operations when FDG-PET specificity was less than 72%. We found that FDG-PET scan for diagnosis of lung cancer may not be cost-effective in regions of the country where specificity is low.

Following on these findings regarding FDG-PET specificity, we discovered through meta-analysis that the accuracy of FDG-PET for diagnosing lung nodules was extremely heterogeneous. Use of FDG-PET combined with computed tomography was less specific in diagnosing malignancy in populations with endemic infectious lung disease (61% [95% CI, 49%-72%]) compared with nonendemic regions (77% [95% CI, 73%-80%]). These data 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.

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

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PUBLICATIONS:

Journal Articles

1. Deppen SA, Blume J, Grogan EL. A reply to Bertolaccini et al. Journal of Thoracic Oncology. 2015 Feb 1; 10(2):e8-9. [view]
2. Maiga AW, Deppen SA, Pinkerman R, Grogan EL. A Successful Institutional Strategy to Increase the Number of Therapeutic Operations Among Patients With Lung Lesions. JAMA surgery. 2016 Feb 1; 151(2):193-4. [view]
3. Deppen SA, Blume JD, Kensinger CD, Morgan AM, Aldrich MC, Massion PP, Walker RC, McPheeters ML, Putnam JB, Grogan EL. Accuracy of FDG-PET to diagnose lung cancer in areas with infectious lung disease: a meta-analysis. JAMA. 2014 Sep 24; 312(12):1227-36. [view]
4. Grogan EL, Deppen SA, Ballman KV, Andrade GM, Verdial FC, Aldrich MC, Chen CL, Decker PA, Harpole DH, Cerfolio RJ, Keenan RJ, Jones DR, D'Amico TA, Shrager JB, Meyers BF, Putnam JB. Accuracy of fluorodeoxyglucose-positron emission tomography within the clinical practice of the American College of Surgeons Oncology Group Z4031 trial to diagnose clinical stage I non-small cell lung cancer. The Annals of thoracic surgery. 2014 Apr 1; 97(4):1142-8. [view]
5. Aldrich MC, Munro HM, Mumma M, Grogan EL, Massion PP, Blackwell TS, Blot WJ. Chronic obstructive pulmonary disease and subsequent overall and lung cancer mortality in low-income adults. PLoS ONE. 2015 Mar 26; 10(3):e0121805. [view]
6. Deppen SA, Davis WT, Green EA, Rickman O, Aldrich MC, Fletcher S, Putnam JB, Grogan EL. Cost-effectiveness of initial diagnostic strategies for pulmonary nodules presenting to thoracic surgeons. The Annals of thoracic surgery. 2014 Oct 1; 98(4):1214-22. [view]
7. Shaver CM, Castilho JL, Cohen DN, Grogan EL, Miller GG, Dummer JS, Gray JN, Lambright ES, Loyd JE, Robbins IM. Fatal Scopulariopsis infection in a lung transplant recipient: lessons of organ procurement. American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons. 2014 Dec 1; 14(12):2893-7. [view]
8. Blume JD, Deppen SA, Grogan EL. Heterogeneity in meta-analysis of FDG-PET studies to diagnose lung cancer--reply. JAMA. 2015 Jan 27; 313(4):419-20. [view]
9. Julien JS, Lang R, Brown TN, Aldrich MC, Deppen SA, Wu H, Feurer ID, Tarpley M, Hill G, Tarpley J, Beauchamp RD, Grogan EL. Minority Underrepresentation in Academia: Factors Impacting Careers of Surgery Residents. Journal of racial and ethnic health disparities. 2014 Dec 1; 1(4):238-246. [view]
10. Ziarnik E, Grogan EL. Postlobectomy Early Complications. Thoracic surgery clinics. 2015 Aug 1; 25(3):355-64. [view]
11. Deppen SA, Blume JD, Aldrich MC, Fletcher SA, Massion PP, Walker RC, Chen HC, Speroff T, Degesys CA, Pinkerman R, Lambright ES, Nesbitt JC, Putnam JB, Grogan EL. Predicting lung cancer prior to surgical resection in patients with lung nodules. Journal of Thoracic Oncology. 2014 Oct 1; 9(10):1477-84. [view]
12. Aldrich MC, Grogan EL, Munro HM, Signorello LB, Blot WJ. Stage-adjusted lung cancer survival does not differ between low-income Blacks and Whites. Journal of Thoracic Oncology. 2013 Oct 1; 8(10):1248-54. [view]
13. Nesbitt JC, St Julien J, Absi TS, Ahmad RM, Grogan EL, Balaguer JM, Lambright ES, Deppen SA, Wu H, Putnam JB. Tissue-based coronary surgery simulation: medical student deliberate practice can achieve equivalency to senior surgery residents. The Journal of Thoracic and Cardiovascular Surgery. 2013 Jun 1; 145(6):1453-8; discussion 1458-9. [view]
14. Deppen SA, Grogan EL. Using Clinical Risk Models for Lung Nodule Classification. Seminars in thoracic and cardiovascular surgery. 2015 Apr 7; 27(1):30-5. [view]

Conference Presentations

1. Deppen SA, Davis WT, Green EA, Rickman O, Aldrich MC, Fletcher S, Putnam JB, Grogan EL. Cost-Effectiveness of Initial Diagnostic Strategies for Solitary Pulmonary Nodules Presenting to Thoracic Surgeons. Paper presented at: Society of Thoracic Surgeons Annual Meeting; 2014 Jan 28; San Diego, CA. [view]
2. Grogan EL, Deppen SA, Chen H, Ballman KV, Verdail G, Aldrich MC, Decker P, Harpole D, Cerfolio R, Keenan R, Jones DR, D'Amico TA, Shrager J, Meyers B, Putnam JB. FDG-PET avidity negatively impacts survival in pStage I NSCLC in the ACOSOG Z4031 trial. Paper presented at: American Association for Cancer Research Annual Meeting; 2013 Apr 10; Washington, DC. [view]

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