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:

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. Pecot CV, Li M, Zhang XJ, Rajanbabu R, Calitri C, Bungum A, Jett JR, Putnam JB, Callaway-Lane C, Deppen S, Grogan EL, Carbone DP, Worrell JA, Moons KG, Shyr Y, Massion PP. Added value of a serum proteomic signature in the diagnostic evaluation of lung nodules. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2012 May 1; 21(5):786-92. [view]
4. 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]
5. 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]
6. St Julien JB, Aldrich MC, Sheng S, Deppen SA, Burfeind WR, Putnam JB, Lambright ES, Nesbitt JC, Grogan EL. Obesity increases operating room time for lobectomy in the society of thoracic surgeons database. The Annals of thoracic surgery. 2012 Dec 1; 94(6):1841-7. [view]
7. St Julien JB, Pinkerman R, Aldrich MC, Chen H, Deppen SA, Callaway-Lane C, Massion P, Putnam JB, Lambright ES, Nesbitt JC, Grogan EL. Poor survival for veterans with pathologic stage I non-small-cell lung cancer. American journal of surgery. 2012 Nov 1; 204(5):637-42. [view]
8. Ziarnik E, Grogan EL. Postlobectomy Early Complications. Thoracic surgery clinics. 2015 Aug 1; 25(3):355-64. [view]
9. Hassanein M, Callison JC, Callaway-Lane C, Aldrich MC, Grogan EL, Massion PP. The state of molecular biomarkers for the early detection of lung cancer. Cancer prevention research (Philadelphia, Pa.). 2012 Aug 1; 5(8):992-1006. [view]
10. Grogan EL, Weinstein JJ, Deppen SA, Putnam JB, Nesbitt JC, Lambright ES, Walker RC, Dittus RS, Massion PP. Thoracic operations for pulmonary nodules are frequently not futile in patients with benign disease. Journal of Thoracic Oncology. 2011 Oct 1; 6(10):1720-5. [view]
11. 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]

Journal Other

1. Grogan EL, Deppen S, Ballman K, Andrade GM, Verdail FC, Aldrich MC, Chen H, Decker PA, Harpole D, Cerfolio R, Keenan R, Jones DR, D’Amico TA, Shrager JB, Meyers BF, Putnam JB. Accuracy of FDG-PET to diagnose lung cancer in the ACOSOG Z4031 trial. [Abstract]. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2012 May 30; 30(15):Suppl: 7008. [view]

Conference Presentations

1. Grogan EL, Deppen S, Ballman K, Andrade GM, Verdail FC, Aldrich MC, Chen H, Decker PA, Harpole D, Cerfolio R, Keenan R, Jones DR, D’Amico TA, Shrager JB, Meyers BF, Putnam JB. Accuracy of FDG-PET to diagnose lung cancer in the ACOSOG Z4031 trial. Paper presented at: American Society of Clinical Oncology Annual Meeting; 2012 Jun 4; Chicago, IL. [view]
2. Grogan EL, Deppen S, Ballman K, Andrade GM, Verdail FC, Aldrich MC, Chen H, Decker PA, Harpole D, Cerfolio R, Keenan R, Jones DR, D’Amico TA, Shrager JB, Meyers BF, Putnam JB. Accuracy of FDG-PET to diagnose lung cancer in the ACOSOG Z4031 trial. Paper presented at: Alliance for Clinical Trials in Oncology Symposium; 2012 Jun 28; Chicago, IL. [view]
3. Grogan EL, Deppen S, Ballman K, Andrade GM, Verdail FC, Aldrich MC, Chen H, Decker PA, Harpole D, Cerfolio R, Keenan R, Jones DR. Accuracy of FDG-PET to diagnose lung cancer in the ACOSOG Z4031 trial. Paper presented at: American Society of Clinical Oncology Best Of Conference; 2012 Jul 13; Chicago, IL. [view]
4. Grogan EL, Deppen S, Ballman K, Andrade GM, Verdail FC, Aldrich MC, Chen H, Decker PA, Harpole D, Cerfolio R, Keenan R, Jones DR, D’Amico TA, Shrager JB, Meyers BF, Putnam JB. Accuracy of FDG-PET to diagnose lung cancer in the ACOSOG Z4031 trial. Paper presented at: American Society of Clinical Oncology Best Of Conference; 2012 Aug 4; Boston, MA. [view]
5. Grogan EL, Deppen S, Ballman K, Andrade GM, Verdail FC, Aldrich MC, Chen H, Decker PA, Harpole D, Cerfolio R, Keenan R, Jones DR, D’Amico TA, Shrager JB, Meyers BF, Putnam JB. Accuracy of FDG-PET to diagnose lung cancer in the ACOSOG Z4031 trial. Paper presented at: American Society of Clinical Oncology Best Of Conference; 2012 Aug 11; San Diego, CA. [view]
6. Deppen SA, Phillips S, Aldrich MC, Shyr Y, Grogan EL. Geographic variation of benign lung disease in the US: Implications for lung cancer screening. Poster session presented at: American College of Epidemiology Annual Scientific Sessions; 2012 Sep 8; Chicago, IL. [view]
7. St. Julien J, Pinkerman R, Deppen S, Callaway-Lane C, Lambright E, Putnam JB, Grogan EL. Poor Survival for Veterans with Pathologic Stage I Non-Small Cell Lung Cancer. Paper presented at: Association of Veteran Affairs Surgeons Annual Meeting; 2012 Apr 1; Miami, FL. [view]

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