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J. Sands
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MA03 - Epidemiology, Risk Factors and Screening (ID 374)
- Event: WCLC 2016
- Type: Mini Oral Session
- Track: Epidemiology/Tobacco Control and Cessation/Prevention
- Presentations: 1
- Moderators:N. Bilir, H. Olschewski
- Coordinates: 12/05/2016, 14:20 - 15:50, Lehar 3-4
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MA03.09 - Retrospective Predictive Performance of a Lung Cancer Screening Risk Prediction Model in a Clinical Lung Cancer Screening Program (ID 5371)
15:20 - 15:26 | Author(s): J. Sands
- Abstract
- Presentation
Background:
United States Preventive Services Task Force (USPSTF) and Centers for Medicare & Medicaid Services (CMS) recommendations for annual screening for lung cancer with low dose CT (LDCT) scans rely on age and smoking history to identify those at high risk for lung cancer. The Tammemagi et al. six year lung cancer risk prediction model, PLCOm2012, developed and validated in large lung cancer screening clinical trials, demonstrated good predictive performance in study participants. A 1.51% PLCOm2012 risk threshold has been reported to outperform CMS/USPSTF entry criteria. This is the first time the model predictive performance has been evaluated in clinical practice.
Methods:
Predictive performance of a reparameterized (no education predictor variable) six year lung cancer risk prediction model, PLCOm2012noEd, was retrospectively assessed in 2297 consecutive individuals that underwent clinical CT lung screening between January 1, 2012 and November 30, 2015. All patients met the National Comprehensive Cancer Network (NCCN) Lung Cancer Screening Guidelines Group 1 or Group 2 high-risk criteria.
Results:
79 cancers were detected in the 2297 screened individuals with a mean follow-up of 2.12 years (75.9% (60/79) – NCCN Group 1). The model six year mean risk for lung cancer was higher for participants with lung cancer, 4.71%, as compared to those without lung cancer, 3.54% (p=0.008). Area under the curve (AUC) of the receiver operator characteristics (ROC) was 0.635 (95% CI 0.577 – 0.693). At the 1.51% predicted risk recommended screening threshold overall sensitivity = 86.1%, specificity = 29.8%, and PPV = 4.2%. For NCCN Group 1 (similar to CMS/USPSTF entry criteria), sensitivity = 91.7%, specificity = 20.7% and PPV = 4.04%. For NCCN Group 2 (younger, lighter smoking history, no limit on time quit with one additional risk factor) mean predicted risk for participants with lung cancer was 2.35% as compared to 1.83% for those without lung cancer but the difference was not statistically significant; p=0.2374. As the incidence of lung cancer was the same in NCCN Group 2 and NCCN Group 1 (3.24% vs 3.51%; p=0.8566) the sensitivity of the model for NCCN Group 2 at the recommended 1.51% screening threshold was reduced to 68.4% with a specificity of 56.3%.
Conclusion:
Lung cancer risk prediction model, PLCOm2012noEd demonstrated reduced sensitivity in individuals meeting NCCN Group 2 high-risk criteria undergoing clinical CT lung screening and may not be appropriate to adequately assess risk of lung cancer in this population.
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P1.03 - Poster Session with Presenters Present (ID 455)
- Event: WCLC 2016
- Type: Poster Presenters Present
- Track: Radiology/Staging/Screening
- Presentations: 1
- Moderators:
- Coordinates: 12/05/2016, 14:30 - 15:45, Hall B (Poster Area)
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P1.03-056 - Implementation of a Prospective Biospecimen Collection Study in an Established Lung Cancer Screening Program (ID 6170)
14:30 - 14:30 | Author(s): J. Sands
- Abstract
Background:
Complexities such as addressing indeterminate pulmonary nodules (IPNs) are an inherent part of a lung screening program, and defining which individuals will benefit from invasive intervention is not always known. With the goal of combining non-invasive biomarkers with imaging to more definitively stratify patients, we initiated an investigational biospecimen collection process into our lung screening program. Ultimately, these biomarkers may improve specificity within the screening population, thereby reducing the overall cost and potential morbidity from false positive results of low-dose computed tomography scan (LDCT) screening. Studies like this are important to the ongoing improvement of lung cancer screening.
Methods:
NCCN high-risk individuals enrolled in a high volume clinical lung screening program were introduced to our IRB approved research biospecimen study at the time of the scheduling of their LDCT. Patients were consented, and routine biospecimens were collected by research staff at the time of their LDCT scan, including nasal epithelial brushings, buccal swabs and blood. When available, additional biospecimens consisting of bronchial airway brushings and tumor samples were collected from subjects who underwent diagnostic interventions for suspicion of malignancy.
Results:
Since 2012 there have been 3856 patients enrolled and 8776 LDCT scans to date with 100 lung cancer diagnoses within our lung screening program. In 2014, funding (LUNGevity and Robert E Wise grants) was obtained for biospecimen collection from subjects enrolled in our institutional LDCT screening program. Initial prospective collection of biospecimens was slower than anticipated due to various factors. After review of the process, a number of adjustments were made, which significantly increased enrollment. This included implementation of a multidisciplinary taskforce consisting of research and clinical staff committed to patient outreach and participation. To date, samples from approximately 1420 subjects have been collected. Of these, 268 (19%) were found to have newly detected IPNs measuring 6-20mm on LDCT, and 28 samples were from patients with subsequent diagnoses of lung cancer.
Conclusion:
Successful coordination of biospecimen collection within a lung screening program is complex, but achievable with multidisciplinary coordination, and has great potential to help further stratify patients that may or may not benefit from invasive diagnostics and therapy. We demonstrate an established lung screening program that is successfully accruing to a prospective diagnostic study and share specific recommendations for how to successfully accrue in other programs.
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P3.02b - Poster Session with Presenters Present (ID 494)
- Event: WCLC 2016
- Type: Poster Presenters Present
- Track: Advanced NSCLC
- Presentations: 1
- Moderators:
- Coordinates: 12/07/2016, 14:30 - 15:45, Hall B (Poster Area)
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P3.02b-010 - Urine Detection of EGFR T790M Mutation in Non-Small-Cell Lung Cancer: An Outcomes and Total Cost of Care Analysis (ID 6147)
14:30 - 14:30 | Author(s): J. Sands
- Abstract
Background:
Third-generation tyrosine kinase inhibitors (3rd-TKIs) have proven effective in patients with EGFR T790M who progress on prior EGFR TKI therapy. Median progression-free survival (PFS) on a 3rd-TKI was 9-10 months for T790M+ patients compared to 2-4 months for T790M- patients. PFS is similar regardless of the specimen used to assess T790M (tissue, plasma, or urine). Using simulation analytics, the primary study aim was to assess the cost effectiveness of a urine-testing strategy (UTS) versus a tissue-testing strategy (TTS) for T790M detection in patients with EGFR-positive lung adenocarcinoma and progression on prior TKI therapy.
Methods:
Analytics followed International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and Society for Medical Decision Making (SMDM) guidelines for Good Modeling Practices, and Consolidated Health Economic Evaluation Reporting Standards (CHEERS) for reporting findings. Outcomes and economic implications were assessed from the perspective of a third-party US payer, stratified by government versus commercial fee rates. Endpoints were PFS, overall survival (OS), direct medical resources used (biopsies, chemotherapy, post-progression) and related costs. Data sources were published reports of randomized drug trials and current data, which includes accuracy results of tissue versus urine testing (Trovagene, San Diego, CA), Medicare fee schedules, and available adjustments for fees in commercial markets. A state-transition analysis and Markov model tracked patients from stable disease, progression, and to death. Full univariate and multivariate sensitivity analyses were performed to assess the robustness of findings and factors that most influenced outcomes and costs.
Results:
Median PFS after treatment with 3rd-TKI was 3.4 months if tumor testing is T790M- versus 9.7 months if T790M+. Because urine testing can be used in patients for whom biopsy cannot be performed or when tissue testing reveals indeterminate results, PFS and OS were slightly increased using the UTS. UTS resulted in avoidance of a biopsy procedure, potential complications, and tissue-based molecular testing in approximately 48% of patients, leading to a 2- to 10-fold total cost savings relative to the unit cost for a urine test. Within the robust variations in input parameters, the cost of a biopsy procedure/complications and tissue-based molecular testing were the most influential factors.
Conclusion:
UTS is a dominant scenario to TTS by saving costs and improving patient experience (e.g., PFS/OS, and reduction in biopsy related complications). This result is based on LEVEL I evidence from a large, randomized trial that showed PFS is similar among patients regardless of urine versus tissue testing for T790M mutation status.
