Professional Education

  • Master of Science, S.U.N.Y. State University at Buffalo (2012)
  • Bachelor of Science, University Of Patras (2009)
  • Doctor of Philosophy, S.U.N.Y. State University at Buffalo (2015)

Stanford Advisors


All Publications

  • Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results A Secondary Analysis of Data From the National Lung Screening Trial JAMA NETWORK OPEN Tammemagi, M. C., ten Haaf, K., Toumazis, I., Kong, C., Han, S. S., Jeon, J., Commins, J., Riley, T., Meza, R. 2019; 2 (3)
  • Development and Validation of a Multivariable Lung Cancer Risk Prediction Model That Includes Low-Dose Computed Tomography Screening Results: A Secondary Analysis of Data From the National Lung Screening Trial. JAMA network open Tammemagi, M. C., Ten Haaf, K., Toumazis, I., Kong, C. Y., Han, S. S., Jeon, J., Commins, J., Riley, T., Meza, R. 2019; 2 (3): e190204


    Importance: Low-dose computed tomography lung cancer screening is most effective when applied to high-risk individuals.Objectives: To develop and validate a risk prediction model that incorporates low-dose computed tomography screening results.Design, Setting, and Participants: A logistic regression risk model was developed in National Lung Screening Trial (NLST) Lung Screening Study (LSS) data and was validated in NLST American College of Radiology Imaging Network (ACRIN) data. The NLST was a randomized clinical trial that recruited participants between August 2002 and April 2004, with follow-up to December 31, 2009. This secondary analysis of data from the NLST took place between August 10, 2013, and November 1, 2018. Included were LSS (n=14?576) and ACRIN (n=7653) participants who had 3 screens, adequate follow-up, and complete predictor information.Main Outcomes and Measures: Incident lung cancers occurring 1 to 4 years after the third screen (202 LSS and 96 ACRIN). Predictors included scores from the validated PLCOm2012 risk model and Lung CT Screening Reporting & Data System (Lung-RADS) screening results.Results: Overall, the mean (SD) age of 22?229 participants was 61.3 (5.0) years, 59.3% were male, and 90.9% were of non-Hispanic white race/ethnicity. During follow-up, 298 lung cancers were diagnosed in 22?229 individuals (1.3%). Eight result combinations were pooled into 4 groups based on similar associations. Adjusted for PLCOm2012 risks, compared with participants with 3 negative screens, participants with 1 positive screen and last negative had an odds ratio (OR) of 1.93 (95% CI, 1.34-2.76), and participants with 2 positive screens with last negative or 2 negative screens with last positive had an OR of 2.66 (95% CI, 1.60-4.43); when 2 or more screens were positive with last positive, the OR was 8.97 (95% CI, 5.76-13.97). In ACRIN validation data, the model that included PLCOm2012 scores and screening results (PLCO2012results) demonstrated significantly greater discrimination (area under the curve, 0.761; 95% CI, 0.716-0.799) than when screening results were excluded (PLCOm2012) (area under the curve, 0.687; 95% CI, 0.645-0.728) (P<.001). In ACRIN validation data, PLCO2012results demonstrated good calibration. Individuals who had initial negative scans but elevated PLCOm2012 six-year risks of at least 2.6% did not have risks decline below the 1.5% screening eligibility criterion when subsequent screens were negative.Conclusions and Relevance: According to this analysis, some individuals with elevated risk scores who have negative initial screens remain at elevated risks, warranting annual screening. Positive screens seem to increase baseline risk scores and may identify high-risk individuals for continued screening and enrollment into clinical trials.Trial Registration: Identifier: NCT00047385.

    View details for PubMedID 30821827

  • A comparative modeling analysis of risk-based lung cancer screening strategies. Journal of the National Cancer Institute Ten Haaf, K., Bastani, M., Cao, P., Jeon, J., Toumazis, I., Han, S. S., Plevritis, S. K., Blom, E. F., Kong, C. Y., Tammemägi, M. C., Feuer, E. J., Meza, R., de Koning, H. J. 2019


    Risk-prediction models have been proposed to select individuals for lung cancer screening. However, their long-term effects are uncertain. This study evaluates long-term benefits and harms of risk-based screening compared to current United States Preventive Services Task Force (USPSTF) recommendations.Four independent natural-history models performed a comparative modeling study evaluating long-term benefits and harms of selecting individuals for lung cancer screening through risk-prediction models. 363 risk-based screening strategies varying by screening starting and stopping age, risk-prediction model used for eligibility (Bach, PLCOm2012, LCDRAT), and risk-threshold were evaluated for a 1950?U.S. birth-cohort. Among the evaluated outcomes were percentage of individuals ever screened, screens required, lung cancer deaths averted, life-years gained and overdiagnosis.Risk-based screening strategies requiring similar screens among individuals aged 55-80 as the USPSTF-criteria (corresponding risk-thresholds: Bach: 2.8%, PLCOm2012: 1.7%, LCDRAT: 1.7%) averted considerably more lung cancer deaths (Bach: 693, PLCOm2012: 698, LCDRAT: 696, USPSTF: 613). However, life-years gained were only modestly higher (Bach: 8,660, PLCOm2012: 8,862, LCDRAT, 8,631,USPSTF: 8,590) and risk-based strategies had more overdiagnosis (Bach: 149, PLCOm2012: 147, LCDRAT: 150, USPSTF: 115). Sensitivity analyses suggests excluding individuals with limited life-expectancies (<5 years) from screening retains the life-years gained by risk-based screening, while reducing overdiagnosis by?>?65.3%.Risk-based lung cancer screening strategies prevent considerably more lung cancer deaths than current recommendations. However, they yield modest additional life-years and increased overdiagnosis due to predominantly selecting older individuals. Efficient implementation of risk-based lung cancer screening requires careful consideration of life-expectancy for determining optimal individual stopping ages.

    View details for DOI 10.1093/jnci/djz164

    View details for PubMedID 31566216

  • Evaluating the impact of varied compliance to lung cancer screening recommendations using a microsimulation model CANCER CAUSES & CONTROL Han, S. S., Erdogan, S., Toumazis, I., Leung, A., Plevritis, S. K. 2017; 28 (9): 947?58


    The US preventive services task force (USPSTF) recently recommended that individuals aged 55-80 with heavy smoking history be annually screened by low-dose computed tomography (LDCT), thereby extending the stopping age from 74 to 80 compared to the national lung screening trial (NLST) entry criterion. This decision was made partly with model-based analyses from cancer intervention and surveillance modeling network (CISNET), which assumed perfect compliance to screening.As part of CISNET, we developed a microsimulation model for lung cancer (LC) screening and calibrated and validated it using data from NLST and the prostate, lung, colorectal, and ovarian cancer screening trial (PLCO), respectively. We evaluated population-level outcomes of the lifetime screening program recommended by the USPSTF by varying screening compliance levels.Validation using PLCO shows that our model reproduces observed PLCO outcomes, predicting 884 LC cases [Expected(E)/Observed(O) = 0.99; CI 0.92-1.06] and 563 LC deaths (E/O = 0.94 CI 0.87-1.03) in the screening arm that has an average compliance rate of 87.9% over four annual screening rounds. We predict that perfect compliance to the USPSTF recommendation saves 501 LC deaths per 100,000 persons in the 1950 U.S. birth cohort; however, assuming that compliance behaviors extrapolated and varied from PLCO reduces the number of LC deaths avoided to 258, 230, and 175 as the average compliance rate over 26 annual screening rounds changes from 100 to 46, 39, and 29%, respectively.The implementation of the USPSTF recommendation is expected to contribute to a reduction in LC deaths, but the magnitude of the reduction will likely be heavily influenced by screening compliance.

    View details for PubMedID 28702814

    View details for PubMedCentralID PMC5880208

  • Comparative Effectiveness of Up To Three Lines of Chemotherapy Treatment Plans for Metastatic Colorectal Cancer. MDM policy & practice Toumazis, I., Kurt, M., Toumazi, A., Karacosta, L. G., Kwon, C. 2017; 2 (2): 2381468317729650


    Modern chemotherapy agents transformed standard care for metastatic colorectal cancer (mCRC) but raised concerns about the financial burden of the disease. We studied comparative effectiveness of treatment plans that involve up to three lines of therapies and impact of treatment sequencing on health and cost outcomes. We employed a Markov model to represent the dynamically changing health status of mCRC patients and used Monte-Carlo simulation to evaluate various treatment plans consistent with existing guidelines. We calibrated our model by a meta-analysis of published data from an extensive list of clinical trials and measured the effectiveness of each plan in terms of cost per quality-adjusted life year. We examined the sensitivity of our model and results with respect to key parameters in two scenarios serving as base case and worst case for patients' overall and progression-free survivals. The derived efficient frontiers included seven and five treatment plans in base case and worst case, respectively. The incremental cost-effectiveness ratio (ICER) ranged between $26,260 and $152,530 when the treatment plans on the efficient frontiers were compared against the least costly efficient plan in the base case, and between $21,256 and $60,040 in the worst case. All efficient plans were expected to lead to fewer than 2.5 adverse effects and on average successive adverse effects were spaced more than 9 weeks apart from each other in the base case. Based on ICER, all efficient treatment plans exhibit at least 87% chance of being efficient. Sensitivity analyses show that the ICERs were most dependent on drug acquisition cost, distributions of progression-free and overall survivals, and health utilities. We conclude that improvements in health outcomes may come at high incremental costs and are highly dependent in the order treatments are administered.

    View details for PubMedID 30288431

    View details for PubMedCentralID PMC6124942

  • Worst-Case Conditional Value-at-Risk Minimization for Hazardous Materials Transportation TRANSPORTATION SCIENCE Toumazis, I., Kwon, C. 2016; 50 (4): 1174-1187
  • Routing hazardous materials on time-dependent networks using conditional value-at-risk TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES Toumazis, I., Kwon, C. 2013; 37: 73-92

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