Iakovos Toumazis is an Assistant Professor at the Department of Health Services Research, Division of Cancer Prevention and Population Sciences at The University of Texas MD Anderson Cancer Center. He is a member of the National Cancer Institute sponsored Cancer Intervention and Surveillance Modeling Network (CISNET) consortium focusing on lung cancer. He received his BS in Mathematics with a major in Probability & Statistics at University of Patras, Greece, and his MS and PhD in Industrial Engineering from the State University of New York at Buffalo. Part of his Doctoral Dissertation received the Best Graduate Student Paper award from the Society for Health Systems (2015). His research is supported by the Duncan Family Institute for Cancer Prevention and Risk Assessment and the National Cancer Institute (R37CA271187). In the past, he received the Ruth L. Kirschstein National Research Service Award (NRSA) Individual Postdoctoral Fellowship (Parent F32) from the National Institutes of Health (NIH). His research interests include sequential decision making under uncertainty, applications of Operations Research in healthcare, simulation systems, robust optimization, and cost-effectiveness analyses of healthcare interventions.

Areas of interest

Adaptive Cancer Treatment Plans

Cancer treatment affects both healthy and cancer cells, thus oncologists must consider the trade-off between tumor size reduction and toxicity. Continuous admission of the same drug might not be effective, since cancer has the ability to adapt and eventually become insensitive to that particular treatment. Hence, mathematical decision models addressing the cancer treatment scheduling problem can assist clinicians in their treatment decision making process.

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Personalized Cancer Screening

Implementation of an effective cancer screening program has the potential to reduce cancer mortality. Maximizing benefits while minimizing the harms of screening requires moving from a “1-size-fits-all” guideline paradigm to more personalized strategies. Cancer incidence is affected by several risk factors which can be used to stratify screening on a personalized level based on a person's risk of developing cancer and screening history.

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Dynamic Programming

Daily, people are making decisions, which have both immediate and long-term rewards. However, decisions made today will affect future decisions as well as their outcomes. Dynamic programming is a field of mathematical optimization that studies sequential decision making under uncertainty. Dynamic programming techniques are used in a variety of disciplines, including healthcare, manufacturing, automated control, and finance.

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Robust Optimization

Robust optimization techniques address the problem of data uncertainty in a particular optimization problem by optimizing the worst-case scenario. Robust optimization methods assume that the uncertainty set is known, with box-constrained set and ellipsoidal sets being the most common forms. Robust optimization has been successfully applied in various different fields such as engineering, finance, and statistics.

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Toumazis I., Cao P, de Nijs K, Bastani M, Munshi V, Hemmati M, Ten Haaf K, Jeon J, Tammemägi M, Gazelle GS, Feuer EJ, Kong CY, Meza R, de Koning HJ, Plevritis SK, Han SS. Risk Model-Based Lung Cancer Screening : A Cost-Effectiveness Analysis. Ann Intern Med. 2023 Feb 7. doi: 10.7326/M22-2216. Epub ahead of print. PMID: 36745885.

Abstract

Background: In their 2021 lung cancer screening recommendation update, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that select people based on their personal lung cancer risk (risk model-based strategies), highlighting the need for further research on the benefits and harms of risk model-based screening.

Objective: To evaluate and compare the cost-effectiveness of risk model-based lung cancer screening strategies versus the USPSTF recommendation and to explore optimal risk thresholds.

Design: Comparative modeling analysis.

Data sources: National Lung Screening Trial; Surveillance, Epidemiology, and End Results program; U.S. Smoking History Generator.

Target population: 1960 U.S. birth cohort.

Time horizon: 45 years.

Perspective: U.S. health care sector.

Intervention: Annual low-dose computed tomography in risk model-based strategies that start screening at age 50 or 55 years, stop screening at age 80 years, with 6-year risk thresholds between 0.5% and 2.2% using the PLCOm2012 model.

Outcome measures: Incremental cost-effectiveness ratio (ICER) and cost-effectiveness efficiency frontier connecting strategies with the highest health benefit at a given cost.

Results of base-case analysis: Risk model-based screening strategies were more cost-effective than the USPSTF recommendation and exclusively comprised the cost-effectiveness efficiency frontier. Among the strategies on the efficiency frontier, those with a 6-year risk threshold of 1.2% or greater were cost-effective with an ICER less than $100 000 per quality-adjusted life-year (QALY). Specifically, the strategy with a 1.2% risk threshold had an ICER of $94 659 (model range, $72 639 to $156 774), yielding more QALYs for less cost than the USPSTF recommendation, while having a similar level of screening coverage (person ever-screened 21.7% vs. USPSTF's 22.6%).

Results of sensitivity analyses: Risk model-based strategies were robustly more cost-effective than the 2021 USPSTF recommendation under varying modeling assumptions.

Limitation: Risk models were restricted to age, sex, and smoking-related risk predictors.

Conclusion: Risk model-based screening is more cost-effective than the USPSTF recommendation, thus warranting further consideration.

Primary funding source: National Cancer Institute (NCI).

 

Maki KG, Talluri R, Toumazis I., Shete S, Volk RJ. Impact of U.S. Preventive Services Task Force lung cancer screening update on drivers of disparities in screening eligibility. Cancer Med. 2023 Feb;12(4):4647-4654. doi: 10.1002/cam4.5066. Epub 2022 Jul 24. PMID: 35871312; PMCID: PMC9972155.

Abstract

Background: In 2021, the U.S. Preventive Services Task Force (USPSTF) updated its recommendation to expand lung cancer screening (LCS) eligibility and mitigate disparities. Although this increased the number of non-White individuals who are eligible for LCS, the update's impact on drivers of disparities is less clear. This analysis focuses on racial disparities among Black individuals because members of this group disproportionately share late-stage lung cancer diagnoses, despite typically having a lower intensity smoking history compared to non-Hispanic White individuals.

Methods: We used data from the National Health Interview Survey to examine the impact of the 2021 eligibility criteria on racial disparities by factors such as education, poverty, employment history, and insurance status. We also examined preventive care use and reasons for delaying medical care.

Results: When comparing Black individuals and non-Hispanic White individuals, our analyses show significant differences in who would be eligible for LCS: Those who do not have a high school diploma (28.7% vs. 17.0%, p = 0.002), are in poverty (26.2% vs. 14.9%, p < 0.001), and have not worked in the past 12 months (66.5% vs. 53.9%, p = 0.009). Further, our analyses also show that more Black individuals delayed medical care due to not having transportation (11.1% vs. 3.6%, p < 0.001) compared to non-Hispanic White individuals.

Conclusions: Our results suggest that despite increasing the number of Black individuals who are eligible for LCS, the 2021 USPSTF recommendation highlights ongoing socioeconomic disparities that need to be addressed to ensure equitable access.

 

Toumazis I., Erdogan S.A., Bastani M., Leung A., Plevritis S.K., "A Cost-Effectiveness Analysis of Lung Cancer Screening With Low-Dose Computed Tomography and a Diagnostic Biomarker," JNCI Cancer Spectrum 2021;5(6):pkab081

Abstract

Background The Lung Computed Tomography Screening Reporting and Data System (Lung-RADS) reduces the false-positive rate of lung cancer screening but introduces prolonged periods of uncertainty for indeterminate findings. We assess the cost-effectiveness of a screening program that assesses indeterminate findings earlier via a hypothetical diagnostic biomarker introduced in place of Lung-RADS 3 and 4A guidelines.

Methods We evaluated the performance of the US Preventive Services Task Force (USPSTF) recommendations on lung cancer screening with and without a hypothetical noninvasive diagnostic biomarker using a validated microsimulation model. The diagnostic biomarker assesses the malignancy of indeterminate nodules, replacing Lung-RADS 3 and 4A guidelines, and is characterized by a varying sensitivity profile that depends on nodules' size, specificity, and cost. We tested the robustness of our findings through univariate sensitivity analyses.

Results A lung cancer screening program per the USPSTF guidelines that incorporates a diagnostic biomarker with at least medium sensitivity profile and 90% specificity, that costs $250 or less, is cost-effective with an incremental cost-effectiveness ratio lower than $100 000 per quality-adjusted life year, and improves lung cancer-specific mortality reduction while requiring fewer screening exams than the USPSTF guidelines with Lung-RADS. A screening program with a biomarker costing $750 or more is not cost-effective. The health benefits accrued and costs associated with the screening program are sensitive to the disutility of indeterminate findings and specificity of the biomarker, respectively.

Conclusions Lung cancer screening that incorporates a diagnostic biomarker, in place of Lung-RADS 3 and 4A guidelines, could improve the cost-effectiveness of the screening program and warrants further investigation.

 

Bastani M., Toumazis I., Hedou' J., Leung A., Plevritis S.K., "Evaluation of Alternative Diagnostic Follow-up Intervals for Lung Reporting and Data System Criteria on the Effectiveness of Lung Cancer Screening," JACR 2021;18(12):1614-1623

Abstract

Purpose The ACR developed the Lung CT Screening Reporting and Data System (Lung-RADS) to standardize the diagnostic follow-up of suspicious screening findings. A retrospective analysis showed that Lung-RADS would have reduced the false-positive rate in the National Lung Screening Trial, but the optimal timing of follow-up examinations has not been established. In this study, we assess the effectiveness of alternative diagnostic follow-up intervals on lung cancer screening.

Methods We used the Lung Cancer Outcome Simulator to estimate population-level outcomes of alternative diagnostic follow-up intervals for Lung-RADS categories 3 and 4A. The Lung Cancer Outcome Simulator is a microsimulation model developed within the Cancer Intervention and Surveillance Modeling Network Consortium to evaluate outcomes of national screening guidelines. Here, among the evaluated outcomes are percentage of mortality reduction, screens performed, lung cancer deaths averted, screen-detected cases, and average number of screens and follow-ups per death averted.

Results The recommended 3-month follow-up interval for Lung-RADS category 4A is optimal. However, for Lung-RADS category 3, a 5-month, instead of the recommended 6-month, follow-up interval yielded a higher mortality reduction (0.08% for men versus 0.05% for women), and a higher number of deaths averted (36 versus 27), a higher number of screen-detected cases (13 versus 7), and a lower number of combined low-dose CTs and diagnostic follow-ups per death avoided (8 versus 5), per one million general population. Sensitivity analysis of nodule progression threshold verifies a higher mortality reduction with a 1-month earlier follow-up for Lung-RADS 3.

Conclusions One-month earlier diagnostic follow-ups for individuals with Lung-RADS category 3 nodules may result in a higher mortality reduction and warrants further investigation.

 

Toumazis I., Alagoz O., Leung A., Plevritis S.K., "A risk-based framework for assessing real-time lung cancer screening eligibility that incorporates life expectancy and past screening findings," Cancer 2021;127(23):4432-4446

Abstract

Background Current lung cancer risk-based screening approaches use a single risk-threshold, disregard life-expectancy, and ignore past screening findings. We address these limitations with a comprehensive analytical framework, the individualized lung cancer screening decision (ENGAGE) tool that aims to optimize lung cancer screening for US ever-smokers under dynamic risk assessment by incorporating life expectancy and past screening findings over time.

Methods ENGAGE employs a partially observable Markov decision process framework that integrates published risk prediction and disease progression models, to dynamically assess the trade-off between the expected health benefits and harms associated with screening. ENGAGE evaluates lung cancer risk annually and provides real-time screening eligibility that maximizes the expected quality-adjusted life-years (QALYs) of ever-smokers. We compare ENGAGE against the 2013 U.S. Preventive Services Task Force (USPSTF) lung cancer screening guideline and single-threshold risk-based screening paradigms.

Results Compared with the 2013 USPSTF guidelines, ENGAGE expands screening coverage among ever-smokers (ENGAGE: 78%, USPSTF: 61%), while reducing the number of screening examinations per person (ENGAGE:10.43, USPSTF:12.07, P < .001), yields higher effectiveness in terms of increased lung cancer-specific mortality reduction (ENGAGE: 19%, USPSTF: 15%, P < .001) and improves screening efficiency (ENGAGE: 696, USPSTF: 819 screens per death avoided, P < .001). When compared against a single-threshold risk-based screening strategy, ENGAGE increases QALY requiring 30% fewer screens per death avoided (ENGAGE: 696, single-threshold: 889, P < .001), and reduces false positives by 40%.

Conclusions ENGAGE provides a comprehensive framework for dynamic risk-based assessment of lung cancer screening eligibility by incorporating life expectancy and past screening findings that can serve to guide future policies on the effectiveness and efficiency of screening.

 

Toumazis I., Kwon C., and Batta R. (2013), "Value-at-Risk and Conditional Value-at-Risk Minimization for Hazardous Materials Routing", in Handbook of OR/MS Models in Hazardous Materials Transportation (Eds.:R. Batta and C. Kwon), Springer

This chapter provides fundamentals of value-at-risk and conditional value-at-risk models applied to routing problems in hazardous materials transportation.    


Toumazis I., Alagoz O., Leung A., Plevritis S., "Personalized Lung Cancer Screening Policies using a Partially Observable Markov Decision Process", 2019 INFORMS Healthcare, Cambridge, MA 

Bastani, M., Plevritis, S.K., Toumazis, I., Leung, A., “Evaluation of alternative diagnostic test intervals and thresholds for Lung-RADS criteria on the effectiveness of lung cancer screening”, INFORMS Annual Meeting 2018, Phoenix, AZ  

Toumazis, I., Tsai, E.B., Erdogan, A., Han, S., Leung, A., Plevritis, S.K., “The effect of indeterminate findings on the cost-effectiveness of lung cancer screening”, INFORMS Annual Meeting 2018, Phoenix, AZ  

Toumazis, I., Alagoz, O., Leung, A., Plevritis, S.K., “ Personalized lung cancer screening strategies using a partially observable Markov decision process”, INFORMS Annual Meeting 2018, Phoenix, AZ 

Toumazis I., Tsai E., Erdogan A., Han S., Wan W., Leung A., Plevritis S.K., "Impact Of False-Positives On The Cost-effectiveness of Lung Cancer Screening" 2017 INFORMS Healthcare Conference, Rotterdam, Netherlands, July 26–28, 2017