Iakovos Toumazis is a postdoctoral scholar at the department of Radiology at Stanford University. He is currently 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 in 2009 and his MS in Industrial Engineering at the State University of New York at Buffalo in 2012. He obtained his PhD in Operation Research in 2015 from the State University of New York at Buffalo. His Doctoral Dissertation project titled “Dynamic Programming approaches to the palliative chemotherapy scheduling problem for metastatic colorectal cancer patients”. His research interests include sequential decision making under uncertainty, applications of Operations Research in healthcare, simulation systems, and robust optimization. In 2017, he received the Ruth L. Kirschstein National Research Service Award (NRSA) Individual Postdoctoral Fellowship (Parent F32) from the National Institutes of Health (NIH). Part of his doctoral dissertation won the Society for Health Systems Best Graduate Student Paper award (2015).

Areas of interest

Chemotherapy Scheduling

Chemotherapy is the most common treatment method for advanced stage cancers. Chemotherapeutic drugs affect both healthy and cancer cells, thus oncologists 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 chemotherapy scheduling problem can assist physicians in their treatment decision making process.


Hazardous Materials Routing

Hazardous materials, also known as HazMats, are defined as substances or materials capable of posing an unreasonable risk to health, safety, or property when transported in commerce. HazMats are commonly transported through vehicular networks using trucks as mode of transportation. Therefore, the need for advanced routing methods that will determine the safest route cannot be over-emphasized.


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.


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.

Han S.S., Erdogan S.A, Toumazis I., Leung A., Plevritis S.K., "Evaluating the impact of varied compliance to lung cancer screening recommendations using a microsimulation model", Cancer Causes & Control, 2017, 28 (9), 947 - 958

Background: The U.S. Preventive Services Task Force (USPSTF) recently recommended that individuals aged 55 to 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. Methods: 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. Results: 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 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. Conclusion: 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.    

Toumazis I., Kurt M., Toumazi A., Karacosta L.G., Kwon C., "Comparative Effectiveness of Up-to-Three Lines of Chemotherapy Treatment Plans for Metastatic Colorectal Cancer", MDM Policy & Practice, 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.    

Toumazis I., Kwon C., "Worst-case conditional value-at-risk minimization for hazardous materials transportation", Transportation Science, 2015, 50 (4), 1174 - 1187

Despite significant advances in risk management, the routing of hazardous materials (hazmat) has relied on relatively simplistic methods. In this paper, we apply an advanced risk measure, called conditional value-at-risk (CVaR), for routing hazmat trucks. CVaR offers a flexible, risk-averse, and computationally tractable routing method that is appropriate for hazmat accident mitigation strategies. The two important data types in hazmat transportation are accident probabilities and accident consequences, both of which are subject to many ambiguous factors. In addition, historical data are usually insufficient to construct a probability distribution of accident probabilities and consequences. This motivates our development of a new robust optimization approach for considering the worst-case CVaR (WCVaR) under data uncertainty. We study important axioms to ensure that both the CVaR and WCVaR risk measures are coherent and appropriate in the context of hazmat transportation. We also devise a computational method for WCVaR and demonstrate the proposed WCVaR concept with a case study in a realistic road network.    

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.    

Latest Publications

Chapters / Books

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

Toumazis I., Erdogan A., Plevritis S.K., "Cost-effectiveness analysis of Alternative Lung Cancer Screening Strategies" 2016 INFORMS International Conference, Hawaii, HA, June 12–15, 2016

Toumazis I., Kurt M., Toumazi A., Karacosta L.G., Kwon C., "Personalized Treatment Decisions For mCRC Patients", 2016 INFORMS International Conference, Hawaii, HA, June 12–15, 2016