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Acceptability and perceptions of personalised risk-based cancer screening among health-care professionals and the general public: a systematic review and meta-analysis
Naomi QP Tan, Renu S Nargund, Elisa E Douglas, Maria A Lopez-Olivo, Paul J Resong, Sayaka Ishizawa, Sara Nofal, Kate Krause, Robert J Volk, Iakovos Toumazis
Abstract
Personalised risk-based screening (PRBS) can enhance the efficiency of cancer screening programnes, but little is known about support for its implementation among the general public and health-care professionals. We aimed to summarise the acceptability and perceptions of PRBS for breast, cervical, colorectal, lung, and prostate cancer screening among these groups.
Impact of comorbidities on the mortality benefits of lung cancer screening: a post-hoc analysis of the PLCO and NLST trials
Sebastien Gendarme, Ehsan Irajizad, James P Long, Johannes F Fahrmann, Jennifer B Dennison, Seyyed Mahmood Ghasemi, Rongzhang Dou, Robert J Volk, Rafael Meza, Iakovos Toumazis, Florence Canoui-Poitrine, Samir M Hanash, Edwin J Ostrin
Abstract
To evaluate how comorbidities affect mortality benefits of lung cancer screening (LCS) with low-dose computed tomography. We developed a comorbidity index (Prostate, Lung, Colorectal, and Ovarian comorbidity index [PLCO-ci]) using LCS-eligible participants' data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) trial (training set) and the National Lung Screening Trial (NLST) (validation set). PLCO-ci predicts five-year non-lung cancer (LC) mortality using a regularized Cox model; with performance evaluated using the area under the receiver operating characteristics curve. In NLST, LC mortality (per original publication) was compared between low-dose computed tomography and chest radiograph arms across the PLCO-ci quintile (Q1-5) using a cause-specific hazard ratio (csHR) with 95% confidence intervals (CIs). Analyses included 34,690 PLCO and 53,452 NLST participants (mean age: 62 y [±5 y] and 61 y [±5 y], 58% and 59% male individuals, and 39% and 41% active smokers, respectively). PLCO-ci predicted five-year non-LC mortality with an area under the receiver operating characteristics curve of 0.72 (95% CI: 0.71-0.74) in PLCO and 0.69 (95% CI: 0.67-0.70) in NLST. In NLST, at a median follow-up of 6.5 years, LC mortality was significantly reduced for participants with intermediate comorbidity (Q2, Q3, and Q4): csHR 0.62 (95% CI: 0.41-0.95), 0.68 (95% CI: 0.48-0.96), and 0.72 (95% CI: 0.54-0.96) respectively, with a nonstatistically significant reduction for Q1 (csHR = 0.72, 95% CI: 0.45-1.17) and no reduction for Q5 participants (csHR = 0.99, 95% CI: 0.79-1.23). Participants in Q2, Q3, and Q4 (60%) accounted for 89% of LC deaths averted among all NLST participants. Q1 participants had low LC incidence, whereas Q5 had higher localized LC lethality, more squamous cell carcinomas, and untreated LC. The PLCO-ci developed in this work shows that individuals with intermediate comorbidity benefited the most from LCS, highlighting the need of addressing comorbidities to achieve LC mortality benefits.
Development and Validation of a Histology-Specific Natural History Model of Ovarian Cancer
Sayaka Ishizawa, Maryam Eghbalizarch, Renu S Nargund, Seyyed Mostafa Mousavi Janbeh Sarayi, Jiangong Niu, Mehdi Hemmati, Maddie Tumbarello, Andrew J Schaefer, Karen Lu, Sharon H Giordano, Larissa A Meyer, Iakovos Toumazis
Abstract
Ovarian cancer is the second leading cause of death from gynecologic cancers, yet no effective screening program exists for the general population. Past screening trials evaluated the effectiveness of annual ovarian cancer screening and concluded that it does not yield substantial mortality reduction. Future investments on ovarian cancer screening trials would require convincing preliminary evidence on the effectiveness of interventions of interest. Simulation modeling is an effective, fast, cost-efficient, and safe approach to gain insights on the effectiveness of interventions, that is increasingly being used to inform guidelines for cancer screening programs. Models that simulate the natural progression of diseases in the absence of any intervention, commonly referred to as natural history models, are the cornerstone of such analyses, because they provide a reference point for evaluating interventions. Currently, no histology-specific natural history model exists for ovarian cancer despite major differences among subtypes. Develop and validate a histology-specific ovarian cancer natural history model. We developed natural history models for the most common histological subtypes of epithelial ovarian cancer: high-grade serous carcinoma, low-grade serous carcinoma, mucinous carcinoma, clear cell carcinoma, endometrioid carcinoma, carcinosarcoma, and not otherwise specified. Each natural history model simulates the natural progression of ovarian cancer from disease's onset until death from any cause. We modeled ovarian cancer progression as a state-transition model comprising of 13 mutually exclusive and collectively exhaustive health states. We informed the model input parameters using observed, nationally representative estimates, whenever possible. Unobserved parameters (eg, preclinical transitions) were estimated through calibration to histology-specific data from the Surveillance, Epidemiology, and End Results registry. We validated the natural history models on the control arms of the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial and the United Kingdom Collaborative Trial of Ovarian Cancer Screening trials, in terms of ovarian cancer incidence and mortality rates, and stage distribution at diagnosis. Differences between observed and estimated outcomes were assessed using traditional statistical tests. The calibrated natural history models reproduced the observed Surveillance, Epidemiology, and End Results data (range of weighted root mean square error across histological subtypes: 0.0081-0.0185) as well as individual calibration targets; survival after diagnosis, stage distribution at diagnosis, and age distribution at diagnosis (ranges of mean square error across histological subtypes: 0.0029-0.0204, 0.0005-0.0203, and 0.0637-0.0816, respectively). The natural history models reproduced the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial's observed incidence and mortality rates, and stage at diagnosis (P value=.411 for incidence, P value=.195 for mortality, and P value=.200 for stage distribution at diagnosis) and the United Kingdom Collaborative Trial of Ovarian Cancer Screening's observed ovarian cancer incidence (P value=.607) and mortality (P value=.624) rates. The average duration of the preclinical phase ranges between 1 and 3 years, which partly explains screening's failure to yield mortality reduction. Moreover, stage II ovarian cancer, independent of histological subtype, is a transient state characterized by noticeably shorter average duration when compared to stages I, III, and IV. The developed natural history models accurately describe the histology-specific natural progression of ovarian cancer and provide important insights into the natural history of the disease. The models may be used to evaluate the impact of future and emerging ovarian cancer interventions, thus providing valuable information to decision-makers and policymakers.
Cost of ovarian cancer by the phase of care in the United States
Naomi N Adjei, Allen M Haas, Charlotte C Sun, Hui Zhao, Paul G Yeh, Sharon H Giordano, Iakovos Toumazis, Larissa A Meyer
Abstract
Ovarian cancer is associated with delayed diagnosis and poor survival; thus, interest is high in identifying predictive and prognostic biomarkers and novel therapeutic agents. Although the costs of ovarian cancer care are likely to increase as newer, more effective, but more expensive treatment regimens become available, information on the current costs of care for ovarian cancer-across the care continuum from diagnosis to the end of life-are lacking. This study aimed to estimate real-world mean and median costs of ovarian cancer care within the first 5 years after diagnosis by patients' phase of care, age, race/ethnicity, and geographic region. We performed a retrospective cohort study of ovarian cancer patients diagnosed between January 1, 2015 and December 31, 2020. We used claims data from Optum's deidentified Clinformatics Data Mart database, which includes inpatient, outpatient, and prescription claims for commercial insurance and Medicare beneficiaries nationwide. Cost of ovarian cancer care were calculated for the start of care (ie, the first 6 months), continuing care (ie, period between the initial and end-of-life care), and end-of-life care (ie, the last 6 months) phases and reported in 2021 U.S. dollar amounts. Ovarian cancer care costs were stratified by age, race/ethnicity, and geographic region. Due to the skewed nature of cost data, the mean cost data were log-transformed for modeling. Ordinary least-squares regression was conducted on the log costs, adjusting for patient categorical age, race/ethnicity, and geographic region. A total of 7913 patients were included in the analysis. The mean cost per year for ovarian cancer care was >$200,000 during the start of care, between $26,000 and $88,000 during the continuing care phase, and >$129,000 during the end-of-life care phase. There were statistically significant associations between age and costs during each phase of care. Compared to younger patients, older patients incurred higher costs during the continuing care phase and lower costs during the end-of-life care phase. Geographic differences in the costs of ovarian cancer care were also noted regardless of the phase of care. There were no associations between cost and race/ethnicity in our cohort. Ovarian cancer care costs are substantial and vary by the phase of care, age category, and geographic region. As more effective but expensive treatment options for ovarian cancer become available with potential survival benefit, sustainable interventions to reduce the cost of care for ovarian cancer will be needed throughout the cancer care continuum.
Examining lung cancer screening uptake in the United States: recent research and limitations of public-use data
Kristin G Maki, Naomi QP Tan, Robert J Volk, Iakovos Toumazis
Abstract
Eligibility criteria for lung cancer screening (LCS) were updated by the Centers for Medicare & Medicaid Services in 2022 following an updated recommendation from the United States Preventive Services Task Force. Recently, research has examined LCS use in the United States following this change, which centered on lowering the age to begin screening from 55 to 50 years and the smoking history threshold from 30 to 20 pack-years. These studies, including the accompanying article from Gudina and colleagues, have used the 2022 Behavioral Risk Factors Surveillance System data, which are publicly available and nationally representative. Although increases in LCS use have been reported in all studies, screening remains low compared with other population-level cancer screening programs, highlighting the need for interventions to improve LCS uptake and adherence. Data limitations from population …
Lung cancer screening among adults older than medicare’s upper age eligibility criteria
Monica Hernandez, Kristin G Maki, Hui Zhao, Iakovos Toumazis, Robert J Volk
Abstract
Implications on the loss of lung cancer screening (LCS) coverage among Medicare recipients aged 77+ years have not been explored. We use a 2022 Behavioral Risk Factor Surveillance System dataset to examine LCS patterns of screen-eligible adults across three age groups: 65 to 70, 71 to 77, and 78 to 79 years. In descriptive analyses, LCS-eligible respondents are compared by screening status across each age category. In regression analyses, we explore various sociodemographic and health-related factors that may help explain age-related differences between these groups. Less than a third of our sample reported LCS in the last year (26.3%). Among eligible respondents, adults aged 78 to 79 years reported the highest LCS rates (32.0%), followed by adults aged 71 to 77 years (28.3%) and 65 to 70 years (24.2%). Respondents aged 78 to 79 years and 65 to 70 years with chronic …
Cost-effectiveness Analysis of Treatments for Bacillus Calmette-Guérin–unresponsive Carcinoma in Situ of the Bladder
Amanda A Myers, Ruchika Talwar, Zhigang Duan, Patrick Hensley, Yair Lotan, Stephen B Williams, Amy N Luckenbaugh, Roger Li, Wassim Kassouf, Andrea Necchi, Vignesh T Packiam, Neal Shore, Gary D Steinberg, J Alfred Witjes, Bogdana Schmidt, Sima Porten, Noah M Hahn, Kelly K Bree, Iakovos Toumazis, Hui Zhao, Ashish M Kamat
Abstract
Treatment options for patients with "bacillus Calmette-Guérin (BCG)-unresponsive" disease who are ineligible for or refuse radical cystectomy (RC) are expanding. Given the lack of direct comparative data, we conducted a cost-effectiveness analysis to guide treatment selection. We developed a Markov decision analytic model to assess five treatments: RC, nadofaragene, nogapendekin, pembrolizumab, and gemcitabine/docetaxel. Cost effectiveness was evaluated over a 5-yr period using a willingness-to-pay threshold of $100 000 per quality-adjusted life year from the US Medicare perspective. Three index patients with BCG-unresponsive carcinoma in situ (CIS) were assessed. For index patient 1, who is willing to try one line of therapy or proceed directly to RC, gemcitabine/docetaxel was the most cost-effective option. For index patient 2, open to two lines of therapy or upfront RC, RC was most cost effective. For index patient 3, willing to try up to two lines of US Food and Drug Administration (FDA)-approved therapy (pembrolizumab, nadofaragene, or nogapendekin) before RC, pembrolizumab was the most cost-effective option. Current pricing of bladder-sparing treatments poses significant financial barriers for patients with BCG-unresponsive CIS. Gemcitabine/docetaxel is most cost effective when only one therapy line is considered before RC, although this varies by clinical scenario. Upfront RC is most cost effective for patients wanting to try up to two lines of therapy. For patients only willing to try FDA-approved options and unwilling to undergo upfront RC, pembrolizumab is the most cost-effective option. Our findings highlight the need for better treatment selection tools and more equitable pricing.
Risk of second primary lung cancer among cancer survivors stratified by the site of first primary cancer and the lung cancer screening eligibility status
Sara Nofal, Edwin J Ostrin, Jianjun Zhang, Jia Wu, Paul Scheet, Mara B Antonoff, John V Heymach, Iakovos Toumazis
Abstract
Personal history of cancer is an independent risk factor for developing lung cancer. However, it is not considered in the current US lung cancer screening (LCS) guidelines. In this study, we assessed the risk of developing lung cancer among cancer survivors across 24 different sites of first primary cancer stratified by their LCS eligibility status. Using data from the Patient History Database at the University of Texas MD Anderson Cancer Center, we calculated and compared the cumulative incidence of second primary lung cancer, the overall and the LCS eligibility status‐specific, stratified by the site of first primary cancer among cancer survivors. We found that among lung, head and neck (H&N), bladder, cervical, breast, and prostate cancer survivors, the risks of second primary lung cancer were statistically significantly higher compared to the overall risk among all cancer survivors (i.e., all cancer sites combined). Risk …
Natural history models for lung Cancer: A scoping review
Renu Sara Nargund, Sayaka Ishizawa, Maryam Eghbalizarch, Paul Yeh, Seyyed Mostafa Mousavi Janbeh Saray, Sara Nofal, Yimin Geng, Pianpian Cao, Edwin J Ostrin, Rafael Meza, Martin C Tammemägi, Robert J Volk, Maria A Lopez-Olivo, Iakovos Toumazis
Abstract
Natural history models (NHMs) of lung cancer (LC) simulate the disease's natural progression providing a baseline for assessing the impact of interventions. NHMs have been increasingly used to inform public health policies, highlighting their utility. The objective of this scoping review was to summarize existing LC NHMs, identify their limitations, and propose a framework for future NHM development. We searched MEDLINE, Embase, Web of Science, and IEEE Xplore from their inception to October 5, 2023, for peer-reviewed, full-length articles with an LC NHM. Model characteristics, their applications, data sources used, and limitations were extracted and narratively synthesized. From 238 publications, 69 publications were included in our review, corresponding to 22 original LC NHMs and 47 model applications. The majority of the models (n = 15, 68 %) used a microsimulation approach. NHM parameters were predominately informed by cancer registries, trial and institutional data, and literature. Model quality and performance were evaluated in 8 (36 %) models. Twenty (91 %) models included at least one carcinogenesis risk factor-primarily age, sex, and smoking history. Three (14 %) LC NHMs modeled progression in never-smokers; one (5 %) addressed recurrence. Non-tobacco smoking, nodule type, and biomarker expression were not considered in existing NHMs. Based on our findings, we proposed a framework for future LC NHM development which incorporates recurrence, nodule type differentiation, biomarker expression levels, biological factors, and non-smoking-related risk factors. Regular updating and future research are warranted to address limitations in existing NHMs thereby ensuring relevance and accuracy of modeling approaches in the evolving LC landscape.
Healthcare Costs in the United States by Demographic Characteristics and Comorbidity Status
Naomi N Adjei, Allen Haas, Charlotte C Sun, Hui Zhao, Paul G Yeh, Sharon H Giordano, Iakovos Toumazis, Larissa A Meyer
Abstract
Current, real-world healthcare cost information is needed to project future expenditures and inform policy. We estimated the healthcare costs for adults in 2019 in the United States by age, sex, race/ethnicity, geographic region, and comorbidity. We aggregated and summarized the healthcare costs in 2021 US dollars using claims data derived from Optum's deidentified Clinformatics® Data Mart Database, which includes inpatient, outpatient, and prescription claims for commercial and Medicare Advantage beneficiaries nationwide. A total of 9 227 901 adults were included in the analysis. The largest group represented was 71 to 75 years old (13%), female (53%), White (68%), received care in the South (41%), and had commercial health insurance (56%). There was a positive relationship between healthcare cost and age. Females had a 1.3-fold multiplicative increase in costs than males (95% CI 1.33-1.34). There were 92.5% of individuals who had health claims in the Northeast, 89.6% in the Midwest, 88.9% in the South, 77.1% in the West, and 12.7% with unknown geographic region. Patients with severe renal failure, heart failure, or metastatic cancer incurred the highest mean yearly costs ($139 844, $113 031, and $85 299, respectively). Metastatic cancer and severe renal failure were associated with a 5.3-fold multiplicative increase in costs than not having these conditions, after adjusting for potential confounders (95% CI 5.26-5.41 and 4.98-5.16, respectively). We identified patient characteristics and medical conditions that are associated with high healthcare cost burden and could benefit from tailored interventions. We provided detailed cost estimates to aid healthcare modeling, cost projection, and cost-minimizing interventions.
Tobacco use and eligibility for lung cancer screening among dental patients at an academic institution in Houston, Texas
Ana S Neumann, Iakovos Toumazis, Jennifer A O'Brien, Diane Beneventi, Sai Keerthi Annam, Anita Joy-Thomas, Robert J Volk
Abstract
Objective: Tobacco use negatively impacts oral and general health and influences dental treatment outcomes. To advance prevention, we surveyed dental patients at an academic institution to characterize their history of tobacco use and eligibility for lung cancer screening (LCS). Methods: Anonymous surveys were administered to adult dental patients at the UTHealth Houston School of Dentistry between April 2022 and October 2022. Surveys collected information on smoking history, pack-year history, health literacy, personal and family history of lung cancer, and previous LCS. Demographic variables included age, gender, race/ethnicity, and education level. Results: Among 432 patients (mean age: 46.4, range 18–88 years, 57.0 % female), 22.7 % were patients who currently smoked cigarettes, and 13.2 % were patients who formerly smoked. Smoking rates were highest among males (36.2 %) and patients younger than 50 (26.0 %). Among patients who currently smoked, 44.1 % met eligibility for LCS based on age and a 20+ pack-year smoking history; 43.2 % of patients who formerly smoked were eligible for LCS. Conclusions: A substantial proportion of patients are eligible for LCS; tailored cessation counseling and electronic-referral pathways could impact screening and cessation support for millions of high-risk adults who visit a dentist each year, closing a critical gap in cancer prevention.
Improving ovarian cancer risk assessment using a machine learning model developed on data from the Prostate Lung Colorectal Ovarian (PLCO) Cancer Screening Trial
Seyyed Mostafa Mousavi Janbeh Sarayi, Martin Tammemägi, Larissa A Meyer, Iakovos Toumazis
Abstract
Ovarian cancer screening for the general population is not effective at reducing mortality primarily due to the low incidence of ovarian cancer. Risk assessment could improve ovarian cancer prevention by targeting available and emerging interventions to high-risk individuals. This study aimed to develop an ovarian cancer risk prediction tool with sufficient predictive performance to guide ovarian cancer prevention. We used data from the PLCO screening trial to develop and validate a 10-year ovarian cancer risk model. We used PLCO's control arm to train the model and the intervention arm for validation. Potential predictors included sociodemographic factors, medical history, and female reproductive history, among others. We compared alternative machine learning algorithms for model development and assessed their performance using the area under the curve, sensitivities, specificities, and positive predictive values. Extreme Gradient Boosting algorithm produced the best performing model, which consisted of 14 trees, each with a maximum of 3 layers (area under the curve 0.80, 95% confidence interval [CI] 0.78 to 0.81 on the PLCO control arm-training set). Seventeen readily available features were included in the final model, among which body mass index, age, and duration of female hormone use increased risk, whereas bilateral oophorectomy and number of live births decreased risk. The model's performance on the PLCO's intervention arm was satisfactory (area under the curve 0.66, 95% CI 0.64 to 0.68), outperforming previously published models when tested on the same validation dataset. At a 58% risk threshold the model yielded sensitivity of 75% (95% CI 72 to 79) and specificity of 70% (95% CI 69 to 70) on the training set. We assessed model's performance on the validation set at various risk thresholds. The developed ovarian cancer incidence risk model demonstrated superior performance over existing models. Nevertheless, more research is warranted to further improve the predictive performance of the models and ensure feasibility of risk-based programs for ovarian cancer prevention.
Radiomics for Dynamic Lung Cancer Risk Prediction in USPSTF-Ineligible Patients
Morteza Salehjahromi, Hui Li, Eman Showkatian, Maliazurina B Saad, Mohamed Qayati, Sherif M Ismail, Sheeba J Sujit, Amgad Muneer, Muhammad Aminu, Lingzhi Hong, Xiaoyu Han, Simon Heeke, Tina Cascone, Xiuning Le, Natalie Vokes, Don L Gibbons, Iakovos Toumazis, Edwin J Ostrin, Mara B Antonoff, Ara A Vaporciyan, David Jaffray, Fernando U Kay, Brett W Carter, Carol C Wu, Myrna CB Godoy, J Jack Lee, David E Gerber, John V Heymach, Jianjun Zhang, Jia Wu
Abstract
Many people who develop lung cancer have never smoked or have smoked very little, and they are often not included in current CT screening programs. We analyzed how small lung nodules change across a series of routine CT scans and turned those changes into simple image-based measurements (“radiomics”) that capture growth, density, and texture. Our approach follows both the nodule and the surrounding lung over time, creating a personalized picture of risk. We also found that widely used tools such as the Brock model and deep learning methods designed for screening heavy smokers were less accurate in our non- and light-smoker cohort. By combining longitudinal imaging features with basic clinical information, our tool more reliably identified higher-risk patients who might otherwise be missed by today’s screening rules. These findings suggest that tracking nodules and lung context over time can support earlier evaluation and intervention for people not covered by existing screening guidelines.
P3. 01.47 Presentation and Validation of Three ENGAGE Variants of the PLCOm2012 Lung Cancer Risk Prediction Model
CM Tammemagi, SM Mousavi Janbeh Sarayi, M Eghbalizarch, S Ishizawa, R Meza, I Toumazis
Abstract
Introduction: Lung cancer is a major public health problem. Computed tomography lung cancer screening (LCS) significantly reduces lung cancer mortality. The most effective and efficient ways to implement LCS are unknown. The ENGAGE framework adapts screening intervals based on a dynamic, individualized assessment of tradeoffs between expected benefits and potential harms related to screening. ENGAGE has been shown to be superior to fixed interval and other adaptive screening-interval approaches. The ENGAGE approach estimates lung cancer risk, incorporates life expectancy, and uses simulation to estimate the optimum time to the next screening. The PLCOm2012 lung cancer risk prediction model was adapted for application in ENGAGE by categorizing some of the continuous predictors. We present ENGAGE versions of PLCOm2012 and their validation. Methods: Cox proportional hazards regression was used to derive models. The outcome was incident lung cancer diagnosed within six years. Predictors included age, race and ethnicity, education, BMI, COPD, personal history of cancer, family history of lung cancer, and smoking status, smoking intensity, smoking duration, and quit-years in former smoking individuals. BMI and smoking intensity were converted from continuous into 4-level categorical variables (Table 1). Three models were produced with varied race/ethnicity categorization (Table 1). Predictive performances of these models were evaluated in PLCO intervention arm and NLST validation data. Comparisons were made between ENGAGE models and a Cox version of the original PLCOm2012 model. Predictive performance and calibration was evaluated using Harrell’s c-statistic or receiver operating characteristic area under the curve (AUC), Brier statistic, Expected/Observed ratio, and Spiegelhalter’s statistic p-value. Results: Table 1 presents the models, and Table 2 compares their predictive performances. All models demonstrated good and comparable discrimination, calibration and validation. Conclusions: Models with categorized continuous variables and variable race/ethnicity predictors demonstrated good predictive performance making them useful in simulation modeling and for different sociodemographic settings.
P1. 17.19 Impact of Race and Ethnicity on the ENGAGE-Derived Lung Cancer Screening Recommendations: Implications for Risk Assessment
M Eghbalizarch, CM Tammemagi, SM Mousavi Janbeh Sarayi, S Ishizawa, R Meza, I Toumazis
Abstract
Introduction: The ENGAGE framework optimizes lung cancer screening based on age, sex, and smoking related risk factors, for ever-smoked individuals at a personalized-level. The inclusion of race and ethnicity in risk prediction models is controversial. We adapted the PLCOm2012 risk model and integrated it into ENGAGE to evaluate the influence of race and ethnicity on the ENGAGE-derived lung cancer screening schedules. Methods: We developed and validated three versions of Cox proportional hazards regression models for lung cancer risk differing by the levels of race and ethnicity included (6-race, 3-race, no-race) and one model to predict death from causes other than lung cancer using data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO). The developed risk models incorporate all PLCOm2012 covariates but converted smoking intensity and body-mass index (BMI) from continuous to categorical variables. The competing causes of death model includes all PLCOm2012 covariates except family history of lung cancer plus sex, diabetes, cardiovascular disease, stroke, and hypertension. We integrated these risk models into ENGAGE and generated personalized screening schedules for the National Lung Screening Trial (NLST) participants who were randomized in the low dose computed tomography (LDCT) arm and were adherent to annual screening. We compared the performance of ENGAGE across the different versions of the risk models (6-race PLCOENGAGE2025, 3-race PLCOENGAGE2025, no-race PLCOENGAGE2025) and the original ENGAGE that uses the Bach risk model, in terms of the number of screen-detected cases, number of screening exams, and number of false-positive results. Results: The PLCOENGAGE2025 models demonstrated good and comparable discrimination, calibration, and validation in both PLCO and NLST data. Screening schedules derived from ENGAGE performed better when based on PLCOENGAGE2025 compared to the Bach risk model. The ENGAGE version utilizing the Bach model reduced screening exams by 81% and false-positive results by 84%, while delaying screen-detected lung cancer diagnoses by 2.8% relative to the NLST results. ENGAGE-derived schedules based on the PLCOENGAGE2025 race-specific models outperformed those using the PLCOENGAGE2025 no-race model for Black individuals, resulting in more screen-detected cases and higher mortality reduction (Figure 1). Conclusions: Incorporating race and ethnicity into lung cancer risk models enhances the effectiveness of screening in a mixed-race population which justifies its consideration in risk prediction models. ENGAGE-derived schedules maintained comparable effectiveness to annual screening despite reducing the number of screening exams and false-positive results.
P1. 17.81 Unveiling the Natural History of Lung Cancer by Sex, Histology, and Nodule Type Using Mathematical Modeling
S Ishizawa, RS Naargund, M Eghbalizarch, CM Tammemagi, R Meza, I Toumazis
Abstract
Introduction: Lung cancer is the leading cause of cancer-related death globally. Mathematical models that simulate the natural course of the disease, commonly referred to as natural history models (NHMs), have been developed and used to provide insights into the effectiveness of interventions. In a recent scoping review of the literature focusing on existing NHMs, we found that no existing lung cancer NHM accounts for the differences in the disease progression by nodule type, and very few explicitly model tumor growth. We thus developed and validated a sex-, histology-, and nodule type-specific NHM for lung cancer that explicitly models tumor growth. Methods: We developed sex-specific state-transition lung cancer NHMs for the most common histological subtypes (small cell, squamous cell, adenocarcinoma, and other non-small cell) using the AJCC staging system. Adenocarcinomas were further stratified by nodule type into solid, part-solid, and non-solid. We assumed that tumors grow according to Gompertz models, which were calibrated to observed data from the Surveillance, Epidemiology, and End Results (SEER) 17 registry from 2000-2021. Because SEER does not provide information on nodule type, we assumed all adenocarcinomas in SEER are solid nodules and adjusted the progression rates for part-solid and non-solid nodules based on estimates of tumor volume doubling time from the literature. Each of the NHMs had a similar structure. We used the observed stage-specific survival after diagnosis, stage distribution at diagnosis, stage-specific tumor size distribution at diagnosis, and stage-specific age distribution at diagnosis as calibration targets. We assessed the goodness of fit of each model using the weighted sum of root mean squared error (RMSE) and validated the NHMs on the control arms of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) and the National Lung Screening Trial (NLST) trials. Results: The number of parameters for each model ranged between 27-33 parameters estimated through calibration. The calibrated NHMs successfully reproduced the observed data from SEER in terms of all calibration targets by sex and histological subtype (Figure 1). The RMSE for all models was less than 3%. The NHMs reproduced the observed outcomes of the PLCO and NLST control arms. Conclusions: The NHMs accurately simulate the sex-, histology-, and nodule type-specific progression of lung cancer while explicitly modeling tumor growth. The resulting NHMs will inform future microsimulation studies to evaluate the effectiveness of alternative lung cancer screening strategies by nodule type.
Use of a Natural History Model to Benchmark Progress in Managing Ovarian Cancer (Reply to Letter-to-the-Editor)
Larissa A Meyer, Sayaka Ishizawa, Iakovos Toumazis
Abstract
We thank Dr Meng for the interest in our ovarian cancer natural history model (NHM) and its related correspondence.1 Dr Meng made several recommendations on how we can enhance the translatability of an ovarian cancer NHM.2 We agree that mathematical/simulation models should be as translational as possible to maximize their utility. However, there are some points we would like to clarify in terms of the intended use of the developed NHM. …
EE71 Cost-Effectiveness of Intensity-Modulated Proton Therapy (IMPT) for the Treatment of Head and Neck Oropharyngeal Carcinoma
Mehdi Hemmati, Seyyed Mostafa Mousavi Janbeh Sarayi, Iakovos Toumazis, Matthew S Ning, Menna Y Teffera, Noveen Ausat, Rasha M Sareyeldin, Mike Hernandez, Robert Foote, Paul Busse, David Rosenthal, Samir H Patel, James W Snider, Brandon Gunn, James Molitaris, Upendra Parvathenini, Shalin J Shah, Jay P Reddy, Alexander Lin, Nancy Lee, Gregory Chronowski, Mark McDonald, Noah Kalmans, Sanford Katz, Gopal K Bajaj, Christian Hyde, Christina E Henson, Roi Dagan, Mohammed Nasiruddin, Adam S Garden, Clifton D Fuller, Daniel J Ma, Steven J Frank
Abstract
Objectives: To evaluate the cost-effectiveness of chemoradiation strategies with intensity-modulated proton therapy (IMPT) versus intensity-modulated photon therapy (IMRT) for the treatment of advanced stage head and neck oropharyngeal carcinoma (OPC). …
Use Patterns of Levonorgestrel-Releasing Intrauterine System among American Women
Paul G Yeh, Allen Haas, Charlotte C Sun, Karen H Lu, Larissa A Meyer, Iakovos Toumazis
Abstract
Levonorgestrel-releasing intrauterine system (LNG-IUS) use is approved by the FDA for contraception and heavy menorrhagia. More importantly, it effectively treats endometrial hyperplasia, a precursor to endometrial cancer. Therefore, LNG-IUS use is associated with potential endometrial cancer risk reduction, but current use patterns in the United States are unknown. We analyzed LNG-IUS use prevalence among women ages 18 to 50 years using a weighted statistical analysis of the 2017 to 2019 National Survey of Family Growth. Summary statistics were stratified by race and ethnic group and known endometrial cancer sociodemographic and health risk factors and assessed statistically with bivariate Rao–Scottχ2tests. A multivariable logistic regression model was developed to explore LNG-IUS use predictors. Current LNG-IUS use in the United States was 6.9% [95% confidence interval (CI), 5.9%–8.1 …
A0796–Cost-effectiveness analysis of treatments for BCG-unresponsive high-risk NMIBC
A Myers, R Talwar, Z Duan, P Hensley, Y Lotan, SB Williams, AN Luckenbaugh, R Li, W Kassouf, A Necchi, VT Packiam, N Shore, GD Steinberg, F Witjes, B Schmidt, S Porten, N Hahn, KK Bree, I Toumazis, H Zhao, AM Kamat
Validation of a blood test for multi-cancer risk stratification in a lung cancer screening cohort
Johannes Fahrmann, Ehsan Irajizad, Hamid Rudsari, Jody Vykoukal, Iakovos Toumazis, Sara Khoramisarvestani, Sara Ansari, Jane Yang, Nicole Kettner, Jennifer B Dennison, Edwin Ostrin, Samir Hanash
Abstract
Purpose: We report a blinded validation study of a ten-protein marker blood test for assessing risk of developing or harboring nine common cancers in a prospective lung cancer screening cohort. …
Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept
Morteza Salehjahromi, Tatiana V Karpinets, Sheeba J Sujit, Mohamed Qayati, Pingjun Chen, Muhammad Aminu, Maliazurina B Saad, Rukhmini Bandyopadhyay, Lingzhi Hong, Ajay Sheshadri, Julie Lin, Mara B Antonoff, Boris Sepesi, Edwin J Ostrin, Iakovos Toumazis, Peng Huang, Chao Cheng, Tina Cascone, Natalie I Vokes, Carmen Behrens, Jeffrey H Siewerdsen, John D Hazle, Joe Y Chang, Jianhua Zhang, Yang Lu, Myrna CB Godoy, Caroline Chung, David Jaffray, Ignacio Wistuba, J Jack Lee, Ara A Vaporciyan, Don L Gibbons, Gregory Gladish, John V Heymach, Carol C Wu, Jianjun Zhang, Jia Wu
Abstract
[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer …
Estimating sojourn time and sensitivity of screening for ovarian cancer using a Bayesian framework
Sayaka Ishizawa, Jiangong Niu, Martin C Tammemagi, Ehsan Irajizad, Yu Shen, Karen H Lu, Larissa A Meyer, Iakovos Toumazis
Abstract
Ovarian cancer is among the leading causes of gynecologic cancer-related death. Past ovarian cancer screening trials using combination of cancer antigen 125 testing and transvaginal ultrasound failed to yield statistically significant mortality reduction. Estimates of ovarian cancer sojourn time—that is, the period from when the cancer is first screen detectable until clinical detection—may inform future screening programs.
Personal history of cancer as a risk factor for second primary lung cancer: Implications for lung cancer screening
Sara Nofal, Jiangong Niu, Paul Resong, Jeff Jin, Kelly W Merriman, Xiuning Le, Hormuzd Katki, John Heymach, Mara B Antonoff, Edwin Ostrin, Jia Wu, Jianjun Zhang, Iakovos Toumazis
Abstract
Personal history of cancer is an independent risk factor for lung cancer but is omitted from existing lung cancer screening eligibility criteria. In this study, we assess the lung cancer risk among cancer survivors and discuss potential implications for screening.
Biomarker trajectory for earlier detection of lung cancer
Ehsan Irajizad, Johannes F Fahrmann, Iakovos Toumazis, Jody Vykoukal, Jennifer B Dennison, Yu Shen, Kim-Anh Do, Edwin J Ostrin, Ziding Feng, Samir Hanash
Abstract
To determine whether an algorithm based on repeated measurements of a panel of four circulating protein biomarkers (4 MP) for lung cancer risk assessment results in improved performance over a single time measurement.
Benchmarking lung cancer screening programmes with adaptive screening frequency against the optimal screening schedules derived from the ENGAGE framework: a comparative …
Mehdi Hemmati, Sayaka Ishizawa, Rafael Meza, Edwin Ostrin, Samir M Hanash, Mara Antonoff, Andrew J Schaefer, Martin C Tammemägi, Iakovos Toumazis
Abstract
Lung cancer screening recommendations employ annual frequency for eligible individuals, despite evidence that it may not be universally optimal. The impact of imposing a structure on the screening frequency remains unknown. The ENGAGE framework, a validated framework that offers fully dynamic, analytically optimal, personalised lung cancer screening recommendations, could be used to assess the impact of screening structure on the effectiveness and efficiency of lung cancer screening. …
The impact of model assumptions on personalized lung cancer screening recommendations
Kevin Ten Haaf, Koen de Nijs, Giulia Simoni, Andres Alban, Pianpian Cao, Zhuolu Sun, Jean Yong, Jihyoun Jeon, Iakovos Toumazis, Summer S Han, G Scott Gazelle, Chung Ying Kong, Sylvia K Plevritis, Rafael Meza, Harry J de Koning
Abstract
Recommendations regarding personalized lung cancer screening are being informed by natural-history modeling. Therefore, understanding how differences in model assumptions affect model-based personalized screening recommendations is essential. …
The evolution of lung adenocarcinoma precursors is associated with chromosomal instability and transition from innate to adaptive immune response/evasion
Xin Hu, Bo Zhu, Natalie Vokes, Junya Fujimoto, Frank R Rojas Alvarez, Simon Heeke, Andre L Moreira, Luisa M Solis, Cara Haymaker, Vamsidhar Velcheti, Daniel H Sterman, Harvey I Pass, Chao Cheng, Jack J Lee, Jianhua Zhang, Zhubo Wei, Jia Wu, Xiuning Le, Edwin Ostrin, Iakovos Toumazis, Don Gibbons, Dan Su, Junya Fukuoka, Mara B Antonoff, David E Gerber, Chenyang Li, Humam Kadara, Linghua Wang, Mark Davis, John V Heymach, Samir Hannash, Ignacio Wistuba, Steven Dubinett, Ludmil Alexandrov, Scott Lippman, Avrum Spira, Andrew P Futreal, Alexandre Reuben, Jianjun Zhang
Abstract
Studying lung adenocarcinoma (LUAD) early carcinogenesis is challenging, primarily due to the lack of LUAD precursors specimens. We amassed multi-omics data from 213 LUAD and LUAD precursors to identify molecular features underlying LUAD precancer evolution. We observed progressively increasing mutations, chromosomal aberrations, whole genome doubling and genomic instability from precancer to invasive LUAD, indicating aggravating chromosomal instability (CIN). Telomere shortening, a crucial genomic alteration linked to CIN, emerged at precancer stage. Moreover, later-stage lesions demonstrated increasing cancer stemness and decreasing alveolar identity, suggesting epithelial de-differentiation during early LUAD carcinogenesis. The innate immune cells progressively diminished from precancer to invasive LUAD, concomitant with a gradual recruitment of adaptive immune cells (except CD8 …
Opportunistic salpingectomy during gynecologic and non-gynecologic abdominopelvic procedures for ovarian cancer primary prevention: a cost-effectiveness analysis
Naomi Adjei, Paul Yeh, Allen Haas, Hui Zhao, Rebecca Stone, Kara Long Roche, Karen Lu, Charlotte Sun, Iakovos Toumazis, Larissa Meyer
Abstract
Objectives: The acceptance of serous tubal intraepithelial carcinoma (STIC) as a precursor to ovarian cancer (OC) has fueled the primary prevention strategy of fallopian tube removal during gynecologic procedures. Opportunistic salpingectomy (OS) during gynecologic procedures among individuals with population-level risk for developing OC is associated with a 49–77 % reduction in ovarian cancer risk. However, there are many additional abdominopelvic procedures during which OS could be offered for OC prevention. The projected impact of the broad adoption of OS at the time of abdominopelvic procedures is unknown. We performed cost-effectiveness analyses to evaluate the impact of OS during 6 common abdominopelvic procedures on OC costs and overall survival. …
Acceptability of personalized lung cancer screening program among primary care providers
Paul J Resong, Jiangong Niu, Gabrielle F Duhon, Lewis E Foxhall, Sanjay Shete, Robert J Volk, Iakovos Toumazis
Abstract
Current lung cancer screening (LCS) guidelines rely on age and smoking history. Despite its benefit, only 5%–15% of eligible patients receive LCS. Personalized screening strategies select individuals based on their lung cancer risk and may increase LCS's effectiveness. We assess current LCS practices and the acceptability of personalized LCS among primary care providers (PCP) in Texas. We surveyed 32,983 Texas-based PCPs on an existing network (Protocol 2019-1257; PI: Dr. Shete) and 300 attendees of the 2022 Texas Academy of Family Physicians (TAFP) conference. We analyzed the responses by subgroups of interest. Using nonparametric bootstrap, we derived an enriched dataset to develop logistic regression models to understand current LCS practices and acceptability of personalized LCS. Response rates were 0.3% (n= 91) and 15% (n= 60) for the 2019–1257 and TAFP …
1238 Predicting lung cancer risk with CT radiomics: implications for immunoprevention
Morteza Salehjahromi, Lingzhi Hong, Hui Li, Yuliya Kitsel, Myrna Godoy, Carol C Wu, Mara Antonoff, Iakovos Toumazis, Edwin Ostrin, Simon Heeke, Natalie I Vokes, Xiuning Le, Ara A Vaporciyan, J Jack Lee, John V Heymach, Jianjun Zhang, Jia Wu
Abstract
Lung cancer develops progressively from normal lung tissue to the formation of small lesions, often beginning with subtle cellular changes that evolve into imaging detectable abnormalities. Current screening approaches and a ‘wait-and-watch’ strategy often miss the optimal window to intercept lung cancer effectively. We are testing immunoprevention strategies on selected high-risk patients. To enhance prevention strategies, it is essential to comprehensively understand the trajectory of tumor evolution and accurately measure the efficacy of prevention interventions.1Addressing this, our study examines lesion progression from normal tissue to precancerous stages and cancer diagnosis in real-world lung cancer patients, and then assess the interception effects on lesion growth in our phase-II Can-Prevent-Lung trial that testing anti-IL-1B antibody in lung precancer setting. …
Proton therapy reduces gastrostomy-tube dependence in a comparative effectiveness analysis of intensity-modulated proton therapy vs intensity-modulated radiotherapy for …
Kathryn Marqueen, Adam Garden, David Swanson, Matthew Ning, David Rosenthal, G Gunn, C Fuller, Jack Phan, Michael Spiotto, Anna Lee, Amy Moreno, Gregory Chronowsk, Jay Reddy, Lauren Mayo, Shalin Shah, Neal Gross, Renata Ferrarotto, Katherine Hutcheson, Iakovos Toumazis, Steven Frank
EPH78 Lung Cancer Screening Eligibility and Use in the US: A Cross-Sectional Analysis of 2022 Behavioral Risk Factor Surveillance System Data
KG Maki, NQP Tan, RJ Volk, I Toumazis
Abstract
This study’s purpose is to assess sociodemographic and health-related variations in lung cancer screening (LCS) use among eligible individuals in the U.S. in 2022. …
A Four-protein Blood Biomarker Panel Refines a Clinical Risk Score for Indeterminate Pulmonary Nodules
E Ostrin, J Fahrmann, J Vykoukal, J Dennison, R Nargund, I Toumazis, A Baron, E Irajizad, S Hanash
Abstract
RationaleIndeterminate pulmonary nodules (IPNs) are a common finding by chest CT. Workup, including risk stratification for biopsy or longitudinal radiographic follow-up, presents a clinical conundrum which has been substantially magnified by increasing uptake of CT-based lung cancer screening. Clinical models of IPN risk, including the Brock and Mayo models, can assist with stratification. We previously reported that a 4-protein biomarker panel (4MP) showed the ability to identify malignant versus benign pulmonary nodules. We investigated whether the 4MP could provide additive discriminative information to a clinical model of lung cancer.MethodsIndividuals aged 50-70 without previous history of cancer presenting to the MD Anderson Suspicion of Cancer Clinic for evaluation of indeterminate pulmonary nodules were enrolled into a biobanking protocol in which demographic and clinical history was also …
Tobacco Use and Eligibility for Lung Cancer Screening Among Dental Patients
Ana Neumann, Iakovos Toumazis, Jennifer A O’Brien, Diane Beneventi, Anita Joy-Thomas, Robert Volk
Abstract
ObjectivesTobacco negatively impacts the quality of both oral and general health and negatively influences dental treatment outcomes. To advance oral and general health, we surveyed dental patients at an academic institution to characterize their history of tobacco use and their eligibility for lung cancer screening (LCS).MethodsData were collected using anonymous surveys of dental patients receiving treatment at academic clinics between April 2022 and October 2022. Surveys collected data on health literacy based on the confidence level in filling out medical forms, smoking history (eg, former and current tobacco use), personal and family history of lung cancer, and previous LCS. Demographic variables included age, gender, race/ethnicity, and education level.ResultsAmong 432 patients (mean age: 46.4, range 18-88 years, 57.0% female), 22.7% currently smoked cigarettes, and 13.2% no longer smoked cigarettes. Smoking rates were highest among males (36.2%) and those younger than 50 (26.0%). Among patients who currently smoked cigarettes, 44.1% met eligibility for LCS based on age and a 20+ pack-year smoking history. In addition, 43.2% of patients who no longer smoked were eligible for LCS.ConclusionsA substantial share of dental patients are eligible for LCS; tailored cessation counseling and e-referral pathways within clinical workflows could impact screening and cessation support for millions of high-risk adults who visit a dentist each year, closing a critical gap in cancer prevention.
Risk model–based lung cancer screening: a cost-effectiveness analysis
Iakovos Toumazis, Pianpian Cao, Koen de Nijs, Mehrad Bastani, Vidit Munshi, Mehdi Hemmati, Kevin Ten Haaf, Jihyoun Jeon, Martin Tammemägi, G Scott Gazelle, Eric J Feuer, Chung Yin Kong, Rafael Meza, Harry J de Koning, Sylvia K Plevritis, Summer S Han
Abstract
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. 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. Comparative modeling analysis. National Lung Screening Trial; Surveillance, Epidemiology, and End Results program; U.S. Smoking History Generator. 1960 U.S. birth cohort. 45 years. U.S. health care sector. 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. Incremental cost-effectiveness ratio (ICER) and cost-effectiveness efficiency frontier connecting strategies with the highest health benefit at a given cost. 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%). Risk model-based strategies were robustly more cost-effective than the 2021 USPSTF recommendation under varying modeling assumptions. Risk models were restricted to age, sex, and smoking-related risk predictors. Risk model-based screening is more cost-effective than the USPSTF recommendation, thus warranting further consideration. National Cancer Institute (NCI).
Prevalence of lung cancer screening among eligible adults in 4 US states in 2021
Kristin G Maki, Naomi QP Tan, Iakovos Toumazis, Robert J Volk
Abstract
Despite recommendations from the US Preventive Services Task Force (USPSTF) endorsing lung cancer screening (LCS), data from the 2019 Behavioral Risk Factor Surveillance System (BRFSS) showed that 12.8% of eligible adults received a computed tomographic (CT) scan to check for lung cancer. 1 Screening rates declined during the COVID-19 pandemic. 2 Increasing LCS among eligible adults is a national priority. 3 We estimated LCS using data from the 2021 BRFSS and examined factors associated with LCS. …
Impact of US Preventive Services Task Force lung cancer screening update on drivers of disparities in screening eligibility
Kristin G Maki, Rajesh Talluri, Iakovos Toumazis, Sanjay Shete, Robert J Volk
Abstract
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. …
Cancer risk assessment in patients with persistent pulmonary nodules and its correlation with cancer-free survival.
Hui Li, Kang Qin, Zheng Zhang, Lingzhi Hong, Carol C Wu, Myrna Cobos Barco Godoy, Mara Antonoff, Edwin J Ostrin, Iakovos Toumazis, Don Lynn Gibbons, John Heymach, J Jack Lee, Jia Wu, Jianjun Zhang
Abstract
With the wide use of CT for lung cancer screening and diagnosis, detection of pulmonary nodules increases drastically. Brock malignancy risk scoring is a validated risk prediction model for distinguishing malignant nodules, but it only provides a snapshot without incorporating the dynamic changes of lung nodules. Our study tested the performance of Brock malignancy risk scoring system in patients with persistent lung nodules. …
Lung cancer screening status and acceptability of a personalized approach in Texas.
Paul Resong, Jiangong Niu, Gabrielle Duhon, Lewis E Foxhall, Sanjay Shete, Robert Joseph Volk, Iakovos Toumazis
Abstract
Current lung cancer screening (LCS) guidelines rely on age and smoking history. Despite its proven benefit, only 5-15% of eligible patients receive LCS, with Texas having one of the lowest rates among states in the US. Personalized screening strategies select people for LCS based on their personal lung cancer risk and may increase LCS’s effectiveness and efficiency. We assess current LCS practices and the acceptability of personalized LCS among primary care providers (PCP) in Texas. …
Disparities in uptake of levonorgestrel-releasing intrauterine system (LNG-IUS): implications for uterine cancer primary prevention
Paul G Yeh, Iakovos Toumazis, Charlotte Sun, Karen Lu, Larissa A Meyer
Abstract
Introduction: Levonorgestrel-releasing intrauterine system (LNG-IUS) is associated with ~50% risk reduction for uterine cancer incidence and can be an effective primary prevention strategy. We aimed to understand current patterns of LNG-IUS use and identify disparities that could inform implementation strategies for more effective and equitable uterine cancer primary prevention. …
EP01. 04-002 The Impact of Enforcing a Structure in Lung Cancer Screening Strategy on the Effectiveness and Efficiency of the Screening Program
M Hemmati, I Toumazis
Abstract
Introduction: In 2021, the US Preventive Services Task Force (USPSTF) recommended annual lung cancer screening for individuals aged 50-80 years, who have at least 20 pack-year smoking history and currently smoke or have quit smoking within 15 years. The USPSTF strategy is practical and easy to implement, but suboptimal in terms of effectiveness and efficiency. Recently, the ENGAGE decision-analytic screening framework has been developed offering dynamic personalized lung cancer screening schedules that maximize individuals’ expected quality-adjusted life years (QALY). The ENGAGE strategy is (analytically) optimal but has no structure and thus its implementation is challenging. In this work, we assess the impact of imposing a structure into screening on the effectiveness and efficiency of the overall screening program by comparing the 2021 USPSTF strategy and 4 alternative structured strategies that allow a single switch in screening frequency against ENGAGE's strategy.
Personalized Lung Cancer Screening–Acceptability among Primary Care Providers
Paul J Resong, Jiangong Niu PhD, Iakovos Toumazis
Abstract
Lung cancer screening (LCS) using Low-Dose Computed Tomography (LDCT) has been proven to reduce mortality. Novel personalized screening approaches for LCS, like use of a risk calculator, are being developed that: …
Evaluation of the benefits and harms of lung cancer screening with low-dose computed tomography: modeling study for the US Preventive Services Task Force
Rafael Meza, Jihyoun Jeon, Iakovos Toumazis, Kevin Ten Haaf, Pianpian Cao, Mehrad Bastani, Summer S Han, Erik F Blom, Daniel E Jonas, Eric J Feuer, Sylvia K Plevritis, Harry J De Koning, Chung Yin Kong
Abstract
The US Preventive Services Task Force (USPSTF) is updating its 2013 lung cancer screening guidelines, which recommend annual screening for adults aged 55 through 80 years who have a smoking history of at least 30 pack-years and currently smoke or have quit within the past 15 years. …
Cost-effectiveness Evaluation of the 2021 US Preventive Services Task Force Recommendation for Lung Cancer Screening
Iakovos Toumazis, Koen de Nijs, Pianpian Cao, Mehrad Bastani, Vidit Munshi, Kevin Ten Haaf, Jihyoun Jeon, G Scott Gazelle, Eric J Feuer, Harry J de Koning, Rafael Meza, Chung Yin Kong, Summer S Han, Sylvia K Plevritis
Abstract
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. 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. Comparative modeling analysis. National Lung Screening Trial; Surveillance, Epidemiology, and End Results program; U.S. Smoking History Generator. 1960 U.S. birth cohort. 45 years. U.S. health care sector. 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. Incremental cost-effectiveness ratio (ICER) and cost-effectiveness efficiency frontier connecting strategies with the highest health benefit at a given cost. 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%). Risk model-based strategies were robustly more cost-effective than the 2021 USPSTF recommendation under varying modeling assumptions. Risk models were restricted to age, sex, and smoking-related risk predictors. Risk model-based screening is more cost-effective than the USPSTF recommendation, thus warranting further consideration. National Cancer Institute (NCI). …
A risk‐based framework for assessing real‐time lung cancer screening eligibility that incorporates life expectancy and past screening findings
Iakovos Toumazis, Oguzhan Alagoz, Ann Leung, Sylvia K Plevritis
Abstract
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. …
A cost-effectiveness analysis of lung cancer screening with low-dose computed tomography and a diagnostic biomarker
Iakovos Toumazis, S Ayca Erdogan, Mehrad Bastani, Ann Leung, Sylvia K Plevritis
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.
Cost-effectiveness of laparoscopic disease assessment in patients with newly diagnosed advanced ovarian cancer
Ross F Harrison, Scott B Cantor, Charlotte C Sun, Mariana Villanueva, Shannon N Westin, Nicole D Fleming, Iakovos Toumazis, Anil K Sood, Karen H Lu, Larissa A Meyer
Abstract
To determine if laparoscopy is a cost-effective way to assess disease resectability in patients with newly diagnosed advanced ovarian cancer. A cost-effectiveness analysis from a health care payer perspective was performed comparing two strategies: (1) a standard evaluation strategy, where a conventional approach to treatment planning was used to assign patients to either primary cytoreduction (PCS) or neoadjuvant chemotherapy with interval cytoreduction (NACT), and (2) a laparoscopy strategy, where patients considered candidates for PCS would undergo laparoscopy to triage between PCS or NACT based on the laparoscopy-predicted likelihood of complete gross resection. A microsimulation model was developed that included diagnostic work-up, surgical and adjuvant treatment, perioperative complications, and progression-free survival (PFS). Model parameters were derived from the literature and our published data. Effectiveness was defined in quality-adjusted PFS years. Results were tested with deterministic and probabilistic sensitivity analysis (PSA). The willingness-to-pay (WTP) threshold was set at $50,000 per year of quality-adjusted PFS. The laparoscopy strategy led to additional costs (average additional cost $7034) but was also more effective (average 4.1 months of additional quality-adjusted PFS). The incremental cost-effectiveness ratio (ICER) of laparoscopy was $20,376 per additional year of quality-adjusted PFS. The laparoscopy strategy remained cost-effective even as the cost added by laparoscopy increased. The benefit of laparoscopy was influenced by mitigation of serious complications and their associated costs. The laparoscopy strategy was cost-effective across a range of WTP thresholds. Performing laparoscopy is a cost-effective way to improve primary treatment planning for patients with untreated advanced ovarian cancer.
Evaluation of alternative diagnostic follow-up intervals for lung reporting and data system criteria on the effectiveness of lung cancer screening
Mehrad Bastani, Iakovos Toumazis, Ann Leung, Sylvia K Plevritis
Abstract
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. 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. 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. 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.
CISNET Lung Model Descriptions
Rafael Meza, Jihyoun Jeon, Iakovos Toumazis, Kevin ten Haaf, Pianpian Cao, Mehrad Bastani, Summer S Han, Erik F Blom, Daniel Jonas, Eric J Feuer, Sylvia K Plevritis, Harry J de Koning, Chung Yin Kong
Abstract
The Erasmus-Microsimulation Screening Analysis (MISCAN)-Lung Model is a microsimulation model that simulates a population of individual life histories. For each individual, a smoking history (including never smoking) is generated using the Smoking History Generator (SHG). 1 Lung cancer is modeled through a multistep procedure. Once a person’s age at death from causes other than lung cancer is generated by the SHG, which is influenced by the person’s smoking history, the integrated Two-Stage Clonal Expansion (TSCE) model is used to determine whether lung cancer develops in that individual. 2, 3 MISCAN-Lung distinguishes four histological types of lung cancer: squamous cell carcinoma, adenocarcinoma, other non-small cell carcinoma, and small-cell carcinoma.
MA05. 09 Evaluation of Alternative Diagnostic Follow-Up Intervals for Lung-RADs Criteria on the Effectiveness of Lung Cancer Screening
M Bastani, I Toumazis, SK Plevritis, J Hedou, A Leung
Abstract
Introduction: The National Lung Screening Trial (NLST) demonstrated that screening for lung cancer significantly reduces lung cancer specific mortality but reporting high rates of false-positive findings. The American College of Radiology (ACR) developed Lung CT screening reporting and data systems (Lung-RADS) to standardize the management of screening findings by specifying a follow-up interval and disease progression threshold before diagnostic imaging or biopsy of small nodules found on screening CT. While Lung-RADS has been shown to reduce the false-positive rate when applied to NLST dataset (27.3% to 13.4%) through retrospective analysis, the optimality of the proposed follow-up strategies has not been fully established. This study estimates the effectiveness of alternative diagnostic follow-up intervals on lung cancer screening.
Risk-based lung cancer screening: a systematic review
Iakovos Toumazis, Mehrad Bastani, Summer S Han, Sylvia K Plevritis
Abstract
Personalised risk-based screening (PRBS) can enhance the efficiency of cancer screening programnes, but little is known about support for its implementation among the general public and health-care professionals. We aimed to summarise the acceptability and perceptions of PRBS for breast, cervical, colorectal, lung, and prostate cancer screening among these groups. We conducted a systematic review and meta-analysis of original research studies reporting on breast, cervical, colorectal, lung, and prostate cancer screening; personalised risk assessments to guide PRBS; and the acceptability of and receptibility towards these approaches among the general public, health-care professionals, or both. We searched MEDLINE, Embase, Cochrane Central, PsycINFO, and CINAHL Plus for articles published between Jan 1, 2010, and April 30, 2024. Studies not reporting on the outcomes of interest and with insufficient data for analysis were excluded. Six reviewers independently screened articles, and risk of bias was assessed using the Mixed Methods Appraisal Tool. Qualitative data were analysed thematically. Quantitative data were analysed with use of random-effects meta-analysis for outcomes that had at least two studies. The study protocol was registered at PROSPERO, CRD42022354287. Our search identified 4491 unique records. After screening, 63 studies were included in our analysis, of which 36 (57%) included the general public, 21 (33%) included health-care professionals, and six (11%) included both. The majority of studies focused on breast cancer screening (43 [68%] studies), and were from North America (28 [44%]) and Europe (28 [44%]). Qualitative findings were analysed thematically, and the extracted quantitative findings were synthesised under the following topics: acceptability and perceptions of personalised risk assessments among the general public; acceptability and perceptions of PRBS among the general public; acceptability and perceptions of PRBS among health-care professionals; and barriers and facilitators to PRBS implementation among health-care professionals. The general public and health-care professionals generally found PRBS acceptable, but they needed more information about how risk was calculated and the accuracy of risk scores. Additionally, both groups were cautious about reducing screening frequencies for individuals at low risk and cited barriers such as the time and resources needed to implement an effective PRBS programme. The pooled estimate for acceptability of PRBS was 78% (95% CI 66-88) among the general public and 86% (64-99) among health-care professionals. The general public and health-care professionals both viewed personalised risk assessments as providing valuable information and PRBS as a logical next step to increase the quality of patient care and improve cancer mortality. However, implementation barriers at the public, health-care professional, and system level need to be addressed.
A comparative modeling analysis of risk-based lung cancer screening strategies
Kevin Ten Haaf, Mehrad Bastani, Pianpian Cao, Jihyoun Jeon, Iakovos Toumazis, Summer S Han, Sylvia K Plevritis, Erik F Blom, Chung Yin Kong, Martin C Tammemägi, Eric J Feuer, Rafael Meza, Harry J De Koning
Abstract
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 with current United States Preventive Services Task Force (USPSTF) recommendations. …
Disparities of national lung cancer screening guidelines in the US population
Summer S Han, Eric Chow, Kevin Ten Haaf, Iakovos Toumazis, Pianpian Cao, Mehrad Bastani, Martin Tammemagi, Jihyoun Jeon, Eric J Feuer, Rafael Meza, Sylvia K Plevritis
Abstract
Current US Preventive Services Task Force (USPSTF) lung cancer screening guidelines are based on smoking history and age (55–80 years). These guidelines may miss those at higher risk, even at lower exposures of smoking or younger ages, because of other risk factors such as race, family history, or comorbidity. In this study, we characterized the demographic and clinical profiles of those selected by risk-based screening criteria but were missed by USPSTF guidelines in younger (50–54 years) and older (71–80 years) age groups. …
Evaluation of the Benefits and Harms of Lung Cancer Screening with Low-Dose Computed Tomography: A Collaborative Modeling Study for the US Preventive Services Task Force. AHRQ …
R Meza, J Jeon, I Toumazis, K Ten Haaf, P Cao, M Bastani
Abstract
Importance: The US Preventive Services Task Force (USPSTF) is updating its 2013 lung cancer screening guidelines, which recommend annual screening for adults aged 55 through 80 years who have a smoking history of at least 30 pack-years and currently smoke or have quit within the past 15 years. Objective: To inform the USPSTF guidelines by estimating the benefits and harms associated with various low-dose computed tomography (LDCT) screening strategies. …
Cost-effectiveness analysis of lung cancer screening in the United States: a comparative modeling study
Steven D Criss, Pianpian Cao, Mehrad Bastani, Kevin Ten Haaf, Yufan Chen, Deirdre F Sheehan, Erik F Blom, Iakovos Toumazis, Jihyoun Jeon, Harry J de Koning, Sylvia K Plevritis, Rafael Meza, Chung Yin Kong
Abstract
Recommendations vary regarding the maximum age at which to stop lung cancer screening: 80 years according to the U.S. Preventive Services Task Force (USPSTF), 77 years according to the Centers for Medicare & Medicaid Services (CMS), and 74 years according to the National Lung Screening Trial (NLST). To compare the cost-effectiveness of different stopping ages for lung cancer screening. By using shared inputs for smoking behavior, costs, and quality of life, 4 independently developed microsimulation models evaluated the health and cost outcomes of annual lung cancer screening with low-dose computed tomography (LDCT). The NLST; Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; SEER (Surveillance, Epidemiology, and End Results) program; Nurses' Health Study and Health Professionals Follow-up Study; and U.S. Smoking History Generator. Current, former, and never-smokers aged 45 years from the 1960 U.S. birth cohort. 45 years. Health care sector. Annual LDCT according to NLST, CMS, and USPSTF criteria. Incremental cost-effectiveness ratios (ICERs) with a willingness-to-pay threshold of $100 000 per quality-adjusted life-year (QALY). The 4 models showed that the NLST, CMS, and USPSTF screening strategies were cost-effective, with ICERs averaging $49 200, $68 600, and $96 700 per QALY, respectively. Increasing the age at which to stop screening resulted in a greater reduction in mortality but also led to higher costs and overdiagnosis rates. Probabilistic sensitivity analysis showed that the NLST and CMS strategies had higher probabilities of being cost-effective (98% and 77%, respectively) than the USPSTF strategy (52%). Scenarios assumed 100% screening adherence, and models extrapolated beyond clinical trial data. All 3 sets of lung cancer screening criteria represent cost-effective programs. Despite underlying uncertainty, the NLST and CMS screening strategies have high probabilities of being cost-effective. CISNET (Cancer Intervention and Surveillance Modeling Network) Lung Group, National Cancer Institute.
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 …
Martin C Tammemägi, Kevin Ten Haaf, Iakovos Toumazis, Chung Yin Kong, Summer S Han, Jihyoun Jeon, John Commins, Thomas Riley, Rafael Meza
Abstract
Low-dose computed tomography lung cancer screening is most effective when applied to high-risk individuals.
Cost-effectiveness analysis of lung cancer screening accounting for the effect of indeterminate findings
Iakovos Toumazis, Emily B Tsai, S Ayca Erdogan, Summer S Han, Wenshuai Wan, Ann Leung, Sylvia K Plevritis
Abstract
Numerous health policy organizations recommend lung cancer screening, but no consensus exists on the optimal policy. Moreover, the impact of the Lung CT screening reporting and data system guidelines to manage small pulmonary nodules of unknown significance (a.k.a. indeterminate nodules) on the cost-effectiveness of lung cancer screening is not well established.
P2. 11-02 individualized risk-based lung cancer screening incorporating past screening findings and changes in smoking behaviors
I Toumazis, O Alagoz, A Leung, S Plevritis
Abstract
Risk-based lung cancer screening guidelines are actively being pursued as an alternative to existing guidelines. However, current risk-based approaches do not capture the dynamic nature of the risk and ignore information collected from past screening findings. …
P1. 11-03 Disparities and National Lung Cancer Screening Guidelines in the US Population
S Han, E Chow, K Ten Haaf, I Toumazis, M Bastani, M Tammemägi, J Jeon, E Feuer, R Meza, S Plevritis
Abstract
Current U.S. Preventive Services Task Force (USPSTF) lung cancer (LC) screening guidelines are based on smoking history and age (55-80). These guidelines may miss those at higher risk, even at younger ages, due to other risk factors such as race or family history. In this study, we characterize the demographic/clinical profiles of those who are selected by risk-based screening criteria, but missed by USPSTF in younger (45-54) or older ages (71-80). …
Evaluating the impact of varied compliance to lung cancer screening recommendations using a microsimulation model
Summer S Han, S Ayca Erdogan, Iakovos Toumazis, Ann Leung, Sylvia K Plevritis
Abstract
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.
Comparative effectiveness of up to three lines of chemotherapy treatment plans for metastatic colorectal cancer
Iakovos Toumazis, Murat Kurt, Artemis Toumazi, Loukia G Karacosta, Changhyun Kwon
Abstract
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 …
Worst-case conditional value-at-risk minimization for hazardous materials transportation
Iakovos Toumazis, Changhyun Kwon
Abstract
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 …
Dynamic Chemotherapy Scheduling for Metastatic Colorectal Cancer Patients: Assessments and Improvements
Iakovos Toumazis
Abstract
Colorectal cancer (CRC) is the third most common and second deadliest type of cancer affecting both sexes. Patients with late-stage CRC undergo sequential chemotherapy treatments, which often yield undesirable toxic outcomes affecting patients' quality of life. Despite recent advancements in screening and treatment methods, only about 13% of metastatic CRC (mCRC) patients survive longer than 5 years. Existing guidelines for mCRC treatment offer flexibility in the decision making but may lead to sub-optimal outcomes. Tumors' drug resistance and high cost of chemotherapy treatments bring extra challenges to physicians, yet continue to be either ignored or overlooked from existing guidelines. In effort to address these challenges, this dissertation leverages the unexploited amount of existing data describing the effectiveness of chemotherapy on CRC patients, recording prior clinical trials in a patient-, disease …
Routing hazardous materials on time-dependent networks using conditional value-at-risk
Iakovos Toumazis, Changhyun Kwon
Abstract
We propose a new method for mitigating risk in routing hazardous materials (hazmat), based on the conditional value-at-risk (CVaR) measure on time-dependent vehicular networks. The CVaR models are shown to be flexible and suitable for hazmat transportation that can be solved efficiently. This paper extends the previous research by considering CVaR for hazmat transportation in the case where accident probabilities and accident consequences are time-dependent. We provide a numerical method to determine an optimal departure time and an optimal route for a given origin–destination pair. The proposed algorithm is tested in a realistic road network in Buffalo, NY, USA and the results are discussed.
Value-at-risk and conditional value-at-risk minimization for hazardous materials routing
Iakovos Toumazis, Changhyun Kwon, Rajan Batta
Abstract
This chapter provides fundamentals of value-at-risk and conditional value-at-risk models applied to routing problems in hazardous materials transportation.
Handbook of OR/MS models in hazardous materials transportation
Rajan Batta, Changhyun Kwon
Abstract
The Pipeline and Hazardous Materials Safety Administration of the US Department of Transportation defines hazardous materials (hazmat) as a substance or material capable of posing an unreasonable risk to health, safety, or property when transported in commerce 1. Hazmat accidents can result in significant impact to the population (death, injuries) and damage to the environment (destroyed or damaged buildings and infrastructure). Further, hazmat, especially explosive materials, can potentially be used by terrorists to attack civilians or to destroy critical infrastructure. This handbook provides models from Operations Research and Management Science that study various activities involving hazmat transportation: risk assessment, route planning, location decisions, evacuation planning, and emergency planning for terrorist attacks. There are two important research areas in hazmat transportation that are widely …
Robust Routing for Hazardous Materials Transportation with Conditional Value-at-Risk on Time-Dependent Networks
Iakovos Toumazis, Changhyun Kwon
Abstract
New methods are proposed for mitigating risk in hazardous materials (hazmat) transportation, based on Conditional Value-at-Risk (CVaR) measure, on time-dependent vehicular networks. While the CVaR risk measure has been popularly used in financial portfolio optimization problems, its application in hazmat transportation has been very recently proposed. The CVaR models are shown to be flexible and general routing models for hazmat transportation, and be solved efficiently. This research project will extend the previous research by considering CVaR for hazmat transportation on time-dependent networks