Modeling the Economic Impact of a CIZ1B Biomarker Blood Test for Lung Cancer Screening in High-Risk Populations
Hinkel, J.; Kavade, R.; Joshi, M.
Show abstract
BackgroundThis study evaluates the economic implications of incorporating a CIZ1B biomarker blood test into lung cancer screening protocols for Medicare-eligible high-risk populations. Despite the proven mortality reduction benefits of low-dose computed tomography (LDCT), its adoption remains low. Barriers such as access disparities, logistical challenges, and the high rate of false positives necessitating invasive follow-up limit LDCTs broader acceptance. The addition of a blood-based biomarker test like CIZ1B could address these barriers by enhancing screening precision, reducing unnecessary diagnostic procedures, and increasing screening participation. MethodsAn economic model was developed to assess the cost savings and public health benefits of integrating CIZ1B in three scenarios: (1) sequential screening with LDCT following a positive biomarker test; (2) using CIZ1B to confirm LDCT-positive cases; and (3) an expanded screening paradigm where CIZ1B reduces barriers, increasing overall screening uptake. The model incorporates Medicare reimbursement rates, prevalence and screening sensitivity data, and cost estimates adjusted for inflation. ResultsResults indicate that integrating CIZ1B testing can yield net savings in the range of $500 Million by avoiding unnecessary biopsies and enabling earlier lung cancer detection and treatment. The expanded screening scenario projects additional savings through higher participation rates. This study highlights the potential of the CIZ1B biomarker to address critical challenges in lung cancer screening, reduce healthcare costs, and improve outcomes for high-risk populations.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Classification performance bias between training and test sets in a limited mammography dataset 90%
- Accuracy of deep learning based computed tomography diagnostic system of COVID-19: a consecutive sampling external validation cohort study 90%
- A flexible formula for incorporating distributive concerns into cost-effectiveness analyses: priority weights 90%
Similar papers in this journal
- A novel decision modeling framework for health policy analyses when outcomes are influenced by social and disease processes 89%
- Forecasting local surges in COVID-19 hospitalizations through adaptive decision tree classifiers 88%
- Emulator-based Bayesian calibration of the CISNET colorectal cancer models 87%
Similar papers in this journal
- Prediction of oncogene mutation status in non-small cell lung cancer: A systematic review and meta-analysis with a special focus on artificial-intelligence-based methods 90%
- Predicting EGFR mutation status in lung adenocarcinoma presenting as ground-glass opacity: utilizing radiomics model in clinical translation 89%
- Assessing GPT-4 Multimodal Performance in Radiological Image Analysis 86%
Similar papers in this journal
- The mobility gap: estimating mobility levels required to control Canada’s winter COVID-19 surge 86%
- Risk factors for retirement home COVID-19 outbreaks in Ontario, Canada: A population-level cohort study 86%
- Mathematical modeling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada 85%
Similar papers in this journal
- Clinical Benefits and Budget Impact of Lenzilumab plus Standard of Care Compared with Standard of Care Alone for the Treatment of Hospitalized Patients with COVID-19 in the United States from the Hospital Perspective 90%
- Modeling the Cost-Effectiveness of the COVID-19 mRNA-1273 vaccine in the United States 86%
- Comparison of healthcare resource use and cost between influenza and COVID-19 vaccine coadministration and influenza vaccination only 86%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.