My research focuses on developing mathematical models and optimization techniques to improve decision-making in cancer screening strategies, treatment protocols, and healthcare delivery. Below are my primary research areas and ongoing projects.
Developing mathematical and computational models to understand cancer natural history, progression, and treatment response
Natural History Models for Lung CancerFeatured
Natural history models are essential for understanding lung cancer progression, informing screening strategies, and predicting clinical outcomes. This research synthesizes existing approaches and develops validated models for personalized cancer care.
Objectives
- Develop and validate natural history models for lung cancer
- Integrate diverse modeling approaches for clinical application
- Improve prediction of cancer progression and outcomes
- Support personalized screening and treatment planning
Methodology
- Scoping review of natural history modeling literature
- Mathematical model development and validation
- Integration of clinical and epidemiological data
- Comparative analysis of modeling approaches
Key Findings
- Natural history models are essential tools for understanding lung cancer progression
- Diverse modeling approaches exist with varying levels of complexity and applicability
- Model selection depends on clinical context and available data
Histology-Specific Natural History Model of Ovarian CancerFeatured
Different histological subtypes of ovarian cancer exhibit distinct natural histories and progression patterns. This research develops validated, histology-specific models to improve outcome prediction and treatment planning.
Objectives
- Develop histology-specific natural history models
- Validate models across different patient populations
- Improve prediction of ovarian cancer progression
- Support personalized treatment and surveillance strategies
Methodology
- Histology-stratified data analysis
- Mathematical model development
- Multi-center validation studies
- Clinical outcome prediction
Key Findings
- Histology-specific models improve understanding of ovarian cancer natural history
- Model validation across different patient populations is essential for clinical applicability
- Distinct progression patterns exist for different ovarian cancer histologies
Developing decision support tools and optimization frameworks for screening, treatment, and risk assessment
Lung Cancer Screening OptimizationFeatured
Lung cancer remains the leading cause of cancer-related deaths worldwide, with early detection being crucial for improving patient outcomes. Current screening guidelines, while effective, may not be optimal for all patient populations, leading to overscreening in some groups and underscreening in others.
Objectives
- Personalize screening intervals based on individual risk factors
- Optimize resource allocation in healthcare systems
- Reduce false positive rates while maintaining high sensitivity
- Improve cost-effectiveness of screening programs
Methodology
- Dynamic programming and stochastic optimization techniques
- Individual risk assessment incorporating smoking history, family history, environmental factors
- Screening technology parameter analysis
- Clinical outcomes modeling
Key Findings
- Personalized screening intervals can be optimized based on individual risk factors
- Dynamic, analytically optimal screening recommendations support improved effectiveness and efficiency
- Screening structure and frequency impact should be assessed through comparative analysis of optimal frameworks
Acceptability and Implementation of Personalized Cancer Screening
Implementation of personalized screening strategies requires understanding patient and provider perspectives. This research synthesizes evidence on acceptability and perceptions of risk-based screening approaches, and evaluates implementation barriers and facilitators across different healthcare settings.
Objectives
- Evaluate acceptability of personalized screening among patients and providers
- Identify implementation barriers and facilitators
- Inform implementation strategies for personalized screening
- Support shared decision-making in cancer screening
- Assess feasibility of personalized screening in diverse populations
Methodology
- Systematic review and meta-analysis
- Qualitative synthesis of acceptability studies
- Analysis of multi-cancer screening approaches
- Implementation science framework
- Provider and patient preference assessment
Key Findings
- Risk-based screening approaches are acceptable across diverse populations
- Patient preferences and concerns vary across cancer types and demographic groups
- Implementation strategies must address both acceptability and feasibility considerations
- Provider perspectives and organizational context influence screening implementation
Economic evaluation of cancer interventions to support resource allocation and value-based decision making
Cost-Effectiveness Analyses of Lung Cancer ScreeningFeatured
Cost-effectiveness analysis is essential for informed healthcare decision-making in lung cancer screening programs. This research portfolio includes multiple cost-effectiveness studies evaluating different screening strategies, risk-based approaches, and biomarker integration. These analyses provide economic evidence to support screening guidelines and healthcare policy.
Objectives
- Evaluate cost-effectiveness of risk-based lung cancer screening strategies
- Compare economic efficiency of different screening approaches and intervals
- Assess impact of biomarkers and diagnostic tools on screening value
- Support evidence-based screening guidelines with economic data
- Inform healthcare resource allocation for lung cancer screening programs
Methodology
- Cost-effectiveness analysis and cost-utility analysis
- Quality-adjusted life year (QALY) estimation
- Probabilistic sensitivity analysis
- Comparative modeling across screening strategies
- Healthcare economic methods and frameworks
Key Findings
- Risk-based lung cancer screening approaches demonstrate favorable cost-effectiveness compared to conventional screening
- Economic evaluation supports optimization of screening intervals based on individual risk
- Cost-effectiveness varies by patient population, smoking history, and healthcare setting
- Integration of biomarkers and risk models can improve economic value of screening programs
Cost of Ovarian Cancer by Phase of Care
Understanding the economic burden of cancer care across different phases of disease is essential for healthcare planning and resource allocation. This research provides comprehensive cost estimates for ovarian cancer across the entire care continuum.
Objectives
- Estimate costs across prevention, treatment, and survivorship phases
- Identify cost drivers and high-burden areas
- Support resource allocation decisions
- Inform policy and reimbursement strategies
Methodology
- Healthcare cost database analysis
- Phase-of-care cost estimation
- Demographic and comorbidity stratification
- Healthcare economics methods
Key Findings
- Ovarian cancer care costs vary substantially across different phases of disease
- Prevention and early detection strategies have economic implications
- Cost analysis informs resource allocation and treatment decision-making
Cost-Effectiveness Analysis of Treatments for BCG-Unresponsive Bladder Cancer
Patients with BCG-unresponsive bladder cancer face multiple treatment options with varying costs and effectiveness profiles. This research provides economic evidence to support treatment selection and value-based care.
Objectives
- Compare cost-effectiveness of treatment options
- Identify optimal treatment pathways
- Support value-based treatment selection
- Inform healthcare resource allocation
Methodology
- Cost-effectiveness analysis
- Treatment outcome data integration
- Quality-adjusted life year analysis
- Sensitivity and scenario analyses
Key Findings
- Economic analysis supports treatment decision-making for BCG-unresponsive bladder cancer
- Different treatment approaches have varying costs and effectiveness profiles
- Cost-effectiveness considerations inform clinical practice recommendations
Healthcare Operations Research and Resource Optimization
Optimizing healthcare delivery systems requires integration of operations research, economic analysis, and data analytics. This research develops methods and frameworks for improved efficiency and value in cancer care delivery.
Objectives
- Optimize resource allocation in healthcare systems
- Improve efficiency of cancer care delivery
- Support value-based decision making
- Enhance healthcare system performance
Methodology
- Operations research modeling
- Healthcare cost and utilization analysis
- Data analytics and simulation
- Multi-objective optimization
Key Findings
- Operations research methods can optimize healthcare resource allocation and patient flow
- Mathematical modeling approaches support improved efficiency in healthcare delivery systems
- Data analytics and simulation provide evidence-based solutions for healthcare system optimization