Ming Wang

PhD

Case Western University

Associate Professor

PLCO (Learn more about this study)

PLCOI-1672

Sep 19, 2024

Advanced data science techniques for prostate, lung, colorectal and ovarian cancer databases to better understand patient disparities, treatment options and outcomes

Although significant progress has been made in cancer care and outcomes in recent years, healthcare disparities, late diagnosis, disease progression to aggressiveness and morbidity, treatment ineffectiveness or delays continue to affect patients with lethal cancer. To better understand these issues, large-scale data (e.g., population-based cancer registries, PLCO) represent valuable resources for exploration. However, fully utilizing these heterogeneous real-world data sources presents significant challenges, as current analytical techniques and toolboxes are limited in their direct practical usage. To address these barriers, we aim to develop a unified analytical framework that enables comprehensive, advanced, and powerful analysis of lethal prostate cancer.

Aim 1: Establish a data warehouse and management system that can integrate and harmonize data sources to meet clinical and research needs and provide demo examples.

Aim 2: Investigate distinct phenotypes of newly diagnosed lethal cancer and gain a better understanding of inequalities and disparities in disease presentation and outcomes.

Aim 3: Identify multi-modal biomarkers for prediction of cancer outcomes with enhanced estimation efficiency and predictive accuracy.

Aim 4: Develop a customizable and interpretable AI framework for investigating and querying cancer, aiding the creation of data-driven decision support systems for treatment planning and optimization.