Prediction for Error-Contaminated Image Data with an Application of the Prostate Cancer Imaging Study

• Activity Number: 543 - Making Sense of Complex Featured Data with Statistical Methods
• Type: Invited
• Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
• Sponsor: SSC
• Author(s): Wenqing He* and Grace Yi and Junhan Fang
• Companies: University of Western Ontario and University of Waterloo and University of Waterloo
• Keywords: Error-contaminated; Classification; Prediction; Prostate cancer

Abstract

Prostate cancer is the most commonly diagnosed cancer and the third highest cause of cancer related mortality in men. Treatment for prostate cancer is quite successful, with about 95% 5 year survival rate for patients with cancer stage below 3. However, this success hinges on an early stage diagnosis and confirmation. While it is imperative to build a powerful predictive model for prostate cancer imaging data, existing methods cannot be applied due to their inadequacy of accommodating the unique features of prostate cancer imaging data. In particular, data imbalance, spatial correlation, and outcome misclassification present great challenges in data analysis. In this talk, I will discuss various statistical approaches to building an effective prediction model. I will examine the data from multiple angles with their features accommodated differently.