Dynamic Survival Prediction for Breast Cancer using Mammogram Imaging Data
报告人：曹际国 (Simon Fraser University)
时间：2026-04-09 15:00-16:00
地点：四元厅
Abstract: With mammography as the primary strategy for breast cancer screening, it is essential to fully leverage imaging data to better identify women at higher or lower than average risk. The primary objective of this study is to extract mammogram-based features that complement established breast cancer risk factors and improve prediction accuracy. We propose a supervised functional principal component analysis over triangulations method to extract features that are explicitly ordered by their association with failure time outcomes. The proposed approach effectively addresses the irregular boundary of the breast region in mammographic images by employing flexible bivariate splines over triangulations. We further develop a computationally efficient algorithm based on eigenvalue decomposition. We apply the method to data from the Joanne Knight Breast Health Cohort at Siteman Cancer Center. Our approach not only delivers superior predictive performance relative to unsupervised FPCA and other benchmark models, but also identifies meaningful risk patterns within mammographic images.
Bio: 曹际国博士, 加拿大温哥华西蒙弗雷泽大学(Simon Fraser University)统计与精算系教授，加拿大数据科学国家特聘教授(Canada Research Chair in Data Science)，现担任Statistics in Medicine, Journal of the Royal Statistical Society: Series A等国际优秀统计期刊副主编。曹际国2006年获得加拿大麦吉尔大学(McGill University) 博士，2007年美国耶鲁大学博士后出站，长期从事人工智能，机器学习，函数型数据分析(functional data analysis) 和估计微分方程的研究。曹际国2021年获得加拿大统计协会(Statistical Society of Canada)和国家数学研究中心(Centre de recherches mathématiques)联合评比的最高奖之一：加拿大国家杰出青年统计学家奖(CRM-SSC award)。曹际国近些年来在国际优秀统计期刊中发表超过100篇文章。
