Statistical and Computation Methods for Cancer Early Detections

Principal Investigator

Name
Yunro Chung

Degrees
Ph.D.

Institution
Arizona Board of Regents for and on behalf of Arizona State University

Position Title
Associate Professor

Email
yunro.chung@asu.edu

About this CDAS Project

Study
PLCO (Learn more about this study)

Project ID
PLCO-2095

Initial CDAS Request Approval
Nov 6, 2026

Title
Statistical and Computation Methods for Cancer Early Detections

Summary
This project aims to develop and evaluate statistical and computational methods for cancer early detection using data from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. The research will focus on two complementary areas. First, we will investigate personalized diagnostic approaches that account for individual characteristics and heterogeneity in cancer risk and biomarker performance. Statistical and computational methods will be developed to improve individualized cancer detection and risk assessment by integrating available demographic, clinical, screening, and biomarker information. Second, we will investigate methods for multi-cancer early detection (MCED), with an emphasis on evaluating and combining information across multiple cancer types. This work will address statistical challenges arising from differences in cancer prevalence, biomarker performance, and patient characteristics across cancer types. The overall goal is to develop rigorous and generalizable statistical methodology that can improve cancer early detection at both the individual and multi-cancer levels.

Aims

Aim 1: Develop statistical and computational methods for personalized cancer diagnostics and early detection. We will investigate approaches that incorporate individual characteristics and heterogeneity in cancer risk and diagnostic performance. Using demographic, clinical, screening, and biomarker information available in PLCO, we will evaluate methods for individualized risk assessment and cancer detection and investigate when personalized approaches improve upon population-level diagnostic strategies.

Aim 2: Develop and evaluate statistical and computational methods for multi-cancer early detection (MCED). We will investigate approaches for identifying, evaluating, and combining diagnostic information across multiple cancer types. The research will consider heterogeneity in cancer prevalence, patient characteristics, and diagnostic performance across cancers and will evaluate approaches for assessing both cancer-specific and overall multi-cancer performance.

Collaborators

Yunro Chung Arizona Board of Regents for and on behalf of Arizona State University