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Supervised two-dimensional functional principal component analysis with time-to-event outcomes and mammogram imaging

Shu Jiang¹, Jiguo Cao², Bernard Rosner³

¹Division of Public Health Sciences, Washington University School of Medicine in St. Louis, Missouri.

|December 2, 2021

Summary

New statistical methods, supervised functional principal component analysis (sFPCA) and functional partial least squares (FPLS), identify breast cancer risk from mammograms. These approaches improve prediction and reveal distinct risk patterns for precision prevention.

Keywords:

functional partial least squares functional principal component analysis image analysis risk prediction survival analysis

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Area of Science:

Biostatistics
Medical Imaging
Radiology

Background:

Screening mammography assesses breast cancer risk and density.
Existing statistical methods for mammogram analysis are limited and do not fully integrate time-to-event data.
Functional principal component analysis (FPCA) extracts image features but is typically independent of risk prediction.

Purpose of the Study:

To develop novel statistical methods for extracting image-based features from mammograms that are associated with breast cancer risk.
To build a prognostic model for precision breast cancer prevention.
To compare the performance of supervised FPCA (sFPCA) and functional partial least squares (FPLS) in predicting breast cancer risk.

Main Methods:

Developed and applied supervised FPCA (sFPCA) and functional partial least squares (FPLS) methods.
These methods extract image features linked to time-to-event data (failure time), accounting for right censoring.
Methods were validated using data from the Joanne Knight Breast Health cohort.

Main Results:

The proposed sFPCA and FPLS methods achieved superior prediction performance compared to benchmark models.
These approaches successfully identified image-based features associated with breast cancer risk.
Distinct risk patterns within mammograms were revealed, offering insights into individual patient risk.

Conclusions:

Supervised FPCA and FPLS are effective tools for prognostic modeling in breast cancer.
These methods enhance the predictive accuracy of mammogram analysis for personalized risk assessment.
The findings support the use of advanced statistical techniques for precision breast cancer prevention strategies.