# Spatial Process Decomposition for Quantitative Imaging Biomarkers Using Multiple Images of Varying Shapes

## TL;DR

This paper introduces a flexible statistical method for extracting quantitative imaging biomarkers from 3D medical images, improving reproducibility and objectivity in radiomics analysis.

## Contribution

A novel model-based spatial process decomposition approach that accounts for patient-specific weights and common component functions across patients.

## Key findings

- Method performs well in simulation studies.
- QIBs extracted show association with clinical endpoints.
- Approach enhances reproducibility of biomarker extraction.

## Abstract

Quantitative imaging biomarkers (QIB) are extracted from medical images in radiomics for a variety of purposes including noninvasive disease detection, cancer monitoring, and precision medicine. The existing methods for QIB extraction tend to be ad-hoc and not reproducible. In this paper, a general and flexible statistical approach is proposed for handling up to three-dimensional medical images in an objective and principled way. In particular, a model-based spatial process decomposition is developed where the random weights are unique to individual patients for component functions common across patients. Model fitting and selection are based on maximum likelihood, while feature extractions are via optimal prediction of the underlying true image. A simulation study evaluates the properties of the proposed methodology and for illustration, a cancer image data set is analyzed and QIBs are extracted in association with a clinical endpoint.