Patient-specific radiomic feature selection with reconstructed healthy persona of knee MR images
Journal:
arXiv
Published Date:
Mar 17, 2025
Abstract
Classical radiomic features have been designed to describe image appearance
and intensity patterns. These features are directly interpretable and readily
understood by radiologists. Compared with end-to-end deep learning (DL) models,
lower dimensional parametric models that use such radiomic features offer
enhanced interpretability but lower comparative performance in clinical tasks.
In this study, we propose an approach where a standard logistic regression
model performance is substantially improved by learning to select radiomic
features for individual patients, from a pool of candidate features. This
approach has potentials to maintain the interpretability of such approaches
while offering comparable performance to DL. We also propose to expand the
feature pool by generating a patient-specific healthy persona via
mask-inpainting using a denoising diffusion model trained on healthy subjects.
Such a pathology-free baseline feature set allows further opportunity in novel
feature discovery and improved condition classification. We demonstrate our
method on multiple clinical tasks of classifying general abnormalities,
anterior cruciate ligament tears, and meniscus tears. Experimental results
demonstrate that our approach achieved comparable or even superior performance
than state-of-the-art DL approaches while offering added interpretability by
using radiomic features extracted from images and supplemented by generating
healthy personas. Example clinical cases are discussed in-depth to demonstrate
the intepretability-enabled utilities such as human-explainable feature
discovery and patient-specific location/view selection. These findings
highlight the potentials of the combination of subject-specific feature
selection with generative models in augmenting radiomic analysis for more
interpretable decision-making. The codes are available at:
https://github.com/YaxiiC/RadiomicsPersona.git
