Patient-specific radiomic feature selection with reconstructed healthy persona of knee MR images

2025-03-17

AI Summary

This study addresses the challenge of balancing interpretability and classification performance in radiomics analysis of knee MRI. We propose a patient-specific radiomic feature selection mechanism, integrated with a denoising diffusion model to construct individualized healthy image baselines—generated via masked inpainting to yield pathology-agnostic references—enabling lesion-guided localization and interpretable feature discovery. Furthermore, we introduce a feature importance re-weighted logistic regression within a multi-task framework (general abnormality, ACL tear, meniscal tear) to enhance discriminative performance. Experiments demonstrate that our method matches or surpasses state-of-the-art deep learning models across all three clinical tasks, while providing clinician-interpretable, feature-level explanations and personalized diagnostic evidence. To our knowledge, this is the first radiomics framework to enhance interpretability through a generative healthy baseline paradigm.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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

Problem

• Improves radiomic feature selection for individual patients
• Generates patient-specific healthy personas using diffusion models
• Enhances interpretability and performance in clinical tasks

Innovation

• Patient-specific radiomic feature selection
• Healthy persona generation via mask-inpainting
• Enhanced interpretability with logistic regression