Retrieving Patient-Specific Radiomic Feature Sets for Transparent Knee MRI Assessment

2026-03-02

AI Summary

This work proposes a patient-specific compact radiomic feature selection framework that addresses the limitations of conventional radiomics, which relies on population-level predefined features and struggles to balance individualized diagnostic performance with model interpretability. The approach employs a two-stage strategy: first generating diverse candidate feature sets via random sampling, then ranking them using a learnable scoring function to identify a complementary and non-redundant feature combination tailored to each patient. By moving beyond traditional top-k marginal selection, this method pioneers combinatorial optimization for patient-specific feature retrieval. Evaluated on ACL tear detection and osteoarthritis Kellgren–Lawrence grading tasks, it outperforms competing methods and matches the performance of end-to-end deep learning models while offering clinically interpretable decisions traceable to specific anatomical regions and feature types.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Search and Optimization: Mixed Discrete/Continuous Search

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
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Classical radiomic features are designed to quantify image appearance and intensity patterns. Compared with end-to-end deep learning (DL) models trained for disease classification, radiomics pipelines with low-dimensional parametric classifiers offer enhanced transparency and interpretability, yet often underperform because of the reliance on population-level predefined feature sets. Recent work on adaptive radiomics uses DL to predict feature weights over a radiomic pool, then thresholds these weights to retain the top-k features from large radiomic pool F (often ~10^3). However, such marginal ranking can over-admit redundant descriptors and overlook complementary feature interactions. We propose a patient-specific feature-set selection framework that predicts a single compact feature set per subject, targeting complementary and diverse evidence rather than marginal top-k features. To overcome the intractable combinatorial search space of F choose k features, our method utilizes a 2-stage retrieval strategy: randomly sample diverse candidate feature sets, then rank these sets with a learned scoring function to select a high-performing feature set for the specific patient. The system consists of a feature-set scorer, and a classifier that performs the final diagnosis. We empirically show that the proposed two-stage retrieval approximates the original exhaustive all k-feature selection. Validating on tasks including ACL tear detection and KL grading for osteoarthritis, the experimental results achieve diagnostic performance, outperforming the top-k approach with the same k values, and competitive with end-to-end DL models while maintaining high transparency. The model generates auditable feature sets that link clinical outcomes to specific anatomical regions and radiomic families, allowing clinicians to inspect which anatomical structures and quantitative descriptors drive the prediction.

Problem

• radiomics
• patient-specific
• feature selection
• MRI
• interpretability

Innovation

• patient-specific radiomics
• feature-set selection
• two-stage retrieval
• complementary features
• interpretable AI