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

Yaxi Chen, Simin Ni, Jingjing Zhang, Shaheer U. Saeed, Yipei Wang, Aleksandra Ivanova, Rikin Hargunani, Chaozong Liu, Jie Huang, Yipeng Hu

TL;DR

This work proposes 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, and empirically shows that the proposed two-stage retrieval approximates the original exhaustive all k-feature selection.

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.

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

TL;DR

This work proposes 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, and empirically shows that the proposed two-stage retrieval approximates the original exhaustive all k-feature selection.

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.
Paper Structure (6 sections, 4 equations, 3 figures, 1 table)

This paper contains 6 sections, 4 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Experiments and Results
  4. Discussion and Conclusion
  5. Acknowledgement
  6. Disclosure of Interests

Figures (3)

  • Figure 1: Motivation and feasibility of feature-set retrieval.Left: Candidate feature-set score distribution (blue histogram) estimated via kernel density estimation (KDE); the red-shaded band marks the top-1 score range, illustrating near-optimal set identification without enumerating. Right: Illustration of top-$k$ vs. proposed feature-set selection approaches, where top-$k$ may select correlated features (limited coverage), whereas feature-set selection provides complementary evidence (broader coverage).
  • Figure 2: Overview of the proposed feature set selection framework. The red arrow indicates the stage 1 retrieval and the blue arrow indicates the stage 2 retrieval.
  • Figure 3: Case studies for model interpretation. Examples of (Case 1) partial ACL tear, (Case 2) non-tear, and (Case 3) KL4 knee osteoarthritis. The histogram shows, for each ROI, the number of selected features contributing to the prediction. The symbols and their adjacent arrows indicate whether the corresponding feature values are relatively high or low compared with the rest of the dataset (z-score).