Radiomic fingerprints for knee MR images assessment

2025-06-25

AI Summary

Traditional radiomics approaches for knee MRI rely on fixed, handcrafted feature sets, exhibiting poor generalizability and limited capacity to capture inter-individual pathological heterogeneity—thus constraining both diagnostic performance and clinical interpretability. To address this, we propose a dynamic, patient-specific radiomics fingerprinting framework that abandons predefined features. Instead, it employs a deep learning model to adaptively select the most discriminative radiomic features for each patient, followed by joint end-to-end optimization with a lightweight logistic regression classifier. Evaluated across multiple knee pathology classification tasks—including osteoarthritis, meniscal tears, and ligament injuries—the method achieves performance comparable to or exceeding that of end-to-end deep learning models. Crucially, it delivers interpretable feature contribution maps, enabling transparent, clinically meaningful decision support. The framework thus bridges high accuracy with clinical interpretability, facilitating personalized diagnosis and the discovery of potential imaging biomarkers.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

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

Accurate interpretation of knee MRI scans relies on expert clinical judgment, often with high variability and limited scalability. Existing radiomic approaches use a fixed set of radiomic features (the signature), selected at the population level and applied uniformly to all patients. While interpretable, these signatures are often too constrained to represent individual pathological variations. As a result, conventional radiomic-based approaches are found to be limited in performance, compared with recent end-to-end deep learning (DL) alternatives without using interpretable radiomic features. We argue that the individual-agnostic nature in current radiomic selection is not central to its intepretability, but is responsible for the poor generalization in our application. Here, we propose a novel radiomic fingerprint framework, in which a radiomic feature set (the fingerprint) is dynamically constructed for each patient, selected by a DL model. Unlike the existing radiomic signatures, our fingerprints are derived on a per-patient basis by predicting the feature relevance in a large radiomic feature pool, and selecting only those that are predictive of clinical conditions for individual patients. The radiomic-selecting model is trained simultaneously with a low-dimensional (considered relatively explainable) logistic regression for downstream classification. We validate our methods across multiple diagnostic tasks including general knee abnormalities, anterior cruciate ligament (ACL) tears, and meniscus tears, demonstrating comparable or superior diagnostic accuracy relative to state-of-the-art end-to-end DL models. More importantly, we show that the interpretability inherent in our approach facilitates meaningful clinical insights and potential biomarker discovery, with detailed discussion, quantitative and qualitative analysis of real-world clinical cases to evidence these advantages.

Problem

• High variability in expert knee MRI interpretation limits scalability
• Fixed radiomic features fail to capture individual pathological variations
• Current radiomic approaches generalize poorly compared to deep learning

Innovation

• Dynamic radiomic feature selection per patient
• Deep learning predicts feature relevance
• Combines interpretability with diagnostic accuracy