Radiomic fingerprints for knee MR images assessment
Journal:
arXiv
Published Date:
Jun 25, 2025
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.
