CT radiomics with transfer learning features for detecting DECT-positive periarticular monosodium urate crystal deposition: a single-center retrospective study.

Objective: To develop and validate single-energy CT (135 kVp)-based radiomics and deep learning models for the non-invasive detection of periarticular monosodium urate (MSU) crystal deposition. Methods: This retrospective study included 605 patients with suspected periarticular MSU deposition, randomly split into a training cohort (n=425) and a validation cohort (n=180). Clinical variables and CT values were collected. Hand-crafted radiomics features were extracted from lesion ROIs and selected using t-test, Pearson correlation, LASSO, and mRMR. Deep features were derived from the maximum cross-sectional ROI using a ResNet50 transfer learning framework, and fused features underwent the same selection. A multilayer perceptron was used to construct the radiomics, deep learning radiomics (DLR), and combined (clinical + DLR) models, as well as a clinical-only model. Results: Serum uric acid (OR 1.003), age (OR 1.017), bone erosion (OR 3.476), and CT value (OR 0.993) were independently associated with MSU deposition (all Conclusions: The single-energy CT-based radiomics and deep learning radiomics models showed comparable performance for identifying periarticular MSU deposition (no statistically significant difference). The combined clinical-imaging model achieved numerically higher performance but did not significantly outperform the deep learning radiomics model.