Systemic evaluation of the relationship between psoriasis, psoriatic arthritis and osteoporosis: observational and Mendelian randomisation study.

OBJECTIVES AND METHODS: With 432 513 samples from UK Biobank dataset, multivariable linear/logistic regression were used to estimate the relationship between psoriasis/psoriatic arthritis (PsA) and estimated bone mineral density (eBMD)/osteoporosis, controlling for potential confounders. Here, confounders were set in three ways: model0 (including age, height, weight, smoking and drinking), model1 (model0 +regular physical activity) and model2 (model1 +medication treatments). The eBMD was derived from heel ultrasound measurement. And 4904 patients with psoriasis and 847 patients with PsA were included in final analysis. Mendelian randomisation (MR) approach was used to evaluate the causal effect between them. RESULTS: Lower eBMD were observed in patients with PsA than in controls in both model0 (ß-coefficient=-0.014, p=0.0006) and model1 (ß-coefficient=-0.013, p=0.002); however, the association disappeared when conditioning on treatment with methotrexate or ciclosporin (model2) (ß-coefficient=-0.005, p=0.28), mediation analysis showed that 63% of the intermediary effect on eBMD was mediated by medication treatment (p<2E-16). Patients with psoriasis without arthritis showed no difference of eBMD compared with controls. Similarly, the significance of higher risk of osteopenia in patients with PsA (OR=1.27, p=0.002 in model0) could be eliminated by conditioning on medication treatment (p=0.244 in model2). Psoriasis without arthritis was not related to osteopenia and osteoporosis. The weighted Genetic Risk Score analysis found that genetically determined psoriasis/PsA were not associated with eBMD (p=0.24 and p=0.88). Finally, MR analysis showed that psoriasis/PsA had no causal effect on eBMD, osteoporosis and fracture. CONCLUSIONS: The effect of PsA on osteoporosis was secondary (eg, medication) but not causal. Under this hypothesis, psoriasis without arthritis was not a risk factor for osteoporosis.

Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review.

Radiomics analysis can potentially characterize salivary gland tumors (SGTs) on magnetic resonance imaging (MRI). The procedures for radiomics analysis were various, and no consistent performances were reported. This review evaluated the methodologies and performances of studies using radiomics analysis to characterize SGTs on MRI. We systematically reviewed studies published until July 2023, which employed radiomics analysis to characterize SGTs on MRI. In total, 14 of 98 studies were eligible. Each study examined 23-334 benign and 8-56 malignant SGTs. Least absolute shrinkage and selection operator (LASSO) was the most common feature selection method (in eight studies). Eleven studies confirmed the stability of selected features using cross-validation or bootstrap. Nine classifiers were used to build models that achieved area under the curves (AUCs) of 0.74 to 1.00 for characterizing benign and malignant SGTs and 0.80 to 0.96 for characterizing pleomorphic adenomas and Warthin's tumors. Performances were validated using cross-validation, internal, and external datasets in four, six, and two studies, respectively. No single feature consistently appeared in the final models across the studies. No standardized procedure was used for radiomics analysis in characterizing SGTs on MRIs, and various models were proposed. The need for a standard procedure for radiomics analysis is emphasized.