2026-11-03

Rationale And Objectives: To assess a noninvasive ultrasound habitat radiomics approach with automatic kidney segmentation for distinguishing pediatric Henoch-Schonlein purpura nephritis (HSPN) from immunoglobulin A nephropathy (IgAN). Materials And Methods: This retrospective multicenter study included 592 biopsy-confirmed pediatric patients from three centers (HSPN, n = 338; IgAN, n = 254). The development cohort (n = 453) was split into training (n = 362) and internal validation (n = 91) cohorts, and an independent external validation cohort (n = 139) was used for final testing. Kidney regions of interest were automatically segmented using nnU-Net. K-means clustering defined three echogenic habitat subregions, and 408 radiomics features were extracted from the whole-kidney region and habitat subregions. Feature selection was performed in the training cohort, and three machine-learning classifiers were evaluated. The selected radiomics model was compared with clinical and combined models. Results: nnU-Net achieved high kidney segmentation performance, with a mean Dice similarity coefficient of 0.957 and a mean intersection over union of 0.918 in the independent healthy-child test set. Fifteen radiomics features were retained, including six whole-kidney features and nine habitat-derived features. Logistic regression showed stable performance, with areas under the receiver operating characteristic curve (AUCs) of 0.806 (95% confidence interval [CI], 0.715-0.897) internally and 0.825 (95% CI, 0.758-0.892) externally. The combined model outperformed the clinical model in AUC in both cohorts (0.846 vs 0.717; 0.805 vs 0.733). Conclusion: This deep-learning-assisted ultrasound habitat radiomics approach enabled automatic kidney delineation and showed encouraging performance as a reproducible imaging adjunct for differentiating pediatric HSPN from IgAN.

Background: Patients with rheumatoid arthritis (RA) initiating biologic or targeted synthetic disease-modifying antirheumatic drugs (b/ts DMARDs) face elevated risk of serious infections, necessitating tools for individualized risk stratification. Objectives: Primary objective was to develop and validate a clinically interpretable machine learning (ML) model to predict 1-year risk of serious infection after b/ts DMARD initiation; secondary objectives were to estimate infection incidence and identify key predictors associated with risk. Methods: We performed a retrospective cohort study using territory-wide EHR from Hong Kong's Clinical Data Analysis and Reporting System (CDARS) for model development and internal validation, with external validation in the U.S. All of Us database. The outcome was first serious infection requiring hospitalization within 1 year. Candidate predictors included demographics, comorbidities, prior infections and medications, laboratory markers. Multiple ML algorithms were trained; model selection was based on AUROC, and interpretability was assessed using SHAP. Results: A total of 3,159 patients from CDARS (8.8% with serious infections) and 1,845 from All of Us (2.8% with serious infections) were included. The model demonstrated the highest AUROC in internal validation (0.840, 95% CI: 0.793-0.888) and maintained robust performance in external validation (AUROC: 0.729, 95% CI: 0.665-0.793). Key predictors included prior infections, diabetes, b/ts DMARD type, and inflammatory markers. Rituximab was linked to the highest infection risk, while tofacitinib and upadacitinib had the lowest. Conclusion: This study developed and validated an ML model using routine clinical data to predict serious infection risk in RA patients, supporting personalised treatment and proactive infection management.

Background: More than 90% of children with familial hypercholesterolemia (FH) remain undiagnosed. Although genetic testing is the diagnostic gold standard, limited availability necessitates the use of clinical diagnostic criteria, which have suboptimal performance in children. Objective: To introduce the recently developed familial hypercholesterolemia pediatric diagnostic score (FH-PeDS) and its online implementation. Methods: FH-PeDS was developed using real-world data from Slovenian children identified through universal cholesterol screening and externally validated in an independent Portuguese pediatric cohort. Results: FH-PeDS demonstrated improved diagnostic accuracy compared with traditional clinical scoring systems and was recently recognized as promising tool in the European Atherosclerosis Society consensus statement on FH in children and adolescents. It is now freely available at https://fh-peds.eu in two versions: a semi-quantitative clinical scoring system and a machine learning model. Conclusion: The online FH-PeDS provides healthcare professionals with an accessible tool to estimate the likelihood of pediatric FH, potentially facilitating earlier diagnosis and treatment. Further external validation is warranted before widespread implementation.