Automatic Ultrasound Habitat Radiomics for Differentiating Henoch-Schonlein Purpura Nephritis from IgA Nephropathy in Children: A Multicenter Study.

Authors

Shuang Gong, Wei Zhang, Tong Xu, Xiao-Feng Zhang, Meng-Juan Zhang, Pan Gao, Xian-Ya Zhang, Qi Wei, Xin-Wu Cui

Published in

Academic radiology. Nov 02, 2026. Epub Nov 02, 2026.

Abstract

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).
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.
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).
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.

PMID:
42971817
Bibliographic data and abstract were imported from PubMed on 03 Nov 2026.

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