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Updated: Sep 9, 2025
03:19
Author Spotlight: Developing a Bedside Protocol for Kidney and Genitourinary Ultrasonography
Published on: June 21, 2024
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Quantifying "Medical Renal Disease": A Pediatric Pilot Study Using Ultrasound Radiomics for Differentiating Acute
Laura De Leon-Benedetti1, Laith R Sultan1, Hansel J Otero1,2
1Radiology Department, Children's Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19146, USA.
Diagnostics (Basel, Switzerland)
|August 28, 2025
Summary
Ultrasound radiomics effectively distinguishes pediatric acute kidney injury (AKI) from chronic kidney disease (CKD) using quantitative image analysis. This non-invasive method shows promise as a biomarker for early diagnosis in children.
Area of Science:
- Medical Imaging
- Radiology
- Pediatric Nephrology
Background:
- Differentiating pediatric acute kidney injury (AKI) from chronic kidney disease (CKD) is challenging due to limited clinical and biochemical markers.
- Conventional ultrasound lacks sensitivity for subtle kidney parenchymal changes.
Purpose of the Study:
- To investigate ultrasound radiomics for non-invasively distinguishing AKI from CKD in children.
- To explore quantitative image analysis for improved diagnostic accuracy in pediatric kidney diseases.
Main Methods:
- Retrospective pilot study of 31 pediatric kidney ultrasound images (8 AKI, 14 CKD, 9 controls).
- Extraction of 124 texture features using ©PyFeats, including GLCM, GLSZM, and WP.
- Machine learning models (XGBoost, SVM, Random Forest) trained on top 10 features identified by PCA.
- Five-fold cross-validation used for model performance evaluation.
Main Results:
- Radiomics revealed significant intergroup differences (p < 0.05) in kidney texture.
- CKD cases showed increased echogenicity and heterogeneity (fibrosis), while AKI cases presented more homogeneous texture (edema/inflammation).
- XGBoost model achieved a high macro-averaged F1 score of 0.90, demonstrating strong classification performance.
Conclusions:
- Ultrasound radiomics offers a promising, non-invasive imaging biomarker for differentiating pediatric AKI from CKD.
- Quantitative texture analysis of ultrasound images can identify subtle parenchymal alterations.
- Further validation in larger, multicenter cohorts is warranted.
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