De Leon-Benedetti Laura, Sultan Laith R, Otero Hansel J, Morales-Tisnés Tatiana, Sims Joya, Fitzpatrick Kate, Fitzgerald Julie C, Furth Susan, Laskin Benjamin L, Viteri Bernarda
Radiology Department, Children's Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19146, USA.
Perlman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Diagnostics (Basel). 2025 Aug 21;15(16):2112. doi: 10.3390/diagnostics15162112.
Differentiating acute kidney injury (AKI) from chronic kidney disease (CKD) in children remains a critical unmet need due to the limitations of current clinical and biochemical markers. Conventional ultrasound lacks the sensitivity to discern subtle parenchymal alterations. This study explores the application of ultrasound radiomics-a novel, non-invasive, and quantitative image analysis method-for distinguishing AKI from CKD in pediatric patients. In this retrospective cross-sectional pilot study, kidney ultrasound images were obtained from 31 pediatric subjects: 8 with oliguric AKI, 14 with CKD, and 9 healthy controls. Renal parenchyma was manually segmented, and 124 advanced texture features were extracted using the open-source ©PyFeats. Features encompassed multiple categories (e.g., GLCM, GLSZM, WP). Statistical comparisons evaluated intergroup differences. Principal Component Analysis identified the top 10 most informative features, which were used to train supervised machine learning models. Model performance used five-fold cross-validation. Radiomic analysis revealed significant intergroup differences ( < 0.05). CKD cases exhibited increased echogenicity and heterogeneity, particularly in GLCM and GLSZM features, consistent with chronic fibrosis. AKI cases displayed more homogeneous texture, likely reflecting edema or acute inflammation. While echogenicity separated diseased from healthy kidneys, it lacked specificity between AKI and CKD. Among ML models, XGBoost achieved the highest macro-averaged F1 score (0.90), followed closely by SVM and Random Forest, demonstrating strong classification performance. Radiomics-based texture analysis of grayscale ultrasound images effectively differentiated AKI from CKD in this pilot study, offering a promising, non-invasive imaging biomarker for pediatric kidney disease. These preliminary findings justify prospective validation in larger, multicenter cohorts.
由于目前临床和生化标志物的局限性，区分儿童急性肾损伤（AKI）和慢性肾脏病（CKD）仍然是一个关键的未满足需求。传统超声缺乏辨别细微实质改变的敏感性。本研究探索了超声放射组学——一种新颖、非侵入性的定量图像分析方法——在区分儿科患者AKI和CKD中的应用。在这项回顾性横断面试点研究中，从31名儿科受试者获取了肾脏超声图像：8名少尿型AKI患者、14名CKD患者和9名健康对照。手动分割肾实质，并使用开源的©PyFeats提取124个高级纹理特征。特征涵盖多个类别（例如，灰度共生矩阵、灰度游程长度矩阵、小波）。统计比较评估组间差异。主成分分析确定了前10个信息量最大的特征，用于训练监督机器学习模型。模型性能采用五折交叉验证。放射组学分析显示组间存在显著差异（<0.05）。CKD病例表现出回声增强和异质性增加，特别是在灰度共生矩阵和灰度游程长度矩阵特征方面，这与慢性纤维化一致。AKI病例显示出更均匀的纹理，可能反映水肿或急性炎症。虽然回声可将患病肾脏与健康肾脏区分开来，但在AKI和CKD之间缺乏特异性。在机器学习模型中，XGBoost获得了最高的宏平均F1分数（0.90），紧随其后的是支持向量机和随机森林，显示出强大的分类性能。在这项试点研究中，基于放射组学的灰度超声图像纹理分析有效地区分了AKI和CKD，为儿科肾脏疾病提供了一种有前景的非侵入性成像生物标志物。这些初步发现证明有必要在更大规模的多中心队列中进行前瞻性验证。
