Prediction of chronic kidney disease stages by renal ultrasound imaging

Chen, Chi-Jim; Pai, Tun-Wen; Hsu, Hui-Huang; Lee, Chien-Hung; Chen, Kuo-Su; Chen, Yung-Chih

doi:10.1080/17517575.2019.1597386

Cited by 21 publications

(8 citation statements)

References 21 publications

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“…5 and 6), consistent with results from Zhu et al [30]. eGFR is an important indicator for estimating kidney function and assessing IFTA severity in patients with CKD [34]. eGFR is calculated based on a standardized formula using Scr, a laboratory index that is widely used for the clinical follow-up of these patients [35].…”

Section: Discussionsupporting

confidence: 78%

Diagnostic accuracy of ultrasound-based multimodal radiomics modeling for fibrosis detection in chronic kidney disease

Lan

et al. 2022

Eur Radiol

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Objectives To predict kidney fibrosis in patients with chronic kidney disease using radiomics of two-dimensional ultrasound (B-mode) and Sound Touch Elastography (STE) images in combination with clinical features. Methods The Mindray Resona 7 ultrasonic diagnostic apparatus with SC5-1U convex array probe (bandwidth frequency of 1–5 MHz) was used to perform two-dimensional ultrasound and STE software. The severity of cortical tubulointerstitial fibrosis was divided into three grades: mild interstitial fibrosis and tubular atrophy (IFTA), fibrotic area < 25%; moderate IFTA, fibrotic area 26–50%; and severe IFTA, fibrotic area > 50%. After extracting radiomics from B-mode and STE images in these patients, we analyzed two classification schemes: mild versus moderate-to-severe IFTA, and mild-to-moderate versus severe IFTA. A nomogram was constructed based on multiple logistic regression analyses, combining clinical and radiomics. The performance of the nomogram for differentiation was evaluated using receiver operating characteristic (ROC), calibration, and decision curves. Results A total of 150 patients undergoing kidney biopsy were enrolled (mild IFTA: n = 74; moderate IFTA: n = 33; severe IFTA: n = 43) and randomized into training (n = 105) and validation cohorts (n = 45). To differentiate between mild and moderate-to-severe IFTA, a nomogram incorporating STE radiomics, albumin, and estimated glomerular filtration (eGFR) rate achieved an area under the ROC curve (AUC) of 0.91 (95% confidence interval [CI]: 0.85–0.97) and 0.85 (95% CI: 0.77–0.98) in the training and validation cohorts, respectively. Between mild-to-moderate and severe IFTA, the nomogram incorporating B-mode and STE radiomics features, age, and eGFR achieved an AUC of 0.93 (95% CI: 0.89–0.98) and 0.83 (95% CI: 0.70–0.95) in the training and validation cohorts, respectively. Finally, we performed a decision curve analysis and found that the nomogram using both radiomics and clinical features exhibited better predictability than any other model (DeLong test, p < 0.05 for the training and validation cohorts). Conclusion A nomogram based on two-dimensional ultrasound and STE radiomics and clinical features served as a non-invasive tool capable of differentiating kidney fibrosis of different severities. Key Points • Radiomics calculated based on the ultrasound imaging may be used to predict the severities of kidney fibrosis. • Radiomics may be used to identify clinical features associated with the progression of tubulointerstitial fibrosis in patients with CKD. • Non-invasive ultrasound imaging-based radiomics method with accuracy aids in detecting renal fibrosis with different IFTA severities.

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Section: Discussionsupporting

confidence: 78%

Diagnostic accuracy of ultrasound-based multimodal radiomics modeling for fibrosis detection in chronic kidney disease

Lan

et al. 2022

Eur Radiol

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“…First, we chose DWI that offers more information on renal parenchymal changes than simple attenuation differences measured in Hounsfield units on CT. Our machine learning algorithms retrieved radiomic features and improved the accuracy for clinical utilization. Second, to the best of our knowledge, this is the first study focusing on the role of DWI-based radiomics in children with CAKUT [15,16]. All available normal or abnormal findings in kidneys from children who underwent MRI/DWI and DMSA/DTPA were included in our cohort.…”

Section: Discussionmentioning

confidence: 99%

A novel clinical-radiomics model predicted renal lesions and deficiency in children on diffusion-weighted MRI

Kang

Zhang

et al. 2022

Front. Phys.

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Background: Assessment of renal lesions and deficiency accurately remains critical in the diagnosis of congenital anomalies of the kidneys and urinary tracts (CAKUT) in children. Advanced imaging such as Magnetic resonance Imaging (MRI) and Diffusion weighted Imaging (DWI) allows structural and functional insufficiency to be detected. Currently, radiomics machine learning models are being explored as full-automated diagnostic tools. We aimed to develop a machine learning integrated radiomics model to predict renal anomalies and deficiency in children.Methods: A retrospective study of 280 children with MRI/DWI were enrolled between 2018 and 202 at a children’s hospital. A total of 1,037 radiomics features were extracted from the DWI images of each participant, which were divided into training set and test set (8:2 split). Using 5-fold cross-validated method, multiple machine learning algorithms were employed to predict renal lesions and deficiency when compared with the radiologist’s diagnosis based on DWI, 99mTc-labeled dimercaptosuccinic acid (DMSA) SPECT cortical renal scintigraphy or 99mTc-labeled diethylenetriamine pentaacetate (DTPA) renal scan.Results: For detecting the kidney lesions, the LASSO + Random Forest algorithm outperformed other classifiers with an accuracy of 0.750 (95% confidence interval, 0.734–0.766) and area under the curve (AUC) of 0.765 (95% confidence interval, 0.700–0.831). The performance of classifiers did not show a significant difference (p > 0.05) in detecting bilateral or unilateral kidney lesions by DWI scanning. The classifiers performed significantly better in bilateral kidney deficit than in unilateral kidney deficit (p < 0.05). We next built prediction models for renal deficiency using the radiomics signature and clinical features compared to renal scintigraphy. The ensemble model had a high-test accuracy of 80.9% ± 4.2% and a sensitivity of 91.7% ± 7.1% with a moderate calibration.Conclusion: An ensemble model integrated with DWI-radiomic and clinical features can be utilized to predict renal lesions and deficiency in children with CAKUT.

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“…Moreover, assessment of fibrosis deposition and renal impairment associated with CKD by textural analysis was performed on ultrasonography (US) renal images (Ardakani et al, 2017;Chen et al, 2019;Iqbal et al, 2017;Sharma and Virmani, 2017). Textures based on Fourier transform that reflect spatial frequencies, succeeded differentiating CKD and healthy kidneys while GLCM-based parameters failed (Iqbal et al, 2017).…”

Section: Texture Analysis and Conventional Machine Learning Techniquesmentioning

confidence: 99%

Recent advances in medical image processing for the evaluation of chronic kidney disease

Alnazer¹,

Bourdon²,

Urruty³

et al. 2021

Medical Image Analysis

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Prediction of chronic kidney disease stages by renal ultrasound imaging

Cited by 21 publications

References 21 publications

Diagnostic accuracy of ultrasound-based multimodal radiomics modeling for fibrosis detection in chronic kidney disease

Diagnostic accuracy of ultrasound-based multimodal radiomics modeling for fibrosis detection in chronic kidney disease

A novel clinical-radiomics model predicted renal lesions and deficiency in children on diffusion-weighted MRI

Recent advances in medical image processing for the evaluation of chronic kidney disease

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