Renal fibrosis assessment in chronic kidney disease: Exploring varied kidney regions with ultrasound-based radiomics analysis

ObjectivesThis study aimed to develop and compare radiomics signatures derived from different renal regions on ultrasound images to assess fibrosis severity in chronic kidney disease (CKD) patients.MethodsA total of 146 CKD patients who underwent renal ultrasound and biopsy were enrolled. Radiomics features were extracted from the whole kidney, parenchyma, and mid-portion to generate region-specific signatures: radscore_whole, radscore_parenchyma, and radscore_mid-portion. Diagnostic performance in distinguishing mild from moderate-to-severe fibrosis was evaluated using receiver operating characteristic (ROC) curve analysis. Performance improvements were assessed via net reclassification improvement (NRI) and integrated discrimination improvement (IDI).ResultsThe radscore_mid-portion displayed the highest discriminatory accuracy, yielding an area under the ROC curve (AUC) of 0.74 (95% CI: 0.65-0.82), which exceeded both the radscore_whole (AUC = 0.61, 95% CI: 0.51-0.70; <i>P</i> = 0.035) and radscore_parenchyma (AUC = 0.66, 95% CI: 0.56-0.74; <i>P</i> = 0.181). Reclassification analysis confirmed the added diagnostic value of the mid-portion signature, with significant improvements compared with both the whole-kidney (NRI = 36.35%; IDI = 10.57%) and parenchyma signatures (NRI = 42.23%; IDI = 10.42%).ConclusionsRadiomics signatures from different renal regions offer varying diagnostic utility. The mid-portion-based signature demonstrated improved performance and added value in identifying moderate-to-severe renal fibrosis in CKD patients.

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Publication Details

Journal
Science Progress
Published
2025-10-01
DOI
https://doi.org/10.1177/00368504251399597
Citations
1
Primary Topic
Chronic Kidney Disease and Diabetes
Type
article
Field-Weighted Citation Impact
1.43
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article

Renal fibrosis assessment in chronic kidney disease: Exploring varied kidney regions with ultrasound-based radiomics analysis

Ziman Chen, Chaoqun Wu, Yingli Wang
1 citations
Science Progress
Chronic Kidney Disease and Diabetes
1.43
article

Renal fibrosis assessment in chronic kidney disease: Exploring varied kidney regions with ultrasound-based radiomics analysis

Ziman Chen, Chaoqun Wu, Yingli Wang
article en
1 citations

Abstract

ObjectivesThis study aimed to develop and compare radiomics signatures derived from different renal regions on ultrasound images to assess fibrosis severity in chronic kidney disease (CKD) patients.MethodsA total of 146 CKD patients who underwent renal ultrasound and biopsy were enrolled. Radiomics features were extracted from the whole kidney, parenchyma, and mid-portion to generate region-specific signatures: radscore_whole, radscore_parenchyma, and radscore_mid-portion. Diagnostic performance in distinguishing mild from moderate-to-severe fibrosis was evaluated using receiver operating characteristic (ROC) curve analysis. Performance improvements were assessed via net reclassification improvement (NRI) and integrated discrimination improvement (IDI).ResultsThe radscore_mid-portion displayed the highest discriminatory accuracy, yielding an area under the ROC curve (AUC) of 0.74 (95% CI: 0.65-0.82), which exceeded both the radscore_whole (AUC = 0.61, 95% CI: 0.51-0.70; <i>P</i> = 0.035) and radscore_parenchyma (AUC = 0.66, 95% CI: 0.56-0.74; <i>P</i> = 0.181). Reclassification analysis confirmed the added diagnostic value of the mid-portion signature, with significant improvements compared with both the whole-kidney (NRI = 36.35%; IDI = 10.57%) and parenchyma signatures (NRI = 42.23%; IDI = 10.42%).ConclusionsRadiomics signatures from different renal regions offer varying diagnostic utility. The mid-portion-based signature demonstrated improved performance and added value in identifying moderate-to-severe renal fibrosis in CKD patients.

Science ProgressVol. 108(4)
Hong Kong Polytechnic University (HK), Sun Yat-sen University (CN), Wenzhou Medical University (CN), First Affiliated Hospital of Wenzhou Medical University (CN), Fifth Affiliated Hospital of Sun Yat-sen University (CN), Edan (China) (CN)
Openalex Percentile: Top 14%
Chronic Kidney Disease and Diabetes
1.43
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