Evaluation of the Degree of Fibrosis in Chronic Kidney Disease via Clinical Radiomics Nomogram Prediction Model
10.3969/j.issn.1005-5185.2025.03.019
- VernacularTitle:基于超声影像组学的临床列线图预测模型在慢性肾脏病纤维化程度诊断中的价值
- Author:
Xiaomin HU
1
;
Weihan XIAO
;
Xuebin LIU
;
Chaoxue ZHANG
;
Xiachuan QIN
Author Information
1. 川北医学院,四川 南充 637000;川北医学院第二临床学院?南充市中心医院超声科,四川 南充 637000
- Publication Type:Journal Article
- Keywords:
Renal insufficiency,chronic;
Fibrosis;
Ultrasonography;
Radiomics;
Nomograms
- From:
Chinese Journal of Medical Imaging
2025;33(3):331-336
- CountryChina
- Language:Chinese
-
Abstract:
Purpose To explore the value of the clinical radiomics nomogram based on ultrasound in evaluating the degree of fibrosis in chronic kidney disease(CKD).Materials and Methods This retrospective study included 350 patients with CKD in Nanchong Central Hospital from January 2014 to July 2022 who underwent renal biopsy.The patients were categorized by the tubule atrophy with interstitial fibrosis(TA/IF)and divided into a training cohort(n=245)and test cohort(n=105).The patient demographics were evaluated to establish a clinical prediction model.The XGBoost machine learning model was constructed by extracting the radiomics features from the ultrasound images.The clinical radiomics nomogram prediction model was constructed by combining the radiomics score(Rad score)and important clinical features.The diagnostic performance of the three models was evaluated using receiver operating characteristic curve analysis.Results Among the 350 patients with CKD,226 had TA/IF 0 and 124 had TA/IF 1.Based on the clinical characteristics and Rad score,the clinical radiomics nomogram prediction model had the highest area under the curve in the training and testing cohorts,with the area under the curve of 0.938(95%CI 0.909-0.969)and 0.933(95%CI 0.891-0.980),respectively.Conclusion The ultrasound-based radiomics prediction model has potential value for the noninvasive diagnosis of TA/IF in CKD.Nomogram prediction models based on renal Rad scores and clinic may help clinicians to manage patients.
