CT radiomics machine learning model for predicting stone free rate of urinary calculi after retrograde intrarenal surgery
10.13929/j.issn.1672-8475.2025.01.012
- VernacularTitle:CT影像组学机器学习模型预测逆行输尿管软镜碎石术后泌尿系结石清石率
- Author:
Cong ZHOU
1
;
Yazhou WANG
;
Qingxia WU
;
Yongyue ZHU
;
Wenxin LIAO
;
Daoqing WANG
Author Information
1. 河南中医药大学第一附属医院放射科,河南 郑州 450046;河南中医药大学第一临床医学院,河南 郑州 450046
- Publication Type:Journal Article
- Keywords:
urolithiasis;
tomography,X-ray computed;
machine learning;
radiomics;
retrograde intrarenal surgery
- From:
Chinese Journal of Interventional Imaging and Therapy
2025;22(1):52-57
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of CT radiomics machine learning(ML)model for predicting stone free rate(SFR)of urinary calculi after retrograde intrarenal surgery(RIRS).Methods Totally 216 patients with urinary calculi who underwent RIRS were retrospectively enrolled and divided into residual group(n=73)and non-residual group(n=143).Univariate and multivariate logistic regression(LR)were performed to analyze clinical data and CT manifestations of stones to screen independent predictors of SFR after RIRS.Window width and window level normalization combined with max-min normalization(denoted as method a),max-min normalization(denoted as method b),window width and window level normalization(denoted as method c)and non-normalization(denoted as method d)of pre-RIRS abdominal CT were performed,respectively,and the best radiomics features of stones were extracted and screened to establish ML models,including support vector machine(SVM),LR and stochastic gradient descent(SGD)models,and the best ML model was screened.RUSS and modified S.T.O.N.E scores were evaluated based on pre-RIRS CT for predicting SFR of urinary calculi after RIRS.A combined model was then constructed with the independent predictors and the best ML model.The predictive efficacy of each model and scoring system were assessed.Results The number of stones,CT value and volume of the maximum stone were all independent predictors of SFR after RIRS(all P＜0.05).The area under the curve(AUC)of SVM model constructed with images preprocessed by method b was the highest(0.861),higher than that of the total scores of RUSS and modified S.T.O.N.E(AUC=0.750,0.759,both P＜0.05)but not different from that of combined model(AUC=0.853,P=0.775).Conclusion Radiomics SVM model based on max-min normalization preprocessed CT could effectively predict SFR of urinary calculi after RIRS.
