吴宇, 付永, 谷明利, 刘建民. 新型肾结石临床-影像组学模型在预测经皮肾镜结石清除率的应用价值[J]. 蚌埠医学院学报, 2023, 48(8): 1050-1055. DOI: 10.13898/j.cnki.issn.1000-2200.2023.08.008
    引用本文: 吴宇, 付永, 谷明利, 刘建民. 新型肾结石临床-影像组学模型在预测经皮肾镜结石清除率的应用价值[J]. 蚌埠医学院学报, 2023, 48(8): 1050-1055. DOI: 10.13898/j.cnki.issn.1000-2200.2023.08.008
    WU Yu, FU Yong, GU Ming-li, LIU Jian-min. Application value of a new clinical-radiomics model of kidney stone in predicting the stone-free rate of percutaneous nephrolithotomy[J]. Journal of Bengbu Medical College, 2023, 48(8): 1050-1055. DOI: 10.13898/j.cnki.issn.1000-2200.2023.08.008
    Citation: WU Yu, FU Yong, GU Ming-li, LIU Jian-min. Application value of a new clinical-radiomics model of kidney stone in predicting the stone-free rate of percutaneous nephrolithotomy[J]. Journal of Bengbu Medical College, 2023, 48(8): 1050-1055. DOI: 10.13898/j.cnki.issn.1000-2200.2023.08.008

    新型肾结石临床-影像组学模型在预测经皮肾镜结石清除率的应用价值

    Application value of a new clinical-radiomics model of kidney stone in predicting the stone-free rate of percutaneous nephrolithotomy

    • 摘要:
      目的构建预测经皮肾镜手术的结石清除率(SFR)的新型临床-影像组学模型并进行验证。
      方法回顾性收集113例行经皮肾镜取石术(PCNL)病人的相关资料, 根据术后复查的泌尿系CT或尿路平片, 将其分为结石清除组和结石残留。收集病人临床和影像组学资料, 利用图像分析软件及计算机程序设计语言工具, 将病人的CT图像划取感兴趣区域并提取出120个影像组学特征。对训练组进行变量选择, 得到最佳的特征选集, 采用多因素logistic回归分析构建新型临床-影像组学预测模型, 使用曲线下面积(AUC)评估模型的预测效果。
      结果113例病人术后1个月复查泌尿系影像, 结石清除者68例, 结石残留者45例, 总体SFR为60.2%。2组病人性别、术后血白细胞(WBC)、住院时间、结石长度、结石宽度及Guy's分级(GSS)差异均有统计学意义(P < 0.05~P < 0.01)。单因素logistic分析显示, 性别、GSS、术后血WBC、结石长度、结石宽度差异均具有统计学意义(P < 0.05~P < 0.01)。多因素logistic分析显示, 性别、GSS、术后血WBC为PCNL术后SFR的独立预测因子(P < 0.05~P < 0.01)。将Lasso回归筛选出的有意义的14个影像组学特征行单因素和多因素logistic分析, 结果显示, 最大三维直径、球度差异均具有统计学意义(P < 0.05和P < 0.01)。将最大三维直径、球度纳入构建临床-影像组学预测模型, 在训练集中曲线下面积为0.923, 在验证集中曲线下面积为0.876, 均优于GSS。
      结论构建的新型临床-影像组学模型结合临床特征, 有助于预测PCNL病人的SFR, 可为泌尿科医生术前沟通及手术方式的选择提供参考。

       

      Abstract:
      ObjectiveTo construct a novel clinical-radiomics model of kidney stone for predicting the stone-free rate (SFR) of percutaneous nephrolithotomy (PCNL) and validate it.
      MethodsThe clinical and imaging data from 113 patients undergoing PCNL were retrospectively collected, and divided into stone removal group and stone residue group according to the results of the CT scan or X-ray of kidney-ureter-bladder review after operation.The clinical and radiomics data of patients were collected, the volume of interest was drawn on the CT images and 120 radiomics features were extracted using image analysis software and computer programming language tool.The variables of the training group were selected to obtain the best feature selection.A novel clinical-radiomics model was established by multivariate logistic regression analysis, and the area under the curve (AUC) was used to evaluate the predictive effect of the model.
      ResultsThe 113 patients underwent a follow-up examination of urological imaging one month after surgery, among them, 68 cases had stones removed and 45 cases had residual stones, and the overall SFR was 60.2%.There were statistically significant differences in gender, postoperative blood white blood cell (WBC), hospital stay, stone length, stone width, and Guy's score (GSS) of patients between the two groups (P < 0.05 to P < 0.01).Univariate logistic analysis showed that there were statistically significant differences in gender, GSS, postoperative blood WBC, stone length, and stone width (P < 0.05 to P < 0.01).Multivariate logistic analysis showed that gender, GSS, and postoperative blood WBC were independent predictors of postoperative SFR of PCNL (P < 0.05 to P < 0.01).The 14 meaningful radiomics features selected by Lasso regression were analyzed using univariate and multivariate logistic analysis, the results showed that there were statistically significant differences in maximum three-dimensional diameter and sphericity (P < 0.05 and P < 0.01).The maximum three-dimensional diameter and sphericity were used to construct a clinical-radiomics prediction model, the AUC in the training set was 0.923, which in the validation set was 0.876, and both was better than GSS.
      ConclusionsThe constructed novel clinical-radiomics model combined with clinical features can help to predict the SFR in PCNL patients, and provide reference for urologists in preoperative communication and selection of surgical methods.

       

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