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A combined model based on CT radiomics and clinical variables to predict uric acid calculi which have a good accuracy

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Abstract

The aim of this study was to develop a CT-based radiomics and clinical variable diagnostic model for the preoperative prediction of uric acid calculi. In this retrospective study, 370 patients with urolithiasis who underwent preoperative urinary CT scans were enrolled. The CT images of each patient were manually segmented, and radiomics features were extracted. Sixteen radiomics features were selected by one-way analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO). Logistic regression (LR), random forest (RF) and support vector machine (SVM) were used to model the selected features, and the model with the best performance was selected. Multivariate logistic regression was used to screen out significant clinical variables, and the radiomics features and clinical variables were combined to construct a nomogram model. The area under the receiver operating characteristic (ROC) curve (AUC), etc., were used to evaluate the diagnostic performance of the model. Among the three machine learning models, the LR model had the best performance and good robustness of the dataset. Therefore, the LR model was used to construct the nomogram. The AUCs of the nomogram model in the training set and validation set were 0.878 and 0.867, respectively, which were significantly higher than those of the radiomics model and the clinical feature model. The CT-based radiomics model based has good performance in distinguishing uric acid stones from nonuric acid stones, and the nomogram model has the best diagnostic performance among the three models. This model can provide an effective reference for clinical decision-making.

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Data available on request from the authors.

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Funding

This work was supported by the National Natural Science Foundation of China under grant numbers 81772713 and 81472411.

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Author notes

  1. Zijie Wang and Guangjie Yang share primary authorship.

Authors and Affiliations

  1. Department of Urology, The Affiliated Hospital of Qingdao University, 16th Jiangsu Road, Qingdao, 266012, China

    Zijie Wang, Xinning Wang, Yuanchao Cao, Wei Jiao & Haitao Niu

  2. PET-CT Center, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China

    Guangjie Yang

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  1. Zijie Wang
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  3. Xinning Wang
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  5. Wei Jiao
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Contributions

ZW, and WJ: study concept and design. ZW, and YC: acquisition of data. ZW: analysis and interpretation of data. ZW, GY, WJ and HN: critical revision of the manuscript and obtained funding. ZW and XW: statistical analysis. All authors contributed to the article and approved the submitted version.

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Correspondence to Wei Jiao or Haitao Niu.

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All authors contributed to the article and approved the submitted version. All authors declare no competing interests.

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Wang, Z., Yang, G., Wang, X. et al. A combined model based on CT radiomics and clinical variables to predict uric acid calculi which have a good accuracy. Urolithiasis 51, 37 (2023). https://doi.org/10.1007/s00240-023-01405-x

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