Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith
Objective. To investigate the clinical application of the three-dimensional (3D) radiomics model of the CT image in the diagnosis and identification of ureteral calculus and phlebolith. Method. Sixty-one cases of ureteral calculus and 61 cases of phlebolith were retrospectively investigated. The enr...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Clinical Practice |
| Online Access: | http://dx.doi.org/10.1155/2022/5478908 |
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| author | Qiuyue Yu Jiaqi Liu Huashan Lin Pinggui Lei Bing Fan |
| author_facet | Qiuyue Yu Jiaqi Liu Huashan Lin Pinggui Lei Bing Fan |
| author_sort | Qiuyue Yu |
| collection | DOAJ |
| description | Objective. To investigate the clinical application of the three-dimensional (3D) radiomics model of the CT image in the diagnosis and identification of ureteral calculus and phlebolith. Method. Sixty-one cases of ureteral calculus and 61 cases of phlebolith were retrospectively investigated. The enrolled patients were randomly categorized into the training set (n = 86) and the testing set (n = 36) with a ratio of 7 : 3. The plain CT scan images of all samples were manually segmented by the ITK-SNAP software, followed by radiomics analysis through the Analysis Kit software. A total of 1316 texture features were extracted. Then, the maximum correlation minimum redundancy criterion and the least absolute shrinkage and selection operator algorithm were used for texture feature selection. The feature subset with the most predictability was selected to establish the 3D radiomics model. The performance of the model was evaluated by the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) was also calculated. Additionally, the decision curve was used to evaluate the clinical application of the model. Results. The 10 selected radiomics features were significantly related to the identification and diagnosis of ureteral calculus and phlebolith. The radiomics model showed good identification efficiency for ureteral calculus and phlebolith in the training set (AUC = 0.98; 95%CI: 0.96–1.00) and testing set (AUC = 0.98; 95%CI: 0.95–1.00). The decision curve thus demonstrated the clinical application of the radiomics model. Conclusions. The 3D radiomics model based on plain CT scan images indicated good performance in the identification and prediction of ureteral calculus and phlebolith and was expected to provide an effective detection method for clinical diagnosis. |
| format | Article |
| id | doaj-art-57830da8d07f48928447ac4e99083ac8 |
| institution | Kabale University |
| issn | 1742-1241 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Clinical Practice |
| spelling | doaj-art-57830da8d07f48928447ac4e99083ac82025-02-03T01:24:37ZengWileyInternational Journal of Clinical Practice1742-12412022-01-01202210.1155/2022/5478908Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and PhlebolithQiuyue Yu0Jiaqi Liu1Huashan Lin2Pinggui Lei3Bing Fan4Department of RadiologyDepartment of RadiologyDepartment of Pharmaceutical DiagnosisDepartment of RadiologyDepartment of RadiologyObjective. To investigate the clinical application of the three-dimensional (3D) radiomics model of the CT image in the diagnosis and identification of ureteral calculus and phlebolith. Method. Sixty-one cases of ureteral calculus and 61 cases of phlebolith were retrospectively investigated. The enrolled patients were randomly categorized into the training set (n = 86) and the testing set (n = 36) with a ratio of 7 : 3. The plain CT scan images of all samples were manually segmented by the ITK-SNAP software, followed by radiomics analysis through the Analysis Kit software. A total of 1316 texture features were extracted. Then, the maximum correlation minimum redundancy criterion and the least absolute shrinkage and selection operator algorithm were used for texture feature selection. The feature subset with the most predictability was selected to establish the 3D radiomics model. The performance of the model was evaluated by the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) was also calculated. Additionally, the decision curve was used to evaluate the clinical application of the model. Results. The 10 selected radiomics features were significantly related to the identification and diagnosis of ureteral calculus and phlebolith. The radiomics model showed good identification efficiency for ureteral calculus and phlebolith in the training set (AUC = 0.98; 95%CI: 0.96–1.00) and testing set (AUC = 0.98; 95%CI: 0.95–1.00). The decision curve thus demonstrated the clinical application of the radiomics model. Conclusions. The 3D radiomics model based on plain CT scan images indicated good performance in the identification and prediction of ureteral calculus and phlebolith and was expected to provide an effective detection method for clinical diagnosis.http://dx.doi.org/10.1155/2022/5478908 |
| spellingShingle | Qiuyue Yu Jiaqi Liu Huashan Lin Pinggui Lei Bing Fan Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith International Journal of Clinical Practice |
| title | Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith |
| title_full | Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith |
| title_fullStr | Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith |
| title_full_unstemmed | Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith |
| title_short | Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith |
| title_sort | application of radiomics model of ct images in the identification of ureteral calculus and phlebolith |
| url | http://dx.doi.org/10.1155/2022/5478908 |
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