Laser lithotripsy for proximal ureteral calculi in adults: can 3D CT texture analysis help predict treatment success?

Wang, Rui; Su, Yunshan; Mao, Chongwen; Li, Song; You, Ming; Xiang, Shutian

doi:10.1007/s00330-020-07498-x

Cited by 4 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ROI is composed of many small areas with significantly different gray levels. These differences in high-order features indicated the heterogeneity of the spatial structure of the ureteral calculus [ 8 , 9 , 21 , 26 , 27 ].…”

Section: Discussionmentioning

confidence: 99%

Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith

Liu

Lin³

et al. 2022

International Journal of Clinical Practice

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith

Liu

Lin³

et al. 2022

International Journal of Clinical Practice

View full text Add to dashboard Cite

show abstract

“…Normalized non-contrast CT (NC-CT) images before TACE in DICOM format (Digital Imaging and Communications in Medicine) were used to extract features via an open access and free software, MaZda, which has proven to be efficient and reliable for quantitative image analysis. [20][21][22] The process of region of interest (ROI) drawing and features extraction is presented in Supplementary Materials and Results (Figure S1). The indicated NC-CT layer of the maximum diameter of the tumor was selected to outline the ROI in each patient and was divided into two groups: a green region was used to define the TACE-response group, including CR and PR patients, while patients with a red region were included in the TACE-nonresponse group, containing PD and SD patients.…”

Section: Extraction Of Radiomic Featuresmentioning

confidence: 99%

Prediction of Hepatocellular Carcinoma Response to Transcatheter Arterial Chemoembolization: A Real-World Study Based on Non-Contrast Computed Tomography Radiomics and General Image Features

Guo¹,

Zhong²,

Xu³

et al. 2021

JHC

View full text Add to dashboard Cite

To construct a predictive model of short-term response and overall survival for transcatheter arterial chemoembolization (TACE) treatment in hepatocellular carcinoma (HCC) patients based on non-contrast computed tomography (NC-CT) radiomics and clinical features. Methods: Ninety-four HCC patients who underwent CT scanning 1 week before the first TACE treatment were retrospectively recruited and divided randomly into a training group (n = 47) and a validation group (n = 47). NC-CT radiomics data were extracted using MaZda software, and the compound model was calculated from radiomics and clinical features by logistic regression. The performance of the different models was compared by examining the area under the receiver operating characteristic curve (AUC). The prediction of prognosis was evaluated using survival analysis. Results: Thirty NC-CT radiomic features were extracted and analyzed. The compound model was formed using four NC-CT run-length matrix (RLM) features and general image features, which included the maximum diameter (cm) of the tumor and the number of tumors (n). The AUCs of the model for TACE response were 0.840 and 0.815, whereas the AUCs of the six-and-twelve grade were 0.754 and 0.750 in the training and validation groups, respectively. HCC patients were divided into two groups using the cutoff value of the model: a group in which the TACE-response led to good survival and a group in which TACE-nonresponse caused poor prognosis. Conclusion: Radiomic features from NC-CT predicted TACE-response. The compound model generated by NC-CT radiomics and clinical features is effective and directly predicts TACEresponse and overall survival. The model may be used repeatedly and is easy to operate.

show abstract

Ureteral calculi lithotripsy for single ureteral calculi: can DNN-assisted model help preoperatively predict risk factors for sepsis?

Chen

Yang

et al. 2022

Eur Radiol

View full text Add to dashboard Cite

Laser lithotripsy for proximal ureteral calculi in adults: can 3D CT texture analysis help predict treatment success?

Cited by 4 publications

References 37 publications

Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith

Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith

Prediction of Hepatocellular Carcinoma Response to Transcatheter Arterial Chemoembolization: A Real-World Study Based on Non-Contrast Computed Tomography Radiomics and General Image Features

Ureteral calculi lithotripsy for single ureteral calculi: can DNN-assisted model help preoperatively predict risk factors for sepsis?

Contact Info

Product

Resources

About