Differentiation of ureteral stones and phleboliths using Hounsfield units on computerized tomography: a new method without observer bias

Tanıdır, Yılören; Şahan, Ahmet; Asutay, Mehmet Kazim; Şener, Tarık Emre; Talibzade, Farhad; Garayev, Asgar; Tınay, İlker; Sekerci, Cagri Akin; Şimşěk, Ferruh

doi:10.1007/s00240-016-0918-1

Cited by 19 publications

(11 citation statements)

References 21 publications

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“…Meanwhile, macroscopically visible lesion morphology, marginal sign, and comet tail sign on unenhanced CT can distinguish ureteral calculus from phlebolith [ 7 , 23 , 29 , 35 , 36 ]. Tanidir et al [ 37 ] measured the density and volume of ureteral calculus and phlebolith on CT images and found differences in CT images when the volume was 171 mm 3 and the density was 643 HU (sensitivity = 75% and 75%, specificity = 100% and 93%). Additionally, Lee et al [ 25 ] applied the artificial neural networks (ANN) method to analyze the morphological features of the ureteral calculus and phlebolith and found some differences.…”

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

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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.

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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

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“…while PCNL is the most effective and safe technique for larger stone sizes more than 2 cm. The prevalence of stone-free rate varied from 73-93% reported in various studies [11][12][13]. SWL is a failure or resistant cases modality while for larger stones, PCNL remains the standard reference.…”

Section: Discussionmentioning

confidence: 99%

Effectiveness and Safety of Percutaneous Nephrolithotomy (PCNL) in Malrotated Kidneys

Rehman¹,

Ali²,

Jehanzeb³

et al. 2021

PJMHS

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Background and Aim: The renal stones of any size could be effectively managed through an essential surgical technique known as Percutaneous Nephrolithotomy (PCNL). The large size renal stones with abnormal kidneys imposed additional challenges for PNCL in anomalous kidneys. The present study aimed to evaluate the effectiveness and safety of Percutaneous Nephrolithotomy in malrotated kidneys. Place and Duration: Conducted at Urology department of Mian Gul Abdulhaq Jehanzeb Kidney Hospital Manglawar, Swat for duration of two years (from May 2019 to April 2021). Materials and Methods. This single-centered retrospective study was conducted on 80 patients who underwent percutaneous nephrolithotomy with malrotation kidneys. The individuals with anomalous kidneys and complex calculi were enrolled in this study. These patients had kidneys anomalies such as horseshoe kidneys, crossed fused ectopia, malrotation kidneys, pelvic and complete stone clearance. Posterior or anterior approaches were followed for the procedure after preoperative evaluation in kidney anomalies. Retrograde catheterization was carried out under spinal and general anesthesia with the patients. For all the patients, stone size and clearance were measured. Results: Of the total 80 kidneys anomalies patients, 55 (68.7%) were male and 25 (31.3%) were females. Overall mean age was 35.26 ± 13.51. The stone size varied from 1.3cm to 7 cm. Patients were categorized into two groups based on stone sizes such as group I (1.3-2.5 cm) had 42 (52.5%) and group II (25-7cm) 38 (47.5%) patients. The patients included renal pelvis 19 (23.6%), the pelvic ureteric junction (PUJ) 8 (10%), horseshoe kidneys 2 (2.5%), crossed fused ectopia 9 (11.3%), and malrotation kidneys 3 (3.8%).The stone clearance was completed in 77 (96.3%) patients. The statistically significant factor for complete clearance was the staghorn stone calculus. Conclusion: Percutaneous nephrolithotomy is a safe and effective procedure for large renal stones management in patients of anomalous kidneys. Higher renal stones clearance can be achieved with minimum morbidity by suitable preoperative evaluation and technical experts. Keywords: Malrotated kidney; Nephrostomy, Percutaneous; Nephrolithiasis; Abnormality

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“…Following the protocol for a recently published study [10], classification of distal ureteral stones and phleboliths was performed based on the cut-off values below:…”

Section: Semi-quantitative Methods Using Attenuation and Volumementioning

confidence: 99%

“…Recently, a semi-quantitative method was applied using cutoff values for the volume and attenuation of the calcification to discriminate stones from phleboliths [10]; while another method used image features that were fed into an artificial network [11]. Irrespective of the CAD method used, a key question for their development is whether the images of the calcification and its local surroundings can provide sufficient information for differentiation, or whether distant information, such as from visible upper ureters, is also needed.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network

2020

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The objectives were to develop and validate a Convolutional Neural Network (CNN) using local features for differentiating distal ureteral stones from pelvic phleboliths, compare the CNN method with a semi-quantitative method and with radiologists' assessments and to evaluate whether the assessment of a calcification and its local surroundings is sufficient for discriminating ureteral stones from pelvic phleboliths in non-contrast-enhanced CT (NECT). We retrospectively included 341 consecutive patients with acute renal colic and a ureteral stone on NECT showing either a distal ureteral stone, a phlebolith or both. A 2.5-dimensional CNN (2.5D-CNN) model was used, where perpendicular axial, coronal and sagittal images through each calcification were used as input data for the CNN. The CNN was trained on 384 calcifications, and evaluated on an unseen dataset of 50 stones and 50 phleboliths. The CNN was compared to the assessment by seven radiologists who reviewed a local 5 × 5 × 5 cm image stack surrounding each calcification, and to a semi-quantitative method using cutoff values based on the attenuation and volume of the calcifications. The CNN differentiated stones and phleboliths with a sensitivity, specificity and accuracy of 94%, 90% and 92% and an AUC of 0.95. This was similar to a majority vote accuracy of 93% and significantly higher (p = 0.03) than the mean radiologist accuracy of 86%. The semi-quantitative method accuracy was 49%. In conclusion, the CNN differentiated ureteral stones from phleboliths with higher accuracy than the mean of seven radiologists' assessments using local features. However, more than local features are needed to reach optimal discrimination. Keywords Computed tomography • Ureteral calculi • Pelvic phlebolith • Deep learning • Convolutional neural networks Abbreviations NECT Non-contrast-enhanced computed tomography CTU CT urography CNN Convolutional neural network 2.5D-CNN 2.5-Dimensional CNN including axial, coronal and sagittal images CAD Computer-aided diagnosis ANN Artificial neural network AI Artificial intelligence FC layer Fully connected layer PACS Picture archiving and communication system MPR Multiplanar reconstructions CI Confidence interval ROC Receiver operating characteristic AUC Area under the curve ROI Region of interest Key points A Convolutional Neural Network for classifying pelvic calcifications was developed and validated.

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Differentiation of ureteral stones and phleboliths using Hounsfield units on computerized tomography: a new method without observer bias

Cited by 19 publications

References 21 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

Effectiveness and Safety of Percutaneous Nephrolithotomy (PCNL) in Malrotated Kidneys

Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network

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