Size matters: The width and location of a ureteral stone accurately predict the chance of spontaneous passage

Jendeberg, Johan; Geijer, Håkan; Alshamari, Muhammed; Cierzniak, Bartosz; Lidén, Mats

doi:10.1007/s00330-017-4852-6

Cited by 101 publications

(91 citation statements)

References 32 publications

(43 reference statements)

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“…Kidney stone moves downwards to ureter due to the flush of urine flow and the size of stone (normal ureter diameter: 3-4 mm). Size does matter for spontaneous passage of ureteric stones, as ureteric stones about 4 mm in width have a spontaneous passage rate over 80% [124].…”

Section: Urolithiasismentioning

confidence: 99%

Oxidative Stress in Urolithiasis

Boonla¹

2018

Reactive Oxygen Species (ROS) in Living Cells

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Oxygen is absolutely essential for the survival of our life. However, metabolic consumption of oxygen inevitably yields reactive oxygen species (ROS). Imbalance of ROS production and antioxidant capacity causes oxidative stress that potentially damages biomolecules leading to cell injury and death. In fact, ROS have two-faceted functions. Under physiologic condition, ROS function as signaling molecules and participate in maintaining redox balance. In pathology, ROS induce oxidative stress that critically involves in the development of several diseases including urolithiasis (UL). UL or urinary stone disease is a common urologic condition in all countries with progressively increasing prevalence. Most of UL are multifactorial with polygenic susceptibility and highly recurrent nature. Formation of urinary stones is driven by supersaturation of urinary lithogenic ions, and calcium oxalate (CaOx) is the most prevalent stone type. Oxidative stress clearly plays an active role in UL development. In vitro, lithogenic crystals induce ROS generation in renal tubular cells leading to oxidative stress, cell injury and release of inflammatory mediators. In nephrolithic rats, oxidative stress and CaOx deposit are gradually increased in the rats' kidneys. Intervention with antioxidants efficiently reduces oxidative damage and crystal deposits. Human studies show that patients with UL have increased oxidative stress and renal tubular injury relative to the non-stone-forming individuals. Increased oxidative lesions and inflammation are observed in the stone-containing kidneys of the patients. Furthermore, renal fibrosis mediated through tubular epithelial-mesenchymal transition is observed in kidneys of stone patients. Increased renal fibrosis is significantly associated with decreased kidney function. From therapeutic point of view, nutraceutical regimens that are able to reduce oxidative stress may be clinically useful alternatives for preventing stone formation and recurrence. This chapter has an intention to provide a basic knowledge of ROS generation and oxidative stress and up-to-date research findings of oxidative stress in UL based on the published articles as well as the author's studies.

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Section: Urolithiasismentioning

confidence: 99%

Oxidative Stress in Urolithiasis

Boonla¹

2018

Reactive Oxygen Species (ROS) in Living Cells

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“…For more than 20 years, non-contrast enhanced computed tomography (NECT) has been the examination of choice for diagnosing ureteral stones. At detection, about two thirds (62-68%) of all ureteral stones are positioned in the lower part of the ureter (defined as overlying or below the sacroiliac joint) [1][2][3]. For a number of reasons, the assessment of this part of the ureter is a challenge even for an experienced radiologist.…”

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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“…Kidney stone disease, also known as nephrolithiasis, is diagnosed based on patient history, physical examination, urine analysis, and abdominal radiography . Studies have been conducted previously to find a relationship between the size and location of renal calculi with its spontaneous passage through the urinary tract . In Mokhless et al, logistic regression models for prediction of spontaneous passage using a clear definition of the stone width and length were created.…”

Section: Introductionmentioning

confidence: 99%

“…Studies have been conducted previously to find a relationship between the size and location of renal calculi with its spontaneous passage through the urinary tract . In Mokhless et al, logistic regression models for prediction of spontaneous passage using a clear definition of the stone width and length were created. Further affirmation of the effect of renal calculi on the ureter can be obtained if an in situ experiment, which incorporates the peristaltic mechanics of the ureter and ureteric stone can be conducted.…”

Section: Introductionmentioning

confidence: 99%

A fluid‐structure interaction (FSI)‐based numerical investigation of peristalsis in an obstructed human ureter

Takaddus

Gautam

Chandy

2018

Numer Methods Biomed Eng

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Urine moves from the kidney to the bladder through the ureter. A series of compression waves facilitates this transport. Due to the highly concentrated mineral deposits in urine, stones are formed in the kidney and travel down through the urinary tract. While passing, a larger stone can get stuck and cause severe damage to ureter wall. Also, stones in the ureter obstructing the urine flow can cause pain and backflow of urine which in turn might require surgical intervention. The current study develops a 2D axisymmetric numerical model to gain an understanding of the ureter obstruction and its effects on the flow, which are critical in assessing the different treatment options. Transient computational analysis involving a two-way fully coupled fluid-structure interaction with the arbitrary Lagrangian-Eulerian method between the ureteral wall and urine flow is conducted with an obstruction in the ureter. The ureter wall is modeled as an anisotropic hyperelastic material, data of which, is based on biaxial tests on human ureter from previous literature, while the incompressible Navier-Stokes equations are solved to calculate urine flow. A finite element-based monolithic solver is used for the simulations here. The obstruction is placed in the fluid domain as a circular stone at the proximal part of the ureter. One of the objectives of this study is to quantify the effect of the ureteral obstruction. A sharp jump in pressure gradient and wall shear stress, as well as retrograde urine flow, is observed as a result of the obstruction.

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Size matters: The width and location of a ureteral stone accurately predict the chance of spontaneous passage

Cited by 101 publications

References 32 publications

Oxidative Stress in Urolithiasis

Oxidative Stress in Urolithiasis

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

A fluid‐structure interaction (FSI)‐based numerical investigation of peristalsis in an obstructed human ureter

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