CT Texture Analysis of <i>Ex Vivo</i> Renal Stones Predicts Ease of Fragmentation with Shockwave Lithotripsy

Cui, Helen; Devlies, Wout; Ravenscroft, Samuel; Heers, Hendrik; Freidin, A; Cleveland, Robin O.; Ganeshan, Balaji; Turney, Benjamin

doi:10.1089/end.2017.0084

Cited by 20 publications

(23 citation statements)

References 25 publications

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“…So far, urogenital applications of TA included an improvement of accuracy for prostate cancer detection in apparent diffusion coefficient maps 22 , and a distinction of histological subtypes of renal and adrenal tumors on CT 23 . Cui et al 17 and our group 16 have recently demonstrated that 2D-TA can predict successful SWL with moderate accuracy in an in-vitro stone model. In this study we were able to improve previous TA methodology in four important aspects.…”

Section: Discussionmentioning

confidence: 92%

“…Texture analysis (TA) detects distinct quantifiable differences of stone characteristics that may not be seen in a purely visual analysis. Our group 16 and Cui et al 17 have recently demonstrated that two dimensional (2D) TA can predict successful SWL in an in-vitro stone model. Latest developments in software technology have enabled three dimensional (3D) TA instead of only 2D-TA analysis.…”

Section: Introductionmentioning

confidence: 99%

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Three-Dimensional Texture Analysis with Machine Learning Provides Incremental Predictive Information for Successful Shock Wave Lithotripsy in Patients with Kidney Stones

et al. 2018

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Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Texture Analysis with Machine Learning Provides Incremental Predictive Information for Successful Shock Wave Lithotripsy in Patients with Kidney Stones

et al. 2018

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“…Obese patients and those with a higher body mass index (BMI) may have a larger skin to stone distance revealed during clinical examination, which could lead to unfavorable SWL success rates [12,23]. Some studies highlighted the importance of anatomical location and the architecture of the stones as measured by CT Houns eld units [8][9][10][11].…”

Section: Discussionmentioning

confidence: 99%

“…Contemporary studies have shown that the success rate of SWL for renal stones ranges from 47 to 92% [2][3][4][5][6][7]. This highly variable result is due to various crucial factors, including stone composition and size [8][9][10][11], skin to stone distance [12], lithotripter energy power [13] and frequency [14,15], patient positioning [16], and patient tolerance and respiration [16,17]. Less than half of the administered shockwaves may be accurately focused on the targeted calculus [18], and the excessive energy and shockwaves can cause damage to the parenchyma and adjacent organs.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Ultrasound-assisted and Pure Fluoroscopy-guided Extracorporeal Shockwave Lithotripsy for Renal Stones

Chang

Lin

Tsai

et al. 2020

Preprint

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Background: In this study, we aimed to compare the efficacy and clinical outcomes of shock wave lithotripsy (SWL) for patients with renal stones using pure fluoroscopy (FS) or ultrasound-assisted (USa) localization with two lithotripters.Methods: We retrospectively identified 425 patients with renal calculi who underwent SWL with either a LiteMed LM-9200 ELMA lithotripter (209 cases), which combined ultrasound and fluoroscopic stone targeting or a Medispec EM-1000 lithotripter machine (216 cases), which used fluoroscopy for stone localization and tracking. The patient demographic data, stone-free rates, stone disintegration rates, retreatment rates and complication rates were analyzed.Results: The USa group had a significantly higher overall stone-free rate (43.6 vs. 28.2%, p<0.001) and stone disintegration rate (85.6 vs. 64.3%, p<0.001), as well as a significantly lower retreatment rate (14.8 vs. 35.6%, p<0.001) and complication rate (1.9 vs. 5.5%, p=0.031) compared with the FS group. This superiority remained significant in the stone size <1cm stratified group. In the stone size >1cm group, the stone-free rate (32.4 vs. 17.8%, p=0.028), disintegration rate (89.2 vs. 54.8%, p=0.031) and retreatment rate (21.6 vs. 53.4%, p<0.001) were still significantly better in the USa group, however there was no significant difference in the complication rate. The most common complication was post-SWL-related flank pain.Conclusion: Extracorporeal SWL using ultrasound combined with fluoroscopy for stone localization and real-time tracking, provides higher stone-free rates and stone disintegration rates, as well as lower retreatment rates and fewer complications.

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“…Another limitation can potentially come from the fact that no intra-or interobserver agreement was assessed. However, previous studies on CT and MRI have demonstrated good reproducibility for filtration-histogram-based TA using multicenter clinical validation [37,38], robustness to variation in image acquisition parameters [44][45][46][47], and good inter-and intraoperator repeatability (good intraclass correlation from test-retest technique) [48,49]. The lack of cytological analysis of the cystic fluid can be considered a major limitation.…”

Section: Study Limitationsmentioning

confidence: 99%

Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts

Lupean

Ștefan

Feier

et al. 2020

JPM

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The imaging diagnosis of malignant ovarian cysts relies on their morphological features, which are not always specific to malignancy. The histological analysis of these cysts shows specific fluid characteristics, which cannot be assessed by conventional imaging techniques. This study investigates whether the texture-based radiomics analysis (TA) of magnetic resonance (MRI) images of the fluid content within ovarian cysts can function as a noninvasive tool in differentiating between benign and malignant lesions. Twenty-eight patients with benign (n = 15) and malignant (n = 13) ovarian cysts who underwent MRI examinations were retrospectively included. TA of the fluid component was undertaken on an axial T2-weighted sequence. A comparison of resulted parameters between benign and malignant groups was undertaken using univariate, multivariate, multiple regression, and receiver operating characteristics analyses, with the calculation of the area under the curve (AUC). The standard deviation of pixel intensity was identified as an independent predictor of malignant cysts (AUC = 0.738; sensitivity, 61.54%; specificity, 86.67%). The prediction model was able to identify malignant lesions with 84.62% sensitivity and 80% specificity (AUC = 0.841). TA of the fluid contained within the ovarian cysts can differentiate between malignant and benign lesions and potentially act as a noninvasive tool augmenting the imaging diagnosis of ovarian cystic lesions.

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CT Texture Analysis of Ex Vivo Renal Stones Predicts Ease of Fragmentation with Shockwave Lithotripsy

Cited by 20 publications

References 25 publications

Three-Dimensional Texture Analysis with Machine Learning Provides Incremental Predictive Information for Successful Shock Wave Lithotripsy in Patients with Kidney Stones

Three-Dimensional Texture Analysis with Machine Learning Provides Incremental Predictive Information for Successful Shock Wave Lithotripsy in Patients with Kidney Stones

Comparison of Ultrasound-assisted and Pure Fluoroscopy-guided Extracorporeal Shockwave Lithotripsy for Renal Stones

Radiomic Analysis of MRI Images is Instrumental to the Stratification of Ovarian Cysts

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