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

Success of extracorporeal shock wave lithotripsy based on CT texture analysis

OBJECTIVE

The aims of the study were to evaluate whether computerised tomography texture analysis (CTTA) based on non-contrast computed tomography (NCCT) has predictive value for the success of extracorporeal-shockwave lithotripsy (ESWL) in upper urinary tract stones (UUTS).

METHODS

This study included 156 of 356 patients undergoing ESWL for UUTS sized 0.5-2 cm from 2015 to 2019. Patients with congenital kidney anomalies, radiolucent stones, multiple stones, treated for upper urinary tract stones previously and lower pole stones were excluded from study. The number of ESWL sessions of the patients was as follows: 78 (50%) patients had 1 session, 30 (19.2%) patients had 2 sessions and 48 (30.8%) patients had >2 sessions. First- and second-order CTTA properties of patients' UUTS were evaluated using texture analysis software (LIFEx Software). Other clinical features such as Hounsfield Unit (HU), initial stone size, body-mass index (BMI) and skin to stone distance (SSD) was recorded. The patients were divided into two groups according to ESWL success. Cases with residual stones larger than 4 mm were considered failed cases.

RESULTS

BMI, the standard deviation of HU, SSD, skewness, kurtosis, entropy and all second-order statistics were found to be statistically different between the two groups except for correlation (P < .05). Multivariate analysis showed longer SSD and four new parameters of CTTA (kurtosis, entropy, dissimilarity and energy by the distribution of pixel grey levels in the UUTS) to be significant predictors for unsuccessful ESWL outcomes. SSD and second-order CTTA properties (dissimilarity and energy) had an area under ROC curve of 0.802, 0.850 and 0.824 at a 95% confidence interval. ESWL success rate in all patients was 76.9%.

CONCLUSION

CTTA can help select patients who will undergo ESWL for upper urinary tract stones. Thus, we can reduce treatment costs and ESWL complications by preventing unnecessary ESWL applications.

Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study

Purpose

To assess if autosegmentation-assisted radiomics can predict disease burden, hydronephrosis, and treatment strategies in patients with renal calculi.

Methods

The local ethical committee-approved, retrospective study included 202 adult patients (mean age: 53 ± 17 years; male: 103; female: 99) who underwent clinically indicated, non-contrast abdomen-pelvis CT for suspected or known renal calculi. All CT examinations were reviewed to determine the presence (n = 123 patients) or absence (n = 79) of renal calculi. On CT images with renal calculi, each kidney stone was annotated and measured (maximum dimension, Hounsfield unit (HU), and combined and dominant stone volumes) using a HU threshold-based segmentation. We recorded the presence of hydronephrosis, number of renal calculi, and treatment strategies. Deidentified CT images were processed with the radiomics prototype (Radiomics, Frontier, Siemens Healthineers), which automatically segmented each kidney to obtain 1690 first-, shape-, and higher-order radiomics. Data were analyzed using multiple logistic regression analysis with areas under the curve (AUC) as output.

Results

Among 202 patients, only 28 patients (18%) needed procedural treatment (lithotripsy or ureteroscopic stone extraction). Gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) differentiated patients with and without procedural treatment (AUC 0.91, 95% CI 0.85–0.92). Higher-order radiomics (gray-level size zone matrix – GLSZM) differentiated kidneys with and without hydronephrosis (AUC: 0.99, p < 0.001) as well those with different stone volumes (AUC up to 0.89, 95% CI 0.89–0.92).

Conclusion

Automated segmentation and radiomics of entire kidneys can assess hydronephrosis presence, stone burden, and treatment strategies for renal calculi with AUCs > 0.85.

Outcome groups and a practical tool to predict success of shock wave lithotripsy in daily clinical routine

PURPOSE

To improve outcome prediction of extracorporeal shock wave lithotripsy (SWL) by development of a model based on easily available clinical and radiographical predictors and suitable for daily clinical use.

MATERIALS AND METHODS

We evaluated predictive factors for SWL success in 517 consecutive patients suffering from urinary calculi who underwent SWL between 2010 and 2018. Analyses included descriptive statistics, receiver operating characteristic statistics and logistic regression. Predictive value was improved by combining parameters using model selection and recursive partitioning.

RESULTS

Of the 517 patients, 310 (60.0%) had a successful SWL. Best individual predictor of SWL success was mean attenuation (MAV), with an area under the curve (AUC) of 0.668, and an optimal cutpoint (OC) of 987.5 HU. The best multivariable model, including MAV, stone size, skin to stone distance (SSD), presence of an indwelling stent, and four interaction effects, yielded an AUC of 0.736. Recursive partitioning would categorize patients into three outcome groups with high (76.9%), intermediate (41%) and low (10%) success probability. High probability of SWL success (76.9%) was found for patients with a stone with MAV ≤ 987 HU or with MAV > 987 HU but stone size ≤ 11 mm and SSD (45°) ≤ 88 mm.

CONCLUSION

A model based on four established predictors, and provided as an Excel^®-Tool, can clearly improve prediction of SWL success. In addition, patients can be classified into three defined outcome groups based on simple cutpoint combinations. Both tools improve informed decision-making in daily clinical practice and might reduce failure rates.

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

OBJECTIVE

To explore whether multiple 3D computed tomography texture analysis (3D-CTTA) parameters can predict the therapeutic effects of holmium: YAG laser lithotripsy (LL) on ureteral calculi.

METHODS

The files from 94 patients (102 stones) with proximal ureteral calculi treated only by LL at a single institution were retrospectively retrieved from January 2016 to March 2019. According to intra-operative observations and postoperative reexamination, samples were divided into a completely crushed and a non-crushed group. Preoperative non-contrast-enhanced computed tomography (NCCT) images obtained by multiple CT scanners were imported to MaZda software for 3D texture analysis (TA). The CT-derived value of each target stone was measured, and 15 TA parameters were extracted by delineating volumes of interest (VOIs). Receiver operating characteristic (ROC) curves were drawn to determine the optimal critical value of each parameter based on the Youden index, and univariable and multivariable logistic regression analyses determined the significant factors for LL success.

RESULTS

In univariable analysis, significant differences (p < 0.05) were observed among 7 parameters. In multivariable analysis, Perc.01 3D > 2062 (p = 0.03) and Z-fraction of image in runs (Z-Fraction) > 0.45570 (p = 0.009) were significant independent predictors, with odds ratios (ORs) of 24.204 and 60.329, respectively. In subgroup analysis based on the cutoff value of the CT-derived value (HU = 960), Perc.01 3D (OR = 44.154, 95% CI (2.379, 819.618), p = 0.011) and Z-Fraction (OR = 14.519, 95% CI (2.088, 100.953), p = 0.007) remained statistically significant.

CONCLUSIONS

The combination of 3D-CTTA parameters and the CT-derived value can be used as a quantitative reference to predict whether a target stone could be completely crushed by LL.

KEY POINTS

• Computed tomography texture analysis (CTTA) may be helpful in selecting suitable laser lithotripsy (LL) patients. • 3D-CTTA better predicts stone fragility than commonly used methods (such as the CT-derived value). • The combination of CTTA and the CT-derived value can be used as a preoperative quantitative reference.

Comparison of ultrasound-assisted and pure fluoroscopy-guided extracorporeal shockwave lithotripsy for renal stones

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 < 1 cm stratified group. In the stone size > 1 cm 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

SWL is a safe and non-invasive way of treating renal stones. This study compared two electromagnetic shock wave machines with different stone tracking systems. LiteMed LM-9200 ELMA lithotripter, which combined ultrasound and fluoroscopic stone targeting outperformed Medispec EM-1000 lithotripter, which used fluoroscopy for stone localization and tracking, with better stone-free rates and disintegration rates, as well as lower retreatment rates and complications with possible reduced radiation exposure.

Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone

High-grade gliomas (HGGs) and solitary brain metastases (BMs) have similar imaging appearances, which often leads to misclassification. In HGGs, the surrounding tissues show malignant invasion, while BMs tend to displace the adjacent area. The surrounding edema produced by the two cannot be differentiated by conventional magnetic resonance (MRI) examinations. Forty-two patients with pathology-proven brain tumors who underwent conventional pretreatment MRIs were retrospectively included (HGGs, n = 16; BMs, n = 26). Texture analysis of the peritumoral zone was performed on the T2-weighted sequence using dedicated software. The most discriminative texture features were selected using the Fisher and the probability of classification error and average correlation coefficients. The ability of texture parameters to distinguish between HGGs and BMs was evaluated through univariate, receiver operating, and multivariate analyses. The first percentile and wavelet energy texture parameters were independent predictors of HGGs (75–87.5% sensitivity, 53.85–88.46% specificity). The prediction model consisting of all parameters that showed statistically significant results at the univariate analysis was able to identify HGGs with 100% sensitivity and 66.7% specificity. Texture analysis can provide a quantitative description of the peritumoral zone encountered in solitary brain tumors, that can provide adequate differentiation between HGGs and BMs.

The utility of automated volume analysis of renal stones before and after shockwave lithotripsy treatment

This study aimed to evaluate the additional utility of an automated method of estimating volume for stones being treated with shockwave lithotripsy (SWL) using computed tomography (CT) images compared to manual measurement. Utility was assessed as the ability to accurately measure stone burden before and after SWL treatment, and whether stone volume is a better predictor of SWL outcome than stone diameter. 72 patients treated with SWL for a renal stone with available CT scans before and after treatment were included. Stone axes measurement and volume estimation using ellipsoid equations were compared to volume estimation using software using CT textural analysis (CTTA) of stone images. There was strong correlation (r > 0.8) between manual and CTTA estimated stone volume. CTTA measured stone volume showed the highest predictive value (r² = 0.217) for successful SWL outcome on binary logistic regression analysis. Three cases that were originally classified as ‘stone-free with clinically insignificant residual fragments’ based on manual axis measurements actually had a larger stone volume based on CTTA estimation than the smallest fragments remaining for cases with an outcome of ‘not stone-free’. This study suggests objective measurement of total stone volume could improve estimation of stone burden before and after treatment. Current definitions of stone-free status based on manual measurements of residual fragment sizes are not accurate and may underestimate remaining stone burden after treatment. Future studies reporting on the efficacy of different stone treatments should consider using objective stone volume measurements based on CT image analysis as an outcome measure of stone-free state.

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

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.

Modern imaging techniques in urinary stone disease

PURPOSE OF REVIEW

Radiological imaging techniques are a fast developing field in medicine. Therefore, the purpose of this review was to identify and discuss the latest changes of modern imaging techniques in the management of urinary stone disease.

RECENT FINDINGS

The introduction of iterative image reconstruction enables low-dose and ultra-low-dose (ULD) protocols. Although current guidelines recommend their utilization in nonobese patients recent studies indicate that low-dose imaging may be feasible in obese (<30 kg/m) but not in bariatric patients. Use of dual energy computed tomography (CT) technologies should balance between additional information and radiation dose aspects. If available on a dose neutral basis, dual energy imaging and analysis should be performed. Current guidelines recommend measuring the largest diameter for clinical decision making; however, recent studies suggest a benefit from measuring the volume based on multiplanar reformation. Quantitative imaging is still an experimental approach.

SUMMARY

The use of low-dose and even ULD CT protocols should be diagnostic standard, even in obese patients. If dual energy imaging is available, it should be limited to specific clinical questions. The stone volume should be reported in addition to the largest diameter for treatment decision and a more valid comparability of upcoming studies.

Predicting shockwave lithotripsy outcome for urolithiasis using clinical and stone computed tomography texture analysis variables

We aimed to develop and evaluate a statistical model, which included known pre-treatment factors and new computed tomography texture analysis (CTTA) variables, for its ability to predict the likelihood of a successful outcome after extracorporeal shockwave lithotripsy (SWL) treatment for renal and ureteric stones. Up to half of patients undergoing SWL may fail treatment. Better prediction of which cases will likely succeed SWL will help patients to make an informed decision on the most effective treatment modality for their stone. 19 pre-treatment factors for SWL success, including 6 CTTA variables, were collected from 459 SWL cases at a single centre. Univariate and multivariable analyses were performed by independent statisticians to predict the probability of a stone free (both with and without residual fragments) outcome after SWL. A multivariable model had an overall accuracy of 66% on Receiver Operator Curve (ROC) analysis to predict for successful SWL outcome. The variables most frequently chosen for the model were those which represented stone size. Although previous studies have suggested SWL can be reliably predicted using pre-treatment factors and that analysis of CT stone images may improve outcome prediction, the results from this study have not produced a useful model for SWL outcome prediction.

Noncontrast Computed Tomography Parameters for Predicting Shock Wave Lithotripsy Outcome in Upper Urinary Tract Stone Cases

Kidney stones are a major public health concern with continuously increasing worldwide prevalence. Shock wave lithotripsy (SWL) is the first line treatment choice for upper urinary tract calculi with ureteroscopy and has advantages of safety and noninvasiveness, but the treatment success rate of SWL is lower than that of other therapies. It is therefore important to identify predictive factors for SWL outcome and select a suitable treatment choice for patients with upper urinary tract calculi. In recent years, computed tomography (CT) has become the gold standard for diagnosis of upper urinary tract calculi. Several factors based on CT images, including skin-to-stone distance, mean stone density, stone heterogeneity index, and variation coefficient of stone density, have been reported to be useful for predicting SWL outcome. In addition, a new method of analysis, CT texture analysis, is reportedly useful for predicting SWL outcomes. This review aims to summarize CT parameters for predicting the outcome of shock wave lithotripsy in stone cases in the upper urinary tract.

Single extracorporeal shock-wave lithotripsy for proximal ureter stones: Can CT texture analysis technique help predict the therapeutic effect?

PURPOSE

To explore whether the computed tomography texture analysis (CTTA) technique can help predict the curative effects of a single extracorporeal shock-wave lithotripsy (ESWL) for proximal ureteral stones.

MATERIALS AND METHODS

In all, 100 patients with proximal ureteral stone underwent non-enhanced multi-detector computed tomography (MDCT) before ESWL. The patients were divided into success and failure groups. Success of ESWL was defined as the patients being stone-free or having residual stone fragments of ≤2 mm. Traditional characteristics, such as stone size, body mass index (BMI), and skin-to-stone distance (SSD), and CTTA metrics, such as the mean Hounsfield unit (HU) density, entropy, kurtosis, and skewness, were analyzed and compared between two groups by univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC) curves were generated to determine Youden index-based cutoff values.

RESULT

Failure of stone removal was observed in 36 patients (36%). Stone height, stone cross-sectional diameter, largest cross-sectional area, stone volume, stone density (mean HU), and CTTA metrics (kurtosis and entropy) were the significant independent predictors of ESWL success on univariate analysis (p < 0.05). On multivariate analysis, mean HU, skewness, and kurtosis were shown to be significant predictors of ESWL success (p < 0.05). In subgroup analysis based on the cutoff value of mean stone density (HU = 857), the only significant independent factor associated with both subgroups was kurtosis (p < 0.05).

CONCLUSIONS

As a quantitative analysis method, CTTA may be helpful in selecting appropriate ESWL patients. High kurtosis and low mean HU values simultaneously indicate a relatively higher ESWL success rate.

Advanced non-contrasted computed tomography post-processing by CT-Calculometry (CT-CM) outperforms established predictors for the outcome of shock wave lithotripsy

OBJECTIVES

To evaluate the predictive value of advanced non-contrasted computed tomography (NCCT) post-processing using novel CT-calculometry (CT-CM) parameters compared to established predictors of success of shock wave lithotripsy (SWL) for urinary calculi.

MATERIALS AND METHODS

NCCT post-processing was retrospectively performed in 312 patients suffering from upper tract urinary calculi who were treated by SWL. Established predictors such as skin to stone distance, body mass index, stone diameter or mean stone attenuation values were assessed. Precise stone size and shape metrics, 3-D greyscale measurements and homogeneity parameters such as skewness and kurtosis, were analysed using CT-CM. Predictive values for SWL outcome were analysed using logistic regression and receiver operating characteristics (ROC) statistics.

RESULTS

Overall success rate (stone disintegration and no re-intervention needed) of SWL was 59% (184 patients). CT-CM metrics mainly outperformed established predictors. According to ROC analyses, stone volume and surface area performed better than established stone diameter, mean 3D attenuation value was a stronger predictor than established mean attenuation value, and parameters skewness and kurtosis performed better than recently emerged variation coefficient of stone density. Moreover, prediction of SWL outcome with 80% probability to be correct would be possible in a clearly higher number of patients (up to fivefold) using CT-CM-derived parameters.

CONCLUSIONS

Advanced NCCT post-processing by CT-CM provides novel parameters that seem to outperform established predictors of SWL response. Implementation of these parameters into clinical routine might reduce SWL failure rates.

Uric acid versus non-uric acid urinary stones: differentiation with single energy CT texture analysis

AIM

To evaluate the accuracy of computed tomography (CT) texture analysis (TA) to differentiate uric acid (UA) stones from non-UA stones on unenhanced CT in patients with urinary calculi with ex vivo Fourier transform infrared spectroscopy (FTIR) as the reference standard.

MATERIALS AND METHODS

Fourteen patients with 18 UA stones and 31 patients with 32 non-UA stones were included. All the patients had preoperative CT evaluation and subsequent surgical removal of the stones. CTTA was performed on CT images using commercially available research software. Each texture feature was evaluated using the non-parametric Mann-Whitney test. Receiver operating characteristic (ROC) curves were created and the area under the ROC curve (AUC) was calculated for texture parameters that were significantly different. The features were used to train support vector machine (SVM) classifiers. Diagnostic accuracy was evaluated.

RESULTS

Compared to non-UA stones, UA stones had significantly lower mean, standard deviation and mean of positive pixels but higher kurtosis (p<0.001) on both unfiltered and filtered texture scales. There were no significant differences in entropy or skewness between UA and non-UA stones. The average SVM accuracy of texture features for differentiating UA from non-UA stones ranged from 88% to 92% (after 10-fold cross validation). A model incorporating standard deviation, skewness, and kurtosis from unfiltered texture scale images resulted in an AUC of 0.965±00.029 with a sensitivity of 94.4% and specificity of 93.7%.

CONCLUSION

CTTA can be used to accurately differentiate UA stones from non-UA stones in vivo using unenhanced CT images.