Precision and Speed at Your Fingertips: An Automated Intracranial Hematoma Volume Calculation

BACKGROUND

Intracranial hemorrhage (ICH) is a severe condition that requires rapid diagnosis and treatment. Automated methods for calculating ICH volumes can reduce human error and improve clinical decisioPlease provide professional degrees (e.g., PhD, MD) for the corresponding author.n-making. A novel automated method has been developed that is comparable to the ABC/2 method in terms of speed and accuracy while providing more accurate volumetric data.

METHODS

We developed a novel automated algorithm for calculating intracranial blood volume from computed tomography (CT) scans. The algorithm consists of a Python script that processes Digital Imaging and Communications in Medicine images and determines the blood volume and ratio. The algorithm was validated against manual calculations performed by neurosurgeons.

RESULTS

Our novel automated algorithm for calculating intracranial blood volume from CT scans demonstrated excellent agreement with the ABC/2 method, with a median overall difference of just 1.46 mL. The algorithm was also validated in patient groups with ICH, epidural hematoma (EDH), and SDH, with agreement coefficients of 0.992, 0.983, and 0.997, respectively.

CONCLUSIONS

The study introduces a novel automated algorithm for calculating the volumes of various ICHs (EDH, and SDH) within CT scans. The algorithm showed excellent agreement with manual calculations and outperformed the commonly used ABC/2 method, which tends to overestimate ICH volume. The automated algorithm offers a more accurate, efficient, and time-saving approach to quantifying ICH, EDH, and SDH volumes, making it a valuable tool for clinical evaluation and decision-making.

Predictors and Prognostic Impact of Hematoma Expansion in Infratentorial Cerebral Hemorrhage

BACKGROUND

Hematoma expansion (HE) is common and predicts poor outcome in patients with supratentorial intracerebral hemorrhage (ICH). We investigated the predictors and prognostic impact of HE in infratentorial ICH.

METHODS

We conducted a retrospective analysis of patients with brainstem and cerebellar ICH admitted at seven sites. Noncontrast computed tomography images were analyzed for the presence of hypodensities according to validated criteria, defined as any hypodense region strictly encapsulated within the hemorrhage with any shape, size, and density. Occurrence of HE (defined as > 33% and/or > 6-mL growth) and mortality at 90 days were the outcomes of interest. Their predictors were investigated using logistic regression with backward elimination at p < 0.1. Logistic regression models for HE were adjusted for baseline ICH volume, antiplatelet and anticoagulant treatment, onset to computed tomography time, and presence of hypodensities. The logistic regression model for mortality accounted for the ICH score and HE.

RESULTS

A total of 175 patients were included (median age 75 years, 40.0% male), of whom 38 (21.7%) had HE and 43 (24.6%) died within 90 days. Study participants with HE had a higher frequency of hypodensities (44.7 vs. 24.1%, p = 0.013), presentation within 3 h from onset (39.5 vs. 24.8%, p = 0.029), and 90-day mortality (44.7 vs. 19.0%, p = 0.001). Hypodensities remained independently associated with HE after adjustment for confounders (odds ratio 2.44, 95% confidence interval 1.13-5.25, p = 0.023). The association between HE and mortality remained significant in logistic regression (odds ratio 3.68, 95% confidence interval 1.65-8.23, p = 0.001).

CONCLUSION

Early presentation and presence of noncontrast computed tomography hypodensities were independent predictors of HE in infratentorial ICH, and the occurrence of HE had an independent prognostic impact in this population.

A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients

BACKGROUND

Haemorrhage transformation (HT) is a serious complication of intravenous thrombolysis (IVT) in acute ischaemic stroke (AIS). Accurate and timely prediction of the risk of HT before IVT may change the treatment decision and improve clinical prognosis. We aimed to develop a deep learning method for predicting HT after IVT for AIS using noncontrast computed tomography (NCCT) images.

METHODS

We retrospectively collected data from 828 AIS patients undergoing recombinant tissue plasminogen activator (rt-PA) treatment within a 4.5-h time window (n = 665) or of undergoing urokinase treatment within a 6-h time window (n = 163) and divided them into the HT group (n = 69) and non-HT group (n = 759). HT was defined based on the criteria of the European Cooperative Acute Stroke Study-II trial. To address the problems of indiscernible features and imbalanced data, a weakly supervised deep learning (WSDL) model for HT prediction was constructed based on multiple instance learning and active learning using admission NCCT images and clinical information in addition to conventional deep learning models. Threefold cross-validation and transfer learning were performed to confirm the robustness of the network. Of note, the predictive value of the commonly used scales in clinics associated with NCCT images (i.e., the HAT and SEDAN score) was also analysed and compared to measure the feasibility of our proposed DL algorithms.

RESULTS

Compared to the conventional DL and ML models, the WSDL model had the highest AUC of 0.799 (95% CI 0.712-0.883). Significant differences were observed between the WSDL model and five ML models (P < 0.05). The prediction performance of the WSDL model outperforms the HAT and SEDAN scores at the optimal operating point (threshold = 1.5). Further subgroup analysis showed that the WSDL model performed better for symptomatic intracranial haemorrhage (AUC = 0.833, F1 score = 0.909).

CONCLUSIONS

Our WSDL model based on NCCT images had relatively good performance for predicting HT in AIS and may be suitable for assisting in clinical treatment decision-making.

Pure uric acid stone prediction model using the variant coefficient of stone density measured by thresholding 3D segmentation-based methods: A multicenter study

Urinary stones are common urological diseases with increasing prevalence and incidence worldwide. Among the various types of stones, uric acid stones can be dissolved by oral chemolysis without any surgical procedure. Therefore, our study demonstrates that variant coefficient of stone density measured by thresholding a three-dimensional segmentation-based method from noncontrast computed tomography images can be used to identify pure uric acid stones from non-pure uric acid stones. This study provides a preoperative pure uric acid stone prediction model that could reduce invasive procedural treatments. The pure uric acid stone prediction model may offer optimized clinical decision-making for patients with urinary stones.

BACKGROUND AND OBJECTIVES

While most urinary stones are managed with interventional therapy, uric acid (UA) stones can be dissolved by oral chemolysis without invasive procedures. This study aimed to develop and validate a pure UA (pUA) stone prediction model using a variant coefficient of stone density (VCSD) measured by thresholding a three-dimensional (3D) segmentation-based method.

METHODS

Patients with urolithiasis treated at Keimyung University Dongsan Hospital between January 2017 and December 2020 were divided into training and internal validation sets, and patients from Kyungpook National University Hospital between January 2017 and December 2018 were used as an external validation set. Each stone was segmented by a thresholding 3D segmentation-based method using an attenuation threshold of 130 Hounsfield units. VCSD was calculated as the stone heterogeneity index divided by the mean stone density.

RESULTS

A total of 1175 urinary stone cases in 1023 patients were enrolled in this study. Of these, 224 (19.1%) were pUA stone cases. Among the potential predictors, thresholding 3D segmentation-based VCSD, age, sex, radio-opacity, hypertension, diabetes, and urine pH were identified as independent pUA stone predictors, and VCSD was the most powerful indicator. The pUA stone prediction model showed good discrimination, yielding area under the receiver operating characteristic curve of 0.960 (95% confidence interval (CI): 0.940-0.979, P < 0.001), 0.931 (95% CI: 0.875-0.987, P < 0.001), and 0.938 (95% CI: 0.912-0.965, P < 0.001) in the training, internal validation, and external validation sets, respectively.

CONCLUSIONS

VCSD measured using 3D segmentation was a decisive independent predictive factor for pUA stones. Furthermore, the established prediction model with VCSD can serve as a noninvasive preoperative tool to identify pUA stones.

A clinical-radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study

OBJECTIVE

To build a clinical-radiomics model based on noncontrast computed tomography images to identify the risk of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) following intravenous thrombolysis (IVT).

MATERIALS AND METHODS

A total of 517 consecutive patients with AIS were screened for inclusion. Datasets from six hospitals were randomly divided into a training cohort and an internal cohort with an 8:2 ratio. The dataset of the seventh hospital was used for an independent external verification. The best dimensionality reduction method to choose features and the best machine learning (ML) algorithm to develop a model were selected. Then, the clinical, radiomics and clinical-radiomics models were developed. Finally, the performance of the models was measured using the area under the receiver operating characteristic curve (AUC).

RESULTS

Of 517 from seven hospitals, 249 (48%) had HT. The best method for choosing features was recursive feature elimination, and the best ML algorithm to build models was extreme gradient boosting. In distinguishing patients with HT, the AUC of the clinical model was 0.898 (95% CI 0.873-0.921) in the internal validation cohort, and 0.911 (95% CI 0.891-0.928) in the external validation cohort; the AUC of radiomics model was 0.922 (95% CI 0.896-0.941) and 0.883 (95% CI 0.851-0.902), while the AUC of clinical-radiomics model was 0.950 (95% CI 0.925-0.967) and 0.942 (95% CI 0.927-0.958) respectively.

CONCLUSION

The proposed clinical-radiomics model is a dependable approach that could provide risk assessment of HT for patients who receive IVT after stroke.

The combination of mean and maximum Hounsfield Unit allows more accurate prediction of uric acid stones

Based on mean Hounsfield Unit (HuMean), we aimed to evaluate the additional use of standard deviation of Hounsfield Unit (HuStd), minimum Hounsfield Unit (HuMin), and maximum Hounsfield Unit (HuMax) in noncontrast computed tomography (NCCT) to evaluate uric acid (UA) stones more accurately. The data of patients who underwent the NCCT examination and infrared spectroscopy in our hospital from August 2017 to December 2021 were analyzed retrospectively. Based on CT scans, the HuMean, HuStd, HuMin, and HuMax of all patients were measured. The patients were divided into groups according to the stone composition. The attenuation value of mixed stones was in the middle of their pure stones. Except for Str, statistically significant differences between UA stones and other pure stones were observed for HuMean, HuStd, HuMin, and HuMax. A moderate correlation was found between HuMean, HuStd, HuMin, and HuMax and UA stones (r_s showed -0.585, -0.409, -0.492, and -0.577, respectively). Receiver operator characteristic (ROC) curve showed that the area under the curve (AUC) of HuMean and HuMax were higher than those of HuStd and HuMin (AUC = 0.896, AUC = 0.891 vs. AUC = 0.777, AUC = 0.833). Higher AUC (0.904), specificity (0.899) and positive predictive value (PPV) (0.712) can be obtained by combining HuMean and HuMax in the diagnosis of UA stones. In conclusion, HuMean and HuMax can better predict UA stones than HuStd and HuMin. The combined use of HuMean and HuMax can lead to higher accuracy.

Value of noncontrast computer tomography in predicting the characteristics of obstructive uropathy

OBJECTIVE

To explore the diagnostic value of noncontrast computed tomography (NCCT) in differentiating pyonephrosis from nonpyogenic hydronephrosis on the basis of CT values (in Horsfield unit [HU]).

METHODS

Data from patients diagnosed with obstructive uropathy at the First affiliated hospital of University of South China from November 2017 to January 2021 were subjected to retrospective analysis. In accordance with the gold standard-the presence of pus during the operation-all patients were divided into the nonpyogenic hydronephrosis group and the pyonephrosis group. The relationship between CT values and the presence or absence of pyonephrosis was performed using binary logistic regression. A receiver operating characteristic (ROC) curve was constructed to determine threshold values for classification on the basis of mean HU.

RESULTS

A total of 207 patients, including 100 males and 107 females, were enrolled. Out of the 207 cases, 124 cases of obstructive uropathy were nonpyogenic hydronephrosis and 83 cases were of pyonephrosis. The CT values of the pyonephrosis group were significantly higher than that of the nonpyogenic hydronephrosis group (t = 9.15, P < 0.05). The CT values were dependent on the presence or absence of pyonephrosis (P < 0.05). A HU threshold value of 9.75 could be applied to diagnose the presence of pyonephrosis.

CONCLUSION

The CT values of hydronephrosis might predict the presence of pyonephrosis in the kidney, and the CT value of 9.75 HU might be the appropriate threshold for its prediction.

Minimal Computed Tomography Attenuation Value Within the Hematoma is Associated with Hematoma Expansion and Poor Outcome in Intracerebral Hemorrhage Patients

BACKGROUND

Early hematoma expansion in intracerebral hemorrhage (ICH) patients is associated with poor outcome. We aimed to investigate whether the minimal computed tomography (CT) attenuation value predicted hematoma expansion and poor outcome.

METHODS

This study involved spontaneous ICH patients of two cohorts who underwent baseline CT scan within 6 h after ICH onset and follow-up CT scan within 24 h after initial CT scan. We determined the critical value of the minimal CT attenuation value via retrospective analysis of the data from a derivation cohort. Then, a prospective study on the validation cohort of three clinical centers was performed for determining the association between the minimal CT attenuation value and hematoma expansion as well as poor outcome (modified Rankin Scale scores > 3) at 90 days by using univariate and multivariate logistic regression analyses.

RESULTS

One hundred and forty eight ICH patients were included in the derivation cohort. Minimal CT attenuation value ≤ 31 Hounsfield units (HU) was demonstrated as the critical value to predict hematoma expansion by using receiver operating characteristic analysis. A total of 311 ICH patients were enrolled in the validation cohort, 86 (27.7%) and 133 (42.8%) of which were found hematoma expansion and poor outcome. Minimal CT attenuation value ≤ 31 HU was positive in 73 patients (23.5%). The multivariate logistic regression analysis demonstrated minimal CT attenuation value and minimal CT attenuation value ≤ 31 HU independently predicted hematoma expansion (p < 0.001) and poor outcome (p < 0.001). The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of minimal CT attenuation value ≤ 31 HU for hematoma expansion and poor outcome prediction were 64.0, 92.0, 75.3, 87.0, 84.2 and 45.1%, 92.7%, 82.2%, 69.3%, 72.3%, respectively.

CONCLUSIONS

The minimal CT attenuation value independently predicts early hematoma expansion and poor outcome in patients with ICH.

White Matter High Signals Interfere with Noncontrast Computed Tomography in the Early Identification of Cerebral Infarction

BACKGROUND

We developed an image patch classification-based method to detect early ischemic stroke. The accuracy of this method was >75%. We aimed to analyze patients' image data to identify interference factors that would affect its accuracy.

METHODS

We conducted a retrospective analysis of 162 patients who were hospitalized with acute ischemic stroke. Factors related to the noncontrast computed tomography (ncCT) determination results were analyzed according to the patient's sex, age, clinical symptoms, cerebral infarction volume, cerebral infarction location, and whether or not the white matter high (WMH) signal was combined.

RESULTS

The volume of cerebral infarction was positively correlated with the predicted results. The correct percentages of patients with volumes >1 and <1 mL were 59.18 and 83.19%, respectively, and the difference was statistically significant (p = 0.001). The correct percentage of the internal capsule region (47.1%) was significantly lower than that of the other groups (p = 0.011). The correct percentage of lateral ventricular paraventricular infarction was significantly lower than that of non-lateral ventricle patients (70.8 vs. 85.7%). In patients with lateral ventricular paraventricular infarction, if the WMH was combined, the correct percentage will decreased further as the Fazekas level increased. The correct percentage of lateral ventricle infarction combined with Fazekas 3 was 40.0%, which was statistically significant compared with the patient having Fazekas 0 with lateral ventricular infarction (p = 0.01).

CONCLUSIONS

WMH had a similar computed tomography appearance to cerebral infarction and could interfere with the prediction of the cerebral infarction region by ncCT. This result provides a reference for clinicians to choose imaging methods for identifying acute cerebral infarction areas.

Endovascular Treatment of Distal Lenticulostriate Artery Aneurysm by Selective Catheterization of Artery with Balloon-Blocking Technique: 2-Dimensional Video Illustration

We report the case of a 15-year-old male patient with polyarteritis nodosa who presented with ruptured lenticulostriate artery (LSA) aneurysm and was successfully treated with endovascular N-butyl-2-cyanoacrylate (Histoacryl, B. Braun, Melsungen, Germany) acrylic glue embolization. Selective catheterization of LSA is sometimes difficult even with a low-profile microcatheter (Magic 1.2 FM, Balt Extrusion, Montmorency, France) due to acute angulation at the origin of the artery. In this 2-dimensional video illustration of the roadmap in digital subtraction angiography, reproduced after informed consent of the patient, we illustrate the balloon blocking technique to safely and effectively navigate the microcatheter through the small perforator with difficult angulation at the origin. A Magic microcatheter was passed via a distal access catheter 070 (Concentric Medical, Mountain View, California, USA) 105 cm in the internal carotid artery. The Magic microcatheter advancement was supported with a 0.008-inch guidewire (Hybrid 008, Balt Extrusion, Montmorency, France). Initial catheterization of LSA even with a low-profile Magic microcatheter was difficult as the origin of LSA was acute angled. While trying the navigate the microcatheter into the perforator, it was continuously flopping into the distal M1 segment of the middle cerebral artery. The balloon microcatheter (Scepter XC 4 × 11mm, Microvention, Tustin, California, USA) was passed separately via 5 French Envoy guiding catheter (Codman, Raynham, Massachusetts, USA) 100 cm in the proximal ICA using a contralateral left femoral artery puncture. The Balloon microcatheter advancement into the middle cerebral artery was supported with a Traxcess 0.014-inch microguidewire (Microvention). It was then inflated just beyond the origin of LSA which provided support to the magic microcatheter and thus allowing its easy navigation into the LSA. Super-selective microcatheter injection confirmed filling of the LSA aneurysm. A dilute 33% concentration of the liquid embolic agent N-butyl-2-cyanoacrylate mixed with Lipiodol (Guerbet, Aulnay-sous-Bois, France) was injected slowly under direct vision. The final-check angiogram revealed complete occlusion of the aneurysm (Video 1). Patient underwent craniotomy and hematoma evacuation 1 day after the procedure in view of his rapidly deteriorating neurological status. He was later discharged with Modified Rankin Scale of 3. Follow up angiography after 3 months showed completely occluded aneurysm (Video 2).

Combination of Ultraearly Hematoma Growth and Hypodensities for Outcome Prediction after Intracerebral Hemorrhage

BACKGROUND

Noncontrast computed tomography hypodensities (HD) and ultraearly hematoma growth (uHG) are reliable markers for outcome prediction in patients with spontaneous intracerebral hemorrhage (sICH). The present study aimed to assess whether the combination of these 2 markers could improve the prognostic value for sICH.

METHODS

We recruited 242 patients with sICH who had been admitted within 6 hours from the onset of symptoms. HD was assessed by 2 independent blinded readers, and uHG was calculated as baseline ICH volume/onset-to-imaging time. We divided the study population into 4 groups: uHG(L) HD(-) (uHG <6.16 mL/hour and HD negative), uHG(L) HD(+) (uHG<6.16 mL/hour and HD positive), uHG(H) HD(-) (uHG ≥6.16 mL/hour and HD negative), and uHG(H) HD(+) (uHG ≥6.16 mL/h and HD positive). The outcome at 90 days was evaluated by the modified Rankin Scale (mRS) score and was dichotomized as good (mRS score 0-3) and poor (mRS score 4-6). The association between the combined indicators and unfavorable outcome was investigated using multivariable logistic regression models.

RESULTS

Patients with poor outcomes were more likely to have HD and higher uHG in univariate analysis. In multivariate logistic regression analysis, uHG(H) HD(+) had a higher risk of unfavorable outcomes compared with uHG(L) HD(-) (odds ratio [OR], 5.710; P < 0.001). In addition, the risk of unfavorable outcomes was increased in uHG(H) HD(-) (OR, 2.957, P = 0.044) and uHG(L) HD(+) (OR, 1.924; P = 0.232). The proportions of unfavorable prognoses were 32.6% in uHG(L) HD(-), 48.3% in uHG(L) HD(+), 72.2% in uHG(H) HD(-), and 87.5% in uHG(H) HD(+) (P < 0.001).

CONCLUSIONS

The combination of uHG and HD improves the stratification of unfavorable prognoses in patients with sICH.

Diagnostic value of swirl sign on noncontrast computed tomography and spot sign on computed tomographic angiography to predict intracranial hemorrhage expansion

OBJECTIVE

Intracranial hemorrhage (ICH) expansion is a predictor of poor clinical outcome. ICH expansion can be predicted with a swirl sign on noncontrast computed tomography (NCCT) and/or a spot sign on computed tomographic angiography (CTA). In this study, we aimed to evaluate the diagnostic value of a swirl sign and a spot sign in identifying hematoma expansion.

PATIENTS AND METHODS

Patients with spontaneous ICH between January 2013 and August 2018 who underwent an initial NCCT and CTA, and a subsequent NCCT at a single center were retrospectively identified. Two experienced neuroradiologists reviewed all images for swirl sign and spot sign presence using a 4-point scale for receiver-operative characteristic analysis. ICH expansion was defined as volume growth of >33% or >6 mL.

RESULTS

A total of 227 patients, including 54 with ICH expansion, qualified for analysis. For both observers, the area under the curve (AUC) of spot sign was significantly higher than that of swirl sign (observer 1: 0.748 vs. 0.577, p = .002; observer 2: 0.749 vs. 0.589, p = .004). The sensitivities of ICH expansion in patients with a spot sign was significantly higher than patients with a swirl sign (observer 1: 54.1% vs. 28.0%, p = .002; observer 2: 56.9% vs. 30.3%, p = .002). Patients with a spot sign had the highest risk of ICH expansion (odds ratio: observer 1 = 8.14, observer 2 = 9.30, p < 0.001).

CONCLUSIONS

A spot sign on CTA was identified and associated with ICH expansion. A swirl sign on NCCT had a relatively low sensitivity and AUC, and will not be able to replace spot sign on CTA.

Accuracy of Shape Irregularity and Density Heterogeneity on Noncontrast Computed Tomography for Predicting Hematoma Expansion in Spontaneous Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis

OBJECTIVE

This systematic review and meta-analysis was aimed to evaluate the predictive values of shape irregularity and density heterogeneity of hematoma on noncontrast computed tomography (NCCT) for hematoma expansion (HE).

METHODS

A literature search was performed in PubMed, Embase, Scopus, Web of Science, and Cochrane Library. Studies about predictive values of shape regularity or density heterogeneity of hematoma on NCCT for HE in spontaneous intracerebral hemorrhage were included. Meta-analysis was performed to pool the data. Publication bias assessment, subgroup analysis, and univariate meta-regression were conducted.

RESULTS

A total of 7 studies with 2294 patients were included. The pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of shape irregularity were 67%, 47%, 1.30, and 0.71, respectively. In contrast, the pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of density irregularity were 52%, 69%, 1.70, and 0.69, respectively.

CONCLUSIONS

Considering the relatively low sensitivity and specificity, the predictive values of shape irregularity and density heterogeneity of hematoma for HE are limited. Further studies are still needed to find optimal NCCT predictors for HE in spontaneous intracerebral hemorrhage patients.

Noncontrast computed tomography factors that predict the renal stone outcome after shock wave lithotripsy

OBJECTIVES

Extracorporeal shock wave lithotripsy (ESWL) is a popular treatment for nephrolithiasis. We took advantage of noncontrast abdominal computed tomography (NCCT) to search the possible prognostic factors including abdominal fat distribution influencing stone-free rate.

METHODS

From August 2008 to August 2010, 145 patients who had renal calculus and had undergone ESWL were retrospectively reviewed. All of them received NCCT assessment before ESWL and were followed up after 1 month for stone clearance. These patients were divided into two groups: one was the stone-free group and the other was the residual-stone group. Affecting parameters included stone size, location, stone surface area, Hounsfield unit density (HU density), skin-to-stone distance (SSD), and abdominal fat area as analyzed between these two groups.

RESULTS

Of 145 patients, 70 were stone-free and 75 had residual stone after ESWL treatment and 1-month follow-up. From univariate analysis, stone size, HU density, SSD, and stone surface area were significant predicting factors for ESWL success. On multivariate analysis, the important factors influencing ESWL outcomes were HU density and stone surface area (odds ratio 1.002 vs. 77.18, respectively; P<.05). Abdominal fat accumulation and distribution had no significant difference between these two groups.

CONCLUSION

This study revealed that stone size, HU density, SSD, and stone surface area were associated with stone-free rate after ESWL treatment. Therefore, these factors could be used to assess the feasibility of ESWL before deciding the treatment strategy. Abdominal fat distribution had no significant impact on ESWL outcome for renal stones.

Stone attenuation value and cross-sectional area on computed tomography predict the success of shock wave lithotripsy

Purpose

To identify the parameters on noncontrast computed tomography (NCCT) that best predict the success of shock wave lithotripsy (SWL).

Materials and Methods

We reviewed the records of 75 patients who underwent SWL for urinary calculi measuring 5 to 20 mm. Using NCCT images, we estimated the largest stone cross-sectional area and contoured the inner edge of the stone. Clinical outcome was classified as successful (stone-free or <4 mm in diameter) or failed (stone fragments, ≥4 mm). The impact of preoperative parameters was evaluated by univariate and multivariate analysis.

Results

The overall success rate was 73.3%. Average stone attenuation value, stone length, and stone cross-sectional area in the success and failure groups were 627.4±166.5 HU (Hounsfield unit) vs. 788.1±233.9 HU (p=0.002), 11.7±3.8 mm vs. 14.2±3.6 mm (p=0.015), and 0.31±0.17 cm² vs. 0.57±0.41 cm² (p<0.001), respectively. In the multivariate analysis, stone attenuation value was the only independent predictor of SWL success (p=0.023), although stone cross-sectional area had a tendency to be associated with SWL success (p=0.053). Patients were then classified into four groups by using cutoff values of 780 HU for stone attenuation value and 0.4 cm² for cross-sectional area. By use of these cutoff values, the group with a low stone attenuation value and a low cross-sectional area was more than 11.6 times as likely to have a successful result on SWL as were all other groups (odds ratio, 11.6; 95% confidence interval, 3.9 to 54.7; p<0.001).

Conclusions

Stone attenuation value and stone cross-sectional area are good predictors of extracorporeal SWL outcome.