A novel use of attenuation value (Hounsfield unit) in non-contrast CT: diagnosis of pyonephrosis in obstructed systems

Machine learning constructs a diagnostic prediction model for calculous pyonephrosis

In order to provide decision-making support for the auxiliary diagnosis and individualized treatment of calculous pyonephrosis, the study aims to analyze the clinical features of the condition, investigate its risk factors, and develop a prediction model of the condition using machine learning techniques. A retrospective analysis was conducted on the clinical data of 268 patients with calculous renal pelvic effusion who underwent ultrasonography-guided percutaneous renal puncture and drainage in our hospital during January 2018 to December 2022. The patients were included into two groups, one for pyonephrosis and the other for hydronephrosis. At a random ratio of 7:3, the research cohort was split into training and testing data sets. Single factor analysis was utilized to examine the 43 characteristics of the hydronephrosis group and the pyonephrosis group using the T test, Spearman rank correlation test and chi-square test. Disparities in the characteristic distributions between the two groups in the training and test sets were noted. The features were filtered using the minimal absolute value shrinkage and selection operator on the training set of data. Auxiliary diagnostic prediction models were established using the following five machine learning (ML) algorithms: random forest (RF), xtreme gradient boosting (XGBoost), support vector machines (SVM), gradient boosting decision trees (GBDT) and logistic regression (LR). The area under the curve (AUC) was used to compare the performance, and the best model was chosen. The decision curve was used to evaluate the clinical practicability of the models. The models with the greatest AUC in the training dataset were RF (1.000), followed by XGBoost (0.999), GBDT (0.977), and SVM (0.971). The lowest AUC was obtained by LR (0.938). With the greatest AUC in the test dataset going to GBDT (0.967), followed by LR (0.957), XGBoost (0.950), SVM (0.939) and RF (0.924). LR, GBDT and RF models had the highest accuracy were 0.873, followed by SVM, and the lowest was XGBoost. Out of the five models, the LR model had the best sensitivity and specificity is 0.923 and 0.887. The GBDT model had the highest AUC among the five models of calculous pyonephrosis developed using the ML, followed by the LR model. The LR model was considered be the best prediction model when combined with clinical operability. As it comes to diagnosing pyonephrosis, the LR model was more credible and had better prediction accuracy than common analysis approaches. Its nomogram can be used as an additional non-invasive diagnostic technique.

The mean Hounsfield unit range acquired from different slices produces superior predictive accuracy for pyonephrosis in obstructive uropathy

PURPOSE

To determine the non-contrast computer tomography imaging features of pyonephrosis and evaluate the predictive value of Hounsfield units (HUs) in different hydronephrotic region slices.

MATERIALS AND METHODS

We retrospectively reviewed data from patients with hydronephrosis who had renal-ureteral calculi. All patients were categorized into pyonephrosis and simple hydronephrosis groups. Baseline characteristics, the mean HU values in the maximal hydronephrotic region (uHU) slice, and the range of uHU in different slices (ΔuHU) were compared between the two groups. Univariate and multivariate analyses were performed to identify risk factors for pyonephrosis.

RESULTS

Among the 181 patients enrolled in the current study, 71 patients (39.2%) were diagnosed with pyonephrosis. The mean dilated pelvis surface areas were comparable between patients with pyonephrosis and simple hydronephrosis (822.61 mm² vs. 877.23 mm², p=0.722). Collecting system debris (p=0.022), a higher uHU (p=0.038), and a higher ΔuHU (p<0.001) were identified as independent risk factors for pyonephrosis based on multivariate analysis. The ΔuHU sensitivity and specificity were 88.7% and 86.4%, respectively, at a cutoff value of 6.56 (p<0.001), whereas the sensitivity and specificity for detecting pyonephrosis at a uHU cutoff value of 7.96 was 50.7% and 70.9%, respectively (p=0.003).

CONCLUSIONS

Non-contrast computer tomography was shown to accurately distinguish simple hydronephrosis from pyonephrosis in patients with obstructive uropathy. Evaluation of the ΔuHU in different slices may be more reliable than the uHU acquired from a single slice in predicting pyonephrosis.

The association between renal pelvis urine density and the risk of severe infectious complications in patient with symptom-free hydronephrosis after shock wave lithotripsy: a multi-center prospective study

Finding reliable and easy-to-obtain predictors of severe infectious complications after shock wave lithotripsy (SWL) is a major clinical need, particular in symptom-free hydronephrosis. Therefore, we aim to prospectively investigate the predictive value of Hounsfield units (HU) in renal pelvis urine for the risk of severe infectious complications in patients with ureteral stones and symptom-free hydronephrosis after SWL. This multi-center prospective study was conducted from June 2020 to December 2023. The HU of renal pelvis urine was measured by non-enhanced computed tomography. The severe infectious complications included systemic inflammatory response syndrome, sepsis, and septic shock. Binary logistic regression models assessed the odds ratios (ORs) and 95% confidence intervals (CIs). Finally, 1,436 patients with ureteral stones were enrolled in this study. 8.9% (128/1,436) of patients experienced severe infectious complications after SWL treatment. After adjusting confounding variables, compared with the patients in the lowest renal pelvis urine density quartile, the OR (95% CI) for the highest quartile was 32.36 (13.32, 78.60). There was a positive linear association between the HU value of renal pelvis urine and the risk of severe infectious complications after SWL (P for trend < 0.001). Furthermore, this association was also observed stratified by age, gender, BMI, stone size, stone location and hydronephrosis grade (all P for interaction > 0.05). Additionally, the nonlinear association employed by restricted cubic splines is not statistically significant (nonlinear P = 0.256). The AUROC and 95%CI of renal pelvis urine density were 0.895 (0.862 to 0.927, P value < 0.001). The cut-off value was 12.0 HU with 78.59% sensitivity and 85.94% specificity. This multi-center prospective study demonstrated a positive linear association between HU in renal pelvis urine and the risk of severe infectious complications in patients with ureteral stones and symptom-free hydronephrosis after SWL, regardless of age, gender, BMI, stone size, stone location, and hydronephrosis grade. These findings might be helpful in the SWL treatment decision-making process.

Importance of the hounsfield unit value measured by computed tomography in the differentiation of hydronephrosis and pyonephrosis

Background

Acute or chronic obstruction of the urinary tract can be due to a lot of different causes. Patients with pyonephrosis usually complain of a triad of fever, loin pain and elevated white blood cell count in cases of acute obstruction; and they may also have hypotension in severe cases of the disease. These patients have to be treated with appropriate decompression, or they may develop septic shock. The urgency of the need for treatment greatly depends on the differentiation between hydronephrosis and pyonephrosis.

There is a lack of reliable clinical prognosticators of pyonephrosis in patients with obstructive hydronephrosis. Hounsfield unit (HU) measurement is considered as an adequate predictor of pyonephrosis and may aid in the diagnosis and management of this disease that may be fatal.

The use of HU values in differentiation between pyonephrosis from hydronephrosis depends on the fact that the pyonephrotic fluid contains infected material, urine, cellular particles and microorganisms, which when combined can increase the HU values on a computed tomography (CT) study.

This study was done to assess the diagnostic value of the HU measured CT in differentiation between hydronephrosis and pyonephrosis.

Results

Thirty-nine patients were included in this study. All patients had loin pain and were diagnosed with pelvicalyceal dilatation by ultrasonographic examination. They then underwent non-contrast CT examination.

Using CT scan, the degree of PC dilatation was significantly higher among hydronephrosis group as hydronephrosis group had 63.1% severe dilatation of PCs versus 30.8% in pyonephrosis group with p value 0.0001.

Pelvic wall thickness > 2 mm was reported in 10 (76.9%) patients of pyonephrosis group versus in three (7.9%) patients among hydronephrosis group with p value 0.0001.

The mean Hounsfield units were significantly higher among pyonephrosis group compared to hydronephrosis group (16 ± 5.2 versus 1.7 ± 5.5) with p value 0.0001.

Sensitivity analysis showed that Hounsfield units can significantly diagnose pyonephrosis using the cutoff point 6.2 units, with sensitivity 92.3%, specificity 93.3%, area under the curve (AUC) 96.9% and p value 0.0001.

Conclusions

Measuring HU in a NCECT scan of the kidney might be helpful for differentiating between hydronephrosis and pyonephrosis especially upon considering 6.2 HU as a cutoff point.

High attenuation value in non-contrast computer tomography can predict pyonephrosis in patients with upper urinary tract stones

To evaluate whether the higher attenuation value [Hounsfield unit (HU)] in non-contrast CT can predict pyonephrosis in patients with upper urinary tract stones (UTS). Between October 2019 and October 2021, patients with hydronephrosis or pyonephrosis secondary to upper UTS were retrospectively searched in our study. All patients with UTS were treated with percutaneous nephrostomy, percutaneous nephrolithotomy, retrograde ureteral stent or transurethral ureteroscope lithotripsy. We excluded patients treated with extracorporeal shock-wave lithotripsy. Patients whose CT was not performed in our hospital or treated in another hospital were also excluded. Clinical data regarding basic information, clinical feature, Calculi-related indicators, HU values of the renal pelvis, the thick wall of the renal pelvis on CT were collected. Univariate and multivariate logistic analyses were performed. Receiver operative characteristic curves were drawn to predict pyonephrosis. A total of 240 patients with UTS were retrospected in this research, 191 patients had hydronephrosis (Group 1), and 49 patients had hydronephrosis with pyonephrosis (Group 2). The HU value of the renal collecting system in Group 2 (mean, 15.46; range, +1/+30) was significantly higher than that in Group 1 (mean, 5.5; 5 range -6/+24) (P = .02); the receiver operative characteristic curve analysis revealed that the best cut-off value of 9.5 could predict the presence of pyonephrosis, with 71.4% sensitivity and 70.2% specificity (area under the curve = 0.613; 95% CI: 0.514-0.713). In this study, we found the HU attenuation value of the renal collecting system can be used to distinguish pyonephrosis from hydronephrosis in patients with UTS.

Development of a novel clinical and radiological risk score to predict septic complications after urinary decompression in patients with obstructive uropathy

BACKGROUND

Well-defined clinical predictors of sepsis after upper tract drainage for obstructive uropathy are lacking. The study aim is to develop a data driven score to predict risk of sepsis after decompression of the upper urinary tract.

MATERIALS AND METHODS

Complete clinical and radiological data from 271 patients entering the emergency department for obstructive uropathy and submitted to stent/nephrostomy tube decompression were evaluated. The Charlson Comorbidity Index (CCI) was used to score comorbidities. The definition of sepsis was an increase in ≥2 SOFA points (or postoperative persistently elevated score +1 additional increase) and documented blood or urine cultures. Descriptive statistics and stepwise multivariable logistic regression modelling with ROC analysis were performed in order to obtain a composite risk score to predict the risk of sepsis after surgery.

RESULTS

Fifty-five (20.3%) patients developed sepsis. At multivariable analysis, CCI ≥2 (OR 3.10; 95%CI 1.36-7.04), max body temperature ≥38°C (OR 4.35; 95%CI 1.89-9.44), grade III-IV hydronephrosis (OR 2.37; 95%CI 1.10-4.98), Hounsfield units of the dilated collecting system ≥7.0 (OR 4.47; 95%CI 2.03-9.81), WBC ≥15x103/mmc (OR 2.77; 95%CI 1.24-6.19) and C-reactive protein ≥10 (OR 3.27; 95%CI 1.41-7.56) were independently associated with sepsis. The PPV of a true sepsis increased incrementally as a function of number of positive variables, ranging from 1.6% to 100.0% among patients with 1 and 6 positive variables, respectively.

CONCLUSION

Our risk score identifies accurately patients with an increased risk of sepsis after urinary decompression for obstructive uropathy, hence improving clinical management.

Value of Hounsfield Units in the Evaluation of Isolated Sphenoid Sinus Lesions

Radiologic findings of fungal sinus disease are generally opacification in paranasal computed tomography (CT) images. The Hounsfield unit (HU) is a standardized objective unit that is also suitable for measuring remodeling and opacifications on CT scans of bone sections of patients with chronic rhinosinusitis. We hypothesized that HU values could provide valuable information in isolated sphenoid sinus lesions before surgery. Between 2012 and 2019, 35 patients underwent functional endoscopic sinus surgery for sphenoid sinus lesions. Tissues obtained from the sphenoid sinus were divided into two groups, fungal and nonfungal, according to the findings of histopathologic examinations. HU values were measured in sphenoid sinus sections on paranasal CT scans of these two groups. Differences in mean and maximum HU values between the two groups were statistically significant (p < .05). The maximum HU values calculated from the sphenoid sinus were 435.08 and 196.23 (p ≤ .05) in the fungal group and nonfungal group, respectively. The mean HU values calculated from the sphenoid sinus were 64.31 and 29 (p ≤ .05) in the fungal and nonfungal groups, respectively. At the maximum cutoff value of 241, the sensitivity and specificity of the HU maximum were 84.6% and 77.3%, respectively. At the mean cutoff value of 41.5, the sensitivity and specificity of the HU mean were 76.9% and 86.4%, respectively. HU is an objective value used in radiographic density measurement. The HU values were higher in fungal lesions than in nonfungal inflammations, and they are useful in preoperative measurement.

Pyonephrosis Ultrasound and Computed Tomography Features: A Pictorial Review

Urinary tract infections (UTIs) are the most frequent community-acquired and healthcare-associated bacterial infections. UTIs are heterogeneous and range from rather benign, uncomplicated infections to complicated UTIs (cUTIs), pyelonephritis and severe urosepsis, depending mostly on the host response. Ultrasound and computed tomography represent the imaging processes of choice in the diagnosis and staging of the pathology in emergency settings. The aim of this study is to describe the common ultrasound (US) and computed tomography (CT) features of pyonephrosis. US can make the diagnosis, demonstrating echogenic debris, fluid/fluid levels, and air in the collecting system. Although the diagnosis appears to be easily made with US, CT is necessary in non-diagnostic US examinations to confirm the diagnosis, to demonstrate the cause and moreover to stage the pathology, defining extrarenal complications. In emergency settings, US and CT are differently used in the diagnosis and staging of pyonephrosis.

Machine learning-assisted decision-support models to better predict patients with calculous pyonephrosis

Background

To develop a machine learning (ML)-assisted model capable of accurately identifying patients with calculous pyonephrosis before making treatment decisions by integrating multiple clinical characteristics.

Methods

We retrospectively collected data from patients with obstructed hydronephrosis who underwent retrograde ureteral stent insertion, percutaneous nephrostomy (PCN), or percutaneous nephrolithotomy (PCNL). The study cohort was divided into training and testing datasets in a 70:30 ratio for further analysis. We developed 5 ML-assisted models from 22 clinical features using logistic regression (LR), LR optimized by least absolute shrinkage and selection operator (Lasso) regularization (Lasso-LR), support vector machine (SVM), extreme gradient boosting (XGBoost), and random forest (RF). The area under the curve (AUC) was applied to determine the model with the highest discrimination. Decision curve analysis (DCA) was used to investigate the clinical net benefit associated with using the predictive models.

Results

A total of 322 patients were included, with 225 patients in the training dataset, and 97 patients in the testing dataset. The XGBoost model showed good discrimination with the AUC, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 0.981, 0.991, 0.962, 1.000, 1.000, and 0.989, respectively, followed by SVM [AUC =0.985, 95% confidence interval (CI): 0.970–1.000], Lasso-LR (AUC =0.977, 95% CI: 0.958–0.996), LR (AUC =0.936, 95% CI: 0.905–0.968), and RF (AUC =0.920, 95% CI: 0.870–0.970). Validation of the model showed that SVM yielded the highest AUC (0.977, 95% CI: 0.952–1.000), followed by Lasso-LR (AUC =0.959, 95% CI: 0.921–0.997), XGBoost (AUC =0.958, 95% CI: 0.902–1.000), LR (AUC =0.932, 95% CI: 0.878–0.987), and RF (AUC =0.868, 95% CI: 0.779–0.958) in the testing dataset.

Conclusions

Our ML-based models had good discrimination in predicting patients with obstructed hydronephrosis at high risk of harboring pyonephrosis, and the use of these models may be greatly beneficial to urologists in treatment planning, patient selection, and decision-making.

Importance of the Hounsfield Unit Value Measured by Computed Tomography in the Differentiation of Hydronephrosis and Pyonephrosis

Objectives

To evaluate the efficacy of the non-contrast-enhanced computed tomography (NCECT) renal pelvis Hounsfield unit (HU) values in differentiating between the hydronephrosis and pyonephrosis in dilated urinary systems.

Materials and methods

Patients who underwent percutaneous nephrostomy (PN) due to urinary system obstruction in the last three years were retrospectively evaluated. Pyonephrosis and hydronephrosis groups were differentiated according to the clarity of percutaneous needle aspiration. The patients’ renal pelvic anteroposterior (AP) diameter, renal pelvic area, and mean HU values were measured on NCECT and compared between two groups.

Results

PN was performed on a total of 523 patients. The study included 159 patients and 214 renal units. Hydronephrosis was detected in 176 renal units and pyonephrosis in 38 renal units. No statistically significant difference was observed between the measured AP diameter and renal pelvic area in the two groups (28.45 ± 10.1 mm vs. 31.13 ± 14.4 mm, p = 0.36 and 658.51 ± 433.1 mm² vs. 755.14 ± 470.6 mm², p = 0.22, respectively). The mean HU value of the pyonephrosis group was significantly higher (2.30 ± 5.02 vs. 10.97 ± 6.68, p < 0.001). At the cut-off value of 8.46, HU had a sensitivity of 68.4% and specificity of 92.6% in the diagnosis of pyonephrosis.

Conclusions

It is possible to determine differential diagnosis between pyonephrosis and hydronephrosis easily and without additional cost by performing dilated renal pelvis HU measurements on NCECT.

The Hounsfield Unit of Perihematomal Edema Is Associated With Poor Clinical Outcomes in Intracerebral Hemorrhage

BACKGROUND

Hounsfield unit (HU) of perihematomal edema (PHE) may be a predictor of prognosis of intracerebral hemorrhage (ICH). Our study evaluated whether PHE mean HU at the 72 hours after ICH predicts outcome, and how it compares against other PHE measures.

METHODS

Patients with ICH from a tertiary medical institution were included. PHE was segmented by the semiautomatic plane method to measure volume and mean HU. Outcomes of interest was poor 90-day prognosis (modified Rankin Scale score ≥3). Logistic regression was used to assess relationships with outcome.

RESULTS

Data from a total of 159 patients with ICH were collected. The median mean HU of PHE at 72 hours was 22.1 (IQR: 19.2-25.0). Binary logistic regression showed that the 72-hour PHE mean HU was negatively correlated with the poor prognosis of patients with ICH (OR 0.59, 95% CI 0.47-0.75, P < 0.05). The receiver operator curves of meaningful indicators revealed that the area under the curve (AUC) of PHE mean HU at 72 hours was larger and the difference of AUC between PHE mean HU with PHE absolute volume or extension distance were statistically significant (P < 0.05). The 72-hour PHE mean HU has a higher value in predicting adverse prognosis of patients with ICH.

CONCLUSIONS

The PHE mean HU at 72 hours was negatively correlated with the poor prognosis of patients with ICH. The prediction ability of PHE mean HU at 72 hours was better than PHE absolute volume and extension distance, contributing to a rather good index for predicting outcome of ICH.

Hounsfield unit attenuation value can differentiate pyonephrosis from hydronephrosis and predict septic complications in patients with obstructive uropathy

We aimed to assess the role of computerized tomography attenuation values (Hounsfield unit-HU) for differentiating pyonephrosis from hydronephrosis and for predicting postoperative infectious complications in patients with obstructive uropathy. We analysed data from 122 patients who underwent nephrostomy tube or ureteral catheter placement for obstructive uropathy. A radiologist drew the region of interest for quantitative measurement of the HU values in the hydronephrotic region of the affected kidney. Descriptive statistics and logistic regression models tested the predictive value of HU determination in differentiating pyonephrosis from hydronephrosis and in predicting postoperative sepsis. A HU cut-off value of 6.3 could diagnose the presence of pyonephrosis with 71.6% sensitivity and 71.5% specificity (AUC 0.76; 95%CI: 0.66-0.85). At multivariable logistic regression analysis HU ≥ 6.3 (p ≤ 0.001) was independently associated with pyonephrosis. Patients who developed sepsis had higher HU values (p ≤ 0.001) than those without sepsis. A HU cut-off value of 7.3 could diagnose the presence of sepsis with 76.5% sensitivity and 74.3% specificity (AUC 0.79; 95%CI: 0.71-0.90). At multivariable logistic regression analysis, HU ≥ 7.3 (p ≤ 0.001) was independently associated with sepsis, after accounting for clinical and laboratory parameters. Measuring HU values of the fluid of the dilated collecting system may be useful to differentiate pyonephrosis from hydronephrosis and to predict septic complications in patients with obstructive uropathy.