Inter- and Intraobserver Agreement in Measuring Urolithiasis Density on Nonenhanced Computed Tomography

The impact of Hounsfield unit-related variables on percutaneous nephrolithotomy outcomes

We aimed to identify the association between Hounsfield Unit(HU)-related variables and percutaneous nephrolithotomy (PCNL) outcomes. We enrolled patients with single renal stones (1-3 cm) who underwent single-tract PCNL between January 2014 and October 2019. Demographics and stone characteristics were retrospectively reviewed. Preoperative computerized tomography (CT) and follow-up CT within at least 3 months after PCNL were included in this analysis. Stone-free status was defined as residual stone measuring ≤ 2 mm within 3 months postoperatively. HU and cross-sectional area (CSA) were measured using the free-draw technique. We analyzed HU-related variables using logistic regression model for outcomes. Altogether, 188 out of 683 patients met the inclusion criteria. The stone-free rate (SFR) was 79.2%. There were no significant differences in age, sex, BMI, ASA class, laterality, pre-op shockwave lithotripsy, stone size, stone burden, skin-to-stone distance, and HU between the stone-free and remnant groups. CSA and HU/CSA in the stone-free and remnant groups were 94.5 ± 46.1 and 128.3 ± 98.5 (p = 0.043) and 10.1 ± 5.6 and 7.3 ± 3.4 (p = 0.001), respectively. Multivariate logistic regression analysis revealed that pelvis, ureteropelvic junction stones, and HU/CSA were independent predictors of SFR. HU did not affect PCNL outcomes. We believe that HU/CSA could be used for determining stone treatment plans and predicting outcomes.

Imaging in stone diagnosis and surgical planning

PURPOSE OF REVIEW

Radiological imaging techniques and applications are constantly advancing. This review will examine modern imaging techniques in the diagnosis of urolithiasis and applications for surgical planning.

RECENT FINDINGS

The diagnosis of urolithiasis may be done via plain film X-ray, ultrasound (US), or contrast tomography (CT) scan. US should be applied in the workup of flank pain in emergency rooms and may reduce unnecessary radiation exposure. Low dose and ultra-low-dose CT remain the diagnostic standard for most populations but remain underutilized. Single and dual-energy CT provide three-dimensional imaging that can predict stone-specific parameters that help clinicians predict stone passage likelihood, identify ideal management techniques, and possibly reduce complications. Machine learning has been increasingly applied to 3-D imaging to support clinicians in these prognostications and treatment selection.

SUMMARY

The diagnosis and management of urolithiasis are increasingly personalized. Patient and stone characteristics will support clinicians in treatment decision, surgical planning, and counseling.

Comprehensive Nomogram for Prediction of the Uric Acid Composition of Ureteral Stones as a Part of Tailored Stone Therapy

BACKGROUND

Oral chemolitholysis is a noninvasive therapy for uric acid (UA) stones. Proper patient selection is crucial for success of the therapy.

OBJECTIVE

To develop a nomogram for prediction of UA stones using parameters gathered during emergency work-up for flank pain.

DESIGN, SETTING, AND PARTICIPANTS

A single-center cohort (459 patients) with singular ureteral stones and available stone analysis was retrospectively reviewed for radiological, urinary, and serological findings indicating UA stones.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

A Mann-Whitney U test or Kruskal-Wallis test was applied for univariate analysis. Categorical variables were compared using a χ² test. Binary logistic regression of significant parameters was performed to design the nomogram.

RESULTS AND LIMITATIONS

Univariate analysis revealed statistically significant differences in parameters between predominantly UA and non-UA groups, including median age (60 yr, interquartile range [IQR] 51.5-70.5 vs 51 yr, IQR 39-62; p < 0.001), body mass index (30.0 kg/m², IQR 27.25-35.0 vs 26.6 kg/m², IQR 24.0-30.467; p < 0.001), stone density (435.0 HU, IQR 329.0-528.0 vs 750.0 HU, IQR 548.0-995.0; p < 0.001), serum UA (437.5 μmol/l, IQR 374.25-478.0 vs 321.0 μmol/l, IQR 273.0-377.0; p < 0.001), and urine pH (5.5, IQR 5.0-5.5 vs 6.0, IQR 5.5-6.5; p < 0.001). Radiolucency was more frequent in the predominantly UA group (88.60% vs 32.70%; p < 0.001). Multivariate binary logistic regression confirmed age, body mass index, stone density, serum UA, urine pH, and radiolucency as independent predictors of UA stones and these parameters were used to design the nomogram.

CONCLUSIONS

We present a nomogram for the prediction of uric acid stones.

PATIENT SUMMARY

We developed a nomogram as a simple tool with potential to be useful in patient counseling regarding chemolitholysis as a tailored stone treatment for uric acid urinary stones.