Strain elastography in the characterization of renal cell carcinoma and angiomyolipoma

Shear wave elastography in the characterization of renal cell carcinoma and angiomyolipoma

BACKGROUND

Detection and characterization of renal lesions are common in daily clinical practice.

PURPOSE

To investigate the effectiveness of shear wave elastography (SWE), a novel radiological examination technique, in the characterization of renal masses.

MATERIAL AND METHODS

The study included a total of 68 patients (33 men, 35 women; mean age = 57.71 ± 12.08 years; age range = 19-83 years) who underwent SWE. SWE measurements were obtained at depths of 2-8 cm from the probe surface in two different positions from an analysis window of approximately 0.5 × 1.0 cm on ultrasound. The cutoff SWE was calculated for the differentiation of renal cell carcinoma (RCC) and angiomyolipoma (AML) by receiver operating characteristic (ROC) analysis. When the result was statistically significant, the sensitivity, specificity, accuracy, and positive and negative predictive values of the test were calculated.

RESULTS

Mass-to-parenchyma SWE ratios of RCCs were significantly higher than those of AMLs (P = 0.003). In ROC curve analysis, the SWE cutoff was 1.215 m/s to differentiate RCCs from AMLs. The area under the ROC curve was calculated as 0.74 (95% CI = 0.610-0.871, sensitivity = 70.7%, specificity = 70.6%, positive predictive value = 87.8%, negative predictive value = 44.4%).

CONCLUSION

The SWE technique is increasingly used and may be useful in distinguishing RCC and AML lesions, and especially clear cell and non-clear cell RCCs.

Elastography in the Urological Practice: Urinary and Male Genital Tract, Prostate Excluded—Review

The aim of this article is to review the utility of elastography in the day-to-day clinical practice of the urologist. An electronic database search was performed on PubMed and Cochrane Library with a date range between January 2000 and December 2021. The search yielded 94 articles that passed the inclusion and exclusion criteria. The articles were reviewed and discussed by organ, pathology and according to the physical principle underlying the elastographic method. Elastography was used in the study of normal organs, tumoral masses, chronic upper and lower urinary tract obstructive diseases, dysfunctions of the lower urinary tract and the male reproductive system, and as a pre- and post-treatment monitoring tool. Elastography has numerous applications in urology, but due to a lack of standardization in the methodology and equipment, further studies are required.

Recent advances in imaging techniques of renal masses

Multiphasic multidetector computed tomography (CT) forms the mainstay for the characterization of renal masses whereas magnetic resonance imaging (MRI) acts as a problem-solving tool in some cases. However, a few of the renal masses remain indeterminate even after evaluation by conventional imaging methods. To overcome the deficiency in current imaging techniques, advanced imaging methods have been devised and are being tested. This review will cover the role of contrast-enhanced ultrasonography, shear wave elastography, dual-energy CT, perfusion CT, MR perfusion, diffusion-weighted MRI, blood oxygen level-dependent MRI, MR spectroscopy, positron emission tomography (PET)/prostate-specific membrane antigen-PET in the characterization of renal masses.

Expanding the Role of Ultrasound for the Characterization of Renal Masses

The incidental detection of renal masses has been steadily rising. As a significant proportion of renal masses that are surgically treated are benign or indolent in nature, there is a clear need for better presurgical characterization of renal masses to minimize unnecessary harm. Ultrasound is a widely available and relatively inexpensive real-time imaging technique, and novel ultrasound-based applications can potentially aid in the non-invasive characterization of renal masses. Evidence acquisition: We performed a narrative review on novel ultrasound-based techniques that can aid in the non-invasive characterization of renal masses. Evidence synthesis: Contrast-enhanced ultrasound (CEUS) adds significant diagnostic value, particularly for cystic renal masses, by improving the characterization of fine septations and small nodules, with a sensitivity and specificity comparable to magnetic resonance imaging (MRI). Additionally, the performance of CEUS for the classification of benign versus malignant renal masses is comparable to that of computed tomography (CT) and MRI, although the imaging features of different tumor subtypes overlap significantly. Ultrasound molecular imaging with targeted contrast agents is being investigated in preclinical research as an addition to CEUS. Elastography for the assessment of tissue stiffness and micro-Doppler imaging for the improved detection of intratumoral blood flow without the need for contrast are both being investigated for the characterization of renal masses, though few studies have been conducted and validation is lacking. Conclusions: Several novel ultrasound-based techniques have been investigated for the non-invasive characterization of renal masses. CEUS has several advantages over traditional grayscale ultrasound, including the improved characterization of cystic renal masses and the potential to differentiate benign from malignant renal masses to some extent. Ultrasound molecular imaging offers promise for serial disease monitoring and the longitudinal assessment of treatment response, though this remains in the preclinical stages of development. While elastography and emerging micro-Doppler techniques have shown some encouraging applications, they are currently not ready for widespread clinical use.

Point Shear Wave Elastography Using Machine Learning to Differentiate Renal Cell Carcinoma and Angiomyolipoma

The question of whether ultrasound point shear wave elastography can differentiate renal cell carcinoma (RCC) from angiomyolipoma (AML) is controversial. This study prospectively enrolled 51 patients with 52 renal tumors (42 RCCs, 10 AMLs). We obtained 10 measurements of shear wave velocity (SWV) in the renal tumor, cortex and medulla. Median SWV was first used to classify RCC versus AML. Next, the prediction accuracy of 4 machine learning algorithms-logistic regression, naïve Bayes, quadratic discriminant analysis and support vector machines (SVMs)-was evaluated, using statistical inputs from the tumor, cortex and combined statistical inputs from tumor, cortex and medulla. After leave-one-out cross validation, models were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). Tumor median SWV performed poorly (AUC = 0.62; p = 0.23). Except logistic regression, all machine learning algorithms reached statistical significance using combined statistical inputs (AUC = 0.78-0.98; p < 7.1 × 10-3). SVMs demonstrated 94% accuracy (AUC = 0.98; p = 3.13 × 10-6) and clearly outperformed median SWV in differentiating RCC from AML (p = 2.8 × 10-4).

Diagnostic value of strain elastography for differentiation between renal cell carcinoma and transitional cell carcinoma of kidney

PURPOSE

The objective of our study was to prospectively evaluate the diagnostic performance of strain elastography for differentiation between renal cell carcinomas (RCCs) and transitional cell carcinomas (TCCs) of kidney.

METHODS

A total of 99 consecutive patients who were referred to our hospital because of a newly diagnosed solid renal mass suspicious for malignancy on radiological screenings were evaluated with sonography, including strain elastography. Strain elastography was used to compare the stiffness of the renal masses and renal cortex. The ratio of strain in a renal mass and nearby renal cortex was defined as the strain index value. Mean strain index values for RCCs and TCCs were compared, and mean strain index values between histological subtypes of RCC were also compared.

RESULTS

Although TCCs were smaller than RCCs (p < 0.001), there were no significant differences in gender distribution and mean age of the patients, and mean probe-tumor distance between RCC and TCC. The mean strain index value ±SD for TCC (5.18 ± 1.12) was significantly higher than the value for RCC (4.04 ± 0.72; p < 0.001). Mean strain index value for papillary cell carcinomas (4.09 ± 0.45) was slightly higher than that for clear cell carcinomas (3.85 ± 0.78): however, the difference was not statistically significant (p = 0.51).

CONCLUSIONS

Strain elastography can be used as a valuable imaging technique for preoperative differentiation between RCC and TCC of kidney.