The Incidental Renal Mass- Update on Characterization and Management

Dual-Energy Computed Tomography Applications in the Genitourinary Tract

By virtue of material differentiation capabilities afforded through dedicated postprocessing algorithms, dual-energy CT (DECT) has been shown to provide benefit in the evaluation of various diseases. In this article, we review the diagnostic use of DECT in the assessment of genitourinary diseases, with emphasis on its role in renal stone characterization, incidental renal and adrenal lesion characterization, retroperitoneal trauma, reduction of radiation, and contrast dose and cost-effectiveness potential. We also discuss future perspectives of the DECT scanning mode, including the use of novel contrast injection strategies and photon-counting detector computed tomography.

Wall Tension and Tubular Resistance in Kidney Cystic Conditions

The progressive formation of single or multiple cysts accompanies several renal diseases. Specifically, (i) genetic forms, such as adult dominant polycystic kidney disease (ADPKD), and (ii) acquired cystic kidney disease (ACKD) are probably the most frequent forms of cystic diseases. Adult dominant polycystic kidney disease (ADPKD) is a genetic disorder characterized by multiple kidney cysts and systemic alterations. The genes responsible for the condition are known, and a large amount of literature focuses on the molecular description of the mechanism. The present manuscript shows that a multiscale approach that considers supramolecular physical phenomena captures the characteristics of both ADPKD and acquired cystic kidney disease (ACKD) from the pathogenetic and therapeutical point of view, potentially suggesting future treatments. We first review the hypothesis of cystogenesis in ADPKD and then focus on ACKD, showing that they share essential pathogenetic features, which can be explained by a localized obstruction of a tubule and/or an alteration of the tubular wall tension. The consequent tubular aneurysms (cysts) follow Laplace's law. Reviewing the public databases, we show that ADPKD genes are widely expressed in various organs, and these proteins interact with the extracellular matrix, thus potentially modifying wall tension. At the kidney and liver level, the authors suggest that altered cell polarity/secretion/proliferation produce tubular regions of high resistance to the urine/bile flow. The increased intratubular pressure upstream increases the difference between the inside (Pi) and the outside (Pe) of the tubules (∆P) and is counterbalanced by lower wall tension by a factor depending on the radius. The latter is a function of tubule length. In adult dominant polycystic kidney disease (ADPKD), a minimal reduction in the wall tension may lead to a dilatation in the tubular segments along the nephron over the years. The initial increase in the tubule radius would then facilitate the progressive expansion of the cysts. In this regard, tubular cell proliferation may be, at least partially, a consequence of the progressive cysts' expansion. This theory is discussed in view of other diseases with reduced wall tension and with cysts and the therapeutic effects of vaptans, somatostatin, SGLT2 inhibitors, and potentially other therapeutic targets.

Automatic renal mass segmentation and classification on CT images based on 3D U-Net and ResNet algorithms

Purpose

To automatically evaluate renal masses in CT images by using a cascade 3D U-Net- and ResNet-based method to accurately segment and classify focal renal lesions.

Material and Methods

We used an institutional dataset comprising 610 CT image series from 490 patients from August 2009 to August 2021 to train and evaluate the proposed method. We first determined the boundaries of the kidneys on the CT images utilizing a 3D U-Net-based method to be used as a region of interest to search for renal mass. An ensemble learning model based on 3D U-Net was then used to detect and segment the masses, followed by a ResNet algorithm for classification. Our algorithm was evaluated with an external validation dataset and kidney tumor segmentation (KiTS21) challenge dataset.

Results

The algorithm achieved a Dice similarity coefficient (DSC) of 0.99 for bilateral kidney boundary segmentation in the test set. The average DSC for renal mass delineation using the 3D U-Net was 0.75 and 0.83. Our method detected renal masses with recalls of 84.54% and 75.90%. The classification accuracy in the test set was 86.05% for masses (<5 mm) and 91.97% for masses (≥5 mm).

Conclusion

We developed a deep learning-based method for fully automated segmentation and classification of renal masses in CT images. Testing of this algorithm showed that it has the capability of accurately localizing and classifying renal masses.

Diagnostic performance of the "drooping" sign in CT diagnosis of exophytic renal angiomyolipoma

OBJECTIVE

To evaluate the prevalence of angular interface and the "drooping" sign in exophytic renal angiomyolipomas (AMLs) and the diagnostic performance in differentiating exophytic lipid-poor AMLs from other solid renal masses.

METHODS

This IRB-approved, two-center study included 185 patients with 188 exophytic solid renal masses < 4 cm with histopathology and pre-operative CT within 30 days of surgical resection or biopsy. Images were reviewed for the presence of angular interface and the "drooping" sign qualitatively by three readers blinded to the final diagnosis, with majority rules applied. Both features were assessed quantitatively by cohort creators (who are not readers) independently. Free-marginal kappa was used to assess inter-reader agreement and agreement between two methods assessing each feature. Fisher's exact test, Mann-Whitney test, and multivariable logistic regression with two-tailed p < 0.05 were used to determine statistical significance. Diagnostic performance was assessed.

RESULTS

Ninety-four patients had 96 AMLs, and 91 patients had 92 non-AMLs. Seventy-four (77%) of AMLs were lipid-poor based on quantitative assessment on CT. The presence of angular interface and the "drooping" sign by both qualitative and quantitative assessment were statistically significantly associated with AMLs (39% (qualitative) and 45% (quantitative) vs 15% (qualitative) and 13% (quantitative), and 48% (qualitative) and 43% (quantitative) vs 4% (qualitative) and 1% (quantitative), respectively, all p < 0.001) in univariable analysis. In multivariable analysis, only the "drooping" sign in either qualitative or quantitative assessment was a statistically significant predictor of AMLs (both p < 0.001). Inter-reader agreement for the "drooping" sign was moderate (k = 0.55) and for angular interface was fair (k = 0.33). Agreement between the two methods of assessing the "drooping" sign was substantial (k = 0.84) and of assessing the angular interface was moderate (k = 0.59). The "drooping" sign both qualitatively and quantitatively, alone or in combination of angular interface, had very high specificity (96-100%) and positive predictive value (PPV) (89-100%), moderate negative predictive value (62-68%), but limited sensitivity (23-49%) for lipid-poor AMLs.

CONCLUSION

The "drooping" sign by both qualitative and quantitative assessment is highly specific for lipid-rich and lipid-poor AMLs. This feature alone or in combination with angular interface can aid in CT diagnosis of lipid-poor AMLs with very high specificity and PPV.

Automatic Detection and Measurement of Renal Cysts in Ultrasound Images: A Deep Learning Approach

Ultrasonography is widely used for diagnosis of diseases in internal organs because it is nonradioactive, noninvasive, real-time, and inexpensive. In ultrasonography, a set of measurement markers is placed at two points to measure organs and tumors, then the position and size of the target finding are measured on this basis. Among the measurement targets of abdominal ultrasonography, renal cysts occur in 20-50% of the population regardless of age. Therefore, the frequency of measurement of renal cysts in ultrasound images is high, and the effect of automating measurement would be high as well. The aim of this study was to develop a deep learning model that can automatically detect renal cysts in ultrasound images and predict the appropriate position of a pair of salient anatomical landmarks to measure their size. The deep learning model adopted fine-tuned YOLOv5 for detection of renal cysts and fine-tuned UNet++ for prediction of saliency maps, representing the position of salient landmarks. Ultrasound images were input to YOLOv5, and images cropped inside the bounding box and detected from the input image by YOLOv5 were input to UNet++. For comparison with human performance, three sonographers manually placed salient landmarks on 100 unseen items of the test data. These salient landmark positions annotated by a board-certified radiologist were used as the ground truth. We then evaluated and compared the accuracy of the sonographers and the deep learning model. Their performances were evaluated using precision-recall metrics and the measurement error. The evaluation results show that the precision and recall of our deep learning model for detection of renal cysts are comparable to standard radiologists; the positions of the salient landmarks were predicted with an accuracy close to that of the radiologists, and in a shorter time.

Clinicopathological characteristics and outcomes of synchronous renal cell carcinoma and urothelial carcinoma: A population-based analysis

Background

To better understand the characteristics, and survival outcomes of synchronous renal cell carcinoma (RCC) and urothelial carcinoma (UC), we described and analyzed the clinical features, factors, and prognosis of patients with synchronous RCC and UC using a large population-based database.

Methods

Within the Surveillance, Epidemiology, and End Results (SEER) database (2004-2016), we identified patient with concurrent RCC and UC at initial diagnosis. Their clinicopathological features and prognosis were evaluated. A logistic regression model was used to examine risk factors for the occurrence of concomitant RCC and UC, and Kaplan-Meier survival curves were used to estimate overall survival.

Results

A total of 61,454 RCC patients were identified from the SEER database, 704 (1.1%) patients presented with synchronous RCC and UC. Among these patients, concurrent bladder tumors (566/704) are more common. Subsequently, subgroup analysis based on the location of UC indicated that patients with concurrent RCC and upper tract urothelial carcinoma (UTUC) had unfavorable UC characteristics (higher tumor stage and grade), compared with patients with concomitant bladder cancer. An increased risk of concurrent UC was observed among older age, male sex, and white race. Meanwhile, papillary RCC histology [odds ratio (OR) 3.23; 95% confidence interval (CI) 2.13-4.90], and smaller tumor (OR 6.63; 95% CI 4.46-9.87) were independent risk factors for concomitant UTUC. In addition, we found that synchronous RCC and UTUC was associated with worse survival by using Kaplan-Meier and multivariable analysis [hazard ratio (HR) 2.36, 95% CI 1.89-2.95]. However, concomitant bladder cancer did not affect survival outcomes of patients with RCC (HR 1.00, 95% CI 0.86-1.17).

Conclusion

We found that synchronous concurrent RCC and UC is relatively uncommon and mostly located in the bladder. Older age, male sex, and white race increase the risk of synchronous RCC and UC. Meanwhile, patients with papillary RCC histology, and smaller tumors are more likely to have concomitant RCC and UTUC. Furthermore, our findings suggest that synchronous RCC and UTUC has a worse prognosis, while, concomitant bladder tumor did not affect the oncological outcomes of RCC.