Differentiation of subtypes of renal cell carcinoma on helical CT scans

Lesion-aware cross-phase attention network for renal tumor subtype classification on multi-phase CT scans

Multi-phase computed tomography (CT) has been widely used for the preoperative diagnosis of kidney cancer due to its non-invasive nature and ability to characterize renal lesions. However, since enhancement patterns of renal lesions across CT phases are different even for the same lesion type, the visual assessment by radiologists suffers from inter-observer variability in clinical practice. Although deep learning-based approaches have been recently explored for differential diagnosis of kidney cancer, they do not explicitly model the relationships between CT phases in the network design, limiting the diagnostic performance. In this paper, we propose a novel lesion-aware cross-phase attention network (LACPANet) that can effectively capture temporal dependencies of renal lesions across CT phases to accurately classify the lesions into five major pathological subtypes from time-series multi-phase CT images. We introduce a 3D inter-phase lesion-aware attention mechanism to learn effective 3D lesion features that are used to estimate attention weights describing the inter-phase relations of the enhancement patterns. We also present a multi-scale attention scheme to capture and aggregate temporal patterns of lesion features at different spatial scales for further improvement. Extensive experiments on multi-phase CT scans of kidney cancer patients from the collected dataset demonstrate that our LACPANet outperforms state-of-the-art approaches in diagnostic accuracy.

AURKB promotes immunogenicity and immune infiltration in clear cell renal cell carcinoma

BACKGROUND

Chromatin regulators (CRs) are capable of causing epigenetic alterations, which are significant features of cancer. However, the function of CRs in controlling Clear Cell Renal Cell Carcinoma (ccRCC) is not well understood. This research aims to discover a CRs prognostic signature in ccRCC and to elucidate the roles of CRs-related genes in tumor microenvironment (TME).

METHODS

Expression profiles and relevant clinical annotations were retrieved from the Cancer Genome Atlas (TCGA) and UCSC Xena platform for progression-free survival (PFS) data. The R package "limma" was used to identify differentially expressed CRs. A predictive model based on five CRs was developed using LASSO-Cox analysis. The model's predictive power and applicability were validated using K-M curves, ROC curves, nomograms, comparisons with other models, stratified survival analyses, and validation with the ICGC cohort. GO and GSEA analyses were performed to investigate mechanisms differentiating low and high riskScore groups. Immunogenicity was assessed using Tumor Mutational Burden (TMB), immune cell infiltrations were inferred, and immunotherapy was evaluated using immunophenogram analysis and the expression patterns of human leukocyte antigen (HLA) and checkpoint genes. Differentially expressed CRs (DECRs) between low and high riskScore groups were identified using log2|FC|> 1 and FDR < 0.05. AURKB, one of the high-risk DECRs and a component of our prognostic model, was selected for further analysis.

RESULTS

We constructed a 5 CRs signature, which demonstrated a strong capacity to predict survival and greater applicability in ccRCC. Elevated immunogenicity and immune infiltration in the high riskScore group were associated with poor prognosis. Immunotherapy was more effective in the high riskScore group, and certain chemotherapy medications, including cisplatin, docetaxel, bleomycin, and axitinib, had lower IC50 values. Our research shows that AURKB is critical for the immunogenicity and immune infiltration of the high riskScore group.

CONCLUSION

Our study produced a reliable prognostic prediction model using only 5 CRs. We found that AURKB promotes immunogenicity and immune infiltration. This research provides crucial support for the development of prognostic biomarkers and treatment strategies for ccRCC.

Contrast-enhanced ultrasound of renal masses in the pre-transplant setting: literature review with case highlights

With the rising incidence of chronic kidney disease worldwide, an increasing number of patients are expected to require renal transplantation, which remains the definitive treatment of end stage renal disease. Medical imaging, primarily ultrasonography and contrast-enhanced CT and/or MRI, plays a large role in pre-transplantation assessment, especially in the characterization of lesions within the native kidneys. However, patients with CKD/ESRD often have relative contraindications to CT- and MR-contrast agents, limiting their utilization within this patient population. Contrast-enhanced ultrasound (CEUS), which combines the high temporal and spatial resolution of ultrasonography with intravascular microbubble contrast agents, provides a promising alternative. This review aims to familiarize the reader with the literature regarding the use of CEUS in the evaluation of cystic and solid renal lesions and provide case examples of its use at our institution in the pre-transplant setting.

Efficacy of 18F-Fluorodeoxyglucose positron emission tomography/computed tomography for detecting renal cell carcinoma in patients with end-stage renal disease

PURPOSE

Dialysis patients are at an increased risk of developing renal cell carcinoma (RCC); however, differentiating between RCC and benign cysts can sometimes be difficult using modalities, such as computed tomography (CT) and ultrasonography. 18F-Fluorodeoxyglucose positron emission tomography (FDG-PET)/CT efficiently detects malignant tumors; however, physiological accumulation of FDG in the kidney limits its efficacy in detecting renal tumors. However, in patients with severely impaired renal function, the renal accumulation of FDG is decreased, possibly improving the detection of renal malignancies in this patient population. This study evaluated the usefulness of FDG-PET/CT as a screening tool for detecting RCC in patients with end-stage renal disease.

MATERIALS AND METHODS

This prospective study recruited 150 participants from 2012 to 2016 who were on dialysis or underwent renal transplantation and were on dialysis until transplantation. FDG-PET/CT was performed to screen for RCC. Three radiologists independently evaluated the images. No protocol was defined for the additional management of positive examinations, leaving decisions to the discretion of each participant. Negative examinations were observed until the end of 2019.

RESULTS

In total, 150 participants (mean age, 58 ± 13 years; 105 men) underwent FDG-PET/CT. Twenty patients (13.4%) were diagnosed as positive. Fifteen patients underwent additional examinations and/or procedures, and RCC was found in seven patients. Of the four patients who underwent surgical resection, the pathological results were clear cell RCC in one, papillary RCC in one, and acquired cystic disease-associated RCC in two. Two participants were diagnosed with RCC on bone biopsy, and one was diagnosed on dynamic CT but opted for observation. The sensitivity, specificity, and negative predictive value were 100%, 93.9%, and 100%, respectively.

CONCLUSION

FDG-PET/CT was useful for detecting RCC in patients with end-stage renal disease. Our findings show the potential use of FDG-PET/CT as a screening tool for RCC in this patient population.

Artificial Intelligence-Based Non-invasive Differentiation of Distinct Histologic Subtypes of Renal Tumors With Multiphasic Multidetector Computed Tomography

INTRODUCTION

With rising cases of renal cell carcinoma (RCC), precise identification of tumor subtypes is essential, particularly for detecting small, heterogenous lesions often overlooked in traditional histopathological examinations. This study demonstrates the non-invasive use of deep learning for Histopathological differentiation of renal tumors through quadriphasic multidetector computed tomography (MDCT).

PATIENTS AND METHODS

This prospective longitudinal study includes 50 subjects (32 males, 18 females) with suspected renal tumors. A deep neural network (DNN) is developed to predict RCC subtypes using peak attenuation values measured in Hounsfield Units (HUs) obtained from quadriphasic MDCT scans. The network then generates confidence scores for each of the four primary subtypes of renal tumors, effectively distinguishing between benign oncocytoma and various malignant subtypes.

RESULTS

Our neural network accurately distinguishes Renal tumor subtypes, including clear cell, papillary, chromophobe, and benign oncocytoma, with a confidence score of 68% with the network's diagnosis aligning with Histopathological examinations. Our network was also able to accurately classify RCC subtypes on a synthetically generated dataset with 20,000 samples.

CONCLUSION

We developed an artificial intelligence-based RCC subtype classification technique. Our approach is non-invasive and has the potential to transform the methodology in Renal oncology by providing accurate and timely diagnostic information and enhancing clinical decisions.

Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics

Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50-61% of all renal tumors are found incidentally. Methods: The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions: The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context.

Transarterial ethiodised oil marking before CT-guided renal cryoablation: evaluation of tumour visibility in various renal cell carcinoma subtypes

AIM

To evaluate ethiodised oil retention of transarterial embolisation using ethiodised oil (ethiodised oil marking) before computed tomography (CT)-guided percutaneous cryoablation (PCA) according to renal cell carcinoma (RCC) subtype.

MATERIALS AND METHODS

Ethiodised oil marking was performed 1-3 days before PCA in 99 patients with 99 RCCs from 2016 to 2020. Ethiodised oil retention on CT images was evaluated retrospectively and CT attenuation values in the tumour were measured. Regions of interest (ROI) were placed on the tumours to calculate: average (ROI-average), maximal (ROI-max), minimum (ROI-min), and standard deviation (ROI-SD). Qualitative scores comprising a five-point scale (5, excellent; 1, poor) were evaluated for the retention scores (RS) of ethiodised oil in the tumour (ethiodised oil-RS) and the visualisation scores (VS) of the boundary between the tumour and renal parenchyma (boundary-VS).

RESULTS

The histological subtypes comprised clear cell (ccRCC; n=85), papillary (pRCC; n=6), and chromophobe/oncocytoma renal cell carcinoma (chrRCC; n=8). The mean ROI-average, ROI-max, and ROI-SD were significantly higher in ccRCCs than in chrRCCs and pRCCs (p<0.05). The mean ethiodised oil-RS was significantly lower in pRCCs than in ccRCCs (p=0.039), and the mean boundary-VS was >4 in all subtypes. Even with poor intratumour ethiodised oil retention (n=6), sufficient boundary-VS was obtained due to "inverted marking." All PCA procedures were completed without additional intravenous contrast material injection at the time of PCA.

CONCLUSION

Regardless of the tumour subtypes, ethiodised oil marking aids in visualising the boundary between the tumour and parenchyma on non-contrast CT in PCA.

CT of the urinary tract revisited

Computed tomography (CT) of the abdomen is usually appropriate for the initial imaging of many urinary tract diseases, due to its wide availability, fast scanning and acquisition of thin slices and isotropic data, that allow the creation of multiplanar reformatted and three-dimensional reconstructed images of excellent anatomic details. Non-enhanced CT remains the standard imaging modality for assessing renal colic. The technique allows the detection of nearly all types of urinary calculi and the estimation of stone burden. CT is the primary diagnostic tool for the characterization of an indeterminate renal mass, including both cystic and solid tumors. It is also the modality of choice for staging a primary renal tumor. Urolithiasis and urinary tract malignancies represent the main urogenic causes of hematuria. CT urography (CTU) improves the visualization of both the upper and lower urinary tract and is recommended for the investigation of gross hematuria and microscopic hematuria, in patients with predisposing factors for urologic malignancies. CTU is highly accurate in the detection and staging of upper tract urothelial malignancies. CT represents the most commonly used technique for the detection and staging of bladder carcinoma and the diagnostic efficacy of CT staging improves with more advanced disease. Nevertheless, it has limited accuracy in differentiating non-muscle invasive bladder carcinoma from muscle-invasive bladder carcinoma. In this review, clinical indications and the optimal imaging technique for CT of the urinary tract is reviewed. The CT features of common urologic diseases, including ureterolithiasis, renal tumors and urothelial carcinomas are discussed.

Magnetic Resonance Imaging Virtual Biopsy of Common Solid Renal Masses-A Pictorial Review

ABSTRACT

The expanded application of radiologic imaging resulted in an increased incidence of renal masses in the recent decade. Clinically, it is difficult to determine the malignant potential of the renal masses, thus resulting in complex management. Image-guided biopsies are the ongoing standard of care to identify molecular variance but are limited by tumor accessibility and heterogeneity. With the evolving importance of individualized cancer therapies, radiomics has displayed promising results in the identification of tumoral mutation status on routine imaging. This article discusses how magnetic resonance imaging features can guide a radiologist toward identifying renal mass characteristics.

The Role of CT Imaging in Characterization of Small Renal Masses

Small renal masses (SRM) are increasingly detected incidentally during imaging. They vary widely in histology and aggressiveness, and include benign renal tumors and renal cell carcinomas that can be either indolent or aggressive. Imaging plays a key role in the characterization of these small renal masses. While a confident diagnosis can be made in many cases, some renal masses are indeterminate at imaging and can present as diagnostic dilemmas for both the radiologists and the referring clinicians. This review focuses on CT characterization of small renal masses, perhaps helping us understand small renal masses. The following aspects were considered for the review: (a) assessing the presence of fat, (b) assessing the enhancement, (c) differentiating renal tumor subtype, and (d) identifying valuable CT signs.

Imaging Features of Renal Masses to Select Optimal Candidates for Tumor Enucleation Partial Nephrectomy

PURPOSE OF REVIEW

The goal of this paper was to critically evaluate preoperative findings that optimally select candidates for renal tumor enucleation partial nephrectomy.

RECENT FINDINGS

Tumor enucleation has been widely accepted as a management option for patients with chronic kidney disease, hereditary renal cell carcinoma, or multifocal disease. Recent evidence suggests safety and efficacy in the management of routine small renal masses. With recent advances in imaging, the literature for ruling out aggressive renal cell carcinoma and selection for tumor enucleation is robust. As the incidence of renal cell carcinoma rises, partial nephrectomy continues to be the mainstay of treatment for localized renal cell carcinoma. Tumor enucleation maximizes preservation of renal parenchyma without hindering oncologic outcomes. It is important to recognize key tumor radiologic findings which urologists may use to optimize patient selection for tumor enucleation.

Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols

Introduction

This study aimed to construct a radiomics-based machine learning (ML) model for differentiation between non-clear cell and clear cell renal cell carcinomas (ccRCC) that is robust against institutional imaging protocols and scanners.

Materials and methods

Preoperative unenhanced (UN), corticomedullary (CM), and excretory (EX) phase CT scans from 209 patients diagnosed with RCCs were retrospectively collected. After the three-dimensional segmentation, 107 radiomics features (RFs) were extracted from the tumor volumes in each contrast phase. For the ML analysis, the cases were randomly split into training and test sets with a 3:1 ratio. Highly correlated RFs were filtered out based on Pearson’s correlation coefficient (r > 0.95). Intraclass correlation coefficient analysis was used to select RFs with excellent reproducibility (ICC ≥ 0.90). The most predictive RFs were selected by the least absolute shrinkage and selection operator (LASSO). A support vector machine algorithm-based binary classifier (SVC) was constructed to predict tumor types and its performance was evaluated based-on receiver operating characteristic curve (ROC) analysis. The “Kidney Tumor Segmentation 2019” (KiTS19) publicly available dataset was used during external validation of the model. The performance of the SVC was also compared with an expert radiologist’s.

Results

The training set consisted of 121 ccRCCs and 38 non-ccRCCs, while the independent internal test set contained 40 ccRCCs and 13 non-ccRCCs. For external validation, 50 ccRCCs and 23 non-ccRCCs were identified from the KiTS19 dataset with the available UN, CM, and EX phase CTs. After filtering out the highly correlated and poorly reproducible features, the LASSO algorithm selected 10 CM phase RFs that were then used for model construction. During external validation, the SVC achieved an area under the ROC curve (AUC) value, accuracy, sensitivity, and specificity of 0.83, 0.78, 0.80, and 0.74, respectively. UN and/or EX phase RFs did not further increase the model’s performance. Meanwhile, in the same comparison, the expert radiologist achieved similar performance with an AUC of 0.77, an accuracy of 0.79, a sensitivity of 0.84, and a specificity of 0.69.

Conclusion

Radiomics analysis of CM phase CT scans combined with ML can achieve comparable performance with an expert radiologist in differentiating ccRCCs from non-ccRCCs.

Long Non-Coding RNAs as Novel Biomarkers in the Clinical Management of Papillary Renal Cell Carcinoma Patients: A Promise or a Pledge?

Papillary renal cell carcinoma (pRCC) represents the second most common subtype of renal cell carcinoma, following clear cell carcinoma and accounting for 10–15% of cases. For around 20 years, pRCCs have been classified according to their mere histopathologic appearance, unsupported by genetic and molecular evidence, with an unmet need for clinically relevant classification. Moreover, patients with non-clear cell renal cell carcinomas have been seldom included in large clinical trials; therefore, the therapeutic landscape is less defined than in the clear cell subtype. However, in the last decades, the evolving comprehension of pRCC molecular features has led to a growing use of target therapy and to better oncological outcomes. Nonetheless, a reliable molecular biomarker able to detect the aggressiveness of pRCC is not yet available in clinical practice. As a result, the pRCC correct prognosis remains cumbersome, and new biomarkers able to stratify patients upon risk of recurrence are strongly needed. Non-coding RNAs (ncRNAs) are functional elements which play critical roles in gene expression, at the epigenetic, transcriptional, and post-transcriptional levels. In the last decade, ncRNAs have gained importance as possible biomarkers for several types of diseases, especially in the cancer universe. In this review, we analyzed the role of long non-coding RNAs (lncRNAs) in the prognosis of pRCC, with a particular focus on their networking. In fact, in the competing endogenous RNA hypothesis, lncRNAs can bind miRNAs, resulting in the modulation of the mRNA levels targeted by the sponged miRNA, leading to additional regulation of the target gene expression and increasing complexity in the biological processes.

Novel Imaging Methods for Renal Mass Characterization: A Collaborative Review

CONTEXT

The incidental detection of localized renal masses has been rising steadily, but a significant proportion of these tumors are benign or indolent and, in most cases, do not require treatment. At the present time, a majority of patients with an incidentally detected renal tumor undergo treatment for the presumption of cancer, leading to a significant number of unnecessary surgical interventions that can result in complications including loss of renal function. Thus, there exists a clinical need for improved tools to aid in the pretreatment characterization of renal tumors to inform patient management.

OBJECTIVE

To systematically review the evidence on noninvasive, imaging-based tools for solid renal mass characterization.

EVIDENCE ACQUISITION

The MEDLINE database was systematically searched for relevant studies on novel imaging techniques and interpretative tools for the characterization of solid renal masses, published in the past 10 yr.

EVIDENCE SYNTHESIS

Over the past decade, several novel imaging tools have offered promise for the improved characterization of indeterminate renal masses. Technologies of particular note include multiparametric magnetic resonance imaging of the kidney, molecular imaging with targeted radiopharmaceutical agents, and use of radiomics as well as artificial intelligence to enhance the interpretation of imaging studies. Among these, 99mTc-sestamibi single photon emission computed tomography/computed tomography (CT) for the identification of benign renal oncocytomas and hybrid oncocytic chromophobe tumors, and positron emission tomography/CT imaging with radiolabeled girentuximab for the identification of clear cell renal cell carcinoma, are likely to be closest to implementation in clinical practice.

CONCLUSIONS

A number of novel imaging tools stand poised to aid in the noninvasive characterization of indeterminate renal masses. In the future, these tools may aid in patient management by providing a comprehensive virtual biopsy, complete with information on tumor histology, underlying molecular abnormalities, and ultimately disease prognosis.

PATIENT SUMMARY

Not all renal tumors require treatment, as a significant proportion are either benign or have limited metastatic potential. Several innovative imaging tools have shown promise for their ability to improve the characterization of renal tumors and provide guidance in terms of patient management.

Value of energy spectrum CT parameters in the differential diagnosis of high-grade clear cell renal cell carcinoma and type II papillary renal cell carcinoma

BACKGROUND

Energy spectrum computed tomography (CT) has become a promising approach for the differential diagnosis of tumor subtypes.

PURPOSE

To explore the value of energy spectrum CT parameters in the differential diagnosis of high-grade clear cell renal cell carcinoma (ccRCC) and type II papillary renal cell carcinoma (pRCC).

MATERIAL AND METHODS

Forty-two cases of high-grade ccRCC and 28 cases of type II pRCC were retrospectively reviewed. All region of interest (ROI) measurements were maintained consistently between the two-phase contrast-enhanced examinations. The ROIs encompassed as much of the enhancing areas of the lesions as possible. Energy spectrum CT parameters of all cases, including the 70 keV (HU) value, normalized iodine concentration (NIC), and energy spectrum curve slope were recorded by two radiologists with over 10 years of experience in abdominal CT diagnosis.

RESULTS

In the cortical phase (CP) and parenchymal phase (PP), the 70 keV (HU) value, NIC, and slope value of the energy spectrum curve of high-grade ccRCC were significantly higher than those of type II pRCC. In the CP, NIC showed the highest differential diagnosis efficiency for the two group tumors, with a sensitivity of 78.9% and a specificity of 77.0%. There was no statistical difference in tumor hemorrhage, tumor envelope, tumor morphology, tumor border, lymph node metastasis, embolism, renal pelvis invasion, or tumor calcification between the two tumor types. However, there was significant difference in the number of tumors (P = 0.019).

CONCLUSION

Energy spectrum CT parameters are valuable for the differential diagnosis of high-grade ccRCC and type II pRCC.

Decoding the multicellular ecosystem of vena caval tumor thrombus in clear cell renal cell carcinoma by single-cell RNA sequencing

Background

Vascular invasion with tumor thrombus frequently occurs in advanced renal cell carcinoma (RCC). Thrombectomy is one of the most challenging surgeries with high rate of perioperative morbidity and mortality. However, the mechanisms driving tumor thrombus formation are poorly understood which is required for designing effective therapy for eliminating tumor thrombus.

Results

We perform single-cell RNA sequencing analysis of 19 surgical tissue specimens from 8 clear cell renal cell carcinoma (ccRCC) patients with tumor thrombus. We observe tumor thrombus has increased tissue resident CD8⁺ T cells with a progenitor exhausted phenotype compared with the matched primary tumors. Remarkably, macrophages, malignant cells, endothelial cells and myofibroblasts from TTs exhibit enhanced remodeling of the extracellular matrix. The macrophages and malignant cells from primary tumors represent proinflammatory states, but also increase the expression of immunosuppressive markers compared to tumor thrombus. Finally, differential gene expression and interaction analyses reveal that tumor-stroma interplay reshapes the extracellular matrix in tumor thrombus associated with poor survival.

Conclusions

Our comprehensive picture of the ecosystem of ccRCC with tumor thrombus provides deeper insights into the mechanisms of tumor thrombus formation, which may aid in the design of effective neoadjuvant therapy to promote downstaging of tumor thrombus and decrease the perioperative morbidity and mortality of thrombectomy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02651-9.

Clinicopathological features of non-conventional renal cell carcinoma histological subtypes: Learning points from a large contemporary series spanning over three decades

Purpose

To perform a retrospective review of the clinicopathological features of patients with conventional and non-conventional renal cell carcinoma (cRCC and ncRCC).

Materials and Methods

A large prospectively maintained uro-oncological registry was accessed to extract clinicopathological data of patients diagnosed with renal tumors who subsequently underwent nephrectomy from 1990–2019. Demographics and operative parameters were extracted. Analyses of overall survival (OS) and cancer-specific survival (CSS) were performed using the Kaplan–Meier method. Cox proportional-hazards analysis was used to identify risk factors which influenced survival.

Results

There were a total of 1,686 consecutive nephrectomies which was retrieved, with 1,286 cRCC and 400 ncRCC. The commonest ncRCC subtypes were papillary (n=198, 11.7%), clear cell papillary (n=50, 3.0%) and chromophobe (n=49, 2.9%) RCC. Kaplan–Meier estimates of OS were higher in cRCC (0.74; 95% confidence interval [CI], 0.71–0.78) than ncRCC (hazard ratio, 1.47; 95% CI, 1.16–1.87). Among individual subtypes, chromophobe RCC had the highest 5-year OS (0.90; 95% CI, 0.79–1.0). Among ncRCC subtypes, acquired cystic RCC demonstrated the highest association with end-stage renal failure and hypertension, with the highest CSS. MiT family translocation RCC had the youngest mean age at presentation (45.6±12.8 y) and excellent CSS. Factors associated with increased OS in the entire cohort included shorter operative time, partial nephrectomy and lower tumor stages.

Conclusions

This study provides a comprehensive contemporary overview of ncRCCs which are yet poorly characterized, in comparison to cRCCs. Data from this study would contribute towards tailored patient counseling and healthcare resource planning.

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.

Transcriptome analysis method based on differential distribution evaluation

Identifying differential genes over conditions provides insights into the mechanisms of biological processes and disease progression. Here we present an approach, the Kullback-Leibler divergence-based differential distribution (klDD), which provides a flexible framework for quantifying changes in higher-order statistical information of genes including mean and variance/covariation. The method can well detect subtle differences in gene expression distributions in contrast to mean or variance shifts of the existing methods. In addition to effectively identifying informational genes in terms of differential distribution, klDD can be directly applied to cancer subtyping, single-cell clustering and disease early-warning detection, which were all validated by various benchmark datasets.

Comparison of cortico-medullary phase contrast-enhanced MDCT and T2-weighted MR imaging in the histological subtype differentiation of renal cell carcinoma: radiology-pathology correlation

Purpose

Renal cell carcinoma (RCC) subtype differentiation is of crucial importance in the management and prognosis of these patients. In this study, we investigated the usefulness of unenhanced and cortico-medullary phase contrast-enhanced multidetector-row computed tomography (MDCT) and T2-weighted fast spin-echo (FSE) magnetic resonance imaging (MRI) modalities in the discrimination of the 3 main subtype RCC patients in correlation with their histopathological findings.

Material and methods

A total of 80 pathologically proven RCC patients who had undergone either partial or total nephrectomy were retrospectively investigated in this study. Their histological subtypes were 54 clear cell renal cell carcinoma (ccRCC), 15 papillary renal cell carcinoma (pRCC), and 11 chromophobe renal cell carcinoma (cRCC), based on pathological evaluation. There were 62 male (77.5%) and 18 female (22.5%) patients. Among the 54 ccRCC patients, 29 patients had both non-contrast and cortico-medullary phase CT, 1 had only non-contrast CT, 5 only had cortico-medullary phase CT, and 38 had MRI examination. In the pRCC group, 10 patients had both non-contrast and cortico-medullary phase CT, 1 had only non-contrast CT, 1 had only cortico-medullary phase CT, and 12 had MRI. Finally, in the remaining 11 cRCC patients, 9 had both non-contrast and cortico-medullary phase CT, and only 5 had MRI. We calculated both tumour attenuation values as HU (Hounsfield units) on unenhanced and cortico-medullary phase MDCT images and also tumour mean signal intensity values on FSE T2-weighted MRI images by using the region of interest (ROI) including normal renal cortex measurements. Besides quantitative evaluation, we also performed qualitative visual assessment of tumours on contrast-enhanced MDCT and FSE T2-weighted MRI.

Results

There was no statistically significant difference among the attenuation values of the 3 tumour subtypes on pre-contrast CT images. ccRCC demonstrated a prominent degree of contrast enhancement compared to the chromophobe and papillary ones on cortico-medullary phase MDCT. We found no statistically significant difference between chromophobe and papillary subtypes, although chromophobe tumours showed slightly higher attenuation values compared to papillary ones. ccRCCs usually demonstrated a heterogenous contrast enhancement on cortico-medullary phase CT images, while the papillary subtype usually had a homogenous appearance on visual assessment. On FSE T2-weighted MR images, the signal intensity values of ccRCC patients were found to be significantly higher than both chromophobe and papillary subtypes. Although cRCC patients had a prominently lower T2 signal intensity than clear cell subtype, there was no statistically significant signal intensity difference between chromophobe and papillary subtypes. Regarding visual assessment, papillary subtype tumours showed a mostly homogenous appearance on T2-weighted images and a statistically significant difference was present. On the other hand, there was no significant difference of visual assessment of the clear cell and chromophobe subtypes.

Conclusions

The measurement of the attenuation values on cortico-medullary phase MDCT and the mean signal intensity values on FSE T2-weighted MRI can provide useful information in the differentiation of RCC main subtypes. Also, visual assessment of tumours on both modalities can contribute to this issue by providing additional imaging properties.

Role of multidetector ct in quantitative enhancement- washout analysis of solid renal masses

Background

Enhancement washout technique in solid renal masses using multidetector computed tomography (MDCT) can differentiate different type of lesions. 99 Patients who are presenting with suspected renal masses or renal tumour for staging are included in this study. CT examination are carried out at urology and nephrology centre using MDCT. The attenuation values (Hounsfield Unit) will be assesed for each lesion on the pre enhanced, corticomedullary, nephrographic and delayed phases. Washout ratio will be calculated for each phase of enhancement in comparison to the unenhanced attenuation value. The characteristics of enhancement-washout will be correlated with the final histopathological diagnosis.

Results

Early enhancement and washout pattern was noted in 54 renal lesions (54.5%) representing 4 types of renal lesions; Oncocytoma (n = 13), clear cell renal cell carcinoma (n = 16), Chromophobe renal cell carcinoma (n = 15) and unclassified renal cell carcinoma (n = 10).Prolonged enhancement pattern was noted 45 lesions (45.4%); PRCC (n = 14), 10 case of lipid poor AML (n = 10), metanephric adenoma (n = 10) and Xp11 RCC (n = 11). High pre-contrast attenuation was noted in Xp 11RCC showing attenuation value 41.7 ± 6.823HU. The highest CMP values were noted in CCRCC (151.9 ± 20.4) followed by oncocytomas (137.6 ± 19.15HU) and then CHRCC (123.6 ± 16.6 HU)while the lowest values were noted in Metanephric adenoma)57.1 ± 17.4HU)and followed by PRCC (59.9 ± 4.8)and followed by lipid poor AML (79.17 ± 13.666) and RCC unclassified (89.06 ± 18.1).

Conclusions

Four-phase MDCT (the unenhanced, corticomedullary, nephrographic, and excretory phases) evaluate role of MDCT in differentiation of solid renal masses.

A Reliable Prediction Model for Renal Cell Carcinoma Subtype Based on Radiomic Features from 3D Multiphase Enhanced CT Images

Background

This study aimed to develop a prediction model to distinguish renal cell carcinoma (RCC) subtypes.

Methods

The radiomic features (RFs) from 5 different computed tomography (CT) phases were used in the prediction models: noncontrast phase (NCP), corticomedullary phase (CMP), nephrographic phase (NP), excretory phase (EP), and all-phase (ALL-P).

Results

For the ALL-P model, all of the RFs obtained from the 4 single-phase images were combined to 420 RFs. The ALL-P model performed the best of all models, with an accuracy of 0.80; the sensitivity and specificity for clear cell RCC (ccRCC) were 0.85 and 0.83; those for papillary RCC (pRCC) were 0.60 and 0.91; those for chromophobe RCC (cRCC) were 0.66 and 0.91, respectively. Binary classification experiments showed for distinguishing ccRCC vs. not-ccRCC that the area under the receiver operating characteristic curve (AUC) of the ALL-P and CMP models was 0.89, but the overall sensitivity/specificity/accuracy of the ALL-P model was better. For cRCC vs. non-cRCC, the ALL-P model had the best performance.

Conclusions

A reliable prediction model for RCC subtypes was constructed. The performance of the ALL-P prediction model was the best as compared to individual single-phase models and the traditional prediction model.

Solid renal masses in adults

With the ever increasing trend of using cross-section imaging in today's era, incidental detection of small solid renal masses has dramatically multiplied. Coincidentally, the number of asymptomatic benign lesions being detected has also increased. The role of radiologists is not only to identify these lesions, but also go a one step further and accurately characterize various renal masses. Earlier detection of small renal cell carcinomas means identifying at the initial stage which has an impact on prognosis, patient management and healthcare costs. In this review article we share our experience with the typical and atypical solid renal masses encountered in adults in routine daily practice.

A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors

Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given renal tumor. The integrated discriminating features may lead to the development of a novel comprehensive renal cancer computer-assisted diagnosis (RC-CAD) system with the ability to discriminate between benign and malignant renal tumors and specify the malignancy subtypes for optimal medical management. Informed consent was obtained from a total of 140 biopsy-proven patients to participate in the study (male = 72 and female = 68, age range = 15 to 87 years). There were 70 patients who had RCC (40 clear cell RCC (ccRCC), 30 nonclear cell RCC (nccRCC)), while the other 70 had benign angiomyolipoma tumors. Contrast-enhanced computed tomography (CE-CT) images were acquired, and renal tumors were segmented for all patients to allow the extraction of discriminating imaging features. The RC-CAD system incorporates the following major steps: (i) applying a new parametric spherical harmonic technique to estimate the morphological features, (ii) modeling a novel angular invariant gray-level co-occurrence matrix to estimate the textural features, and (iii) constructing wash-in/wash-out slopes to estimate the functional features by quantifying enhancement variations across different CE-CT phases. These features were subsequently combined and processed using a two-stage multilayer perceptron artificial neural network (MLP-ANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype as well. Using the combined features and a leave-one-subject-out cross-validation approach, the developed RC-CAD system achieved a sensitivity of 95.3%±2.0%, a specificity of 99.9%±0.4%, and Dice similarity coefficient of 0.98±0.01 in differentiating malignant from benign tumors, as well as an overall accuracy of 89.6%±5.0% in discriminating ccRCC from nccRCC. The diagnostic abilities of the developed RC-CAD system were further validated using a randomly stratified 10-fold cross-validation approach. The obtained results using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, relational functional gradient boosting, etc.). Hence, integrating morphological, textural, and functional features enhances the diagnostic performance, making the proposal a reliable noninvasive diagnostic tool for renal tumors.

Contrast -Enhanced Ultrasound: State of the Art in North America

The Society of Radiologists in Ultrasound convened a panel of specialists in contrast-enhanced ultrasound (CEUS) to produce a white paper on noncardiac CEUS in North America. The panel met in Chicago, Illinois, on October 24 and 25, 2017. The recommendations are based on analysis of current literature and common practice strategies and are thought to represent a reasonable approach to introduce the advantages of this safe and noninvasive technique for the benefit of our patients. Characterization of liver nodules, and pediatric vascular and intravesicular applications comprise the approved indications for CEUS in the United States. They, along with the very successful off-label use of CEUS for the kidney, are included in this publication.Other off-label uses are presented with emphasis on their value and literature support in the online version.

CT evaluation of patent artery after percutaneous cryoablation of renal cell carcinoma

PURPOSE

The purpose of this retrospective study was to determine the incidence of persistent patent artery after percutaneous cryoablation of renal cell carcinoma (RCC) and the relationship between patent arteries one month after cryoablation and early tumor progression.

MATERIALS AND METHODS

One hundred and fifty-nine patients (112 men, 47 women; mean age, 63.6±14.6 [SD] years; age range: 21-91 years) who underwent percutaneous cryoablation for 186 RCCs (mean diameter, 1.9±0.6 [SD] cm; range: 0.7-4.0cm) were retrospectively included. After cryoablation, patients underwent contrast-enhanced computed tomography (CT) with ≤2-mm slice thickness within one week from cryoablation, and at one, three, and six months. The time course of patent artery in the ablated renal parenchyma after cryoablation was the primary endpoint. The relationships between patent arteries one month after cryoablation and treatment effectiveness, tumor vascularity, tumor enhancement one month after cryoablation, tumor subtype, and renal function changes were evaluated as secondary endpoints.

RESULTS

CT showed patent arteries in the ablated renal parenchyma within one week in 166 RCCs (89.2%), at one month in 54 RCCs (29.0%), at three months in 8 RCCs (4.3%), and at six months in 2 RCCs (1.1%). The presence of patent artery one month after cryoablation was significantly associated with tumor enhancement at the same time point (P=0.015). There was no association between patent arteries one month after cryoablation and treatment effectiveness (P=0.693).

CONCLUSION

Patent arteries in the ablated renal parenchyma are commonly observed on CT examination after percutaneous cryoablation of RCC. However, they gradually disappear and do not require specific treatment.

Papillary renal cell carcinoma: Review

Kidney cancer is the 13th most common malignancy globally, and the incidence is rising. Papillary renal cell carcinoma is the second most common subtype, comprising 10-15% of renal cell carcinomas. Though the histologic features of this subtype were initially described in the 1990's, our understanding of the genetic and molecular characteristics of this disease have rapidly evolved over the past decade. In this review, we summarize the contemporary understanding of the clinical, morphologic, radiographic, and genetic characteristics of papillary renal cell carcinoma, as well as clinical considerations, current options for management, and prognosis.

Multi-Phase Multiple Detector Computed Tomography (MDCT) Enhancement Patterns and Morphological Features of Chromophobe Renal Cell Carcinoma: An Analysis of 67 Cases

Background

Chromophobe renal cell carcinoma (ChRCC) is difficult to diagnose preoperatively. We investigated multiple detector computed tomography (MDCT) plain scan and multi-phase CT enhancement features to aid ChRCC preoperative diagnosis.

Material/Methods

MDCT data of patients with pathologically confirmed ChRCC were retrospectively analyzed. We calculated the ratios of the CT value for the solid part of the mass to those of the renal cortex, aorta, and inferior vena cava. These ratios were designated as L01–3 for the CT plain scan images, La1–3 for the cortical phase, Lv1–3 for the nephrographic phase, and Lp1–3 for the pelvic phase. We classified the masses into types I, II, III, and IV by type of enhancement.

Results

Sixty-eight masses were included and divided into 3 groups by tumor size (groups A, B, and C). Percentages of calcification, central scars, and small vessel signs were significantly different during the cortical phase for masses in all groups (all P<0.01). Significant differences in enhancement were observed between tumors with severe and mild degrees of enhancement (P<0.01); and among La1, Lv1, and Lp1; La2, Lv2, and Lp2; and La3, Lv3, and Lp3 after enhancement during the cortical, nephrographic, and renal pelvic phases (all P<0.01). The most common type of mass enhancement was type II, followed by type I, and differences between these 2 types were significant (P<0.001).

Conclusions

Although the MDCT features for ChRCC are diverse, MDCT helped preoperatively diagnose ChRCC. Multiple MDCT features are needed to improve the accuracy of preoperative diagnosis.

Is hypervascular papillary renal cell carcinoma present?

PURPOSE

We aimed to investigate atypical papillary renal cell carcinoma (PRCC) presenting with early contrast enhancement and late washout and to investigate the correlation between the CT attenuation value of the corticomedullary phase (CMP) of contrast-enhanced CT in PRCCs and the endothelial cell counts of these tumors.

METHODS

Twenty-two patients with pathologically confirmed PRCC were enrolled in this study. PRCCs were categorized into 18 typical PRCCs and 4 atypical PRCCs. The CT attenuation value of the lesion in the CMP was measured in the maximal section of the tumor using the region of interest. Microvessel density (MVD) was evaluated as a histopathologic parameter using tissue specimens immunohistochemically stained with an anti-ERG antibody. The CT attenuation value and MVD were compared between atypical and typical PRCCs using the Mann-Whitney U test, where p < 0.05 was considered significant. The correlations between CT attenuation value and MVD were evaluated in all PRCCs using single linear regression analysis.

RESULTS

The mean CT attenuation value and the MVD were significantly higher in atypical than in typical PRCCs. Correlation analyses revealed a weak positive correlation between the CT attenuation value and MVD.

CONCLUSIONS

We confirmed several cases of atypical PRCC that present with early contrast enhancement, such as clear cell renal cell carcinoma. In addition, a positive correlation was found between the CT attenuation value in the CMP of PRCCs and the vascular endothelial cell count.

Imaging analysis of 13 rare cases of renal collecting (Bellini) duct carcinoma in northern China: a case series and literature review

Background

Collecting (Bellini) duct carcinoma (CDC) is a highly malignant and rare kidney tumor. We report our 12-year experience with CDC and the results of a retrospective analysis of patients and tumor characteristics, clinical manifestations, and imaging features by computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET)/CT.

Methods

Retrospective examination of tumors between January 2007 and December 2019 identified 13 cases of CDC from three medical centers in northern China. All 13 patients underwent CT scan, among which eight underwent dynamic enhanced CT scan, two underwent PET/CT scan, and one underwent magnetic resonance cholangiopancreatography (MRCP) examination. The lesions were divided into nephritis type and mass type according to the morphology of the tumors.

Results

The study group included ten men and three women with an average age of 64.23 ± 10.74 years. The clinical manifestations were gross hematuria, flank pain, and waist discomfort. The mean tumor size was 8.48 ± 2.48 cm. Of the 13 cases, six (46.2%) were cortical-medullary involved type and seven (53.8%) were cortex–medullary–pelvis involved type. Eleven (84.6%) cases were nephritis type and two (15.4%) were mass type. The lesions appeared solid or complex solid and cystic on CT and MRI. The parenchymal area of the tumors showed isodensity or slightly higher density on unenhanced CT scan in the 13 cases. PET/CT in two cases showed increased radioactivity intake. Evidence of intra-abdominal metastatic disease was present on CT in nine (69.2%) cases.

Conclusions

The imaging characteristics of CDC differ from those of other renal cell carcinomas. In renal tumors located in the junction zone of the renal cortex and medulla that show unclear borders, slight enhancement, and metastases in the early stage, a diagnosis of CDC needs to be considered. PET/CT provides crucial information for the diagnosis of CDC, as well as for designing treatment strategies including surgery.

A Concise Review of the Multimodality Imaging Features of Renal Cell Carcinoma

The evaluation of renal cell carcinoma (RCC) is routinely performed using the multimodality imaging approach, including ultrasonography, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Ultrasonography is the most frequently used imaging modality for the initial diagnosis of renal masses. The modality of choice for the characterization of the renal mass is multiphasic CT. Recent advances in CT technology have led to its widespread use as a powerful tool for preoperative planning, reducing the need for catheter angiography for the evaluation of vascular invasion. CT is also the standard imaging modality for staging and follow-up. MRI serves as a problem-solving tool in selected cases of undefined renal lesions. Newer MRI techniques, such as arterial spin labeling and diffusion-weighted imaging, have the potential to characterize renal lesions without contrast media, but these techniques warrant further investigation. PET may be a useful tool for evaluating patients with suspected metastatic disease, but it has modest sensitivity in the diagnosis and staging of RCC. The newer radiotracers may increase the accuracy of PET for RCC diagnosis and staging. In summary, the main imaging modality used for the characterization, staging, and surveillance of RCC is multiphasic CT. Other imaging modalities, such as MRI and PET, are used for selected indications.

Wunderlich Syndrome: Wonder What It Is

Wunderlich syndrome (WS) refers to spontaneous renal or perinephric hemorrhage occurring in the absence of known trauma. WS is much less common than hemorrhage occurring after iatrogenic or traumatic conditions. Lenk's triad of acute onset flank pain, flank mass, and hypovolemic shock is a classic presentation of WS but seen in less than a quarter of patients. The majority of patients present only with isolated flank pain and often imaged with an unenhanced CT in the emergency department. The underlying etiology is varied with most cases attributed to neoplasms, vascular disease, cystic renal disease and anticoagulation induced; the etiology is often occult on the initial exam and further evaluation is necessary. Urologists are familiar with this unique entity but radiologists, who are more likely to be the first to diagnose WS, may not be familiar with the imaging work up and management options. In the last decade or so, there has been a conspicuous shift in the approach to WS and thus it will be worthwhile to revisit WS in detail. In our review, we will review the multimodality imaging approach to WS, describe optimal follow up and elaborate on management.

Perineal Metastatic Clear Cell Renal Cell Carcinoma: A Case Report

신세포암의 회음부 전이는 매우 드물고 영상 소견은 거의 보고되지 않았다. 저자들은 투명세포 신세포암의 회음부 전이에 의한 회음부 통증을 주소로 내원한 76세 여자 환자의 표적 치료 전후의 영상 소견을 보고하고자 한다.

Differentiation of Clear Cell Renal Cell Carcinoma from other Renal Cell Carcinoma Subtypes and Benign Oncocytoma Using Quantitative MDCT Enhancement Parameters

Background and objectives: The use of non-invasive techniques to predict the histological type of renal masses can avoid a renal mass biopsy, thus being of great clinical interest. The aim of our study was to assess if quantitative multiphasic multidetector computed tomography (MDCT) enhancement patterns of renal masses (malignant and benign) may be useful to enable lesion differentiation by their enhancement characteristics. Materials and Methods: A total of 154 renal tumors were retrospectively analyzed with a four-phase MDCT protocol. We studied attenuation values using the values within the most avidly enhancing portion of the tumor (2D analysis) and within the whole tumor volume (3D analysis). A region of interest (ROI) was also placed in the adjacent uninvolved renal cortex to calculate the relative tumor enhancement ratio. Results: Significant differences were noted in enhancement and de-enhancement (diminution of attenuation measurements between the postcontrast phases) values by histology. The highest areas under the receiver operating characteristic curves (AUCs) of 0.976 (95% CI: 0.924–0.995) and 0.827 (95% CI: 0.752–0.887), respectively, were demonstrated between clear cell renal cell carcinoma (ccRCC) and papillary RCC (pRCC)/oncocytoma. The 3D analysis allowed the differentiation of ccRCC from chromophobe RCC (chrRCC) with a AUC of 0.643 (95% CI: 0.555–0.724). Wash-out values proved useful only for discrimination between ccRCC and oncocytoma (43.34 vs 64.10, p < 0.001). However, the relative tumor enhancement ratio (corticomedullary (CM) and nephrographic phases) proved useful for discrimination between ccRCC, pRCC, and chrRCC, with the values from the CM phase having higher AUCs of 0.973 (95% CI: 0.929–0.993) and 0.799 (95% CI: 0.721–0.864), respectively. Conclusions: Our observations point out that imaging features may contribute to providing prognostic information helpful in the management strategy of renal masses.

Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

Purpose

To explore the value of CT-enhanced quantitative features combined with machine learning for differential diagnosis of renal chromophobe cell carcinoma (chRCC) and renal oncocytoma (RO).

Methods

Sixty-one cases of renal tumors (chRCC = 44; RO = 17) that were pathologically confirmed at our hospital between 2008 and 2018 were retrospectively analyzed. All patients had undergone preoperative enhanced CT scans including the corticomedullary (CMP), nephrographic (NP), and excretory phases (EP) of contrast enhancement. Volumes of interest (VOIs), including lesions on the images, were manually delineated using the RadCloud platform. A LASSO regression algorithm was used to screen the image features extracted from all VOIs. Five machine learning classifications were trained to distinguish chRCC from RO by using a fivefold cross-validation strategy. The performance of the classifier was mainly evaluated by areas under the receiver operating characteristic (ROC) curve and accuracy.

Results

In total, 1029 features were extracted from CMP, NP, and EP. The LASSO regression algorithm was used to screen out the four, four, and six best features, respectively, and eight features were selected when CMP and NP were combined. All five classifiers had good diagnostic performance, with area under the curve (AUC) values greater than 0.850, and support vector machine (SVM) classifier showed a diagnostic accuracy of 0.945 (AUC 0.964 ± 0.054; sensitivity 0.999; specificity 0.800), showing the best performance.

Conclusions

Accurate preoperative differential diagnosis of chRCC and RO can be facilitated by a combination of CT-enhanced quantitative features and machine learning.

Effect of phase of enhancement on texture analysis in renal masses evaluated with non-contrast-enhanced, corticomedullary, and nephrographic phase-enhanced CT images

OBJECTIVE

To compare texture analysis (TA) features of solid renal masses on renal protocol (non-contrast enhanced [NECT], corticomedullary [CM], nephrographic [NG]) CT.

MATERIALS AND METHODS

A total of 177 consecutive solid renal masses (116 renal cell carcinoma [RCC]; 51 clear cell [cc], 40 papillary, 25 chromophobe, and 61 benign masses; 49 oncocytomas, 12 fat-poor angiomyolipomas) with three-phase CT between 2012 and 2017 were studied. Two blinded radiologists independently assessed tumor heterogeneity (5-point Likert scale) and segmented tumors. TA features (N = 25) were compared between groups and between phases. Accuracy (area under the curve [AUC]) for RCC versus benign and cc-RCC versus other masses was compared.

RESULTS

Subjectively, tumor heterogeneity differed between phases (p < 0.01) and between tumors within the same phase (p = 0.03 [NECT] and p < 0.01 [CM, NG]). Inter-observer agreement was moderate to substantial (intraclass correlation coefficient = 0.55-0.73). TA differed in 92.0% (23/25) features between phases (p < 0.05) except for GLNU and f6. More TA features differed significantly on CM (80.0% [20/25]) compared with NG (40.0% [10/25]) and NECT (16.0% [4/25]) (p < 0.01). For RCC versus benign, AUCs of texture features did not differ comparing CM and NG (p > 0.05), but were higher for 20% (5/25) and 28% (7/25) of features comparing CM and NG with NECT (p < 0.05). For cc-RCC versus other, 36% (9/25) and 40% (10/25) features on CM had higher AUCs compared with NECT and NG images (p < 0.05).

CONCLUSION

Texture analysis of renal masses differs, when evaluated subjectively and quantitatively, by phase of CT enhancement. The corticomedullary phase had the highest discriminatory value when comparing masses and for differentiating cc-RCC from other masses.

KEY POINTS

• Subjectively evaluated renal tumor heterogeneity on CT differs by phase of enhancement. • Quantitative CT texture analysis features in renal tumors differ by phases of enhancement with the corticomedullary phase showing the highest number and most significant differences compared with non-contrast-enhanced and nephrographic phase images. • For diagnosis of clear cell RCC, corticomedullary phase texture analysis features had improved accuracy of classification in approximately 40% of features studied compared with non-contrast-enhanced and nephrographic phase images.

Importance of phase enhancement for machine learning classification of solid renal masses using texture analysis features at multi-phasic CT

OBJECTIVE

To compare machine learning (ML) of texture analysis (TA) features for classification of solid renal masses on non-contrast-enhanced CT (NCCT), corticomedullary (CM) and nephrographic (NG) phase contrast-enhanced (CE) CT.

MATERIALS AND METHODS

With IRB approval, we retrospectively identified 177 consecutive solid renal masses (116 renal cell carcinoma [RCC]; 51 clear cell [cc], 40 papillary, 25 chromophobe and 61 benign tumors; 49 oncocytomas and 12 fat-poor angiomyolipomas) with renal protocol CT between 2012 and 2017. Tumors were independently segmented by two blinded radiologists. Twenty-five 2-dimensional TA features were extracted from each phase. Diagnostic accuracy for 1) RCC versus benign tumor and 2) cc-RCC versus other tumor was assessed using XGBoost.

RESULTS

ML of texture analysis features on different phases achieved mean area under the ROC curve (AUC [SD]), sensitivity/specificity for 1) RCC vs benign = 0.70(0.19), 96%/32% on CM-CECT and 0.71(0.14), 83%/58% on NG-CECT and; 2) cc-RCC vs other = 0.77(0.12), 49%/90% on CM-CECT and 0.71(0.16), 22%/94% on NG-CECT. There was no difference in AUC comparing CECT to NCCT (p = 0.058-0.54) and no improvement when combining data across all three phases compared single-phase assessment (p = 0.39-0.68) for either outcome. AUCs decreased when ML models were trained with one phase and tested on a different phase for both outcomes (RCC;p = 0.045-0.106, cc-RCC; < 0.001).

CONCLUSION

Accuracy of machine learning classification of renal masses using texture analysis features did not depend on phase; however, models trained using one phase performed worse when tested on another phase particularly when associating NCCT and CECT. These findings have implications for large registries which use varying CT protocols to study renal masses.

Usefulness of multidetector computed tomography to differentiate between renal cell carcinoma and oncocytoma. A model validation

OBJECTIVE

The purpose of this study is to validate a multivariable predictive model previously developed to differentiate between renal cell carcinoma (RCC) and oncocytoma using CT parameters.

METHODS AND MATERIALS

We included 100 renal lesions with final diagnosis of RCC or oncocytoma studied before surgery with 4-phase multidetector CT (MDCT). We evaluated the characteristics of the tumors and the enhancement patterns at baseline, arterial, nephrographic and excretory MDCT phases.

RESULTS

Histopathologically 15 tumors were oncocytomas and 85 RCCs. RCCs were significantly larger (median 4.4 cm vs 2.8 cm, p = 0.006). There were significant differences in nodule attenuation in the excretory phase compared to baseline (median: 31 vs 42, p = 0.015), with RCCs having lower values. Heterogeneous enhancement patterns were also more frequent in RCCs (85.9% vs 60%, p = 0.027).Multivariable analysis showed that the independent predictors of malignancy were the enhancement pattern, with oncocytomas being more homogeneous in the nephrographic phase [Odds Ratio (OR) 0.16 (95% CI 0.03 to 0.75, p = 0.02)], nodule enhancement in the excretory phase compared to baseline, with RCCs showing lower enhancement [OR 0.96 (95% CI 0.93 to 0.99, p = 0.005)], and a size > 4 cm, with RCCs being larger [OR 5.89 (95% CI 1.10 to 31.58), p = 0.038].

CONCLUSION

The multivariable predictive model previously developed which combines different MDCT parameters, including lesion size > 4 cm, lesion enhancement in the excretory phase compared to baseline and enhancement heterogeneity, can be successfully applied to distinguish RCC from oncocytoma.

ADVANCES IN KNOWLEDGE

This study confirms that multiparametric assessment using MDCT (including parameters such as size, homogeneity and enhancement differences between the excretory and the baseline phases) can help distinguish between RCCs and oncocytomas. While it is true that this multiparametric predictive model may not always correctly classify renal tumors such as RCC or oncocytoma, it can be used to determine which patients would benefit from pre-surgical biopsy to confirm that the tumor is in fact an oncocytoma, and thereby avoid unnecessary surgical treatments.

Discrimination of papillary renal cell carcinoma from benign proteinaceous cyst based on iodine and water content on rapid kV-switching dual-energy CT

PURPOSE

We aimed to evaluate whether rapid kV-switching dual energy CT (rsDECT) can discriminate between papillary renal cell carcinoma (RCC) and benign proteinaceous cysts (BPCs) based on iodine and water content.

METHODS

Twenty-four patients with histopathologically proven papillary RCC and 38 patients with 41 BPCs were retrospectively included. Patients with BPCs were eligible for inclusion when the cysts were stable in size and appearance for at least 2 years or proved to be a cyst on ultrasound or MRI. All patients underwent delayed phase (70-90 s) rsDECT. Iodine and water content of each lesion was measured on the workstation.

RESULTS

Of papillary RCC patients, 4 (16%) were female and 20 (84%) were male. Mean tumor size was 39±20 mm. Mean iodine and water content was 2.08±0.7 mg/mL and 1021±14 mg/mL, respectively. Of BPC patients, 9 were female and 29 were male. Mean cyst size was 20±7 mm. Mean iodine and water content was 0.82±0.4 mg/mL and 1012±14 mg/mL, respectively. There were significant differences between iodine and water contents of papillary RCCs and BPCs (P < 0.001). The best cutoff of iodine content for differentiating papillary RCC from BPC was 1.21 mg/mL (area under the curve [AUC]=0.97, P < 0.001, sensitivity 96%, specificity 88%, positive predictive value [PPV] 82%, negative predictive value [NPV] 97%, accuracy 91%,); the best cutoff of water content was 1015.5 mg/mL (AUC=0.68, P = 0.016, sensitivity 83%, specificity 56%, PPV 52%, NPV 85%, accuracy 66%).

CONCLUSION

An iodine content threshold of 1.21 mg/mL accurately differentiates papillary RCC from BPCs on a single postcontrast rsDECT. Despite having a high sensitivity, water content has inferior diagnostic accuracy.

Multiparametric MR for Solid Renal Mass Characterization

Multiparametric MR provides a noninvasive means for improved differentiation between benign and malignant solid renal masses. Although most large, heterogeneous renal masses are due to renal cell carcinoma, smaller "indeterminate" renal masses are being identified on cross-sectional imaging. Although definitive diagnosis of a solid renal mass may not always be possible by MR imaging, integrated evaluation of multiple MR imaging parameters can result in concise differential diagnosis. Multiparametric MR should be considered a critical step in the triage of patients with a solid renal mass for whom treatment options are being considered in the context of morbidity, prognosis, and mortality.

Disease characterization using a partial correlation-based sample-specific network

A single-sample network (SSN) is a biological molecular network constructed from single-sample data given a reference dataset and can provide insights into the mechanisms of individual diseases and aid in the development of personalized medicine. In this study, we proposed a computational method, a partial correlation-based single-sample network (P-SSN), which not only infers a network from each single-sample data given a reference dataset but also retains the direct interactions by excluding indirect interactions (https://github.com/hyhRise/P-SSN). By applying P-SSN to analyze tumor data from the Cancer Genome Atlas and single cell data, we validated the effectiveness of P-SSN in predicting driver mutation genes (DMGs), producing network distance, identifying subtypes and further classifying single cells. In particular, P-SSN is highly effective in predicting DMGs based on single-sample data. P-SSN is also efficient for subtyping complex diseases and for clustering single cells by introducing network distance between any two samples.

Small renal masses (≤ 4 cm): differentiation of oncocytoma from renal clear cell carcinoma using ratio of lesion to cortex attenuation and aorta-lesion attenuation difference (ALAD) on contrast-enhanced CT

PURPOSE

We investigate the use of ratio of lesion to cortex (L/C) attenuation and aorta-lesion attenuation difference (ALAD) on multiphase contrast-enhanced CT to help distinguish oncocytoma from clear cell RCC in small renal masses (diameter < 4 cm).

METHODS

We retrospectively identified 76 patients that undergo CT before surgery for a suspicious small renal mass between January 2014 and December 2018 with pathological diagnosis of 21 oncocytomas (ROs), 25 clear cell RCCs, 7 chromophobe RCCs, 7 papillary RCCs, 7 multilocular cystic RCCs, 7 angiomyolipomas and 2 leiomyomas. CT attenuation values were obtained for the tumor, the normal renal cortex and the aorta, placing a circular region of interest (ROI) in the same slice by two radiologists, independently.

RESULTS

In the corticomedullary phase, ROs showed isodense enhancement to the renal cortex (ratio L/C 0.92 ± 0.12), while clear cell RCCs appeared hypodense to the renal cortex (ratio L/C 0.69 ± 0.20; p < 0.01) with an accuracy of 80% for diagnosing RO. In nephrographic phase, the ratio L/C attenuation was lower than the corticomedullary phase in ROs (0.78 ± 0.11) showing an early washout pattern, while the ratio L/C was similar to the corticomedullary phase in clear cell RCCs (0.69 ± 0.13; p = 0.025, with an accuracy of 65% for diagnosing RO). The ratio L/C attenuation showed considerable overlap between ROs and clear cell RCCs in the excretory phase (p = 0.27). Mean ALAD values in the nephrographic phase were 21.95 ± 16.24 for ROs and 36.96 ± 30.53 for clear cell RCCs (p = 0.049).

CONCLUSION

The ratio L/C attenuation in corticomedullary phase may be useful to differentiate RO from clear cell RCC.

Contrast-enhanced ultrasound findings of adult renal cell carcinoma associated with Xp11.2 translocation/TFE3 gene fusion: comparison with clear cell renal cell carcinoma and papillary renal cell carcinoma

BACKGROUND

To investigate the contrast-enhanced ultrasound (CEUS) findings of renal cell carcinoma (RCC) associated with Xp11.2 translocation/TFE3 gene fusion (Xp11.2/TFE3) in adult patients by comparison with those of clear cell RCC (ccRCC) and papillary RCC (pRCC).

METHODS

In total, 110 patients (110 renal masses) who underwent CEUS examinations were enrolled in this study. The cases included 18 Xp11.2/TFE3 RCCs, 60 ccRCCs and 32 pRCCs. All masses were confirmed by operative pathology. The CEUS imaging data of these patients were retrospectively analysed by two readers. The conventional US and CEUS features of Xp11.2/TFE3 RCC were compared with those of ccRCC and pRCC.

RESULTS

The age of the patients with Xp11.2/TFE3 RCC ranged from 20 to 68 years, with a mean age of 38.3 ± 16.3 years and a slight female predominance. The weighted kappa value that interprets the concordance between the interobserver agreement of the US and CEUS features ranged from 0.61 to 0.89. On conventional US and CEUS imaging of Xp11.2/TFE3 RCCs, the tumours were hypoechoic (6/18, 33.3%), isoechoic (8/18, 44.4%), and hyperechoic (4/18, 22.2%). The cystic component was present in 5 cases (27.8%), calcification was present in 9 cases (50.0%), and colour flow signal was present in 7 cases (38.9%). Most cases showed simultaneous wash-in (11/18, 61.1%); the peak enhancement showed hypoenhancement (6/18, 33.3%), isoenhancement (10/18, 55.6%), and hyperenhancement (2/18, 11.1%); most cases exhibited heterogeneous enhancement (12/18, 66.7%) and fast- or simultaneous-out (16/18, 88.9%); and a pseudocapsule was present in 6 cases (33.3%). In the multivariate logistic regression analysis, calcification and lower peak enhancement were more likely to be present in Xp11.2/TFE3 RCC than in ccRCC (P < 0.05), and younger age and relatively high peak enhancement were more likely to be present in Xp11.2/TFE3 RCC than in pRCC (P < 0.05). The calcification combined peak enhancement model differentiated Xp11.2/TFE3 RCC from ccRCC, and the age combined peak enhancement model differentiated Xp11.2/TFE3 RCC from pRCC with an AUC, a sensitivity and a specificity of 0.896, 94.4% and 73.3% and 0.786, 50.0% and 100.0%, respectively.

CONCLUSIONS

The specific CEUS features combined with demographic information and clinical symptoms may be helpful for differentiating Xp11.2/TFE3 RCC from ccRCC and pRCC.

Characterization of clear cell renal cell carcinoma and other renal tumors: evaluation of dual-energy CT using material-specific iodine and fat imaging

OBJECTIVE

This study aimed to assess material-specific iodine and fat images for diagnosis of clear cell renal cell carcinoma (cc-RCC) compared to papillary RCC (p-RCC) and other renal masses.

MATERIALS AND METHODS

With IRB approval, we identified histologically confirmed solid renal masses that underwent rapid-kVp-switch DECT between 2016 and 2018: 25 cc-RCC (7 low grade versus 18 high grade), 11 p-RCC, and 6 other tumors (2 clear cell papillary RCC, 2 chromophobe RCC, 1 oncocytoma, 1 renal angiomyomatous tumor). A blinded radiologist measured iodine and fat concentration on material-specific iodine-water and fat-water basis pair images. Comparisons were performed between groups using univariate analysis and diagnostic accuracy calculated by ROC.

RESULTS

Iodine concentration was higher in cc-RCC (6.14 ± 1.79 mg/mL) compared to p-RCC (1.40 ± 0.54 mg/mL, p < 0.001), but not compared to other tumors (5.0 ± 2.2 mg/mL, p = 0.370). Intratumoral fat was seen in 36.0% (9/25) cc-RCC (309.6 ± 234.3 mg/mL [71.1-762.3 ng/mL]), 9.1% (1/11) papillary RCC (97.11 mg/mL), and no other tumors (p = 0.036). Iodine concentration ≥ 3.99 mg/mL achieved AUC and sensitivity/specificity of 0.88 (CI 0.76-1.00) and 92.31%/82.40% to diagnose cc-RCC. To diagnose p-RCC, iodine concentration ≤ 2.5 mg/mL achieved AUC and sensitivity/specificity of 0.99 (0.98-1.00) and 100%/100%. The presence of intratumoral fat had AUC 0.64 (CI 0.53-0.75) and sensitivity/specificity of 34.6%/93.8% to diagnose cc-RCC. A logistic regression model combining iodine concentration and presence of fat increased AUC to 0.91 (CI 0.81-1.0) with sensitivity/specificity of 80.8%/93.8% to diagnose cc-RCC.

CONCLUSION

Iodine concentration values are highly accurate to differentiate clear cell RCC from papillary RCC; however, they overlap with other tumors. Fat-specific images may improve differentiation of clear cell RCC from other avidly enhancing tumors.

KEY POINTS

• Clear cell renal cell carcinoma (RCC) has significantly higher iodine concentration than papillary RCC, but there is an overlap in values comparing clear cell RCC to other renal tumors. • Iodine concentration ≤ 2.5 mg/mL is highly accurate to differentiate papillary RCC from clear cell RCC and other renal tumors. • The presence of microscopic fat on material-specific fat images was specific for clear cell RCC, helping to differentiate clear cell RCC from other avidly enhancing renal tumors.

Efficient Gaussian sample specific network marker discovery and drug enrichment analysis validation

Identifying stable gene markers at an individual level can help to understand the genetic mechanisms of each individual patient and accomplish personalized medicine. In this paper, we propose an efficient framework to identify sample-specific markers. Gene expression data first is transformed to a corresponding likelihood matrix to alleviate inherent noise besides adding population information to each sample. Then those significantly differential genes or gene pairs are further mapped to a STRING network for analysis by assuming that the likelihood of each gene or gene pairs in the control group follows a Gaussian distribution. The proposed method is applied to three benchmark datasets including lung adenocarcinoma, kidney renal clear cell carcinoma, and uterine corpus endometrial carcinoma. It is found that disease gene markers identified by the proposed methods outperform the previous sample-specific network (SSN) method in both subtyping and survival analysis. Furthermore, we exploit the application of the subtype markers in following drug selection. The difference of the enriched drug set may reflect some underlying mechanisms of the subtypes and shed light on selecting appropriate drugs for each cancer subtype.

Surgical margin follow-up after nephron-sparing surgery: the possible role of CEUS

PURPOSE

To evaluate the possible role of CEUS in the management of patients who underwent nephron-sparing surgery (NSS) and presented questionable findings on the surgical margins at the CECT follow-up exam.

METHODS

In our retro-prospective study, we included 952 patients with small renal masses (SRMs) treated with NSS between 2012 and 2015 and followed with CECT for at least 3 years at Careggi University Hospital. Twenty-two of them presented solid masses on the site of surgery with questionable enhancement at CECT and were further studied with CEUS. This examination was followed by a quantitative analysis of the enhancement pattern.

RESULTS

Out of the 22 masses, 18 were considered possible granulomas, presenting slow wash-in and low enhancement peaks compared to the surrounding parenchyma and persistent delayed wash-out at CEUS. Four lesions presented a suspicious malignant enhancement pattern, with rapid wash-in, high peak and rapid wash-out. In accordance with instructions from the urologist, the first group of 18 patients was strictly monitored, revealing that the mass dimensions and enhancement pattern were stable for at least 3 years of follow-up, while the other 4 patients underwent a second intervention and their masses were confirmed as tumor recurrence at the histopathological evaluation.

CONCLUSIONS

CEUS can play a key role in the surgical margin follow-up after NSS when a suspicious enhancing mass is detected by CECT, giving an accurate depiction of the enhancement pattern and thus helping the clinician in the management of the patient.

FORCE dual-energy CT in pathological grading of clear cell renal cell carcinoma

The aim of the present study was to examine the value of FORCE dual-energy CT in grading the clear cell renal cell carcinoma (ccRCC). A total of 35 cases of ccRCC were included. Hematoxylin and eosin staining was performed, and the cases were divided into low- (Fuhrman I-II) and high-grade (Fuhrman III-IV) groups. FORCE dual-energy CT parameters, including virtual network computing CT value (VNCV), iodine overlay value (IOV), mixed energy CT value (MEV), iodine concentration (IC), normalized iodine concentration (NIC), NIC based on aorta (NICA), NIC based on cortex (NICC) and NIC based on medulla (NICM), were analyzed and compared. Receiver operating characteristic analysis was also performed. There were significant differences in the arterial phase IOV, MEV and IC, and the venous phase IOV and IC between the low- and high-grade groups. No significant differences were observed in VNCV and MEV between the low -and high-grade groups in the venous phase. Significant differences were observed in the NICA and NICC between these two groups, however no difference was observed in NICM. There were significant differences in the tumor CT values for the arterial phase at the 40, 60, 80 and 100 kiloelectron volt (keV) between the low- and high-grade groups, while no significant differences were observed at the 120-140 keV levels. The k-slope for the low-grade group was significantly higher than the high-grade group. In addition, the area under curve for the arterial phase IOV, arterial phase MEV, arterial phase IC, aortic NIC, cortical NIC, venous phase IOV, venous phase IC and curve slope K of mono-energy CT value suggested high value in diagnosis of low- and high-grade ccRCC cases.