Multiphasic enhancement patterns of small renal masses (≤4 cm) on preoperative computed tomography: utility for distinguishing subtypes of renal cell carcinoma, angiomyolipoma, and oncocytoma

Lack of effect of renal function on uptake of 99m Tc-sestamibi in renal masses

OBJECTIVE

The appropriate clinical management of indeterminate small renal masses can be improved based on accurate risk stratification. This study aimed to investigate the impact of renal function on the uptake of technetium-99m ( 99m Tc)-sestamibi, a widely available imaging agent that can be utilized to identify oncocytomas and other benign/indolent renal masses.

METHODS

A retrospective cohort study was conducted, involving 100 consecutive patients who underwent 99m Tc-sestamibi single-photon emission computed tomography/computed tomography. Renal function was evaluated based on creatinine levels and glomerular filtration rate (GFR). Statistical analyses, including correlation and regression analyses, were performed to explore the relationship between renal function and 99m Tc-sestamibi uptake.

RESULTS

The results revealed that discrepancies can occur between imaging and pathology results, emphasizing the need for careful interpretation of imaging findings. Correlation analysis demonstrated no significant correlation between relative tumor uptake of 99m Tc-sestamibi and GFR or serum creatinine levels. These findings highlight the relative independence of renal function and 99m Tc-sestamibi uptake and suggest sestamibi may be of use even in patients with relatively poor renal function.

CONCLUSIONS

However, further research with larger sample sizes is required to validate and generalize these findings. Ultimately, understanding the broad variety of applicability of 99m Tc-sestamibi uptake for risk stratification of renal malignancy could aid in appropriate clinical decision-making.

Multimodality Imaging Features of Papillary Renal Cell Carcinoma

Objectives: To describe the US, CEUS, CT, and MRI features of papillary renal cell carcinoma (PRCC) and to underline the imaging characteristics that are helpful in the differential diagnosis. Methods: Patients with histologically proven papillary renal cell carcinoma who underwent at least two imaging examinations (US, CEUS, CT, and MRI) were included in the study. Tumor size, homogeneity, morphology, perilesional stranding, contrast enhancement locoregional extension were assessed. A comparison and the characteristics of the imaging features for each imaging modality were analyzed. Results: A total of 27 patients with an histologically confirmed diagnosis of PRCC were included in the study. US was highly accurate in distinguishing solid masses from cystic masses, supporting the differential diagnosis of PRCC, as well as in patients with a poor representation of the solid component. CEUS significantly increased diagnostic accuracy in delineating the solid intralesional component. Furthermore, when using CEUS, in the arterial phase, PRCC exhibited hypo-enhancement, and in the late phase it showed an inhomogeneous and delayed wash-out compared with the surrounding renal parenchyma. At MRI, PRCC showed a marked restiction of DWI and was hypointense in the T2-weighted compared to the renal parenchyma. Conclusions: In our study, the characteristic hypodensity and hypoenhancement of PRCC make CT the weakest method of their recognition, while US/CEUS and MRI are necessary to reach a definitive diagnosis. Knowledge of the appearance of PRCC can support an early diagnosis and prompt management, and radiologists should be aware that PRCC, when detected using CT, may resemble spurious non-septate renal cyst.

Fully automated segmentation and classification of renal tumors on CT scans via machine learning

BACKGROUND

To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.

MATERIALS AND METHODS

The model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas. Renal tumor masks manually drawn on contrast-enhanced CT images were used to develop a 3D U-Net-based deep learning model for fully automated tumor segmentation. After segmentation, the entire classification pipeline, including feature extraction and subtype classification, was conducted without any manual intervention. Both conventional radiological features (Hounsfield units, HUs) and radiomic features extracted from areas predicted by the deep learning models were used to develop an algorithm for classifying renal tumor subtypes via a random forest classifier. The performance of the segmentation model was evaluated using the Dice similarity coefficient, while the classification model was assessed based on accuracy, sensitivity, and specificity.

RESULTS

For tumors larger than 4 cm, the Dice similarity coefficient (DSC) for automated segmentation was 0.83, while for tumors smaller than 4 cm, the DSC was 0.65. The classification accuracy (ACC) for distinguishing RCC subtypes was 0.77 for tumors larger than 4 cm and 0.68 for tumors smaller than 4 cm. Additionally, the accuracy for benign versus malignant classification was 0.85.

CONCLUSIONS

Our automatic segmentation and classifier model showed promising results for renal tumor segmentation and classification.

Radiomics and machine learning for renal tumor subtype assessment using multiphase computed tomography in a multicenter setting

OBJECTIVES

To distinguish histological subtypes of renal tumors using radiomic features and machine learning (ML) based on multiphase computed tomography (CT).

MATERIAL AND METHODS

Patients who underwent surgical treatment for renal tumors at two tertiary centers from 2012 to 2022 were included retrospectively. Preoperative arterial (corticomedullary) and venous (nephrogenic) phase CT scans from these centers, as well as from external imaging facilities, were manually segmented, and standardized radiomic features were extracted. Following preprocessing and addressing the class imbalance, a ML algorithm based on extreme gradient boosting trees (XGB) was employed to predict renal tumor subtypes using 10-fold cross-validation. The evaluation was conducted using the multiclass area under the receiver operating characteristic curve (AUC). Algorithms were trained on data from one center and independently tested on data from the other center.

RESULTS

The training cohort comprised n = 297 patients (64.3% clear cell renal cell cancer [RCC], 13.5% papillary renal cell carcinoma (pRCC), 7.4% chromophobe RCC, 9.4% oncocytomas, and 5.4% angiomyolipomas (AML)), and the testing cohort n = 121 patients (56.2%/16.5%/3.3%/21.5%/2.5%). The XGB algorithm demonstrated a diagnostic performance of AUC = 0.81/0.64/0.8 for venous/arterial/combined contrast phase CT in the training cohort, and AUC = 0.75/0.67/0.75 in the independent testing cohort. In pairwise comparisons, the lowest diagnostic accuracy was evident for the identification of oncocytomas (AUC = 0.57-0.69), and the highest for the identification of AMLs (AUC = 0.9-0.94) CONCLUSION: Radiomic feature analyses can distinguish renal tumor subtypes on routinely acquired CTs, with oncocytomas being the hardest subtype to identify.

CLINICAL RELEVANCE STATEMENT

Radiomic feature analyses yield robust results for renal tumor assessment on routine CTs. Although radiologists routinely rely on arterial phase CT for renal tumor assessment and operative planning, radiomic features derived from arterial phase did not improve the accuracy of renal tumor subtype identification in our cohort.

Imaging of Chromophobe Renal Cell Carcinoma with 99mTc-Sestamibi SPECT/CT: Considerations Regarding Risk Stratification and Histologic Reclassification

PURPOSE

Indeterminate renal masses are increasingly incidentally found on cross-sectional imaging. 99mTc-sestamibi single-photon emission computed tomography/computed tomography (SPECT/CT) scans can be used to identify oncocytomas and oncocytic renal neoplasms, including a subset of chromophobe renal cell carcinomas (chRCCs), which are viewed as false-positive.

PROCEDURE

Patients imaged with renal sestamibi scans between 2014 and 2023 were reviewed. Those patients with solitary tumors that were originally classified as chRCC were included in the analysis. Imaging with SPECT/CT from the liver dome down had been carried out 75 min after the administration of 925 MBq of 99mTc-sestamibi. All available H&E and immunostained slides were re-reviewed and classified according to WHO 2022 criteria. Confirmatory immunohistochemical stains were performed in tumors considered morphologically suspicious for non-chRCC entities.

RESULT

A total of 18 patients with solitary tumors were included in the final analysis. 13/18 (72.2%) tumors in this cohort remained classified as chRCC, with 4/18 (22.2%) being eosinophilic-variant chRCC. The reclassified tumors (5/18 [27.8%]) included 2/18 (11.1%) low-grade oncocytic tumor (LOT), 1/18 (5.5%) eosinophilic vacuolated tumor (EVT), and 2/18 (11.1%) unclassified low-grade oncocytic neoplasms. As such, only 2/9 (22.2%) qualitatively "hot" tumors were chRCC other than eosinophilic-variant and only 1/9 (11.1%) "cold" tumors was a histology other than chRCC.

CONCLUSION

Based on current histopathologic classification methods, it is likely that the "false-positive" rate of uptake on renal sestamibi scans with chRCC has been over-stated. Further study is warranted to better refine the optimal utility of renal sestamibi scans for non-invasive risk stratification of indeterminate renal masses.

Molecular imaging for non-invasive risk stratification of renal masses

Anatomic imaging with contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI) has long been the mainstay of renal mass characterization. However, those modalities are often unable to adequately characterize indeterminate, solid, enhancing renal masses - with some exceptions, such as the development of the clear-cell likelihood score on multi-parametric MRI. As such, molecular imaging approaches have gained traction as an alternative to anatomic imaging. Mitochondrial imaging with 99mTc-sestamibi single-photon emission computed tomography/CT is a cost-effective means of non-invasively identifying oncocytomas and other indolent renal masses. On the other end of the spectrum, carbonic anhydrase IX agents, most notably the monoclonal antibody girentuximab - which can be labeled with positron emission tomography radionuclides such as zirconium-89 - are effective at identifying renal masses that are likely to be aggressive clear cell renal cell carcinomas. Renal mass biopsy, which has a relatively high non-diagnostic rate and does not definitively characterize many oncocytic neoplasms, nonetheless may play an important role in any algorithm targeted to renal mass risk stratification. The combination of molecular imaging and biopsy in selected patients with other advanced imaging methods, such as artificial intelligence/machine learning and the abstraction of radiomics features, offers the optimal way forward for maximization of the information to be gained from risk stratification of indeterminate renal masses. With the proper application of those methods, inappropriately aggressive therapy for benign and indolent renal masses may be curtailed.

Hibernoma in the renal sinus: A case mimicking malignancy

Introduction

Hibernomas are benign tumors of brown adipose tissue. Hibernoma in the renal sinus is extremely rare. Herein, we present the third known case of renal hibernoma.

Case presentation

A 71-year-old man reported to our department with a left kidney tumor with an average growth rate of 5 mm/year and a progressive contrast effect on computed tomography. It was diagnosed as a hibernoma following a laparoscopic radical nephrectomy.

Conclusion

We encountered a rare case of a hibernoma in the renal sinus. Development of new and accurate diagnostic methods for hibernoma, without resorting to nephrectomy, is essential.

PET/Computed Tomography Transformation of Oncology: Kidney and Urinary Tract Cancers

Renal cell carcinoma (RCC) and urothelial carcinoma (UC) are two of the most common genitourinary malignancies. 2-deoxy-2-[18F]fluoro-d-glucose (18F-FDG) can play an important role in the evaluation of patients with RCC and UC. In addition to the clinical utility of 18F-FDG PET to evaluate for metastatic RCC or UC, the shift in molecular imaging to focus on specific ligand-receptor interactions should provide novel diagnostic and therapeutic opportunities in genitourinary malignancies. In combination with the rise of artificial intelligence, our ability to derive imaging biomarkers that are associated with treatment selection, response assessment, and overall patient prognostication will only improve.

99mTc-MIBI SPECT/CT Evaluation of a Renal Collision Tumor

ABSTRACT

Preoperative differentiation of oncocytomas from renal cell carcinoma (RCC) is often challenging. 99mTc-MIBI imaging could play a potential role in differentiating oncocytoma from RCC, which in turn could guide surgical decision-making. We present the use of 99mTc-MIBI SPECT/CT to characterize a renal mass in a 66-year-old man with a complex medical history, including history of bilateral oncocytomas. 99mTc-MIBI SPECT/CT showed features suspicious of a malignant tumor, which was confirmed postnephrectomy as a chromophobe and papillary RCC collision tumor. This case supports 99mTc-MIBI imaging for preoperative differentiation of benign versus malignant renal tumors.

Contemporary Clinical Definitions, Differential Diagnosis, and Novel Predictive Tools for Renal Cell Carcinoma

Despite significant progress regarding clinical detection/imaging evaluation modalities and genetic/molecular characterization of pathogenesis, advanced renal cell carcinoma (RCC) remains an incurable disease and overall RCC mortality has been steadily rising for decades. Concomitantly, clinical definitions have been greatly nuanced and refined. RCCs are currently viewed as a heterogeneous series of cancers, with the same anatomical origin, but fundamentally different metabolisms and clinical behaviors. Thus, RCC pathological diagnosis/subtyping guidelines have become increasingly intricate and cumbersome, routinely requiring ancillary studies, mainly immunohistochemistry. Meanwhile, RCC-associated-antigen targeted systemic therapy has been greatly diversified and emerging, novel clinical applications for RCC immunotherapy have already reported significant survival benefits, at least in the adjuvant setting. Even so, systemically disseminated RCCs still associate very poor clinical outcomes, with currently available therapeutic modalities only being able to prolong survival. In lack of a definitive cure for advanced RCCs, integration of the amounting scientific knowledge regarding RCC pathogenesis into RCC clinical management has been paramount for improving patient outcomes. The current review aims to offer an integrative perspective regarding contemporary RCC clinical definitions, proper RCC clinical work-up at initial diagnosis (semiology and multimodal imaging), RCC pathological evaluation, differential diagnosis/subtyping protocols, and novel clinical tools for RCC screening, risk stratification and therapeutic response prediction.

The value of CT features and demographic data in the differential diagnosis of type 2 papillary renal cell carcinoma from fat-poor angiomyolipoma and oncocytoma

PURPOSES

To determine the CT features and demographic data predictive of type 2 papillary renal cell carcinoma (PRCC) that can help distinguish this neoplasm from fat-poor angiomyolipoma (fpAML) and oncocytoma.

METHODS

Fifty-four patients with type 2 PRCC, 48 with fpAML, and 47 with oncocytoma in the kidney from multiple centers were retrospectively reviewed. The demographic data and CT features of type 2 PRCC were analyzed and compared with those of fpAML and oncocytoma by univariate analysis and multiple logistic regression analysis to determine the predictive factors for differential diagnosis. Then, receiver operating characteristic (ROC) curve analysis was performed to further assess the logistic regression model and set the threshold level values of the numerical parameters.

RESULTS

Older age (≥ 46.5 years), unenhanced lesion-to-renal cortex attenuation (RLRCA) < 1.21, corticomedullary ratio of lesion to renal cortex net enhancement (RLRCNE) < 0.32, and size ≥ 30.1 mm were independent predictors for distinguishing type 2 PRCC from fpAML (OR 14.155, 8.332, and 57.745, respectively, P < 0.05 for all). The area under the curve (AUC) of the multiple logistic regression model in the ROC curve analysis was 0.970. In the combined evaluation, the four independent predictors had a sensitivity and specificity of 0.896 and 0.889, respectively. A corticomedullary RLRCNE < 0.61, irregular shape, and male sex were independent predictors for the differential diagnosis of type 2 PRCC from oncocytoma (OR 15.714, 12.158, and 6.175, respectively, P < 0.05 for all). In the combined evaluation, the three independent predictors had a sensitivity and specificity of 0.889 and 0.979, respectively. The AUC of the multiple logistic regression model in the ROC curve analysis was 0.964.

CONCLUSION

The combined application of CT features and demographic data had good ability in distinguishing type 2 PRCC from fpAML and oncocytoma, respectively.

Molecular imaging in oncology: Current impact and future directions

The authors define molecular imaging, according to the Society of Nuclear Medicine and Molecular Imaging, as the visualization, characterization, and measurement of biological processes at the molecular and cellular levels in humans and other living systems. Although practiced for many years clinically in nuclear medicine, expansion to other imaging modalities began roughly 25 years ago and has accelerated since. That acceleration derives from the continual appearance of new and highly relevant animal models of human disease, increasingly sensitive imaging devices, high-throughput methods to discover and optimize affinity agents to key cellular targets, new ways to manipulate genetic material, and expanded use of cloud computing. Greater interest by scientists in allied fields, such as chemistry, biomedical engineering, and immunology, as well as increased attention by the pharmaceutical industry, have likewise contributed to the boom in activity in recent years. Whereas researchers and clinicians have applied molecular imaging to a variety of physiologic processes and disease states, here, the authors focus on oncology, arguably where it has made its greatest impact. The main purpose of imaging in oncology is early detection to enable interception if not prevention of full-blown disease, such as the appearance of metastases. Because biochemical changes occur before changes in anatomy, molecular imaging-particularly when combined with liquid biopsy for screening purposes-promises especially early localization of disease for optimum management. Here, the authors introduce the ways and indications in which molecular imaging can be undertaken, the tools used and under development, and near-term challenges and opportunities in oncology.

Diagnosis and Treatment of Small Renal Masses: Where Do We Stand?

PURPOSE OF REVIEW

To present an overview of the current evidence-based studies covering diagnostic and management of SRM.

RECENT FINDINGS

Renal cell carcinoma (RCC) represents 3% of the cancers. Nowadays, partial nephrectomy (PN) represents gold standard treatment. New nephron-sparing approaches such as active surveillance and ablative therapies have been increasingly used as an alternative to surgical intervention. Due to novel comprehension of RCC and widespread use of imaging techniques, diagnosis at early stage in elderly patients has increased. Treatment decision-making should be based on patient and tumour characteristics. With expanding treatment options, the management of SRMs has become a debate and should be adjusted to patient and tumour characteristics. In a shared decision manner, both active surveillance with possible delayed intervention and focal therapy should be discussed with the patient as an alternative to partial nephrectomy.

Role of PSMA-ligands imaging in Renal Cell Carcinoma management: current status and future perspectives

Background

Renal masses detection is continually increasing worldwide, with Renal Cell Carcinoma (RCC) accounting for approximately 90% of all renal cancers and remaining one of the most aggressive urological malignancies. Despite improvements in cancer management, accurate diagnosis and treatment strategy of RCC by computed tomography (CT) and magnetic resonance imaging (MRI) are still challenging. Prostate-Specific Membrane Antigen (PSMA) is known to be highly expressed on the endothelial cells of the neovasculature of several solid tumors other than prostate cancer, including RCC. In this context, recent preliminary studies reported a promising role for positron emission tomography (PET)/CT with radiolabeled molecules targeting PSMA, in alternative to fluorodeoxyglucose (FDG) in RCC patients.

Purpose

The aim of our review is to provide an updated overview of current evidences and major limitations regarding the use of PSMA PET/CT in RCC.

Methods

A literature search, up to 31 December 2021, was performed using the following electronic databases: PubMed, SCOPUS, Web of Science, and Google Scholar.

Results

The findings of this review suggest that PSMA PET/CT could represent a valid imaging option for diagnosis, staging, and therapy response evaluation in RCC, particularly in clear cell RCC.

Conclusions

Further studies are needed for this “relatively” new imaging modality to consolidate its indications, timing, and practical procedures.

Renal Cell Carcinoma or Oncocytoma? The Contribution of Diffusion-Weighted Magnetic Resonance Imaging to the Differential Diagnosis of Renal Masses

Background and Objectives: Renal Cell Carcinoma (RCC) accounts for 85% and oncocytomas constitute 3–7% of solid renal masses. Oncocytomas can be confused, especially with hypovascular RCC. The purpose of this research was to evaluate the contribution of diffusion-weighted imaging (DWI) and contrast-enhanced MRI sequences in the differential diagnosis of RCC and oncocytoma Materials and Methods: 465 patients with the diagnosis of RCC and 45 patients diagnosed with oncocytoma were retrospectively reviewed between 2009 to 2020. All MRI acquisitions were handled by a 1.5 T device (Achieva, Philips Healthcare, Best, The Netherlands) and all images were evaluated by the consensus of two radiologists with 10–15 years’ experience. The SPSS package program version 15.0 software was used for statistical analysis of the study. Chi-square test, Mann–Whitney U test or the Kruskal–Wallis tests were used in the statistical analysis. A receiver operating characteristic (ROC) curve was used to calculate the cut-off values Results: The results were evaluated with a 95% confidence interval and a significance threshold of p < 0.05. ADC values (p < 0.001) and enhancement index (p < 0.01) were significantly lower in the RCC group than the oncocytoma group. Conclusion: DWI might become an alternative technique to the contrast-enhanced MRI in patients with contrast agent nephropathy or with a high risk of nephrogenic systemic fibrosis, calculation of CI of the oncocytoma and RCCs in the contrast-enhanced acquisitions would contribute to the differential diagnosis.

Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC)

PURPOSE

The purpose of this study was to evaluate the use of CT radiomics features and machine learning analysis to identify aggressive tumor features, including high nuclear grade (NG) and sarcomatoid (sarc) features, in large renal cell carcinomas (RCCs).

METHODS

CT-based volumetric radiomics analysis was performed on non-contrast (NC) and portal venous (PV) phase multidetector computed tomography images of large (> 7 cm) untreated RCCs in 141 patients (46W/95M, mean age 60 years). Machine learning analysis was applied to the extracted radiomics data to evaluate for association with high NG (grade 3-4), with multichannel analysis for NG performed in a subset of patients (n = 80). A similar analysis was performed in a sarcomatoid rich cohort (n = 43, 31M/12F, mean age 63.7 years) using size-matched non-sarcomatoid controls (n = 49) for identification of sarcomatoid change.

RESULTS

The XG Boost Model performed best on the tested data. After manual and machine feature extraction, models consisted of 3, 7, 5, 10 radiomics features for NC sarc, PV sarc, NC NG and PV NG, respectively. The area under the receiver operating characteristic curve (AUC) for these models was 0.59, 0.65, 0.69 and 0.58 respectively. The multichannel NG model extracted 6 radiomic features using the feature selection strategy and showed an AUC of 0.67.

CONCLUSIONS

Statistically significant but weak associations between aggressive tumor features (high nuclear grade, sarcomatoid features) in large RCC were identified using 3D radiomics and machine learning analysis.

CT-based multi-phase Radiomic models for differentiating clear cell renal cell carcinoma

Background

The aim of the study is to compare the diagnostic value of models that based on a set of CT texture and non-texture features for differentiating clear cell renal cell carcinomas(ccRCCs) from non-clear cell renal cell carcinomas(non-ccRCCs).

Methods

A total of 197 pathologically proven renal tumors were divided into ccRCC(n = 143) and non-ccRCC (n = 54) groups. The 43 non-texture features and 296 texture features that extracted from the 3D volume tumor tissue were assessed for each tumor at both Non-contrast Phase, NCP; Corticomedullary Phase, CMP; Nephrographic Phase, NP and Excretory Phase, EP. Texture-score were calculated by the Least Absolute Shrinkage and Selection Operator (LASSO) to screen the most valuable texture features. Model 1 contains the three most distinctive non-texture features with p < 0.001, Model 2 contains texture scores, and Model 3 contains the above two types of features.

Results

The three models shown good discrimination of the ccRCC from non-ccRCC in NCP, CMP, NP, and EP. The area under receiver operating characteristic curve (AUC)values of the Model 1, Model 2, and Model 3 in differentiating the two groups were 0.748–0.823, 0.776–0.887 and 0.864–0.900, respectively. The difference in AUC between every two of the three Models was statistically significant (p < 0.001).

Conclusions

The predictive efficacy of ccRCC was significantly improved by combining non-texture features and texture features to construct a combined diagnostic model, which could provide a reliable basis for clinical treatment options.

Comparison of Different Machine Models Based on Contrast-Enhanced Computed Tomography Radiomic Features to Differentiate High From Low Grade Clear Cell Renal Cell Carcinomas

Purpose

This study was to investigate the role of different radiomics models with enhanced computed tomography (CT) scan in differentiating low from high grade renal clear cell carcinomas.

Materials and Methods

CT data of 190 cases with pathologically confirmed renal cell carcinomas were collected and divided into the training set and testing set according to different time periods, with 122 cases in the training set and 68 cases in the testing set. The region of interest (ROI) was delineated layer by layer.

Results

A total of 402 radiomics features were extracted for analysis. Six of the radiomic parameters were deemed very valuable by univariate analysis, rank sum test, LASSO cross validation and correlation analysis. From these six features, multivariate logistic regression model, support vector machine (SVM), and decision tree model were established for analysis. The performance of each model was evaluated by AUC value on the ROC curve and decision curve analysis (DCA). Among the three prediction models, the SVM model showed a high predictive efficiency. The AUC values of the training set and the testing set were 0.84 and 0.83, respectively, which were significantly higher than those of the decision tree model and the multivariate logistic regression model. The DCA revealed a better predictive performance in the SVM model that possessed the highest degree of coincidence.

Conclusion

Radiomics analysis using the SVM radiomics model has highly efficiency in discriminating high- and low-grade clear cell renal cell carcinomas.

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.

A CT-Based Radiomics Approach for the Differential Diagnosis of Sarcomatoid and Clear Cell Renal Cell Carcinoma

This study was aimed at building a computed tomography- (CT-) based radiomics approach for the differentiation of sarcomatoid renal cell carcinoma (SRCC) and clear cell renal cell carcinoma (CCRCC). It involved 29 SRCC and 99 CCRCC patient cases, and to each case, 1029 features were collected from each of the corticomedullary phase (CMP) and nephrographic phase (NP) image. Then, features were selected by using the least absolute shrinkage and selection operator regression method and the selected features of the two phases were explored to build three radiomics approaches for SRCC and CCRCC classification. Meanwhile, subjective CT findings were filtered by univariate analysis to construct a radiomics model and further selected by Akaike information criterion for integrating with the selected image features to build the fifth model. Finally, the radiomics models utilized the multivariate logistic regression method for classification and the performance was assessed with receiver operating characteristic curve (ROC) and DeLong test. The radiomics models based on the CMP, the NP, the CMP and NP, the subjective findings, and the combined features achieved the AUC (area under the curve) value of 0.772, 0.938, 0.966, 0.792, and 0.974, respectively. Significant difference was found in AUC values between each of the CMP radiomics model (0.0001 ≤ p ≤ 0.0051) and the subjective findings model (0.0006 ≤ p ≤ 0.0079) and each of the NP radiomics model, the CMP and NP radiomics model, and the combined model. Sarcomatoid change is a common pathway of dedifferentiation likely occurring in all subtypes of renal cell carcinoma, and the CT-based radiomics approaches in this study show the potential for SRCC from CCRCC differentiation.

Prognostic Effect of Preoperative Apolipoprotein B Level in Surgical Patients with Clear Cell Renal Cell Carcinoma

BACKGROUND

The aim of this study was to assess the prognostic value of the preoperative apolipoprotein B (ApoB) level in surgical patients with clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

The study included 307 ccRCC patients receiving radical or partial nephrectomy between 2003 and 2012 in our center. The correlations among the preoperative ApoB, clinicopathological parameters, and overall survival (OS) were evaluated.

RESULTS

A total of 193 males (62.9%) and 114 females (37.1%) with ccRCC who underwent radical or partial nephrectomy were enrolled in the present study. The OS at 5 years after the operation was 90.6% for all patients, 87.4% for the lower ApoB group, and 97.0% for the higher-ApoB group. The cause-specific survival (CSS) at 5 years after surgery was 90.2% for all patients, 86.7% for the lower-ApoB group, and 97.0% for the higher-ApoB group. A higher-ApoB level was related to a better OS and CSS in ccRCC patients (p = 0.001 and p < 0.001, respectively). In multivariate analysis, age >60 years (p = 0.008 and p = 0.023) and a lower Apo B level (p = 0.019 and p = 0.018) were independent prognostic factors for OS and CSS, respectively.

CONCLUSIONS

In the Apo apolipoprotein family, the preoperative ApoB level had an important clinical significance for predicting the prognosis and survival rate of ccRCC patients.

Discriminating malignant and benign clinical T1 renal masses on computed tomography: A pragmatic radiomics and machine learning approach

The aim of this study was to discriminate malignant and benign clinical T1 renal masses on routinely acquired computed tomography (CT) images using radiomics and machine learning techniques.Adult patients undergoing surgical resection and histopathological analysis of clinical T1 renal masses were included. Preoperative CT studies in venous phase from multiple referring centers were included, without restriction to specific CT scanners, slice thickness, or degrees of artifacts. Renal masses were segmented and 120 standardized radiomic features extracted. Machine learning algorithms were used to predict malignancy of renal masses using radiomics features and cross-validation. Diagnostic accuracy of machine learning models and assessment by independent blinded radiologists were compared based on the gold standard of histopathologic diagnosis.A total of 94 patients met inclusion criteria (benign renal masses: n = 18; malignant: n = 76). CT studies from 18 different scanners were assessed with median slice thickness of 2.5 mm and artifacts in 15 cases (15.9%).Area under the receiver-operating-characteristics curve (AUC) of random forest (random forest [RF], AUC = 0.83) was significantly higher compared to the radiologists (AUC = 0.68, P = .047). Sensitivity was significantly higher for RF versus radiologists (0.88 vs 0.80, P = .045), whereas specificity was numerically higher for RF (0.67 vs 0.50, P = .083).Although limited by an overall small sample size and few benign renal tumors, a radiomic features and machine learning approach suggests a high diagnostic accuracy for discrimination of malignant and benign clinical T1 renal masses on venous phase CT. The presented algorithm robustly outperforms human readers in a real-life scenario with nonstandardized imaging studies from various referring centers.

Usefulness of contrast-enhanced ultrasonography for diagnosis of renal cell carcinoma in dialysis patients: Comparison with computed tomography

AIMS

To investigate the usefulness of contrast-enhanced ultrasonography for diagnosing renal cell carcinoma (RCC) in dialysis patients.

MATERIAL AND METHODS

Of 1301 dialysis patients who underwent abdominal computed tomography (CT) between January 2012 and March 2017, 19 were suspected to have solid renal lesions; of these patients, 18 gave consent for and underwent contrast-enhanced ultrasonography with perflubutane in addition to CT; 13 underwent dynamic contrast-enhanced CT, and 5, who could not be administered iodinated contrast media, underwent unenhanced CT. The final diagnoses were based on histopathological findings or the presence/absence of enlargement of the lesion during follow-up.

RESULTS

Of the 19 lesions in 18 patients, 14 were diagnosed as RCC and 5 as benign cysts. CT facilitated accurate diagnosis in 10/19 lesions (52.6%) with obvious enhancement (≥20 Hounsfield units [HU]), while definitive diagnosis by CT was difficult in 9 lesions: 2 lesions showed ambiguous enhancement (10-20 HU), 1 lesion was an inflammatory cyst with obvious enhancement, and 6 lesions were assessed by unenhanced CT. Compared with CT, contrast-enhanced ultrasonography allowed more accurate diagnosis (McNemar test, P = .02) in 17/19 lesions (89.5%, 14 RCC and 3 cysts; including all lesions assessed by unenhanced CT and 2 with ambiguous enhancement on CT), with 1 false-positive (inflammatory cyst with hyper-enhancement) and 1 false-negative result due to deep location of the lesion.

CONCLUSIONS

Contrast-enhanced ultrasonography was useful for the diagnosis of RCC in dialysis patients with suspected solid renal lesions especially when contrast enhancement was not obvious on CT or contrast-enhanced CT could not be performed.

Renal Cell Carcinoma: the Oncologist Asks, Can PSMA PET/CT Answer?

PURPOSE OF REVIEW

To critically review the potential clinical applications of prostate-specific membrane antigen (PSMA) radioactive ligands in renal cell carcinoma (RCC).

RECENT FINDINGS

Radioactive probes targeting PSMA hold promise in several malignancies in addition to prostate cancer, owing to the expression of PSMA by tumor neovasculature. The majority of clear cell RCCs (ccRCC), the most malignant RCC subtype, express PSMA on tumor-associated neovasculature. The endothelium of less aggressive RCC subtypes is PSMA positive in a lower, but still significant percentage of cases. PSMA might therefore represent an interesting theragnostic target in RCC. The preliminary data available suggest a potential role for PSMA-targeting radiopharmaceuticals in complementing conventional imaging for staging ccRCC patients at risk of nodal involvement and oligometastatic disease. Additional applications of PSMA imaging may be the selection and the response assessment of patients receiving anti-angiogenic treatments. The effectiveness of PSMA-targeting radionuclide therapy should also be investigated.

Relationship between plasma Atherogenic index and final pathology of Bosniak III-IV renal masses: a retrospective, single-center study

Background

There is an increased incidence of renal cell carcinoma (RCC) in patients with metabolic syndrome who usually have high levels of serum triglyceride (TG) and low high-density lipoprotein-cholesterol (HDL-C). Plasma atherogenic index (PAI) is the logarithmic ratio of serum TG level to HDL-C and related to cardiovascular diseases. In this study, we aimed to determine the accuracy of PAI in determining renal malignancy in localized renal masses preoperatively.

Methods

Totally 169 patients who were diagnosed with Bosniak III-IV lesions by imaging modalities and treated in our hospital with partial or radical nephrectomy were retrospectively analyzed using institutional renal cancer database between 2013 and 2018. Preoperative images were evaluated by two experienced radiologists. The patients were divided into two groups according to their postoperative pathological diagnosis as malignant or benign tumors. The PAI of each patient was calculated and the statistical significance of PAI in predicting malignancy for renal masses was analyzed using uni- and multivariable analyses.

Results

Of patients, 109 (64.5%) were males and 60 (35.5%) were females with a median age of 61 (33–84) years. Median tumor size was 6.5 (2–18) cm. Pathological diagnosis was malignant in 145 (85.8%) and benign in 24 (14.2%) patients. There was no statistically significant difference in serum TG levels between malignant and benign cases (p > 0.05). The HDL-C levels were significantly lower in malignant cases (p = 0.001). Median PAI value was 0.63 (0.34–1.58) and significantly higher in malignant cases (p = 0.003). The PAI cut-off value for malignancy was ≥0.34. The sensitivity was calculated as 88.2% and specificity as 45.8%, the positive predictive value as 90.8, negative predictive value as 39.3, and odds ratio as 6.37 (95% CI: 2.466–16.458). In multivariable analysis, gender, smoking status, and hypertension had no effect on malignancy, whereas PAI and HDL-C were independent risk factors (p = 0.003 and p = 0.003, respectively). The risk of malignancy was 5.019 times higher, when PAI was > 0.34 (95% CI: 1.744–14.445) in multivariable logistic regression analysis.

Conclusions

The PAI can be used as a predictive tool in suspicion of malignant renal masses. In case of a benign pathology, PAI levels may be encouraging for surgeons for nephron-sparing surgery.

Roles for active surveillance in renal cancer

PURPOSE OF REVIEW

With this review, we describe the most recent advances in active surveillance as well as diagnosis and management of small renal masses (SRMs).

RECENT FINDINGS

We discuss diagnosis, differentiation of solid from cystic lesions, risk prediction and treatment of the SRM. A better understanding of the disease facilitates the use of more conservatory treatments, such as active surveillance. Active surveillance has been increasingly accepted not only for SRM, but also for larger tumors and even metastatic patients. Exiting advances in risk prediction will help us define which patients can be safely managed with active surveillance and which require immediate treatment. Meanwhile, the use of renal tumor biopsies is still an important tool for these cases.

SUMMARY

Active surveillance is an option for many patients with renal masses. Noninvasive methods for diagnosis and risk prediction are being developed, but meanwhile, renal tumor biopsy is a useful tool. A better understanding of the disease increases the number of patients who can undergo active surveillance fully certain of the safety of their management.

Texture analysis of small renal cell carcinomas at MDCT for predicting relevant histologic and protein biomarkers

PURPOSE

To assess CT texture features of small renal cell carcinomas (≤ 4cm) for association with key pathologic features including protein biomarkers.

METHODS

Quantitative CT texture analysis (CTTA) of small renal cancers (≤ 4cm) was performed on non-contrast and portal venous phase abdominal MDCT scans with an ROI drawn at the largest cross-sectional diameter of the tumor using commercially available software. Texture parameters including mean pixel attenuation, the standard deviation (SD) of the pixel distribution histogram, entropy, the mean of positive pixels, the skewness (i.e., asymmetry) of the pixel histogram, kurtosis (i.e., peakness) of the pixel histogram, and the percentage of positive pixels were correlated with pathologic data from surgical resection, including histology and nuclear grade, as well as microarray analysis in a subset (n = 40) including Ki67 index, CRP, and neovascularization (CD105/CD31).

RESULTS

Portal venous phase images were available in 249 patients (105 women, 144 men; mean age, 56.7 years) with tumors ≤ 4cm (mean, median, range, ± SD; 2.66, 2.60, 0.3-4.0 ± 0.85 cm). CT texture features of standard deviation, mean of the positive pixels, and entropy of the pixel histogram were significantly associated with histologic cell type (clear vs. non-clear; p < 0.001). Entropy and mean of the positive pixels also showed an association with nuclear grade, although not statistically significant. In the microarray analysis subset, kurtosis of the pixel histogram was associated with CD105/CD31 (p = 0.05). SD also showed some association with CD 105 positivity (p = 0.02) and CAIX expression (p = 0.01). Non-contrast CT images were available in 174 patients (72 women, 102 men; mean age, 57.5 years). Although the association with histology was not as strong as on the portal venous phase, in the subset of patients with microarray data, SD was found to correlate with CRP (p = 0.08), kurtosis with CRP (p = 0.004), CD105/CD31 (p = 0.002), and with Ki 67 index (p < 0.001).

CONCLUSION

CT texture features were significantly associated with important histopathologic features in small renal cancers. These non-invasive measures can be performed retrospectively and may provide useful information when determining follow-up and treatment of small renal cancers.

Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT

PURPOSE

Currently, all solid enhancing renal masses without microscopic fat are considered malignant until proven otherwise and there is substantial overlap in the imaging findings of benign and malignant renal masses, particularly between clear cell RCC (ccRCC) and benign oncocytoma (ONC). Radiomics has attracted increased attention for its utility in pre-operative work-up on routine clinical images. Radiomics based approaches have converted medical images into mineable data and identified prognostic imaging signatures that machine learning algorithms can use to construct predictive models by learning the decision boundaries of the underlying data distribution. The TensorFlow™ framework from Google is a state-of-the-art open-source software library that can be used for training deep learning neural networks for performing machine learning tasks. The purpose of this study was to investigate the diagnostic value and feasibility of a deep learning-based renal lesion classifier using open-source Google TensorFlow™ Inception in differentiating ccRCC from ONC on routine four-phase MDCT in patients with pathologically confirmed renal masses.

METHODS

With institutional review board approval for this 1996 Health Insurance Portability and Accountability Act compliant retrospective study and a waiver of informed consent, we queried our institution's pathology, clinical, and radiology databases for histologically proven cases of ccRCC and ONC obtained between January 2000 and January 2016 scanned with a an intravenous contrast-enhanced four-phase renal mass protocol (unenhanced (UN), corticomedullary (CM), nephrographic (NP), and excretory (EX) phases). To extract features to be used for the machine learning model, the entire renal mass was contoured in the axial plane in each of the four phases, resulting in a 3D volume of interest (VOI) representative of the entire renal mass. We investigated thirteen different approaches to convert the acquired VOI data into a set of images that adequately represented each tumor which was used to train the final layer of the neural network model. Training was performed over 4000 iterations. In each iteration, 90% of the data were designated as training data and the remaining 10% served as validation data and a leave-one-out cross-validation scheme was implemented. Accuracy, sensitivity, specificity, positive (PPV) and negative predictive (NPV) values, and CIs were calculated for the classification of the thirteen processing modes.

RESULTS

We analyzed 179 consecutive patients with 179 lesions (128 ccRCC and 51 ONC). The ccRCC cohort had a mean size of 3.8 cm (range 0.8-14.6 cm) and the ONC cohort had a mean lesion size of 3.9 cm (range 1.0-13.1 cm). The highest specificity and PPV (52.9% and 80.3%, respectively) were achieved in the EX phase when we analyzed the single mid-slice of the tumor in the axial, coronal and sagittal plane, and when we increased the number of mid-slices of the tumor to three, with an accuracy of 75.4%, which also increased the sensitivity to 88.3% and the PPV to 79.6%. Using the entire tumor volume also showed that classification performance was best in the EX phase with an accuracy of 74.4%, a sensitivity of 85.8% and a PPV of 80.1%. When the entire tumor volume, plus mid-slices from all phases and all planes presented as tiled images, were submitted to the final layer of the neural network we achieved a PPV of 82.5%.

CONCLUSIONS

The best classification result was obtained in the EX phase among the thirteen classification methods tested. Our proof of concept study is the first step towards understanding the utility of machine learning in the differentiation of ccRCC from ONC on routine CT images. We hope this could lead to future investigation into the development of a multivariate machine learning model which may augment our ability to accurately predict renal lesion histology on imaging.

CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade

OBJECTIVE

CT texture analysis (CTTA) using filtration-histogram-based parameters has been associated with tumor biologic correlates such as glucose metabolism, hypoxia, and tumor angiogenesis. We investigated the utility of these parameters for differentiation of clear cell from papillary renal cancers and prediction of Fuhrman grade.

METHODS

A retrospective study was performed by applying CTTA to pretreatment contrast-enhanced CT scans in 290 patients with 298 histopathologically confirmed renal cell cancers of clear cell and papillary types. The largest cross section of the tumor on portal venous phase axial CT was chosen to draw a region of interest. CTTA comprised of an initial filtration step to extract features of different sizes (fine, medium, coarse spatial scales) followed by texture quantification using histogram analysis.

RESULTS

A significant increase in entropy with fine and medium spatial filters was demonstrated in clear cell RCC (p = 0.047 and 0.033, respectively). Area under the ROC curve of entropy at fine and medium spatial filters was 0.804 and 0.841, respectively. An increased entropy value at coarse filter correlated with high Fuhrman grade tumors (p = 0.01). The other texture parameters were not found to be useful.

CONCLUSION

Entropy, which is a quantitative measure of heterogeneity, is increased in clear cell renal cancers. High entropy is also associated with high-grade renal cancers. This parameter may be considered as a supplementary marker when determining aggressiveness of therapy.

KEY POINTS

• CT texture analysis is easy to perform on contrast-enhanced CT. • CT texture analysis may help to separate different types of renal cancers. • CT texture analysis may enhance individualized treatment of renal cancers.

Usefulness of rapid kV-switching dual energy CT in renal tumor characterization

PURPOSE

To investigate whether iodine content can discriminate between benign or malignant renal tumors, malign tumor subtypes, low-grade and high-grade tumors on rapid kv-switching dual-energy CT (rsDECT).

METHODS

This prospective study enrolled 95 patients with renal tumors who underwent rsDECT for tumor characterization between 2016 and 2018. Attenuation on true and virtual unenhanced images, absolute enhancement and enhancement ratio and iodine content of each lesion on nephrographic phase iodine density images were measured. Histopathological diagnosis was obtained following either surgery or core biopsy.

RESULTS

Eighty-five tumors were renal cell carcinoma (RCC) (56 clear cell, 20 papillary, 9 chromophobe) and 10 were benign (6 angiomyolipoma,4 oncocytoma). 46 tumors were low-grade and 23 high-grade. There was significant difference between iodine content of clear cell and non-clear cell (papillary + chromophobe) RCC (p < 0.001). However, no significant iodine content differences were found between papillary and chromophobe RCC, benign and malignant tumors, low-grade and high-grade tumors. The best cut-off iodine content for differentiating clear cell from non-clear cell RCC was 3.2 mg/ml and clear cell from papillary RCC was 2.9 mg/ml with a high sensitivity and specificity. Also, significant difference was found between attenuation values of true and virtual unenhanced images (p = 0.007). Mean iodine content, absolute enhancement and enhancement ratio were highly correlated.

CONCLUSION

rsDECT contributes to renal tumor characterization by showing higher iodine content in clear cell RCCs compared with non-clear cell RCCs.

The value of quantitative CT texture analysis in differentiation of angiomyolipoma without visible fat from clear cell renal cell carcinoma on four-phase contrast-enhanced CT images

AIM

To investigate the diagnostic performance and usefulness of texture analysis in differentiating angiomyolipoma (AML) without visible fat from clear cell renal cell carcinoma (ccRCC) on four-phase contrast-enhanced computed tomography (CECT).

MATERIALS AND METHODS

Seventeen patients with AML without visible fat and 50 patients with ccRCC of size ≤4.5 cm who had also undergone preoperative four-phase CECT were included in this study. The histogram, grey-level co-occurrence matrix (GLCM), and grey-level run length matrix (GLRLM) were evaluated. Sequential feature selection (SFS) and support vector machine (SVM) classifier with leave-one-out cross validation were used.

RESULTS

Using the SFS and SVM classifiers, five texture features were selected; mean (unenhanced), standard deviation (unenhanced and excretory), cluster prominence (nephrographic), and long-run high grey-level emphasis (corticomedullary). Diagnostic performance of the five selected texture features for all CT phases was as follows: 82% sensitivity, 76% specificity, 85% accuracy, and 85 area under the receiver operating characteristic curve (AUC). In the subgroup analysis, the AUCs of each phase were significantly >0.5 (p<0.05). In the pairwise comparison of AUCs between four phases, there were no significant differences between the four phases except the unenhanced and corticomedullary phases (p=0.015), i.e., the unenhanced phase showed slightly higher AUC than the corticomedullary phase.

CONCLUSIONS

Texture analysis of small renal masses (≤4.5 cm) on four-phase CECT can accurately differentiate AML without visible fat from ccRCC and showed good diagnostic performance for both the unenhanced and enhanced phases.

Quantitative multiparametric MR analysis of small renal lesions: correlation with surgical pathology

PURPOSE

The purpose of the study is to evaluate the utility of apparent diffusion coefficient (ADC), chemical shift signal intensity index (SII), and contrast enhancement in distinguishing between benign lesions and renal cell carcinoma (RCC) and between subtypes of renal lesions.

METHODS

This retrospective study included 98 renal lesions (≤ 3 cm) on MRI with correlative surgical pathology. Scanner field strength, lesion location, and size were recorded. Two readers blinded to surgical pathology independently measured ADC ratio (ADC lesion/ADC non-lesion kidney), SII, and absolute/relative enhancement in the corticomedullary and nephrographic phases of contrast.

RESULTS

There were 76 malignant and 22 benign lesions. 42 RCC were clear cell (ccRCC), 19 papillary (pRCC), 5 chromophobe (cbRCC). Benign lesions included both solid and cystic lesions. Interreader agreement for all variables was good-excellent (ICC 0.70-0.91). There was no difference in ADC or SII between benign and malignant lesions. There was greater absolute corticomedullary enhancement of benign versus malignant lesions (150.0 ± 111.5 vs. 81.1 ± 74.8, p = 0.0115), which did not persist when excluding pRCC. For lesion subtype differentiation, ADC_ratio for pRCC was lower than benign lesions (0.74 ± 0.35 vs. 1.03 ± 0.46, p = 0.0246). ccRCC demonstrated greater SII than other RCC (0.09 ± 0.22 vs. 0.001 ± 0.26, p = 0.0412). Oncocytomas and angiomyolipoma (AML) showed greater absolute corticomedullary enhancement than ccRCC and pRCC (145.6 ± 65.2 vs. 107.2 ± 85.3, p = 0.043 and 186.2 ± 93.9 vs. 37.6 ± 35.3, p = 0.0108), respectively.

CONCLUSIONS

While corticomedullary-phase enhancement was a differentiating feature, quantitative metrics from diffusion and chemical shift imaging cannot reliably differentiate benign from malignant lesions. Quantitative assessment may be useful in differentiating some benign and malignant lesion subtypes.

Sonographic Features of Small (< 4 cm) Renal Tumors With Low Signal Intensity on T2-Weighted MR Images: Differentiating Minimal-Fat Angiomyolipoma From Renal Cell Carcinoma

OBJECTIVE

The purpose of this study is to characterize and assess the diagnostic utility of sonographic features of minimal-fat angiomyolipoma (AML) and renal cell carcinoma (RCC) with regard to small (< 4 cm) renal masses with a predominantly low signal intensity (SI) on T2-weighted MR images.

MATERIALS AND METHODS

Fifty small renal masses with a predominantly low SI on T2-weighted MR images and no macroscopic fat, all of which had US images available, were assessed. MRI variables (T2 ratio, signal intensity index [SII], and tumor-to-spleen ratio on chemical-shift images), CT features (enhancement patterns and attenuations values on unenhanced images and images obtained in the corticomedullary and nephrographic phases), and sonographic features (echogenicity, heterogeneity, and the presence of acoustic shadowing, a hypoechoic rim, or an intratumoral cyst) were recorded in a blinded manner. Echo-genicity was classified as hypo-, iso-, or hyperechoic compared with the renal parenchyma or markedly hyperchoic when equivalent to that of the renal sinus fat.

RESULTS

Minimal-fat AML and RCC were confirmed in 22 and 28 patients, respectively. T2 ratios were significantly lower for minimal-fat AML versus RCCs (p = 0.044). Minimal-fat AMLs exhibited echogenicities that were considered hypoechoic (31.8%), isoechoic (4.5%), hyperechoic (18.2%), or markedly hyperechoic (45.5%). No RCC showed marked hyperechogenicity. CT attenuation values were significantly higher for the minimal-fat AMLs seen in all imaging phases. When the combination of the T2 ratio, nephrographic phase attenuation, and echogenicity was assessed, the AUC value was 0.93 (95% CI, 0.81-0.98), which was a significant increase over the AUC value of 0.83 (95% CI, 0.69-0.92) for noted the combination of the T2 ratio and nephrographic phase attenuation.

CONCLUSION

Additional reviews of the echogenicity of small renal masses with low SI on T2-weighted MR images may aid the diagnosis of minimal-fat AML.

Localized chromophobe renal cell carcinoma: preoperative imaging judgment and laparoscopic simple enucleation for treatment

Objective:

To evaluate the preoperative imaging manifestation and therapeutic effect of laparoscopic simple enucleation (SE) for localized chromophobe renal cell carcinoma (chRCC).

Materials and Methods:

Clinical data of 36 patients who underwent laparoscopic SE of localized chRCC at our institute were retrospectively analyzed. All patients underwent preoperative renal protocol CT (unenhanced, arterial, venous, and delayed images). CT scan characteristics were evaluated. After intraoperative occlusion of the renal artery, the tumor was free bluntly along the pseudocapsule and enucleated totally. The patients were followed up regularly after the operation.

Results:

Mean tumor diameter was 3.9±1.0 cm, 80% of tumors were homogeneous and all the tumors had complete pseudocapsule. The attenuation values were slightly lower than normal renal cortex and degree of enhancement of the tumors were significantly lower than normal renal cortex. Mean operation time was 104.3±18.2 min. Mean warm ischemia time (WIT) was 21.3±3.5 min. Mean blood loss was 78.6±25.4 mL. No positive surgical margin was identified. Mean postoperative hospital stay was 5.3±1.5 d. Hematuria occurred in 3 patients and all disappeared within 3 days. After a mean follow-up of 32.1±20.6 months, no patient had local recurrence or metastatic progression.

Conclusion:

Localized chRCCs have a great propensity for homogeneity and complete pseudocapsule. The attenuation values were slightly lower than normal renal cortex and small degree of enhancement. Laparoscopic SE is a safe and effective treatment for localized chRCC. The oncological results were satisfactory.

Identification and Validation of Radiographic Enhancement for Reliable Differentiation of CD117(+) Benign Renal Oncocytoma and Chromophobe Renal Cell Carcinoma

Purpose: The diagnostic differential for CD117/KIT(+) oncocytic renal tumor biopsies is limited to benign renal oncocytoma versus chromophobe renal cell carcinoma (ChRCC); however, further differentiation is often challenging and requires surgical resection. We investigated clinical variables that might improve preoperative differentiation of CD117(+) renal oncocytoma versus ChRCC to avoid the need for benign tumor resection.Experimental Design: A total of 124 nephrectomy patients from a single institute with 133 renal oncocytoma or ChRCC tumors were studied. Patients from 2003 to 2012 comprised a retrospective cohort to identify clinical/radiographic variables associated with renal oncocytoma versus ChRCC. Prospective validation was performed among consecutive renal oncocytoma/ChRCC tumors resected from 2013 to 2017.Results: Tumor size and younger age were associated with ChRCC, and multifocality with renal oncocytoma; however, the most reliable variable for ChRCC versus renal oncocytoma differentiation was the tumor:cortex peak early-phase enhancement ratio (PEER) using multiphase CT. Among 54 PEER-evaluable tumors in the retrospective cohort [19 CD117(+), 13 CD117(-), 22 CD117-untested], PEER classified each correctly as renal oncocytoma (PEER >0.50) or ChRCC (PEER ≤0.50), except for four misclassified CD117(-) ChRCC variants. Prospective study of PEER confirmed 100% accuracy of renal oncocytoma/ChRCC classification among 22/22 additional CD117(+) tumors. Prospective interobserver reproducibility was excellent for PEER scoring (intraclass correlation coefficient, ICC = 0.97) and perfect for renal oncocytoma/ChRCC assignment (ICC = 1.0).Conclusions: In the largest clinical comparison of renal oncocytoma versus ChRCC to our knowledge, we identified and prospectively validated a reproducible radiographic measure that differentiates CD117(+) renal oncocytoma from ChRCC with potentially 100% accuracy. PEER may allow reliable biopsy-based diagnosis of CD117(+) renal oncocytoma, avoiding the need for diagnostic nephrectomy. Clin Cancer Res; 24(16); 3898-907. ©2018 AACR.

Angiomyolipoma of the Kidneys: Current Perspectives and Challenges in Diagnostic Imaging and Image-Guided Therapy

Angiomyolipomas (AML) are benign tumors of the kidneys frequently encountered in radiologic practice in large tertiary centers. In comparison to renal cell carcinomas (RCC), AML are seldom treated unless they are large, undergo malignant transformation or develop complications like acute hemorrhage. The common garden triphasic (classic) AML is an easy diagnosis, however, some variants lack macroscopic fat in which case the radiologic differentiation from RCC becomes challenging. Several imaging features, both qualitative and quantitative, have been described in differentiating the 2 entities. Although minimal fat AML is not entirely a radiologic diagnosis, the suspicion raised on imaging necessitates sampling and potentially avoids an unwanted surgery. Recently a new variant, epitheloid AML has been described which often has atypical imaging features and is at a higher risk for malignant transformation. Apart from the diagnosis, the radiologist also needs to convey information regarding nephrometric scores which help in surgical decision-making. Recently, more and more AMLs are managed with selective arterial embolization and percutaneous ablation, both of which are associated with less morbidity when compared to surgery. The purpose of this article is to review the imaging and pathologic features of classic AML as well as the differentiation of minimal fat AML from RCC. In addition, an overview of nephrometric scoring and image-guided interventions is also provided.

[64Cu]XYIMSR-06: A dual-motif CAIX ligand for PET imaging of clear cell renal cell carcinoma

Carbonic anhydrase IX (CAIX) is a cell surface enzyme that is over-expressed in approximately 95% of cases of clear cell renal cell carcinoma (ccRCC), the most common renal cancer. We synthesized and performed in vitro and in vivo evaluation of a dual-motif ligand, [64Cu]XYIMSR-06, for imaging CAIX expression on ccRCC tumors using positron emission tomography (PET). [64Cu]XYIMSR-06 was generated in yields of 51.0 ± 4.5% (n=5) and specific activities of 4.1 - 8.9 GBq/μmol (110-240 Ci/mmol). Tumor was visualized on PET images by 1 h post-injection with high tumor-to-background levels (>100 tumor-to-blood and -muscle) achieved within 24 h. Biodistribution studies demonstrated a maximum tumor uptake of 19.3% injected dose per gram of radioactivity at 4 h. Tumor-to-blood, -muscle and -kidney ratios were 129.6 ± 18.8, 84.3 ± 21.0 and 2.1 ± 0.3, respectively, at 8 h post-injection. At 24 h a tumor-to-kidney ratio of 7.1 ± 2.5 was achieved. These results indicate pharmacokinetics superior to those of previously reported imaging agents binding to CAIX. [64Cu]XYIMSR-06 is a new low-molecular-weight PET ligand targeting CAIX, which can image localized and metastatic ccRCC.

Management of Small Renal Masses

With the ubiquitous use of cross-sectional abdominal imaging in recent years, the incidence of small renal masses (SRMs) has increased, and the evaluation and management of SRMs have become important clinical issues. Diagnosing a mass in the early stages theoretically allows for high rates of cure but simultaneously risks overtreatment. In the past 20 years, surgical treatment of SRMs has transitioned from radical nephrectomy for all renal tumors, regardless of size, to elective partial nephrectomy whenever technically feasible. Additionally, newer approaches, including renal mass biopsy, active surveillance for select patients, and renal mass ablation, have been increasingly used. In this chapter, we review the current evidence-based papers covering aspects of the diagnosis and management of SRMs.

Use of quantitative SPECT/CT reconstruction in 99mTc-sestamibi imaging of patients with renal masses

OBJECTIVE

Technetium-99m (^99mTc)-sestamibi single-photon emission computed tomography/computed tomography (SPECT/CT) has previously been shown to allow for the accurate differentiation of benign renal oncocytomas and hybrid oncocytic/chromophobe tumors (HOCTs) apart from other malignant renal tumor histologies, with oncocytomas/HOCTs showing high uptake and renal cell carcinoma (RCC) showing low uptake based on uptake ratios from non-quantitative single-photon emission computed tomography (SPECT) reconstructions. However, in this study, several tumors fell close to the uptake ratio cutoff, likely due to limitations in conventional SPECT/CT reconstruction methods. We hypothesized that application of quantitative SPECT/CT (QSPECT) reconstruction methods developed by our group would provide more robust separation of hot and cold lesions, serving as an imaging framework on which quantitative biomarkers can be validated for evaluation of renal masses with ^99mTc-sestamibi.

METHODS

Single-photon emission computed tomography data were reconstructed using the clinical Flash 3D reconstruction and QSPECT methods. Two blinded readers then characterized each tumor as hot or cold. Semi-quantitative uptake ratios were calculated by dividing lesion activity by background renal activity for both Flash 3D and QSPECT reconstructions.

RESULTS

The difference between median (mean) hot and cold tumor uptake ratios measured 0.655 (0.73) with the QSPECT method and 0.624 (0.67) with the conventional method, resulting in increased separation between hot and cold tumors. Sub-analysis of 7 lesions near the separation point showed a higher absolute difference (0.16) between QPSECT and Flash 3D mean uptake ratios compared to the remaining lesions.

CONCLUSIONS

Our finding of improved separation between uptake ratios of hot and cold lesions using QSPECT reconstruction lays the foundation for additional quantitative SPECT techniques such as SPECT-UV in the setting of renal ^99mTc-sestamibi and other SPECT/CT exams. With robust quantitative image reconstruction and biomarker analysis, there may be an expanded role for SPECT/CT imaging in renal masses and other pathologic conditions.

Correlation of 99mTc-sestamibi uptake in renal masses with mitochondrial content and multi-drug resistance pump expression

Background

^99mTc-sestamibi single-photon emission computed tomography/computed tomography (SPECT/CT) has recently been explored for the characterization of indeterminate renal masses. As judged by increased intra-tumoral radiotracer uptake, we have previously reported the excellent diagnostic performance characteristics of this test for identifying benign/indolent oncocytomas and hybrid oncocytic/chromophobe tumors (HOCTs). In this study, we investigated potential molecular mechanisms underlying the discriminatory ability of ^99mTc-sestamibi SPECT/CT for renal masses.

Fifty renal masses imaged with ^99mTc-sestamibi SPECT/CT prior to surgical resection were evaluated by immunohistochemistry for mitochondrial content and expression of the multi-drug resistance pump 1 (MDR1/P-gp). Immunohistochemical staining was scored semi-quantitatively, and results were compared across renal tumor histologies and correlated with ^99mTc-sestamibi uptake.

Results

In total, 6/6 (100%) and 2/2 (100%) HOCTs demonstrated strong mitochondrial content staining combined with low MDR1 staining. Clear cell renal cell carcinoma showed an opposite pattern with the majority having low mitochondrial (14/26, 54%) and high MDR1 staining (18/26, 69%). Other tumor types were more variable in staining pattern, although the staining pattern reliably predicted ^99mTc-sestamibi uptake in almost all tumors except chromophobe renal cell carcinoma.

Conclusions

Our findings confirm that renal tumors with high mitochondrial content and relatively low MDR pump expression activity accumulate ^99mTc-sestamibi and allow for the accurate diagnosis of the benign/indolent tumor class that includes oncocytomas and HOCTs. For masses in which MDR activity outweighs the presence of mitochondria, the tumors appear cold on ^99mTc-sestamibi SPECT/CT, allowing for high confidence in the diagnosis of renal cell carcinoma.

Role of Contrast Enhancement and Corrected Attenuation Values of Renal Tumors in Predicting Renal Cell Carcinoma (RCC) Subtypes: Protocol for a Triphasic Multi-Slice Computed Tomography (CT) Procedure

BACKGROUND

To distinguish RCC subtypes based on contrast enhancement features of CT images.

MATERIAL/METHODS

In total, 59 lesions from 57 patients were included. All patients underwent multi-slice CT imaging with a triphasic protocol, which included non-contrast, corticomedullary, nephrographic and urographic phases. Contrast enhancement features of renal masses were evaluated in terms of CT attenuation values (AV) and differences in contrast density; the aorta or renal parenchyma were evaluated based on corrected or relative values.

RESULTS

Clear cell RCC (ccRCC) showed more intense contrast enhancement than other RCC subtypes. When differentiating ccRCC from other RCC subtypes, a cut-off AV of 86-89 HU, aorta-based corrected AV of 89-95 HU and renal parenchyma-based corrected AV of 87-95 HU showed a diagnostic accuracy of 81-86%, 86-88% and 74-78%, respectively, in the corticomedullary phase. Furthermore, a cutoff of 2.42-2.72 for the relative contrast enhancement ratio, a cutoff of 2.59-2.74 for the aorta-based corrected relative contrast enhancement ratio and a cutoff of 2.63-2.76 for the renal parenchyma-based attenuation ratio showed a diagnostic accuracy of 83-88%, 88-90% and 81%, respectively.

CONCLUSIONS

The most reliable parameters for differentiating ccRCC from other RCC subtypes are aorta-based corrected AV and aorta-based corrected relative contrast enhancement values in the corticomedullary phase.

Quantitative computer-aided diagnostic algorithm for automated detection of peak lesion attenuation in differentiating clear cell from papillary and chromophobe renal cell carcinoma, oncocytoma, and fat-poor angiomyolipoma on multiphasic multidetector computed tomography

OBJECTIVE

To evaluate the performance of a novel, quantitative computer-aided diagnostic (CAD) algorithm on four-phase multidetector computed tomography (MDCT) to detect peak lesion attenuation to enable differentiation of clear cell renal cell carcinoma (ccRCC) from chromophobe RCC (chRCC), papillary RCC (pRCC), oncocytoma, and fat-poor angiomyolipoma (fp-AML).

MATERIALS AND METHODS

We queried our clinical databases to obtain a cohort of histologically proven renal masses with preoperative MDCT with four phases [unenhanced (U), corticomedullary (CM), nephrographic (NP), and excretory (E)]. A whole lesion 3D contour was obtained in all four phases. The CAD algorithm determined a region of interest (ROI) of peak lesion attenuation within the 3D lesion contour. For comparison, a manual ROI was separately placed in the most enhancing portion of the lesion by visual inspection for a reference standard, and in uninvolved renal cortex. Relative lesion attenuation for both CAD and manual methods was obtained by normalizing the CAD peak lesion attenuation ROI (and the reference standard manually placed ROI) to uninvolved renal cortex with the formula [(peak lesion attenuation ROI - cortex ROI)/cortex ROI] × 100%. ROC analysis and area under the curve (AUC) were used to assess diagnostic performance. Bland-Altman analysis was used to compare peak ROI between CAD and manual method.

RESULTS

The study cohort comprised 200 patients with 200 unique renal masses: 106 (53%) ccRCC, 32 (16%) oncocytomas, 18 (9%) chRCCs, 34 (17%) pRCCs, and 10 (5%) fp-AMLs. In the CM phase, CAD-derived ROI enabled characterization of ccRCC from chRCC, pRCC, oncocytoma, and fp-AML with AUCs of 0.850 (95% CI 0.732-0.968), 0.959 (95% CI 0.930-0.989), 0.792 (95% CI 0.716-0.869), and 0.825 (95% CI 0.703-0.948), respectively. On Bland-Altman analysis, there was excellent agreement of CAD and manual methods with mean differences between 14 and 26 HU in each phase.

CONCLUSION

A novel, quantitative CAD algorithm enabled robust peak HU lesion detection and discrimination of ccRCC from other renal lesions with similar performance compared to the manual method.

Percutaneous radiofrequency ablation for renal cell carcinoma vs. partial nephrectomy: Comparison of long-term oncologic outcomes in both clear cell and non-clear cell of the most common subtype

OBJECTIVES

To compare the clinical outcomes of percutaneous radiofrequency ablation (PRFA) and partial nephrectomy (PN) in patients with clear cell renal cell carcinoma (ccRCC) and non-clear cell RCC (nccRCC) of the most common subtypes.

MATERIALS AND METHODS

A retrospective study was conducted to review the records of all the patients who underwent PRFA or PN between February 2005 and April 2014 at our institution. Patients with histologic confirmation of ccRCC, papillary RCC, and chromophobe RCC were included. The Mann-Whitney U test was applied to compare PRFA to PN in the ccRCC and nccRCC groups. The Kaplan-Meier method was used to generate the survival curves that were compared to the log-rank test.

RESULTS

A total of 264 patients meeting the selection criteria were included in this study. The tumor size ranged from 0.9 to 7.0cm. The median follow-up period was 78 months (range: 8-132 mo). Although PRFA provided comparable 10-year overall survival rates and 10-year disease-free survival (DFS) rates to PN both in ccRCC ≤4cm and nccRCC, the 10-year DFS for patients treated with PRFA was lower than that of PN in ccRCC >4cm. The DFS survival curve between the 2 operations and 2 subtypes was statistically significant in patients with tumor size >4cm. Limitations include retrospective review and selection bias.

CONCLUSIONS

Patients with T1b ccRCC treated with PRFA have less favorable outcomes than those with PN whereas PRFA provides comparable oncologic outcomes to PN in patients with T1b nccRCC. It is necessary to take RCC subtypes into consideration when choosing a surgical approach to treat T1b RCC between PFRA and PN.