Comparisons of outcome and prognostic features among histologic subtypes of renal cell carcinoma

Racial Differences in Survival for Locally Advanced Renal Cell Carcinoma

INTRODUCTION

African Americans with renal cell carcinoma (RCC) often have more aggressive tumors and worse outcomes compared to other racial groups. The impact of race on survival in locally advanced RCC with tumor thrombus and metastatic RCC (mRCC) remains unclear. This study evaluates racial disparities in survival among RCC patients with tumor thrombus.

METHODS

This IRB-approved retrospective study analyzed 11,520 RCC patients aged 18 to 80 with tumor thrombus who underwent nephrectomy (2010-2015) using the National Cancer Database. Demographic factors (age, sex, race/ethnicity) and clinical variables (tumor stage, grade, thrombus level, surgery type, comorbidities) were included. Statistical analyses utilized Kaplan-Meier curves, Cox proportional hazards, and multinomial logistic regression, with significance set at P < .05.

RESULTS

African Americans (6% of patients) had a 22% higher overall mortality hazard (HR: 1.22, P < .001) and 24% higher hazard in metastatic RCC (HR: 1.24, P = .019) compared to non-Hispanic Whites (83%). Five-year survival rates for advanced thrombus levels (I-IV) were comparable between races. Overall mortality was 55%, rising to 82% in metastatic cases.

CONCLUSION

Among patients with RCC and tumor thrombus, African American patients face 22% higher mortality hazard compared to non-Hispanic white patients. They often present with more locally advanced disease and mRCC. The mortality hazard for metastatic RCC is increased by 24% among African Americans compared to Caucasians. The results highlight the demographic impact of race and supports clinical consideration when managing African American patients.

Pembrolizumab-axitinib versus nivolumab-cabozantinib as first-line therapy in patients with metastatic renal cell carcinoma: a retrospective real-world comparison (ARON-1)

BACKGROUND

The optimal first-line therapy for metastatic renal cell carcinoma (mRCC) remains uncertain, despite recent advancements in immune-based combinations. This retrospective study compares the effectiveness of pembrolizumab plus axitinib (PA) and nivolumab plus cabozantinib (NC) as first-line treatments for mRCC in a real-world setting.

METHODS

Patient data were collected from 55 centers across 16 countries, encompassing individuals diagnosed with mRCC receiving first-line treatment with PA or NC between January 2016 and October 2023. Clinical and tumor features and treatment responses were recorded. The primary endpoints were overall response rate (ORR), overall survival (OS), progression-free survival (PFS), and time to second progression. Statistical analyses included Kaplan-Meier survival estimates, Cox proportional hazard models, and chi-square tests.

RESULTS

A total of 760 patients with a median age of 64 years (range, 29-88) were included. Of them, 607 received PA, and only 153 NC. In the overall study population, ORR was 59% for and 49% for PA. Median OS was 55.7 months and not reached (NR) for PA and NC, respectively (P = .51), while median PFS was longer with NC (27.6 months) than for PA (16.2 months, P = .003). Subgroup analysis suggested a PFS benefits for NC in male, younger patients, intermediate risk group, clear cell histology, and lung involvement, as well as ORR favored NC in good risk patients. Multivariate analysis identified first-line therapy as a significant factor associated with PFS.

CONCLUSIONS

In this certainly biased retrospective comparison, NC demonstrated superior ORR and longer PFS compared to PA in mRCC. These findings underscore the importance of considering individual patient characteristics and risk profiles when selecting first-line therapy for mRCC.

State of the art review of AI in renal imaging

Renal cell carcinoma (RCC) as a significant health concern, with incidence rates rising annually due to increased use of cross-sectional imaging, leading to a higher detection of incidental renal lesions. Differentiation between benign and malignant renal lesions is essential for effective treatment planning and prognosis. Renal tumors present numerous histological subtypes with different prognoses, making precise subtype differentiation crucial. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), shows promise in radiological analysis, providing advanced tools for renal lesion detection, segmentation, and classification to improve diagnosis and personalize treatment. Recent advancements in AI have demonstrated effectiveness in identifying renal lesions and predicting surveillance outcomes, yet limitations remain, including data variability, interpretability, and publication bias. In this review we explored the current role of AI in assessing kidney lesions, highlighting its potential in preoperative diagnosis and addressing existing challenges for clinical implementation.

Applicability of the regression approach for histological multi-class grading in clear cell renal cell carcinoma

The histological grading of carcinoma has been one of the central applications of task-specific deep learning in pathology. The deep learning method has pushed away the regression approach, which has been exploited for two-class classification, to address multi-class classification. However, the applicability of the regression approach on multi-class carcinoma grading has not been extensively investigated. Here, we show that the regression approach is sufficiently compatible with classification regarding the four-class grading of clear cell renal cell carcinoma using 11,826 histological image patches from 16 whole slide images. Using convolutional neural network models (DenseNet-121 and Inception-v3), we found that regression models predict as accurately as classification models, achieving an accuracy of 0.990 at the highest, with fewer prediction errors by two or more grades. Furthermore, we found that the predictions by the regression models qualitatively capture intra-tumor heterogeneity of grades using the composite image patches. Our results demonstrate that the regression approach offers advantages in making a core of the multi-class grade prediction tools for practice.

Primary tumor characteristics and immunohistochemical profile of renal cell carcinoma in serous fluid cytology

INTRODUCTION

Renal cell carcinoma (RCC) involves serosal surfaces in 2%-3% of cases, and thus few papers describe serous fluid cytology (SFC) involvement by RCC. This diagnosis is challenging, given its rarity, nondescript cytomorphologic features and infrequent expression of widely used epithelial markers MOC31 and BerEP4. We describe our institutional experience with RCC in SFC specimens.

MATERIALS AND METHODS

Our institutional laboratory information system was queried for SFC specimens from patients with RCC between 2013 and 2023. Cases signed out as "Suspicious for Malignant Cells" and "Positive for Malignant Cells" were included. Patient demographics, immunohistochemical results, primary tumor characteristics, and molecular data were recorded.

RESULTS

Sixty-one cases, 50 pleural, and 11 peritoneal fluid specimens were identified. Fifty (50) were signed out as positive for malignancy and 11 were signed out as suspicious for malignancy. MOC31 and BerEP4 were positive in 59% and 55% of cases, respectively. PAX-8, CA9, CD10, and RCC were positive in 85%, 82%, 73%, and 29% of cases, respectively. Primary tumor histologic subtypes included 39 clear cell, 6 papillary, 1 chromophobe, and 15 were not further subclassified. Fifty-nine percent (59%) of cases had a nuclear grade of 4%, and 37% had sarcomatoid or rhabdoid differentiation. Seventy-one percent (71%) of cases had stage 3 or 4 disease.

CONCLUSIONS

RCC metastases to serosal surfaces are more likely to be seen in patients with higher disease stage, high nuclear grade, and sarcomatoid or rhabdoid differentiation. MOC31 and BerEP4 performed poorly in this setting. We recommend the addition of cytokeratins, PAX-8, CD10, and CA-9 to confirm metastatic involvement.

Computed tomography-based delta-radiomics analysis for preoperative prediction of ISUP pathological nuclear grading in clear cell renal cell carcinoma

BACKGROUND

Nuclear grading of clear cell renal cell carcinoma (ccRCC) plays a crucial role in diagnosing and managing the disease.

OBJECTIVE

To develop and validate a CT-based Delta-Radiomics model for preoperative assessment of nuclear grading in renal clear cell carcinoma.

MATERIALS AND METHODS

This retrospective analysis included surgical cases of 146 ccRCC patients from two medical centers from December 2018 to December 2023, with 117 patients from Hospital and 29 from the *Hospital Affiliated to University of **. Radiomic features were extracted from whole-abdomen CT images, and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used for feature selection. The Multi-Layer Perceptron (MLP) approach was employed to construct five predictive models (RAD_NE, RAD_AP, RAD_VP, RAD_Delta1, RAD_Delta2). The models were evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity, while clinical utility was assessed through Decision Curve Analysis (DCA).

RESULTS

A total of 1,834 radiomic features were extracted from the three phases of the CT images for each model. The models demonstrated strong classification performance, with AUC values ranging from 0.837 to 0.911 in the training set and 0.608 to 0.869 in the test set. The Rad_Delta1 and Rad_Delta2 models demonstrated advantages in predicting ccRCC pathological grading.The AUC value of the Rad_Delta1 is 0.911in the training set and 0.771 in the external verifcation set.The AUC value of the Rad_Delta2 is 0.881 in the training set and0.608 in the external verifcation set. DCA curves confirmed the clinical applicability of these models.

CONCLUSION

CT-based delta-radiomics shows potential in predicting the pathological grading of clear cell renal cell carcinoma (ccRCC).

Computed tomography and magnetic resonance imaging characteristics of renal cell carcinoma: Differences between subtypes and clinical evaluation

This review discusses the evaluation of renal cell carcinoma (RCC) subtypes using computed tomography (CT) and magnetic resonance imaging (MRI). RCC is a malignancy with different histopathological subtypes, constituting approximately 90% of adult kidney tumors. It has been reported that these subtypes show significant differences in terms of clinical behavior, treatment response, and prognosis. In the study, CT and MRI findings of subtypes such as clear cell RCC (ccRCC), papillary RCC (pRCC), chromophobe RCC (chRCC), medullary RCC (mRCC), collecting duct RCC (cdRCC), and multiloculated cystic RCC (mcRCC) were compared. It was stated that CT is the first-choice imaging method in the staging and surgical planning of RCC and provides detailed information about the tumor size, vascularity, and metastatic spread. On the other hand, it has been emphasized that MRI allows better characterization of RCC subtypes with its soft-tissue resolution and contrast agent usage advantage. The study draws attention to the different imaging features of each subtype and details the role of these findings in the clinical decision-making process. It has been stated that ccRCC exhibits intense contrast enhancement and rapid washout pattern in the corticomedullary phase on CT and appears hyperintense on T2A and hypointense on T1 weighted imaging (T1A) on MRI. It has been stated that pRCC has hypovascular features, has lower contrast enhancement, and has homogeneous borders. It has been stated that chRCC has a less vascular structure and exhibits moderate contrast enhancement in the corticomedullary phase. It has been reported that mRCC has invasive features and is usually diagnosed at an advanced stage while cdRCC has a very aggressive clinical course. It has been stated that mcRCC contains distinct cystic areas between the septa, has a well-circumscribed structure, and generally has a low malignancy potential. As a result, it has been stated that detailed evaluation of CT and MRI findings of RCC subtypes plays a critical role in the diagnosis, treatment, and prognosis of these subtypes. It has been emphasized that the findings presented in this study will contribute to the development of more targeted treatment approaches in RCC management.

Response Gene to Complement 32 is associated with poor patient survival and an inflamed tumor-immune microenvironment in clear cell renal cell carcinoma

It has been well established that tumor-infiltrating lymphocytes (TILs) play a critical role in the pathogenesis and progression of clear cell renal cell carcinoma (ccRCC). However, the mechanism on the interactions between TILs and tumor cells in the tumor-immune microenvironment remains unclear. In the present study, the expression of Response Gene to Complement 32 (RGC-32) was evaluated using immunohistochemistry. We analyzed the associations of RGC-32 expression with patient characteristics and survival. We also assessed TILs and their subsets (CD3+, CD4+, CD8+ and PD-1+) in the tumor nest. The level of RGC-32 expression was positively correlated with ISUP grade and Ki67 expression and was an independent poor prognosis factor of patients with ccRCC. RGC-32 expression was negatively correlated with the infiltration of TIL and CD3+T cells, but positively correlated with infiltration of PD-1+cells. In vitro studies showed that RGC-32 expression in renal cancer cells was downregulated by activated immune cells. Further investigation revealed that RGC-32 expression in renal cancer cells was inhibited by TNF-α and IL-1β secreted by activated immune cells. Collectively, these data indicate that RGC-32 could be a novel prognostic and druggable target related to the tumor-immune microenvironment in renal cancer.

Prediction of Distant Metastasis of Renal Cell Carcinoma Based on Interpretable Machine Learning: A Multicenter Retrospective Study

Background 

The traditional tool for predicting distant metastasis in renal cell carcinoma (RCC) is still insufficient. We aimed to establish an interpretable machine learning model for predicting distant metastasis in RCC patients.

Methods

We involved a population-based cohort of 121433 patients (mean age = 63 years; 63.58% men) diagnosed with RCC between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. The lightGBM algorithm was used to develop prediction model and assessed by the area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, and specificity. The LightGBM model was then externally validated in 36395 RCC patients enrolled from the SEER database between 2016 and 2018. Shapley Additive exPlanations (SHAP) method was applied to provide insights into the model's outcome or prediction.

Results

Of 121433 patients involved in the study cohort, 10730 (8.84%) had distant metastasis. The LightGBM model showed good performance in the internal validation set (AUC: 0.955, 95% CI: 0.951-0.959) and temporal external validation sets (0.963, 95% CI: 0.959-0.967; 0.961, 95% CI: 0.954-0.966). Performance for the prediction model was also well performed in different sub-cohort stratified by age, gender, and ethnicity. The calibration curve indicated that the predicted values are highly consistent with the actual observed values. SHAP plots demonstrated that chemotherapy was the most vital variable for prediction of distant metastasis of RCC patients.

Conclusion

We developed an interpretable machine learning model that is capable of accurately predicting the risk of distant metastasis of RCC patients. The presented model could help identify high-risk patients who require additional treatment strategies and follow-up regimens.

Survival analysis of clear cell renal cell carcinoma based on radiomics and deep learning features from CT images

PURPOSE

To create a nomogram for accurate prognosis of patients with clear cell renal cell carcinoma (ccRCC) based on computed tomography images.

METHODS

Eight hundred twenty-two ccRCC patients with contrast-enhanced computed tomography images involved in this study were collected. A rectangular region of interest surrounding the tumor was used to extract quantitative radiomics and deep-learning features, which were filtered by Cox proportional hazard regression model and least absolute shrinkage and selection operator. Then the selected features formed a fusion signature, which was assessed by Cox proportional hazard regression model method, Kaplan-Meier analysis, receiver operating characteristic curves, and concordance index (C-index) in different clinical subgroups. Finally, a nomogram constructed with this signature and clinicopathologic risk factors was assessed by C-index and survival calibration curves.

RESULTS

The fusion signature performed better than the radiomics signature. Then we combined this signature and 2 clinicopathologic risk factors. This nomogram showed an increase of about 20% in C-index values when compared to clinical nomogram in both datasets. Its prediction probability was also in good agreement with the actual ratio.

CONCLUSION

The proposed fusion nomogram provided a noninvasive and easy-to-use model for survival prognosis of ccRCC patients in future clinical use, without the requirement to perform a detailed segmentation for radiologists.

Evaluation of American Urological Association Renal Cell Carcinoma Risk Groups for Chromophobe Renal Cell Carcinoma

OBJECTIVE

To develop and compare various models for risk stratification in chromophobe renal cell carcinoma (chrRCC). Models have been developed to predict progression-free (PFS) and cancer-specific survival (CSS) following surgery for localized renal cell carcinoma (RCC). Notably, chromophobe RCC (chrRCC) is not included in American Urological Association (AUA) risk stratification, as nuclear grading is not recommended.

METHODS

We queried our institutional registry to identify patients managed surgically for unilateral, sporadic, M0, chrRCC from 1970-2012. AUA risk groups were defined using reported criteria, excluding grade, and were compared to the Mayo system incorporating nodal involvement, perinephric/renal sinus fat invasion, and sarcomatoid differentiation. PFS and CSS were estimated using the Kaplan-Meier method. Predictive ability was summarized using c-indexes from Cox proportional hazard regression models.

RESULTS

A total of 257 patients were identified. Thirty-nine patients experienced disease progression at a median 30 months (IQR 5.0-84) and 25 died from chrRCC at a median 34 months (IQR 15-79) following surgery. PFS and CSS rates at 10 years after surgery were 84% and 90%, respectively. C-indexes for modified AUA and Mayo risk groups were similar at 0.76 and 0.75, respectively, for PFS, and 0.77 and 0.76, respectively for CSS.

CONCLUSION

The modified AUA and Mayo risk stratification systems have similarly robust c-indexes for PFS and CSS in chrRCC. These models can be used to counsel patients based on pathologic features, inform clinicians on appropriate follow-up pathways, and identify patients at risk of disease progression for enrollment in adjuvant systemic therapy trials.

A systematic investigation of clear cell renal cell carcinoma using meta-analysis and systems biology approaches

Renal cell carcinoma with clear cells (ccRCC) is the most frequent kind; it accounts for almost 70% of all kidney cancers. A primary objective of current research was to find genes that may be used in ccRCC gene therapy to understand better the molecular pathways underlying the disease. Based on PubMed microarray searches and meta-analyses, we compared overall survival and recurrence-free survival rates in ccRCC patients with those in healthy samples. The technique was followed by a KEGG pathway and Gene Ontology (GO) function analyses, both performed in conjunction with the approach. Tumor immune estimate and multi-gene biomarkers validation for clinical outcomes were performed at the molecular and clinical cohort levels. Our analysis included fourteen GEO datasets based on inclusion and exclusion criteria. A meta-analysis procedure, network construction using PPIs, and four significant gene identification standard algorithms indicated that 11 genes had the most important differences. Ten genes were upregulated, and one was downregulated in the study. In order to analyze RFS and OS survival rates, 11 genes expressed in the GEPIA2 database were examined. Nearly nine of eleven significant genes have been found to beinvolved in tumor immunity. Furthermore, it was found that mRNA expression levels of these genes were significantly correlated with experimental literature studies on ccRCCs, which explained these findings. This study identified eleven gene panels associated with ccRCC growth and metastasis, as well as their immune system infiltration.

Development of prognostic signatures and risk index related to lipid metabolism in ccRCC

Background

Clear cell renal cell carcinoma (ccRCC) is a metabolic disorder characterized by abnormal lipid accumulation in the cytoplasm. Lipid metabolism-related genes may have important clinical significance for prognosis prediction and individualized treatment.

Methods

We collected bulk and single-cell transcriptomic data of ccRCC and normal samples to identify key lipid metabolism-related prognostic signatures. qPCR was used to confirm the expression of signatures in cancer cell lines. Based on the identified signatures, we developed a lipid metabolism risk score (LMRS) as a risk index. We explored the potential application value of prognostic signatures and LMRS in precise treatment from multiple perspectives.

Results

Through comprehensive analysis, we identified five lipid metabolism-related prognostic signatures (ACADM, ACAT1, ECHS1, HPGD, DGKZ). We developed a risk index LMRS, which was significantly associated with poor prognosis in patients. There was a significant correlation between LMRS and the infiltration levels of multiple immune cells. Patients with high LMRS may be more likely to respond to immunotherapy. The different LMRS groups were suitable for different anticancer drug treatment regimens.

Conclusion

Prognostic signatures and LMRS we developed may be applied to the risk assessment of ccRCC patients, which may have potential guiding significance in the diagnosis and precise treatment of ccRCC patients.

To characterize small renal cell carcinoma using diffusion relaxation correlation spectroscopic imaging and apparent diffusion coefficient based histogram analysis: a preliminary study

PURPOSE

To study the capability of diffusion-relaxation correlation spectroscopic imaging (DR-CSI) on subtype classification and grade differentiation for small renal cell carcinoma (RCC). Histogram analysis for apparent diffusion coefficient (ADC) was studied for comparison.

MATERIALS AND METHODS

A total of 61 patients with small RCC (< 4 cm) were included in the retrospective study. MRI data were reviewed, including a multi-b (0-1500 s/mm2) multi-TE (51-200 ms) diffusion weighted imaging (DWI) sequence. Region of interest (ROI) was delineated manually on DWI to include solid tumor. For each patient, a D-T2 spectrum was fitted and segmented into 5 compartments, and the volume fractions VA, VB, VC, VD, VE were obtained. ADC mapping was calculated, and histogram parameters ADC 90th, 10th, median, standard deviation, skewness and kurtosis were obtained. All MRI metrices were compared between clear cell RCC (ccRCC) and non-ccRCC group, and between high-grade and low-grade group. Receiver operator curve analysis was used to assess the corresponding diagnostic performance.

RESULTS

Significantly higher ADC 90th, ADC 10th and ADC median, and significantly lower DR-CSI VB was found for ccRCC compared to non-ccRCC. Significantly lower ADC 90th, ADC median and significantly higher VB was found for high-grade RCC compared to low-grade. For identifying ccRCC from non-ccRCC, VB showed the highest area under curve (AUC, 0.861) and specificity (0.882). For differentiating high- from low-grade, ADC 90th showed the highest AUC (0.726) and specificity (0.786), while VB also displayed a moderate AUC (0.715).

CONCLUSION

DR-CSI may offer improved accuracy in subtype identification for small RCC, while do not show better performance for small RCC grading compared to ADC histogram.

Clinical T1/2 renal cell carcinoma: multiparametric dynamic contrast-enhanced MRI features-based model for the prediction of individual adverse pathology

BACKGROUND

The detection of renal cell carcinoma (RCC) has been rising due to the enhanced utilization of cross-sectional imaging and incidentally discovered lesions with adverse pathology demonstrate potential for metastasis. The purpose of our study was to determine the clinical and multiparametric dynamic contrast-enhanced magnetic resonance imaging (CEMRI) associated independent predictors of adverse pathology for cT1/2 RCC and develop the predictive model.

METHODS

We recruited 105 cT1/2 RCC patients between 2018 and 2022, all of whom underwent preoperative CEMRI and had complete clinicopathological data. Adverse pathology was defined as RCC patients with nuclear grade III-IV; pT3a upstage; type II papillary RCC, collecting duct or renal medullary carcinoma, unclassified RCC; sarcomatoid/rhabdoid features. The qualitative and quantitative CEMRI parameters were independently reviewed by two radiologists. Univariate and multivariate binary logistic regression analyses were utilized to determine the independent predictors of adverse pathology for cT1/2 RCC and construct the predictive model. The receiver operating characteristic (ROC) curve, confusion matrix, calibration plot, and decision curve analysis (DCA) were conducted to compare the diagnostic performance of different predictive models. The individual risk scores and linear predicted probabilities were calculated for risk stratification, and the Kaplan-Meier curve and log-rank tests were used for survival analysis.

RESULTS

Overall, 45 patients were pathologically confirmed as RCC with adverse pathology. Clinical characteristics, including gender, and CEMRI parameters, including RENAL score, tumor margin irregularity, necrosis, and tumor apparent diffusion coefficient (ADC) value were identified as independent predictors of adverse pathology for cT1/2 RCC. The clinical-CEMRI predictive model yielded an area under the curve (AUC) of the ROC curve of 0.907, which outperformed the clinical model or CEMRI signature model alone. Good calibration, better clinical usefulness, excellent risk stratification ability of adverse pathology and prognosis were also achieved for the clinical-CEMRI predictive model.

CONCLUSIONS

The proposed clinical-CEMRI predictive model offers the potential for preoperative prediction of adverse pathology for cT1/2 RCC. With the ability to forecast adverse pathology, the predictive model could significantly benefit patients and clinicians alike by providing enhanced guidance for treatment planning and decision-making.

Detection of two synchronous histologically different renal cell carcinoma subtypes in the same kidney: a case report and review of the literature

INTRODUCTION

Renal cell carcinoma (RCC) is the dominant primary renal malignant neoplasm, encompassing a significant portion of renal tumors. The presence of synchronous yet histologically distinct ipsilateral RCCs, however, is an exceptionally uncommon phenomenon that is rather under-described in the literature regarding etiology, diagnosis, management, and later outcomes during follow-up.

CASE PRESENTATION

We aim to present the 9th case of a combination chromophobe RCC (ChRCC) and clear cell RCC (ccRCC) in literature, according to our knowledge, for a 69-year-old North African, Caucasian female patient who, after complaining of loin pain and hematuria, was found to have two right renal masses with preoperative computed tomography (CT) and underwent right radical nephrectomy. Pathological examination later revealed the two renal masses to be of different histologic subtypes.

CONCLUSION

The coexistence of dissimilar RCC subtypes can contribute to diverse prognostic implications. Further research should focus on enhancing the complex, yet highly crucial, preoperative detection and pathological examination to differentiate multiple renal lesions. Planning optimal operative techniques (radical or partial nephrectomy), selecting suitable adjuvant regimens, and reporting long-term follow-up outcomes of patients in whom synchronous yet different RCC subtypes were detected are of utmost importance.

Update on Renal Cell Carcinoma Diagnosis with Novel Imaging Approaches

This review highlights recent advances in renal cell carcinoma (RCC) imaging. It begins with dual-energy computed tomography (DECT), which has demonstrated a high diagnostic accuracy in the evaluation of renal masses. Several studies have suggested the potential benefits of iodine quantification, particularly for distinguishing low-attenuation, true enhancing solid masses from hyperdense cysts. By determining whether or not a renal mass is present, DECT could avoid the need for additional imaging studies, thereby reducing healthcare costs. DECT can also provide virtual unenhanced images, helping to reduce radiation exposure. The review then provides an update focusing on the advantages of multiparametric magnetic resonance (MR) imaging performance in the histological subtyping of RCC and in the differentiation of benign from malignant renal masses. A proposed standardized stepwise reading of images helps to identify clear cell RCC and papillary RCC with a high accuracy. Contrast-enhanced ultrasound may represent a promising diagnostic tool for the characterization of solid and cystic renal masses. Several combined pharmaceutical imaging strategies using both sestamibi and PSMA offer new opportunities in the diagnosis and staging of RCC, but their role in risk stratification needs to be evaluated. Although radiomics and tumor texture analysis are hampered by poor reproducibility and need standardization, they show promise in identifying new biomarkers for predicting tumor histology, clinical outcomes, overall survival, and the response to therapy. They have a wide range of potential applications but are still in the research phase. Artificial intelligence (AI) has shown encouraging results in tumor classification, grade, and prognosis. It is expected to play an important role in assessing the treatment response and advancing personalized medicine. The review then focuses on recently updated algorithms and guidelines. The Bosniak classification version 2019 incorporates MRI, precisely defines previously vague imaging terms, and allows a greater proportion of masses to be placed in lower-risk classes. Recent studies have reported an improved specificity of the higher-risk categories and better inter-reader agreement. The clear cell likelihood score, which adds standardization to the characterization of solid renal masses on MRI, has been validated in recent studies with high interobserver agreement. Finally, the review discusses the key imaging implications of the 2017 AUA guidelines for renal masses and localized renal cancer.

An epidemiological and clinicopathological study of type 1 vs. type 2 morphological subtypes of papillary renal cell carcinoma- results from a nation-wide study covering 50 years in Iceland

INTRODUCTION

Papillary renal cell carcinoma (pRCC) is the second most common histology of renal cell carcinoma (RCC), accounting for 10-15% of cases. Traditionally, pRCC is divided into type 1 and type 2, although this division is currently debated as a prognostic factor of survival. Our aim was to investigate the epidemiology and survival of the pRCC subtypes in a whole nation cohort of patients during a 50-year period.

MATERIALS AND METHODS

A Population based retrospective study including consecutive cases of RCC in Iceland from 1971-2020. Comparisons were made between histological classifications of RCC, with emphasis on pRCC subtypes (type 1 vs. 2) for outcome estimation. Changes in RCC incidence were analyzed in 5-year intervals after age standardization. The Kaplan-Meier method and Cox regression were used for outcome analysis.

RESULTS

A total of 1.725 cases were identified, with 74.4%, 2.1% and 9.2% having clear cell (ccRCC), chromophobe (chRCC), and pRCC, respectively. The age standardized incidence (ASI) of pRCC was 1.97/100.000 for males and 0.5/100.000 for females, and the proportion of pRCC increased from 3.7% to 11.5% between the first and last intervals of the study (p < 0.001). Age standardized cancer specific mortality (ASCSM) of pRCC was 0.6/100.000 and 0.19/100.000 for males and females, respectively. The annual average increase in ASI was 3.6% for type 1 pRCC, but the ASI for type 2 pRCC and ASCSM for both subtypes did not change significantly. Male to female ratio was 4.4 for type 1 pRCC and 2.3 for type 2. The average tumor size for type 1 and 2 was 58.8 and 73.7 mm, respectively. Metastasis at diagnosis was found in 8.7% in the type 1 pRCC, compared to 30.0% of patients with type 2 pRCC (p < 0.001). Estimated 5-year cancer-specific survival (CSS) were 94.4%, 80.7%, and 69.3% for chRCC, pRCC and ccRCC, respectively (p < 0.001). For the pRCC subtypes, type 1 was associated with better 5-year CSS than type 2 (86.3% vs. 66.0%, p < 0.001), although this difference was not significant after adjusting for cancer stage and grading.

CONCLUSIONS

pRCC histology was slightly less common in Iceland than in other countries. Males are more than three times more likely to be diagnosed with pRCC, compared to other RCC histologies. The subtype of pRCC was not found to be an independent risk factor for worse survival, and as suggested by the most recent WHO Classification of Urinary Tumors, grade and TNM-stage seem to be the most important factors for estimation of survival for pRCC patients.

Acetylation- and ubiquitination-regulated SFMBT2 acts as a tumor suppressor in clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (RCC) is the most common kidney tumor. The analysis from medical database showed that Scm-like with four MBT domains protein 2 (SFMBT2) was decreased in advanced clear cell RCC cases, and its downregulation was associated with the poor prognosis. This study aims to investigate the role of SFMBT2 in clear cell RCC.

METHODS

The expression of SFMBT2 in clear cell RCC specimens were determined by immunohistochemistry staining and western blot. The overexpression and knockdown of SFMBT2 was realized by infection of lentivirus loaded with SFMBT2 coding sequence or silencing fragment in 786-O and 769-P cells, and its effects on proliferation and metastasis were assessed by MTT, colony formation, flow cytometry, wound healing, transwell assay, xenograft and metastasis experiments in nude mice. The interaction of SFMBT2 with histone deacetylase 3 (HDAC3) and seven in absentia homolog 1 (SIAH1) was confirmed by co-immunoprecipitation.

RESULTS

In our study, SFMBT2 exhibited lower expression in clear cell RCC specimens with advanced stages than those with early stages. Overexpression of SFMBT2 inhibited the growth and metastasis of clear cell RCC cells, 786-O and 769-P, in vitro and in vivo, and its silencing displayed opposites effects. HDAC3 led to deacetylation of SFMBT2, and the HDAC3 inhibitor-induced acetylation prevented SFMBT2 from SIAH1-mediated ubiquitination modification and proteasome degradation. K687 in SFMBT2 protein molecule may be the key site for acetylation and ubiquitination.

CONCLUSIONS

SFMBT2 exerted an anti-tumor role in clear cell RCC cells, and HDAC3-mediated deacetylation promoted SIAH1-controlled ubiquitination of SFMBT2. SFMBT2 may be considered as a novel clinical diagnostic marker and/or therapeutic target of clear cell RCC, and crosstalk between its post-translational modifications may provide novel insights for agent development.

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.

Matrix- and Surface-Assisted Laser Desorption/Ionization Mass Spectrometry Methods for Urological Cancer Biomarker Discovery-Metabolomics and Lipidomics Approaches

Urinary tract cancers, including those of the bladder, the kidneys, and the prostate, represent over 12% of all cancers, with significant global incidence and mortality rates. The continuous challenge that these cancers present necessitates the development of innovative diagnostic and prognostic methods, such as identifying specific biomarkers indicative of cancer. Biomarkers, which can be genes, proteins, metabolites, or lipids, are vital for various clinical purposes including early detection and prognosis. Mass spectrometry (MS), particularly soft ionization techniques such as electrospray ionization (ESI) and laser desorption/ionization (LDI), has emerged as a key tool in metabolic profiling for biomarker discovery, due to its high resolution, sensitivity, and ability to analyze complex biological samples. Among the LDI techniques, matrix-assisted laser desorption/ionization (MALDI) and surface-assisted laser desorption/ionization (SALDI) should be mentioned. While MALDI methodology, which uses organic compounds as matrices, is effective for larger molecules, SALDI, based on the various types of nanoparticles and nanostructures, is preferred for smaller metabolites and lipids due to its reduced spectral interference. This study highlights the application of LDI techniques, along with mass spectrometry imaging (MSI), in identifying potential metabolic and lipid biomarkers for urological cancers, focusing on the most common bladder, kidney, and prostate cancers.

The role of lncRNA HCG18 in human diseases

A substantial number of long noncoding RNAs (lncRNAs) have been identified as potent regulators of human disease. Human leukocyte antigen complex group 18 (HCG18) is a new type of lncRNA that has recently been proven to play an important role in the occurrence and development of various diseases. Studies have found that abnormal expression of HCG18 is closely related to the clinicopathological characteristics of many diseases. More importantly, HCG18 was also found to promote disease progression by affecting a series of cell biological processes. This article mainly discusses the expression characteristics, clinical characteristics, biological effects and related regulatory mechanisms of HCG18 in different human diseases, providing a scientific theoretical basis for its early clinical application.

Understanding Factors that Influence Prognosis and Response to Therapy in Clear Cell Renal Cell Carcinoma

In this review, we highlight and contextualize emerging morphologic prognostic and predictive factors in renal cell carcinoma. We focus on clear cell renal cell carcinoma (ccRCC), the most common histologic subtype. Our understanding of the molecular characterization of ccRCC has dramatically improved in the last decade. Herein, we highlight how these discoveries have laid the foundation for new approaches to prognosis and therapeutic decision-making for patients with ccRCC. We explore the clinical relevance of common mutations, established gene expression signatures, intratumoral heterogeneity, sarcomatoid/rhabdoid morphology and PD-L1 expression, and discuss their impact on predicting response to therapy.

UroPredict: Machine learning model on real-world data for prediction of kidney cancer recurrence (UroCCR-120)

Renal cell carcinoma (RCC) is most often diagnosed at a localized stage, where surgery is the standard of care. Existing prognostic scores provide moderate predictive performance, leading to challenges in establishing follow-up recommendations after surgery and in selecting patients who could benefit from adjuvant therapy. In this study, we developed a model for individual postoperative disease-free survival (DFS) prediction using machine learning (ML) on real-world prospective data. Using the French kidney cancer research network database, UroCCR, we analyzed a cohort of surgically treated RCC patients. Participating sites were randomly assigned to either the training or testing cohort, and several ML models were trained on the training dataset. The predictive performance of the best ML model was then evaluated on the test dataset and compared with the usual risk scores. In total, 3372 patients were included, with a median follow-up of 30 months. The best results in predicting DFS were achieved using Cox PH models that included 24 variables, resulting in an iAUC of 0.81 [IC95% 0.77-0.85]. The ML model surpassed the predictive performance of the most commonly used risk scores while handling incomplete data in predictors. Lastly, patients were stratified into four prognostic groups with good discrimination (iAUC = 0.79 [IC95% 0.74-0.83]). Our study suggests that applying ML to real-world prospective data from patients undergoing surgery for localized or locally advanced RCC can provide accurate individual DFS prediction, outperforming traditional prognostic scores.

Integration of basement membrane-related genes in a risk signature for prognosis in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is characterized by high heterogeneity and recurrence rates, posing significant challenges for stratification and treatment. Basement membrane-related genes (BMGs) play a crucial role in tumor initiation and progression. Clinical and transcriptomic data of ccRCC patients were extracted from TCGA and GEO databases. We employed univariate regression and LASSO-Cox stepwise regression analysis to construct a BMscore model based on BMGs expression level. A nomogram combining clinical features and BMscore was constructed to predict individual survival probabilities. Further enrichment analysis and immune-related analysis were conducted to explore the enriched pathways and immune features associated with BMGs. High-risk individuals predicted by BMscore exhibited poorer overall survival, which was consistent with the validation dataset. BMscore was identified as an independent risk factor for ccRCC. Functional analysis revealed that BMGs were related to cell-matrix and tumor-associated signaling pathways. Immune profiling suggests that BMGs play a key role in immune interactions and the tumor microenvironment. BMGs serve as a novel prognostic predictor for ccRCC and play a role in the immune microenvironment and treatment response. Targeting the BM may represent an alternative therapeutic approach for ccRCC.

Surgical Management and Oncologic Outcomes of Renal Cell Carcinoma and Inferior Vena Caval Thrombi With Aggressive Histologic Variants

OBJECTIVE

To characterize the surgical management, perioperative, and cancer-specific outcomes, and the influence of aggressive histologic variants (AHV) on operative management among patients with renal cell carcinoma (RCC) and inferior vena cava (IVC) thrombus. RCC with rhabdoid and/or sarcomatoid differentiation, which we defined as AHV, portends a worse prognosis. AHV can be associated with a desmoplastic reaction which may complicate resection.

METHODS

We reviewed patients undergoing radical nephrectomy and IVC thrombectomy between 1990 and 2020. Comparative statistics were employed as appropriate. Survival analysis was performed according to the Kaplan-Meier method, and intergroup analysis performed with log-rank statistics. Multivariable cox proportional hazards regression was used to assess the effect of AHV, age, thrombus level, vena cavectomy, metastases, and medical comorbidities on recurrence and overall survival (OS).

RESULTS

Ninety-four of 403 (23.3%) patients had AHV, including 43 (46%) rhabdoid, 39 (41%) sarcomatoid, and 12 (13%) with both. AHV were more likely to present with advanced disease; however, increased perioperative complications or decreased OS were not observed. Median (IQR) survival was 16.7 (4.8-47) months without AHV and 12.6 (4-29) months with AHV (P = .157). Sarcomatoid differentiation was independently associated with worse OS (HR = 2.016, CI 1.38-2.95, P <.001), whereas rhabdoid alone or with sarcomatoid demonstrated similar OS (P = 0.063).

CONCLUSION

RCC and IVC thrombus with AHV are more likely to present with metastatic disease, and sarcomatoid differentiation is associated with a worse OS. Resection of tumors with and without AHV have similar perioperative complications, suggesting that surgery can be safely accomplished in patients with RCC and IVC thrombus with AHV.

Chromophobe Renal Cell Carcinoma with Sarcomatoid Differentiation: Clinicopathologic Correlation and Molecular Findings

Introduction: Sarcomatoid differentiation has been reported in approximately 8% of chromophobe renal cell carcinoma (RCC) and is associated with a worse prognosis. We aim to describe the clinicopathologic and molecular findings of chromophobe RCC with sarcomatoid differentiation. Methods: Surgical pathology database was searched to identify chromophobe RCC with sarcomatoid differentiation from January 2015 to December 2021. Results: Five patients were diagnosed with chromophobe RCC with sarcomatoid differentiation. The median age at the time of diagnosis was 57 years (range 51-61 years). Three patients died after median follow-up of 12.1 months (range 1.6-18.2 months). The median tumor size was 10.7 cm (range 5.6-13.6 cm). The median percentage of sarcomatoid component was 60% (range 10-90%), and the median percentage of necrosis was 30% (range 10-50%). One tumor demonstrated osteoid formation. PAX8, keratin 7, KIT (CD117), and Hale colloidal iron were positive in the epithelial component, whereas the sarcomatoid component was positive for vimentin, CD10, and high Ki67 proliferative index. Molecular testing was performed in three specimens: all were TP53 mutated and microsatellite stable. One aggressive tumor had RB1 frameshift mutation and copy number gains for TERT and CUL4A. Conclusion: Chromophobe RCC with sarcomatoid differentiation is a rare entity with aggressive behavior. Percentage of sarcomatoid component, necrosis, and the occurrence of metastasis is associated with worse prognosis. Molecular profiling reveals frequent TP53 mutation. While TERT promoter mutation has no prognostic implication, FLCN inactivation may be associated with a less aggressive course. The clinical significance of RB1 loss is unclear.

Genetic Profiling Uncovers Genome-Wide Loss of Heterozygosity and Provides Insight into Mechanisms of Sarcomatoid Transformation in Chromophobe Renal Cell Carcinoma

Sarcomatoid transformation occurs in ∼8% of chromophobe renal cell carcinoma (chRCC) and is associated with aggressive clinical behavior. In recent years, several studies have identified genomic, transcriptomic, and epigenomic correlates of aggressive behavior in chRCC; however, the molecular mechanisms associated with sarcomatoid transformation remain incompletely understood. In this study, we analyzed paired conventional and sarcomatoid histologic components of individual chRCC to elucidate the genomic alterations that underlie sarcomatoid transformation in this tumor type. Massively parallel sequencing was performed on paired (conventional and sarcomatoid) components from 8 chRCCs. All cases harbored TP53 variants (87.5% showing TP53 variants in both components and 12.5% only in the sarcomatoid component). Intratumor comparisons revealed that TP53 variants were concordant in 71% and discordant in 29% of cases. Additional recurrent single-nucleotide variants were found in RB1 (37.5% of cases) and PTEN (25% of cases), with the remaining single-nucleotide variants detected in these tumors (PBRM1, NF1, and ASXL1) being nonrecurrent. Copy number variant analysis showed the characteristic pattern of chromosomal losses associated with chRCC (1, 2, 6, 10, 13, 17, and 21) in the conventional histologic components only. Interestingly, the sarcomatoid components of these tumors demonstrated widespread loss of heterozygosity but lacked the above chromosomal losses, likely as a consequence of whole-genome duplication/imbalanced chromosomal duplication events. Overall, the findings suggest that TP53 variants followed by whole-genome duplication/imbalanced chromosomal duplication events underlie sarcomatoid transformation in chRCC.

A promising natural killer cell-based model and a nomogram for the prognostic prediction of clear-cell renal cell carcinoma

BACKGROUND

Clear-cell renal cell carcinoma (ccRCC) is one of prevalent kidney malignancies with an unfavorable prognosis. There is a need for a robust model to predict ccRCC patient survival and guide treatment decisions.

METHODS

RNA-seq data and clinical information of ccRCC were obtained from the TCGA and ICGC databases. Expression profiles of genes related to natural killer (NK) cells were collected from the Immunology Database and Analysis Portal database. Key NK cell-related genes were identified using consensus clustering algorithms to classify patients into distinct clusters. A NK cell-related risk model was then developed using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to predict ccRCC patient prognosis. The relationship between the NK cell-related risk score and overall survival, clinical features, tumor immune characteristics, as well as response to commonly used immunotherapies and chemotherapy, was explored. Finally, the NK cell-related risk score was validated using decision tree and nomogram analyses.

RESULTS

ccRCC patients were stratified into 3 molecular clusters based on expression of NK cell-related genes. Significant differences were observed among the clusters in terms of prognosis, clinical characteristics, immune infiltration, and therapeutic response. Furthermore, six NK cell-related genes (DPYSL3, SLPI, SLC44A4, ZNF521, LIMCH1, and AHR) were identified to construct a prognostic model for ccRCC prediction. The high-risk group exhibited poor survival outcomes, lower immune cell infiltration, and decreased sensitivity to conventional chemotherapies and immunotherapies. Importantly, the quantitative real-time polymerase chain reaction (qRT-PCR) confirmed significantly high DPYSL3 expression and low SLC44A4 expression in ACHN cells. Finally, the decision tree and nomogram consistently show the dramatic prediction performance of the risk score on the survival outcome of the ccRCC patients.

CONCLUSIONS

The six-gene model based on NK cell-related gene expression was validated and found to accurately mirror immune microenvironment and predict clinical outcomes, contributing to enhanced risk stratification and therapy response for ccRCC patients.

Comprehensive analyses of immune tumor microenvironment in papillary renal cell carcinoma

BACKGROUND

Papillary renal cell carcinoma (pRCC) is the most common non-clear cell RCC, and associated with poor outcomes in the metastatic setting. In this study, we aimed to comprehensively evaluate the immune tumor microenvironment (TME), largely unknown, of patients with metastatic pRCC and identify potential therapeutic targets.

METHODS

We performed quantitative gene expression analysis of TME using Microenvironment Cell Populations-counter (MCP-counter) methodology, on two independent cohorts of localized pRCC (n=271 and n=98). We then characterized the TME, using immunohistochemistry (n=38) and RNA-sequencing (RNA-seq) (n=30) on metastatic pRCC from the prospective AXIPAP trial cohort.

RESULTS

Unsupervised clustering identified two "TME subtypes", in each of the cohorts: the "immune-enriched" and the "immune-low". Within AXIPAP trial cohort, the "immune-enriched" cluster was significantly associated with a worse prognosis according to the median overall survival to 8 months (95% CI, 6 to 29) versus 37 months (95% CI, 20 to NA, p=0.001). The two immune signatures, Teff and JAVELIN Renal 101 Immuno signature, predictive of response to immune checkpoint inhibitors (CPI) in clear cell RCC, were significantly higher in the "immune-enriched" group (adjusted p<0.05). Finally, five differentially overexpressed genes were identified, corresponding mainly to B lymphocyte populations.

CONCLUSION

For the first time, using RNA-seq and immunohistochemistry, we have highlighted a specific immune TME subtype of metastatic pRCC, significantly more infiltrated with T and B immune population. This "immune-enriched" group appears to have a worse prognosis and could have a potential predictive value for response to immunotherapy, justifying the confirmation of these results in a cohort of metastatic pRCC treated with CPI and in combination with targeted therapies.

TRIAL REGISTRATION NUMBER

NCT02489695.

Exploring the role of natural bioactive molecules in genitourinary cancers: how far has research progressed?

The primary approaches to treat cancerous diseases include drug treatment, surgical procedures, biotherapy, and radiation therapy. Chemotherapy has been the primary treatment for cancer for a long time, but its main drawback is that it kills cancerous cells along with healthy ones, leading to deadly adverse health effects. However, genitourinary cancer has become a concern in recent years as it is more common in middle-aged people. So, researchers are trying to find possible therapeutic options from natural small molecules due to the many drawbacks associated with chemotherapy and other radiation-based therapies. Plenty of research was conducted regarding genitourinary cancer to determine the promising role of natural small molecules. So, this review focused on natural small molecules along with their potential therapeutic targets in the case of genitourinary cancers such as prostate cancer, renal cancer, bladder cancer, testicular cancer, and so on. Also, this review states some ongoing or completed clinical evidence in this regard.

Prognostic factors in renal cell carcinoma: A single‑center study

Renal cell carcinoma (RCC) is a heterogeneous and complex disease with numerous pathophysiologic variants. ~40% of patients succumb due to the progression of the disease, making RCC the most fatal of the common urologic malignancies. Prognostic factors are indicators of the progression of the disease, and the precise determination of these factors is important for evaluating and managing RCC. In the present study, it was aimed to determine and find associations among the histopathological features of RCCs and their impact on survival and metastasis. This is a cross-sectional study of RCC cases who have undergone partial or radical nephrectomy from March 2008 to October 2021 and have been pathologically reviewed at Shorsh General Teaching Hospital in Sulaimani, Iraq. The data in the pathology studies were supplemented by follow-up of the patients to obtain information about survival, recurrence and metastasis. In total, 228 cases of RCC were identified, among whom 60.5% were men and 39.5% were women, with a median age of 51 years. The main tumor types were clear cell RCC (71.1%), papillary RCC (13.6%), and chromophobe RCC (11%). Various measures of aggressiveness, including tumor necrosis, sarcomatoid change, microvascular invasion, and parameters of invasiveness (invasion of the renal sinus and other structures), were significantly correlated with each other, and they were also associated with reduced overall survival and an increased risk of metastasis on univariate analysis. However, on multivariate analysis, only tumor size and grade, and microvascular invasion retained statistical significance and were associated with a lower survival rate. In conclusion, pathological parameters have an impact on prognosis in RCC. The most consistent prognostic factors can be tumor size and grade, and microvascular invasion.

Tyrosine kinase and immune checkpoints inhibitors in favorable risk metastatic renal cell carcinoma: Trick or treat?

Over the past decade, the management of metastatic renal cell carcinoma (RCC) has undergone rapid evolution, culminating in a significant improvement in prognosis with frontline immunotherapy. RCC is a highly immunogenic and pro-angiogenic cancer, and mounting evidence has established the immunosuppressive effects of pro-angiogenic factors on the host's immune system. Anti-angiogenic agents such as tyrosine kinase inhibitors (TKIs) and bevacizumab, which obstruct the vascular endothelial growth factor pathway, have demonstrated the potential to enhance antitumor activity and improve the efficacy of immune checkpoint inhibitors (ICIs). Consequently, various combinations of TKIs and ICIs have been assessed and are currently considered the preferred regimens for all metastatic RCC patients, regardless of their prognostic risk score. Nevertheless, some inquiries have arisen within the medical community, as metastatic RCC patients with favorable risk scores who received ICIs and TKIs in combination showed no statistically significant advantage in overall survival compared to those treated with sunitinib alone. Considering these concerns, this review aims to elucidate the rationale behind TKI and ICI combination therapies, provide a summary of current first-line metastatic RCC combinations approved for use, with a focus on favorable-risk patients, and outline present challenges and future perspectives in this context.

Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma

Objective

This study aimed to evaluate the effectiveness of multi-phase-combined contrast-enhanced CT (CECT) radiomics methods for noninvasive Fuhrman grade prediction of clear cell renal cell carcinoma (ccRCC).

Methods

A total of 187 patients with four-phase CECT images were retrospectively enrolled and then were categorized into training cohort (n=126) and testing cohort (n=61). All patients were confirmed as ccRCC by histopathological reports. A total of 110 3D classical radiomics features were extracted from each phase of CECT for individual ccRCC lesion, and contrast-enhanced variation features were also calculated as derived radiomics features. These features were concatenated together, and redundant features were removed by Pearson correlation analysis. The discriminative features were selected by minimum redundancy maximum relevance method (mRMR) and then input into a C-support vector classifier to build multi-phase-combined CECT radiomics models. The prediction performance was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC).

Results

The multi-phase-combined CECT radiomics model showed the best prediction performance (AUC=0.777) than the single-phase CECT radiomics model (AUC=0.711) in the testing cohort (p value=0.039).

Conclusion

The multi-phase-combined CECT radiomics model is a potential effective way to noninvasively predict Fuhrman grade of ccRCC. The concatenation of first-order features and texture features extracted from corticomedullary phase and nephrographic phase are discriminative feature representations.

Comparison and validation of different risk models for papillary renal cell carcinoma

BACKGROUND

Several prognostic algorithms were specifically or nonspecifically used for papillary renal cell carcinoma (PRCC). No consensus was reached upon their efficacy of discrimination. We aim to compare the stratifying ability of current models or systems in predicting the risk of recurrence of PRCC.

METHODS

A PRCC cohort consisting of 308 patients from our institution and 279 patients from The Cancer Genome Atlas (TCGA) was generated. With ISUP grade, TNM classification, UCLA Integrated Staging System (UISS), STAGE, SIZE, GRADE AND NECROSIS (SSIGN), Leibovich model and VENUSS system, recurrence-free survival (RFS), disease-specific survival (DSS) and overall survival (OS) were studied using Kaplan-Meier method and concordance index (c-index) was compared. Differences between risk groups in gene mutation and infiltration of inhibitory immune cells were studied with TCGA database.

RESULTS

All the algorithms were able to stratify patients in RFS as well as DSS and OS (all P < 0.001). VENUSS score and risk group generally had the highest and balanced c-index (0.815 and 0.797 for RFS). ISUP grade, TNM stage and Leibovich model had the lowest c-indexes in all analysis. Among the 25 most frequently mutated genes in PRCC, eight had different mutation frequency between VENUSS low- and intermediate-/high-risk patients and mutated KMT2D and PBRM1 resulted in worsened RFS (P = 0.053 and P = 0.007). Increased Treg cells in tumors of intermediate-/high- risk patients were also identified.

CONCLUSIONS

VENUSS system showed better predictive accuracy in RFS, DSS and OS compared with SSIGN, UISS and Leibovich risk models. VENUSS intermediate-/high-risk patients had increased frequency of mutation in KMT2D and PBRM1 and increased infiltration of Treg cells.

Oncological outcomes of papillary versus clear cell renal cell carcinoma in pT1 and pT2 stage: Results from a contemporary Turkish patient cohort

OBJECTIVES

To compare overall survival (OS), recurrence free survival (RFS), and cancer-specific survival (CSS) in the long-term follow-up of T1 and T2 clear-cell-Renal Cell Carcinoma (ccRCC) and papillary Renal Cell Carcinoma (pRCC) patients, as well as to determine the risk factors for recurrence and overall mortality.

MATERIAL AND METHOD

Data of patients with kidney tumors obtained from the Urologic Cancer Database - Kidney (UroCaD-K) of Turkish Urooncology Association (TUOA) were evaluated retrospectively. Out of them, patients who had pathological T1-T2 ccRCC and pRCC were included in the study. According to the two histological subtype, recurrence and mortality status, RFS, OS and CSS data were analyzed.

RESULTS

RFS, OS and CSS of pRCC and ccRCC were found to be similar. Radiological local invasion was shown to be a risk factor for recurrence in pRCC, and age was the only independent factor affecting overall mortality.

CONCLUSIONS

There were no differences in survivals (RFS, OS and CSS) of patients with localized papillary and clear cell RCC. While age was the only factor affecting overall mortality, radiological local invasion was a risk factor for recurrence in papillary RCC.

Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review

Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.

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.

Quantification of structural heterogeneity in H&E stained clear cell renal cell carcinoma using refractive index tomography

Clear cell renal cell carcinoma (ccRCC) is a common histopathological subtype of renal cancer and is notorious for its poor prognosis. Its accurate diagnosis by histopathology, which relies on manual microscopic inspection of stained slides, is challenging. Here, we present a correlative approach to utilize stained images and refractive index (RI) tomography and demonstrate quantitative assessments of the structural heterogeneities of ccRCC slides obtained from human patients. Machine-learning-assisted segmentation of nuclei and cytoplasm enabled the quantification at the subcellular level. Compared to benign regions, malignant regions exhibited a considerable increase in structural heterogeneities. The results demonstrate that RI tomography provides quantitative information in synergy with stained images on the structural heterogeneities in ccRCC.

Expression of RSUME is associated with poor prognosis in clear cell Renal Carcinoma: involvement of ROS related metabolism

INTRODUCTION

RSUME (RWD domain-containing protein SUMO Enhancer), RWD domain containing 3 (RWDD3) gene product, is upregulated by hypoxia and expressed in organs prone to develop von Hippel-Lindau (VHL) syndrome tumors.

MATERIALS AND METHODS

We evaluated RSUME prognostic value in clear cell renal cell carcinoma (ccRCC) based mainly on the dataset (KIRC) from patients in The Cancer Genome Atlas (TCGA). Wilcoxon signed-rank test and one-way analysis of variance (ANOVA) followed by Tukey's test were used to evaluate relationships between clinicopathological features and RSUME expression and univariate and multivariate Cox regression analysis methods were used to evaluate prognostic factors. The biological function of RSUME was assessed by gene set enrichment analysis (GSEA). For validation, total amount of ROS was detected in ccRCC cell lines using dichlorofluorescin diacetate.

RESULTS

RSUME is highly expressed in tumor tissues compared with normal tissues (P = .006, P = .039, P = .002, P = .036, P < .001) and associates with tumor T (P = .018) and tumor M (P = .036) advanced stages and higher extent cysts (P = .005). RSUME expression appears to be an independent risk factor for overall survival (OS) (P = .002) and disease-specific survival (DSS) (P = .026) in ccRCC patients. GSEA showed enrichment of relevant glycerophospholipid- and ROS-related pathways in RSUME high-expression phenotype. ROS diminished levels in RSUME-silenced ccRCC cell lines validated RSUME relevance in ROS-related pathways.

CONCLUSION

RSUME high expression may predict poor prognosis in ccRCC and impact through its action on metabolism and ROS related pathways.

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.

Imaging Recommendations for Diagnosis, Staging, and Management of Renal Tumors

Renal cell carcinomas accounts for 2% of all the cancers globally. Most of the renal tumors are detected incidentally. Ultrasound remains the main screening modality to evaluate the renal masses. A multi -phase contrast enhanced computer tomography is must for characterizing the renal lesions. Imaging plays an important role in staging, treatment planning and follow up of renal cancers. In this review , we discuss the imaging guidelines for the management of renal tumors.

Pembrolizumab Plus Axitinib for Metastatic Papillary and Chromophobe Renal Cell Carcinoma: NEMESIA (Non Clear MEtaStatic Renal Cell Carcinoma Pembrolizumab Axitinib) Study, a Subgroup Analysis of I-RARE Observational Study (Meet-URO 23a)

Non-clear cell renal cell carcinoma (nccRCC) represents a heterogeneous histological group which is 20-25% of those with renal cell carcinoma (RCC). Patients with nccRCC have limited therapeutic options due to their exclusion from phase III randomized trials. The aim of the present study was to investigate the effectiveness and tolerability of pembrolizumabaxitinib combination in chromophobe and papillary metastatic RCC (mRCC) patients enrolled in the I-RARE (Italian Registry on rAre genitor-uRinary nEoplasms) observational ongoing study (Meet-URO 23). Baseline characteristics, objective response rate (ORR), disease control rate (DCR) and progression-free survival (PFS) and toxicities were retrospectively and prospectively collected from nccRCC patients treated in 14 Italian referral centers adhering to the Meet-Uro group, from December 2020 to April 2022. Only patients with chromophobe and papillary histology were considered eligible for the present pre-specified analysis. There were 32 eligible patients who received pembrolizumab-axitinib as first-line treatment, of whom 13 (40%) had chromophobe histology and 19 (60%) were classified as papillary RCC. The DCR was 78.1% whereas ORR was 43.7% (11 patients achieved stable disease and 14 patients obtained partial response: 9/19 papillary, 5/13 chromophobe). Six patients (18.7%) were primary refractory. Median PFS was 10.8 months (95%CI 1.7-11.5). Eleven patients (34.3%) interrupted the full treatment due to immune-related adverse events (irAEs): G3 hepatitis (n = 5), G3 hypophisitis (n = 1), G3 diarrhea (n = 1), G3 pancreatitis (n = 1), G3 asthenia (n = 1). Twelve patients (37.5%) temporarily interrupted axitinib only due to persistent G2 hand-foot syndrome or G2 hypertension. Pembrolizumab-axitinib combination could be an active and feasible first-line treatment option for patients with papillary or chromophobe mRCC.

Updated National Study of Functional Graft Renal Cell Carcinomas: Are They a Different Entity?

OBJECTIVE

To analyze de novo graft carcinoma characteristics from our updated national multicentric retrospective cohort.

METHODS

Thirty-two transplant centers have retrospectively completed the database. This database concerns all kidney graft tumors including urothelial, and others type but excludes renal lymphomas over 31 years.

RESULTS

One hundred and fifty twokidney graft carcinomas were diagnosed in functional grafts. Among them 130 tumors were Renal Cell Carcinomas. The calculated incidence was 0.18%. Median age of the allograft at diagnosis was 45.4 years old. The median time between transplantation and diagnosis was 147.1 months. 60 tumors were papillary carcinomas and 64 were clear cell carcinomas. Median tumor size was 25 mm. 18, 64, 21 and 1 tumors were respectively Fuhrman grade 1, 2, 3 and 4. Nephron sparing surgery (NSS) was performed on 68 (52.3%) recipients. Ablative therapy was performed in 23 cases (17.7%). Specific survival rate was 96.8%.

CONCLUSION

This study confirmed that renal graft carcinomas are a different entity: with a younger age of diagnosis; a lower stage at diagnosis; a higher incidence of papillary subtypes.

Differentiation of Clear Cell and Non-clear-cell Renal Cell Carcinoma through CT-based Radiomics Models and Nomogram

PURPOSE

The aim of the study was to investigate the feasibility of discriminating between clear-cell renal cell carcinoma (ccRCC) and non-clear-cell renal cell carcinoma (non-ccRCC) via radiomics models and nomogram.

METHODS

The retrospective study included 147 patients (ccRCC=100, non-ccRCC=47) who underwent enhanced CT before surgery. CT images of the corticomedullary phase (CMP) were collected and features from the images were extracted. The data were randomly grouped into training and validation sets according to 7:3, and then the training set was normalized to extract the normalization rule for the training set, and then the rule was applied to the validation set. First, the T-test, T'-test or Wilcoxon rank-sum test were executed in the training set data to keep the statistically different parameters, and then the optimal features were picked based on the least absolute shrinkage and selection operator (LASSO) algorithm. Five machine learning (ML) models were trained to differentiate ccRCC from noccRCC, rad+cli nomogram was constructed based on clinical factors and radscore (radiomics score), and the performance of the classifier was mainly measured by area under the curve (AUC), accuracy, sensitivity, specificity, and F1. Finally, the ROC curves and radar plots were plotted according to the five performance parameters.

RESULTS

1130 radiomics features were extracted, there were 736 radiomics features with statistical differences were obtained, and 4 features were finally selected after the LASSO algorithm. In the validation set of this study, three of the five ML models (logistic regression, random forest and support vector machine) had excellent performance (AUC 0.9-1.0) and two models (adaptive boosting and decision tree) had good performance (AUC 0.7-0.9), all with accuracy ≥ 0.800. The rad+cli nomogram performance was found excellent in both the training set (AUC = 0.982,0.963-1.000, accuracy=0.941) and the validation set (AUC = 0.949,0.885-1.000, accuracy=0.911). The random forest model with perfect performance (AUC = 1, accuracy=1) was found superior compared to the model performance in the training set. The rad+cli nomogram model prevailed in the comparison of the model's performance in the validation set.

CONCLUSION

The ML models and nomogram can be used to identify the relatively common pathological subtypes in clinic and provide some reference for clinicians.

Clinical pathological characteristics and treatment outcomes of renal cell carcinoma (RCC): a retrospective study from Sudan

Background

Worldwide, renal cell carcinoma comprises 2.2% and 1.8% of global cancer incidence and mortality, respectively. Studies of epidemiology, treatment modalities and outcomes of renal cell carcinoma (RCC) in Sudan are scarce. To address this shortcoming, we evaluated baseline information on the epidemiology, types of treatment and outcomes of RCC at Gezira Hospital for Renal Diseases and Surgery (GHRDS) and the National Cancer Institute (NCI).

Methods

We performed a retrospective, descriptive study of all patients with RCC, who were treated in GHRDS and NCI from January 2000 to December 2015.

Results

A total of 189 patients with RCC were identified over the study period. Tumours were more common among male patients (56%) and involved the left kidney in 52% of cases. The median age at diagnosis was 57 years (range: 21-90 years). Loin pain was the most frequent symptom (n = 103 patients) followed by weight loss (n = 103 patients) and haematuria (n = 65 patients). The most common histopathologic type of RCC was clear cell (73.5%), followed by papillary (13.8%) and chromophobe (1.6%). The relative frequencies of stages I-IV were 3.2%, 14.3%, 29.1% and 53.4%, respectively. The overall median survival rate was 24 months, and the 5-year survival rate was 40%. The 5-year survival rate in stages I-IV was 95%, 83%, 39%, and 17%, respectively. Advanced stages and higher-grade tumour were associated with worse survival. The median survival of stage IV patients was better for patients who underwent nephrectomy (11.0 months) compared to those who did not undergo nephrectomy (4.0 months) (p value = 0.28).

Conclusion

Our findings reveal poor outcomes for patients with RCC in Sudan, which is most likely due to the high proportion of patients presenting with advanced stages at the time of initial presentation.

Multiparametric magnetic resonance imaging for characterizing renal tumors: A validation study of the algorithm presented by Cornelis et al

Objectives

In the last decade, the incidence of renal cell carcinoma (RCC) has been rising, with the greatest increase observed for solid tumors. Magnetic resonance imaging (MRI) protocols and algorithms have recently been available for classifying RCC subtypes and benign subtypes. The objective of this study was to prospectively validate the MRI algorithm presented by Cornelis et al. for RCC classification.

Material and Methods

Over a 7-month period, 38 patients with 44 renal tumors were prospectively included in the study and received an MRI examination in addition to the conventional investigation program. The MRI sequences were: T2-weighted, dual chemical shift MRI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced T1-weighted in wash-in and wash-out phases. The images were evaluated according to the algorithm by two experienced, blinded radiologists, and the histopathological diagnosis served as the gold standard.

Results

Of 44 tumors in 38 patients, only 8 tumors (18.2%) received the same MRI diagnosis according to the algorithm as the histopathological diagnosis. MRI diagnosed 16 angiomyolipoma, 14 clear cell RCC (ccRCC), 12 chromophobe RCC (chRCC), and two papillary RCC (pRCC), while histopathological examination diagnosed 24 ccRCC, four pRCC, one chRCC, and one mixed tumor of both pRCC and chRCC. Malignant tumors were statistically significantly larger than the benign (3.16 ± 1.34 cm vs. 2.00 ± 1.04 cm, P = 0.006).

Conclusion

This prospective study could not reproduce Cornelis et al.'s results and does not support differentiating renal masses using multiparametric MRI without percutaneous biopsy in the future. The MRI algorithm showed few promising results to categorize renal tumors, indicating histopathology for clinical decisions and follow-up regimes of renal masses are still required.

Prediction of the Prognosis of Clear Cell Renal Cell Carcinoma by Cuproptosis-Related lncRNA Signals Based on Machine Learning and Construction of ceRNA Network

Background

Clear cell renal cell carcinoma's (ccRCC) occurrence and development are strongly linked to the metabolic reprogramming of tumors, and thus far, neither its prognosis nor treatment has achieved satisfying clinical outcomes.

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively, provided us with information on the RNA expression of ccRCC patients and their clinical data. Cuproptosis-related genes (CRGS) were discovered in recent massive research. With the help of log-rank testing and univariate Cox analysis, the prognostic significance of CRGS was examined. Different cuproptosis subtypes were identified using consensus clustering analysis, and GSVA was used to further investigate the likely signaling pathways between various subtypes. Univariate Cox, least absolute shrinkage and selection operator (Lasso), random forest (RF), and multivariate stepwise Cox regression analysis were used to build prognostic models. After that, the models were verified by means of the C index, Kaplan-Meier (K-M) survival curves, and time-dependent receiver operating characteristic (ROC) curves. The association between prognostic models and the tumor immune microenvironment as well as the relationship between prognostic models and immunotherapy were next examined using ssGSEA and TIDE analysis. Four online prediction websites-Mircode, MiRDB, MiRTarBase, and TargetScan-were used to build a lncRNA-miRNA-mRNA ceRNA network.

Results

By consensus clustering, two subgroups of cuproptosis were identified that represented distinct prognostic and immunological microenvironments.

Conclusion

A prognostic risk model with 13 CR-lncRNAs was developed. The immune microenvironment and responsiveness to immunotherapy are substantially connected with the model, which may reliably predict the prognosis of patients with ccRCC.

New insights into fibrotic signaling in renal cell carcinoma

Fibrotic signaling plays a pivotal role in the development and progression of solid cancers including renal cell carcinoma (RCC). Intratumoral fibrosis (ITF) and pseudo-capsule (PC) fibrosis are significantly correlated to the disease progression of renal cell carcinoma. Targeting classic fibrotic signaling processes such as TGF-β signaling and epithelial-to-mesenchymal transition (EMT) shows promising antitumor effects both preclinically and clinically. Therefore, a better understanding of the pathogenic mechanisms of fibrotic signaling in renal cell carcinoma at molecular resolution can facilitate the development of precision therapies against solid cancers. In this review, we systematically summarized the latest updates on fibrotic signaling, from clinical correlation and molecular mechanisms to its therapeutic strategies for renal cell carcinoma. Importantly, we examined the reported fibrotic signaling on the human renal cell carcinoma dataset at the transcriptome level with single-cell resolution to assess its translational potential in the clinic.

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.