Differentiation Of Renal Cell Carcinoma Research Articles

The Efficiency of Acoustic Radiation Force Impulse (ARFI) Elastography in the Differentiation of Renal Cell Carcinoma and Oncocytoma.

This study is to investigate the effectiveness of Acoustic Radiation Force Impulse (ARFI) elastography in differentiating radiologically similar renal cell carcinoma (RCC) and oncocytoma in solid masses of the kidney. The patients with solid renal mass histopathological diagnosed after excision or tru-cat biopsy who underwent a preoperative ARFI elastography of the lesion during a 4-year period were included in this study. Preoperative shear wave velocity (SWV) values were measured in all the lesions. SWV results of RCCs and oncocytomas were compared by an independent t-test, and cut-off, sensitivity and specificity values were calculated. Forty-two of the 60 patients included in the study were men (70%) and, 18 were women (30%), and the mean age was 59.7 ± 14 (27-94) years. Among 46 RCCs (76.6%), 23 and 14 oncocytomas, 5 (23.4%) were located in the right kidney (p:0.34722). Mean SWV values were found to be significantly higher in RCCs (2.87± 0.74 (0.96-4.14) m/s) than oncocytomas (1.83 ± 0.78 (0.80-3.76) m/s) (p <0.001). In the ROC analysis, a cutoff value of 2.29 m/s was found to havean 80.4% sensitivity and a 78.6% specificity for the discrimination of RCCs from oncocytomas. ARFI elastography measurements may be useful in distinguishing RCC and oncocytomas that may have similar solid radiological imaging features.

Abstract A029: Epigenomic profiling nominates master transcription factors (TFs) driving sarcomatoid differentiation of renal cell carcinoma (RCC)

Abstract Introduction Sarcomatoid RCC (sRCC) is associated with poor prognosis. Recent studies showed promising response rates of sRCC to immune checkpoint blockade (ICB). Although distinct patterns of gene expression in sRCC have been reported, the gene regulatory programs that drive SD remain unknown. This study aims to nominate TFs driving SD and to evaluate their impact on the clinical outcomes of patients (pts) with RCC. Methods Chromatin immunoprecipitation and sequencing (ChIP-seq) for H3K27ac was performed on histologically annotated areas of sRCC and non-sRCC fresh frozen tissue samples collected at Dana-Farber Cancer Institute. Regulatory elements that were differentially active between the two groups were identified (cutoff p&amp;lt;0.01, log-fold change (LFC) threshold=1). Enrichment of specific TF binding motifs at activated regulatory elements in sRCC was assessed using HOMER. Differential gene expression analysis of TFs was performed using DESeq2 on RNA-seq data from TCGA (Benjamini-Hochberg q &amp;lt;0.01, LFC threshold=1). An unpaired student’s t-test was performed to compare mean gene expression levels in transcript per million (TPM) on baseline sRCC and non-sRCC tissue samples from the IMmotion151 trial (sunitinib vs. atezolizumab-bevacizumab in metastatic RCC). Pts enrolled in the IMmotion151 trial were divided into quartiles based on gene expression levels of candidate TFs. Progression-free survival (PFS) was compared between the quartiles using a multivariable Cox proportional-hazards model accounting for age and IMDC risk score. The association of TF expression with the objective radiographic response (ORR) was analyzed using a logistic regression model. Results ChIP-seq was performed on 9 sRCC &amp; 17 non-sRCC samples. We identified 278 candidate regulatory elements with increased H3K27ac levels in sRCC vs. non-sRCC that were enriched for nucleotide motifs bound by the TFs FOSL1 and E2F7. Differential expression analysis between 48 sRCC vs. 493 non-sRCC samples showed that FOSL1 &amp; E2F7 were significantly upregulated in sRCC vs. non-sRCC (LFC=1.7, q=5e-11; LFC=1.8, q=1.3e-20; resp.). Mean TPMs of FOSL1 &amp; E2F7 were significantly increased in sRCC vs. non-sRCC in IMmotion151 cohort (2.5 vs. 1.5 TPM for FOSL1, and 2.9 vs. 1.9 TPM for E2F7, all p&amp;lt;0.001). Pts in the highest quartile of FOSL1 and E2F7 expression showed significantly shorter PFS in IMmotion151 pts receiving sunitinib alone (HR=1.6, 95%CI=1.3-2.2, p=0.008 &amp; HR=2.6, 95%CI=1.8-3.7, p&amp;lt;0.001; resp.). We observed similar results even after controlling for the presence of SD. Increased expression of E2F7 was associated with worse ORR in IMmotion151 pts who received sunitinib alone, even after controlling for the presence of SD (OR=0.24±0.4, p&amp;lt;0.001). Conclusion This is the first study to analyze the epigenomic landscape of sRCC by integrating ChIP-seq and RNA-seq data. Our findings implicated FOSL1 &amp; E2F7 as transcriptional regulators of SD with prognostic relevance. Further studies are underway to validate these results. Citation Format: Karl Semaan, Talal El Zarif, Marc Eid, Valisha Shah, Brad Fortunato, Renee Maria Saliby, Amin H. Nassar, Sarah Abou Alaiwi, Ji-Heui Seo, Chris Labaki, Ziad Bakouny, Sayed Matar, Nourhan El Ahmar, Yasmin Nabil Laimon, Gwo-Shu Mary Lee, Mark Pomerantz, Jacob Berchuck, Sabina Signoretti, Eliezer Van Allen, Toni Choueiri, David Braun, Matthew Freedman, Sylvan Baca. Epigenomic profiling nominates master transcription factors (TFs) driving sarcomatoid differentiation of renal cell carcinoma (RCC) [abstract]. In: Proceedings of the AACR Special Conference: Advances in Kidney Cancer Research; 2023 Jun 24-27; Austin, Texas. Philadelphia (PA): AACR; Cancer Res 2023;83(16 Suppl):Abstract nr A029.

Gut and urinary microbiota: the causes and potential treatment measures of renal cell carcinoma.

Mounting evidence suggests that the gut microbiota plays a crucial role in the development and treatment of various cancers. Recent research on the urinary microbiota challenges the long-standing belief that urine is sterile, as urinary microbiota has been implicated in the development of bladder and prostate cancers, similar to the role of gut microbiota in cancer development. Although the precise involvement of microbiota in the proliferation and differentiation of renal cell carcinoma (RCC) remains unclear, dysbiosis is considered one possible mechanism by which microbiota may contribute to RCC development and treatment. This review summarizes potential mechanisms by which gut microbiota may contribute to the development of RCC, and provides evidence for the involvement of urinary microbiota in RCC. We also explore the role of gut microbiota in RCC treatment and propose that the composition of gut microbiota could serve as a predictive marker for the potential efficacy of immune checkpoint inhibitors (ICIs) in RCC patients. Additionally, evidence suggests that modulating the abundance and distribution of microbiota can enhance the therapeutic effects of drugs, suggesting that microbiota may serve as a promising adjuvant therapy for RCC. Overall, we believe that further investigation into the gut and urinary microbiome of RCC patients could yield valuable insights and strategies for the prevention and personalized treatment of RCC.

Diagnostic performance of multiparametric magnetic resonance imaging in the differentiation of clear cell renal cell cancer.

This study aimed to evaluate the diagnostic performance of multiparametric magnetic resonance imaging (mpMRI) in the differentiation of renal cell carcinoma (RCC) subtypes. This is a retrospective diagnostic performance study, in which the diagnostic performances of mpMRI features were evaluated to differentiate clear cell RCC (ccRCC) from non-clear cell RCC (non-ccRCC). Adult patients who were evaluated using a 3-Tesla dynamic contrast-enhanced mpMRI before undergoing partial or radical nephrectomy for possible malignant renal tumors were included in the study. Signal intensity change percentages (SICP) between contrast-enhanced phases and pre-administration period for both the tumor and normal renal cortex, and tumor-to-cortex enhancement index (TCEI); tumor apparent diffusion coefficient (ADC) values; tumor-to-cortex ADC ratio; and a scale which was developed according to the tumor signal intensities on the axial fat-suppressed T2-weighted Half-Fourier Acquisition Single-shot Turbo spin Echo (HASTE) images were used in ROC analysis to estimate the presence of ccRCC in the patients. The reference test positivity was the histopathologic examination of the surgical specimens. Ninety-eight tumors from 91 patients were included in the study, and 59 of them were ccRCC, 29 were pRCC, and 10 were chRCC. The mpMRI features that had the three highest sensitivity rates were excretory phase SICP, T2-weighted HASTE scale score, and corticomedullary phase TCEI (93.2%, 91.5%, and 86.4%, respectively). However, those with the three highest specificity rates were nephrographic phase TCEI, excretory phase TCEI, and tumor ADC value (94.9%, 94.9%, and 89.7%, respectively). Several parameters on mpMRI showed an acceptable performance to differentiate ccRCC from non-ccRCC.

7 - Similarities and differences of selected gene expression in metastasizing and differentiation of renal cell carcinoma

7 - Similarities and differences of selected gene expression in metastasizing and differentiation of renal cell carcinoma

Characterization of Patients With Metastatic Renal Cell Carcinoma Experiencing Complete Response to First-line Therapies: Results From the International Metastatic Renal Cell Carcinoma Database Consortium.

Clinical trials have demonstrated higher complete response rates in the immuno-oncology-based combination arms than in the tyrosine kinase inhibitor arms in patients with metastatic renal cell carcinoma. We aimed to characterize real-world patients who experienced complete response to the contemporary first-line therapies. Using the International Metastatic Renal Cell Carcinoma Database Consortium, response-evaluable patients who received frontline immuno-oncology-based combination therapy or tyrosine kinase inhibitor monotherapy were analyzed. Baseline characteristics of patients and post-landmark overall survival were compared based on best overall response, as per RECIST 1.1. A total of 52 (4.6%) of 1,126 and 223 (3.0%) of 7,557 patients experienced complete response to immuno-oncology-based and tyrosine kinase inhibitor therapies, respectively (P = .005). An adjusted odds ratio for complete response achieved by immuno-oncology-based combination therapy (vs tyrosine kinase inhibitor monotherapy) was 1.56 (95% CI 1.11-2.17; P = .009). Among patients who experienced complete response, the immuno-oncology-based cohort had a higher proportion of non-clear cell histology (15.9% and 4.7%; P = .016), sarcomatoid dedifferentiation (29.8% and 13.5%; P = .014), and multiple sites of metastases (80.4% and 50.0%; P < .001) than the tyrosine kinase inhibitor cohort. Complete response was independently associated with post-landmark overall survival benefit in both the immuno-oncology-based and tyrosine kinase inhibitor cohorts, giving respective adjusted hazard ratios of 0.17 (95% CI 0.04-0.72; P = .016) and 0.28 (95% CI 0.21-0.38; P < .001). The complete response rate was not as high in the real-world population as in the clinical trial population. Among those who experienced complete response, several adverse clinicopathological features were more frequently observed in the immuno-oncology-based cohort than in the tyrosine kinase inhibitor cohort. Complete response was an indicator of favorable overall survival.

A discrimination model for differentiation of renal cell carcinoma from renal angiomyolipoma without visible fat: based on hierarchical fusion framework of multi-classifier

To investigate the capabilities of classification models based on hierarchical fusion framework of multi-classifier using a random projection strategy for differentiation of renal cell carcinoma (RCC) from small renal angiomyolipoma (< 4 cm) without visible fat (AMLwvf). We retrospectively collected the clinical data from 163 patients with pathologically proven small renal mass, including 118 with RCC and 45 with AMLwvf.Target region of interest (ROI) delineation was performed on an unenhanced phase (UP) CT image slice displaying the largest lesion area.The radiomics features were used to establish a hierarchical fusion method.On the projection-based level, the homogeneous classifiers were fused, and the fusion results were further fused at the classifier-based level to construct a multi-classifier fusion system based on random projection for differentiation of AMLwvf and RCC.The discriminative capability of this model was quantitatively evaluated using 5-fold cross validation and 4 evaluation indexes[specificity, sensitivity, accuracy and area under ROC curve (AUC)].We quantitatively compared this multi-classifier fusion framework against different classification models using a single classifier and several multi-classifier ensemble models. When the projection number was set at 10, the proposed hierarchical fusion differentiation framework achieved the best results on all the evaluation measurements.At the optimal projection number of 10, the specificity, sensitivity, average accuracy and AUC of the multi-classifier ensemble classification system for differentiation between AMLwvf and RCC were 0.853, 0.693, 0.809 and 0.870, respectively. The proposed model constructed based on a multi-classifier fusion system using random projection shows better performance to differentiate RCC from AMLwvf than the AMLwvf and RCC discrimination models based on a single classification algorithm and the currently available benchmark ensemble methods.

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

Detection and characterization of renal lesions are common in daily clinical practice. To investigate the effectiveness of shear wave elastography (SWE), a novel radiological examination technique, in the characterization of renal masses. The study included a total of 68 patients (33 men, 35 women; mean age = 57.71 ± 12.08 years; age range = 19-83 years) who underwent SWE. SWE measurements were obtained at depths of 2-8 cm from the probe surface in two different positions from an analysis window of approximately 0.5 × 1.0 cm on ultrasound. The cutoff SWE was calculated for the differentiation of renal cell carcinoma (RCC) and angiomyolipoma (AML) by receiver operating characteristic (ROC) analysis. When the result was statistically significant, the sensitivity, specificity, accuracy, and positive and negative predictive values of the test were calculated. Mass-to-parenchyma SWE ratios of RCCs were significantly higher than those of AMLs (P = 0.003). In ROC curve analysis, the SWE cutoff was 1.215 m/s to differentiate RCCs from AMLs. The area under the ROC curve was calculated as 0.74 (95% CI = 0.610-0.871, sensitivity = 70.7%, specificity = 70.6%, positive predictive value = 87.8%, negative predictive value = 44.4%). The SWE technique is increasingly used and may be useful in distinguishing RCC and AML lesions, and especially clear cell and non-clear cell RCCs.

The Current Status of Kidney Cancer Urine Markers – A Systematic Review

BACKGROUND: Renal cell carcinoma is the 9th most common malignant disease in the Western World. Typically, patients develop symptoms in a late stage of the disease and most of them are diagnosed by chance. Up to 30% of the patients at the time of diagnosis had metastatic disease. Therefore, highly specific and sensitive biomarkers for the detection and progression of kidney cancer are of great importance. Here, urine markers can be a major advantage and can have a huge clinical impact on the diagnosis, differentiation and prognosis of kidney cancer. At the moment there are several approaches to improve these conditions.. METHODS: A systematic literature research was performed according to the PRISMA guidelines to identify studies reporting urine markers for kidney cancer between 2012 and 2021. A two-step process for the selection of the studies was initiated. In total 287 studies were considering for the final analysis. In total, 6 studies, which presented potential urinary biomarker were analyzed in depth. RESULTS: The major focus was on urinary markers for the detection, progression and differentiation of renal cell carcinoma. In total, a study population of 1099 patients were investigated in the different studies that were analyzed in depth. The median patient sample size of the different studies was 157 patients. The focus was based on the investigation of different microRNAs and proteins as urinary marker for kidney cancer detection. CONCLUSION: Overall, there are different approaches present for the detection, prognosis and differentiation of kidney cancer in urine but most of the studies are based on a small sample size and need to be validated in a greater collective. Furthermore, the standard should be improved to bring these biomarkers into routine clinical practice.

Genomic alteration of MTAP/CDKN2A predicts sarcomatoid differentiation and poor prognosis and modulates response to immune checkpoint blockade in renal cell carcinoma.

Sarcomatoid differentiation is a highly aggressive pathological characteristic of renal cell carcinoma (RCC) and is characterized by susceptibility to progression and extremely poor prognosis. In this study, we included all genomic alteration events that led to a loss of protein function of MTAP and CDKN2A, and enrolled 5,307 RCC patients with genomic sequencing data from Western and Chinese cohorts. Notably, MTAP/CDKN2A MUT occurred in the Chinese population ~2 times more frequently than in the Western cohort and showed significant co-mutation trends. We found significantly higher proportions of sarcomatoid-positive patients with MTAP MUT or CDKN2A MUT compared with MTAP/CDKN2A wild-type (WT) patients (P < 0.001). Of the 574 RCC samples from the FUSCC cohort and 3,563 RCC samples from 17 independent cohorts, the MTAP/CDKN2A MUT significantly predicted extremely poor outcomes (P < 0.0001). The Western cohort suggested a concordant relationship between MTAP/CDKN2A MUT and sarcomatoid differentiation in RCC. Moreover, although MTAP/CDKN2A MUT RCC may be insensitive to targeted therapy, the high degree of tumor heterogeneity and higher PD-L1 and CXCL13 expression characterizations reflected that MTAP/CDKN2A-deficient features could benefit from immunotherapy for patients with RCC. This study utilized RCC samples from large-scale, global, multicenter sequencing cohorts and first proved that MTAP/CDKN2A deficiency significantly correlates with sarcomatoid differentiation in RCC and predicts aggressive progression, poor prognosis, and primary resistance to targeted therapy and potential favorable responses to immune checkpoint blockade. Unlike conventional targeted therapies, emerging drugs such as immunotherapies or synthetic lethal PRMT5 inhibitors may become novel therapeutic options for patients with MTAP/CDKN2A MUT RCC.

Papillary Type Renal Cell Carcinoma with Sarcomatoid Differentiation

Renal cell carcinoma is the most common renal tumor in adult population. Sarcomatoid differentiation of renal cell carcinoma is quite aggressive in nature and has poor prognosis. In this study, a case of 69-year-old female patient who initially presented with hematuria was discussed.

Differentiation of Benign Solid Renal Lesions from Malignant Lesions Using Dual-Energy CT

Objective: To evaluate the quantitative measurement of effective atomic number (Zeff) and iodine density reconstructed from dual-energy computed tomography (CT) in the discrimination of malignant renal lesions from benign renal lesions. Materials and Methods: The present study was a retrospective study that included patients with renal lesions who underwent dual-energy CT between January 2017 and June 2020. All subjects had a pathological diagnosis or follow-up imaging of at least 24 months. Two observers independently measured the Zeff, iodine density, and conventional CT attenuation of the renal lesions. Associations between the different tumor types and the quantitative CT measurements reconstructed from dual-energy CT were assessed, and interobserver agreement was evaluated. Results: Sixty-one renal lesions from 48 patients were included in the present study. No statistical differences were found in mean Zeff, iodine density, or CT attenuation between the benign renal lesions and the malignant renal lesions. However, significant differences were observed in Zeff, iodine density, and CT attenuation between the renal cell carcinoma (RCC) and the other malignant tumors, and between the clear cell RCC and the papillary RCC. The optimal thresholds for Zeff, iodine density, and CT attenuation for distinguishing RCC from other malignant tumors were 8.22, 1.71 mg/mL, and 79.50 HU, respectively, which provided sensitivity of 76%, 76%, and 80%, respectively. Conclusion: Benign solid renal lesions cannot be differentiated from malignant renal tumors by Zeff and iodine density measurements alone. However, dual-energy Zeff and iodine density have potential for the non-invasive differentiation of RCC from the other malignant renal tumors, and for the differentiation of RCC subtypes. Keywords: Dual-energy CT; Spectral CT; Renal cell carcinoma; Renal mass; Iodine density; Effective atomic number

Cross-sectional imaging assessment of renal masses with emphasis on MRI.

Magnetic resonance imaging (MRI) is a useful complementary imaging tool for the diagnosis and characterization of renal masses, as it provides both morphologic and functional information. A core MRI protocol for renal imaging should include a T1-weighted sequence with in- and opposed-phase images (or, alternatively with DIXON technique), T2-weighted and diffusion-weighted images as well as a dynamic contrast-enhanced sequence with subtraction images, followed by a delayed post-contrast T1-weighted sequence. The main advantages of MRI over computed tomography include increased sensitivity for contrast enhancement, less sensitivity for detection of calcifications, absence of pseudoenhancement, and lack of radiation exposure. MRI may be applied for renal cystic lesion characterization, differentiation of renal cell carcinoma (RCC) from benign solid renal tumors, RCC histologic grading, staging, post-treatment follow-up, and active surveillance of patients with treated or untreated RCC.

Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI

Sarcomatoid differentiation in RCC (sRCC) is associated with a poor prognosis, necessitating more aggressive management than RCC without sarcomatoid components (nsRCC). Since suspected renal cell carcinoma (RCC) tumors are not routinely biopsied for histologic evaluation, there is a clinical need for a non-invasive method to detect sarcomatoid differentiation pre-operatively. We utilized unsupervised self-organizing map (SOM) and supervised Learning Vector Quantizer (LVQ) machine learning to classify RCC tumors on T2-weighted, non-contrast T1-weighted fat-saturated, contrast-enhanced arterial-phase T1-weighted fat-saturated, and contrast-enhanced venous-phase T1-weighted fat-saturated MRI images. The SOM was trained on 8 nsRCC and 8 sRCC tumors, and used to compute Activation Maps for each training, validation (3 nsRCC and 3 sRCC), and test (5 nsRCC and 5 sRCC) tumor. The LVQ classifier was trained and optimized on Activation Maps from the 22 training and validation cohort tumors, and tested on Activation Maps of the 10 unseen test tumors. In this preliminary study, the SOM-LVQ model achieved a hold-out testing accuracy of 70% in the task of identifying sarcomatoid differentiation in RCC on standard multiparameter MRI (mpMRI) images. We have demonstrated a combined SOM-LVQ machine learning approach that is suitable for analysis of limited mpMRI datasets for the task of differential diagnosis.

Liq_ccRCC: Identification of Clear Cell Renal Cell Carcinoma Based on the Integration of Clinical Liquid Indices.

Currently, preoperative diagnosis and differentiation of renal clear cell carcinoma and other subtypes remain a serious challenge for doctors. The liquid biopsy technique and artificial intelligence have inspired the pursuit of distinguishing clear cell renal cell carcinoma using clinically available test data. In this work, a method called liq_ccRCC based on the integration of clinical blood and urine indices through machine learning approaches was successfully designed to achieve this goal. Clinically available biochemical blood data and urine indices were collected from 306 patients with renal cell carcinoma. Finally, the integration of 18 top-ranked clinical liquid indices (13 blood samples and 5 urine samples) was proven to be able to distinguish renal clear cell carcinoma from other subtypes of renal carcinoma by cross-valuation with an AUC of 0.9372. The successful introduction of this identification method suggests that subtype differentiation of renal cell carcinoma can be accomplished based on clinical liquid test data, which is noninvasive and easy to perform. It has huge potential to be developed as a promising innovation strategy for preoperative subtype differentiation of renal cell carcinoma with the advantages of convenience and real-time testing. liq_ccRCC is available online for the free test of readers at http://lishuyan.lzu.edu.cn/liq_ccRCC.

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

PurposeRenal cell carcinoma (RCC) subtype differentiation is of crucial importance in the management and prognosis of these patients. In this study, we investigated the usefulness of unenhanced and cortico-medullary phase contrast-enhanced multidetector-row computed tomography (MDCT) and T2-weighted fast spin-echo (FSE) magnetic resonance imaging (MRI) modalities in the discrimination of the 3 main subtype RCC patients in correlation with their histopathological findings.Material and methodsA total of 80 pathologically proven RCC patients who had undergone either partial or total nephrectomy were retrospectively investigated in this study. Their histological subtypes were 54 clear cell renal cell carcinoma (ccRCC), 15 papillary renal cell carcinoma (pRCC), and 11 chromophobe renal cell carcinoma (cRCC), based on pathological evaluation. There were 62 male (77.5%) and 18 female (22.5%) patients. Among the 54 ccRCC patients, 29 patients had both non-contrast and cortico-medullary phase CT, 1 had only non-contrast CT, 5 only had cortico-medullary phase CT, and 38 had MRI examination. In the pRCC group, 10 patients had both non-contrast and cortico-medullary phase CT, 1 had only non-contrast CT, 1 had only cortico-medullary phase CT, and 12 had MRI. Finally, in the remaining 11 cRCC patients, 9 had both non-contrast and cortico-medullary phase CT, and only 5 had MRI. We calculated both tumour attenuation values as HU (Hounsfield units) on unenhanced and cortico-medullary phase MDCT images and also tumour mean signal intensity values on FSE T2-weighted MRI images by using the region of interest (ROI) including normal renal cortex measurements. Besides quantitative evaluation, we also performed qualitative visual assessment of tumours on contrast-enhanced MDCT and FSE T2-weighted MRI.ResultsThere was no statistically significant difference among the attenuation values of the 3 tumour subtypes on pre-contrast CT images. ccRCC demonstrated a prominent degree of contrast enhancement compared to the chromophobe and papillary ones on cortico-medullary phase MDCT. We found no statistically significant difference between chromophobe and papillary subtypes, although chromophobe tumours showed slightly higher attenuation values compared to papillary ones. ccRCCs usually demonstrated a heterogenous contrast enhancement on cortico-medullary phase CT images, while the papillary subtype usually had a homogenous appearance on visual assessment. On FSE T2-weighted MR images, the signal intensity values of ccRCC patients were found to be significantly higher than both chromophobe and papillary subtypes. Although cRCC patients had a prominently lower T2 signal intensity than clear cell subtype, there was no statistically significant signal intensity difference between chromophobe and papillary subtypes. Regarding visual assessment, papillary subtype tumours showed a mostly homogenous appearance on T2-weighted images and a statistically significant difference was present. On the other hand, there was no significant difference of visual assessment of the clear cell and chromophobe subtypes.ConclusionsThe measurement of the attenuation values on cortico-medullary phase MDCT and the mean signal intensity values on FSE T2-weighted MRI can provide useful information in the differentiation of RCC main subtypes. Also, visual assessment of tumours on both modalities can contribute to this issue by providing additional imaging properties.

Quantitative Differentiation of Renal Cell Carcinoma From Fat-Poor Angiomyolipoma and Between Renal Cell Carcinoma Subtypes by Using Three-Phase MDCT

Background: Renal cell carcinoma (RCC) can be differentiated from angiomyolipoma by detection of macroscopic fat at multidetector computed tomography (MDCT). Measurement of enhancement at MDCT help classifying between RCC subtypes, which possibly predict tumor prognosis.&#x0D; Objective: Retrospectively assess whether quantitative measurements (percentage enhancement ratio [PER] and absolute washout ratio [AWR]) of renal mass enhancement during three-phase MDCT help differentiating RCC from fat-poor angiomyolipoma and other RCC subtypes.&#x0D; Methods: The retrospective review of the preoperative three-phase MDCT (unenhanced, corticomedullary, and early excretory phases) performed between January 2008 and July 2017, a total of 75 renal lesions (74 consecutive patients) were assessed for attenuation values in each phase. The enhancement values (PER and AWR) were compared by ANOVA tests. Cutoff analysis of enhancement values was performed to determine optimal threshold for each histologic subtype.&#x0D; Results: The attenuation value of fat-poor angiomyolipoma was significantly higher than clear cell RCCs in unenhanced phase (P = .02). The PER of the clear cell RCCs was significantly lower than that of papillary RCCs, chromophobe RCCs, and fat-poor angiomyolipomas (P &lt; .001). The AWR of the clear cell RCCs showed significantly greater than that of papillary RCCs and fat-poor angiomyolipoma (P &lt; .001). The PER and AWR thresholds for differentiating RCCs from fat-poor angiomyolipoma were 93.0 and 31.6 with accuracy of 74.7% and 77.3%, respectively.&#x0D; Conclusions: Quantitative measurement of enhancement (PER and AWR) might help differentiating RCCs from fat-poor angiomyolipoma, and differentiating clear cell RCCs from papillary RCCs.&#x0D;

The value of 99mTc-sestamibi single-photon emission computed tomography-computed tomography in the evaluation and risk stratification of renal masses.

Differentiation of renal cell carcinoma (RCC) from oncocytoma is a common diagnostic dilemma. A few studies have shown that 99mTc-sestamibi (MIBI) imaging has the potential to characterize indeterminate renal masses. This comparative study evaluated the utility of MIBI single-photon emission computed tomography-computed tomography (SPECT-CT) in the assessment and risk stratification of renal masses. A total of 29 patients with 31 renal masses who had cross-sectional imaging and MIBI SPECT-CT were included. Lesions were categorized as either MIBI-positive or -negative on SPECT-CT. Individual lesion density ranged from 22-56 Hounsfield units (HU) on the non-contrast CT part of SPECT-CT. Quantitative relative MIBI uptake was calculated by measuring tumor to ipsilateral renal parenchymal uptake. The imaging results were correlated with histopathology. All oncocytic lesions, including seven oncocytomas and one hybrid oncocytic chromophobe tumor (100%), were positive on MIBI. One chromophobe RCC showed low-grade MIBI uptake. The remaining RCC subtypes, including 15 clear-cell, four papillary, two mixed clear-cell and papillary, and one chromophobe, were MIBI-negative. The quantitative relative tumor uptake showed statistically significant higher uptake in the low-risk/oncocytic lesions compared to RCCs. This study demonstrates that MIBI SPECT-CT is valuable in the characterization of indeterminate renal masses. The combination of MIBI uptake on SPECT and lesion density on non-contrast CT can be used for risk stratification of renal masses. This technique may reduce the need for further imaging (multiphasic CT or magnetic resonance imaging), renal mass biopsy, or surgical resection of low-risk renal masses. Subsequently, more patients could be followed with active surveillance.

Characterization of solid renal neoplasms using MRI-based quantitative radiomics features

To assess the diagnostic value of magnetic resonance imaging (MRI)-based radiomics features using machine learning (ML) models in characterizing solid renal neoplasms, in comparison/combination with qualitative radiologic evaluation. Retrospective analysis of 125 patients (mean age 59years, 67% males) with solid renal neoplasms that underwent MRI before surgery. Qualitative (signal and enhancement characteristics) and quantitative radiomics analyses (histogram and texture features) were performed on T2-weighted imaging (WI), T1-WI pre- and post-contrast, and DWI. Mann-Whitney U test and receiver-operating characteristic analysis were used in a training set (n = 88) to evaluate diagnostic performance of qualitative and radiomics features for differentiation of renal cell carcinomas (RCCs) from benign lesions, and characterization of RCC subtypes (clear cell RCC [ccRCC] and papillary RCC [pRCC]). Random forest ML models were developed for discrimination between tumor types on the training set, and validated on an independent set (n = 37). We assessed 104 RCCs (51 ccRCC, 29 pRCC, and 24 other subtypes) and 21 benign lesions in 125 patients. Significant qualitative and quantitative radiomics features (area under the curve [AUC] between 0.62 and 0.90) were included for ML analysis. Models with best diagnostic performance on validation sets showed AUC of 0.73 (confidence interval [CI] 0.5-0.96) for differentiating RCC from benign lesions (using combination of qualitative and radiomics features); AUC of 0.77 (CI 0.62-0.92) for diagnosing ccRCC (using radiomics features), and AUC of 0.74 (CI 0.53-0.95) for diagnosing pRCC (using qualitative features). ML models incorporating MRI-based radiomics features and qualitative radiologic assessment can help characterize renal masses.

Impact of sarcomatoid differentiation and rhabdoid differentiation on prognosis for renal cell carcinoma with vena caval tumour thrombus treated surgically

BackgroundSarcomatoid differentiation in renal cell carcinoma (RCC) with vena caval tumour thrombus has been shown to be associated with aggressive behaviours and poor prognosis; however, evidence of the impact of rhabdoid differentiation on prognosis is lacking. This study evaluated the impact of sarcomatoid differentiation and rhabdoid differentiation on oncological outcomes for RCC with vena caval tumour thrombus treated surgically.MethodsWe retrospectively analysed patients treated surgically for RCC with vena caval tumour thrombus at our institute from Jan 2015 to Nov 2018. Prognostic variables were evaluated for associations with progression-free survival (PFS) and cancer-specific survival (CSS) by Kaplan–Meier survival analysis and log-rank test. Univariate and multivariate analyses were performed to determine independent prognostic variables.ResultsWe identified 125 patients with RCC and vena caval tumour thrombus, including 17 (13.6%) with sarcomatoid differentiation alone, 8 (6.4%) with rhabdoid differentiation alone and 3 (2.4%) with both sarcomatoid and rhabdoid differentiation. Compared to pure RCC, patients with sarcomatoid differentiation but not rhabdoid differentiation have worse PFS (p = 0.018 and p = 0.095, respectively). The univariate and multivariate analyses both showed sarcomatoid differentiation as a significant predictor of PFS. Compared to pure RCC, patients with sarcomatoid differentiation (p = 0.002) and rhabdoid differentiation (p = 0.001) both had significantly worse CSS. The univariate analysis showed sarcomatoid differentiation, rhabdoid differentiation, metastasis and blood transfusion as significant predictors of CSS (All, p < 0.05). In the multivariate analysis, sarcomatoid differentiation (HR 3.90, p = 0.008), rhabdoid differentiation (HR 3.01, p = 0.042), metastasis (HR 3.87, p = 0.004) and blood transfusion (HR 1.34, p = 0.041) all remained independent predictors of CSS.ConclusionsSarcomatoid differentiation and rhabdoid differentiation are both independent predictors of poor prognosis in RCC with vena caval tumour thrombus treated surgically.