Subtypes Of Renal Tumors Research Articles

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

BackgroundTo develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.Materials and methodsThe model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas. Renal tumor masks manually drawn on contrast-enhanced CT images were used to develop a 3D U-Net-based deep learning model for fully automated tumor segmentation. After segmentation, the entire classification pipeline, including feature extraction and subtype classification, was conducted without any manual intervention. Both conventional radiological features (Hounsfield units, HUs) and radiomic features extracted from areas predicted by the deep learning models were used to develop an algorithm for classifying renal tumor subtypes via a random forest classifier. The performance of the segmentation model was evaluated using the Dice similarity coefficient, while the classification model was assessed based on accuracy, sensitivity, and specificity.ResultsFor tumors larger than 4 cm, the Dice similarity coefficient (DSC) for automated segmentation was 0.83, while for tumors smaller than 4 cm, the DSC was 0.65. The classification accuracy (ACC) for distinguishing RCC subtypes was 0.77 for tumors larger than 4 cm and 0.68 for tumors smaller than 4 cm. Additionally, the accuracy for benign versus malignant classification was 0.85.ConclusionsOur automatic segmentation and classifier model showed promising results for renal tumor segmentation and classification.

Multi-instance learning for identifying high-risk subregions associated with synchronous distant metastasis in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is one of the most common histological subtypes of renal tumors. To identify high-risk subregions associated with synchronous distant metastasis. This study enrolled a total of 277 patients with ccRCC. Voxel intensity and local entropy values were compiled within the region of interest for all patients. Unsupervised k-means clustering yielded three subregions per tumor. Radiomic features were extracted, and random forest-based feature selection was conducted. The selected features were used in a multi-instance support vector machine (mi-SVM) model for training, and predictions were made on the validation cohort. Model performance was evaluated using five-fold cross-validation. The subregion with the highest score for patients with synchronous distant metastasis was identified across all cohorts. The mi-SVM model yielded an average area under the curve (AUC) of 0.812 in the training cohort and 0.805 in the validation cohort. In the entire cohort of patients with synchronous distant metastasis, subregion 2, characterized by tumor periphery and intratumoral transitional components, accounted for the highest proportion (48.57%, 30.6/63) among all subregions. It represents a high-risk subregion for synchronous distant metastasis of clear cell renal cell carcinoma. The peripheral and intratumoral transition zones of clear cell renal cell carcinoma are high-risk subregions associated with synchronous distant metastasis.

Renal Solitary Fibrous Tumor With Müllerian Cysts: A Novel Differential Diagnosis for Biphasic Renal Neoplasms.

Solitary fibrous tumor is an uncommon mesenchymal neoplasm, initially described in the pleura and infrequently found in the kidney. It is characterized by haphazardly arranged spindle cells, staghorn vasculature, coexpression of CD34 and signal transducer and activator of transcription 6 (STAT6), and a NAB2::STAT6 gene fusion. We report a 64-year-old woman who presented with a 2.5 cm multilocular cystic renal mass. On microscopic examination, the tumor consisted of a spindle cell component closely intermingled with ciliated and hemorrhagic epithelial cysts. The spindle cell component was positive for CD34, B-cell lymphoma 2 (BCL2), and STAT6, confirming the diagnosis of a solitary fibrous tumor. The epithelial cysts expressed keratin 7, PAX8, BCL2, estrogen receptor 1, and progesterone receptor, indicative of Müllerian cysts. The occurrence of solitary fibrous tumor in the kidney is extremely rare, and its association with Müllerian cysts has not been previously reported in the kidney. This morphologic variant has a striking similarity with biphasic renal neoplasms, especially with mixed epithelial and stromal tumors and angiomyolipoma with epithelial cysts. This report describes a novel renal solitary fibrous tumor subtype within this differential and underscores clinical and pathological distinctions of biphasic renal neoplasms.

Renal Neoplasms with Concurrent Castleman-Like Regional Lymphadenopathy.

Renal cell neoplasms are known to be associated with paraneoplastic syndromes, and the association with Castleman-like regional lymphadenopathy has been rarely reported. We aim to characterize the association between renal neoplasms and Castleman-like lymphadenopathy. A search for renal neoplasms with concurrent Castleman-like lymphadenopathy in one single medical institution from 2000 to 2023 resulted in 4 specimens. A literature search for "Castleman" and "renal neoplasm" resulted in 8 reports. Patients' demographics, clinical presentation, gross and histologic features, results of ancillary studies, treatment, and follow-up were evaluated. Our patients included 3 men and 1 woman, with a mean age of 60 years. Four different subtypes of renal neoplasms were diagnosed, including clear cell renal cell carcinoma (RCC), papillary RCC, chromophobe RCC, and mucinous cystadenoma of the renal pelvis. For Castleman-like regional lymphadenopathy, 2 were plasma-cell predominant, and 2 were hyaline-vascular. After a median follow-up of 84 months, all patients were alive with no recurrence or progression of Castleman-like features following nephrectomies. Castleman-like regional lymphadenopathy should be considered in patients with renal tumors and lymphadenopathy. Although more prevalent in clear cell RCC, it can be also associated with other renal neoplasms. The concurrent lymphadenopathy was remitted following the renal tumor resections.

Clear Cell Renal Cell Carcinoma: A Comprehensive Review of its Histopathology, Genetics, and Differential Diagnosis.

Clear cell renal cell carcinoma (ccRCC) is the predominant subtype of renal epithelial tumor, accounting for roughly 2% of all malignancies. Clinically, it often presents in the sixth to seventh decade of life, predominantly in men. Pathologically, these tumors exhibit a distinctive golden yellow cut surface, usually arising from the renal cortex. Their microscopic features are characterized by solid and nested architectures of cells with clear or eosinophilic granular cytoplasm and a prominent vascular network. A hallmark genetic feature is the inactivation of the VHL gene situated on chromosome 3p25. The majority of ccRCCs are sporadic (over 95%), typically presenting as a single mass; and a small percentage have a hereditary basis, often associated with VHL disease, characterized by multiple bilateral tumors with an earlier onset. Immunohistochemically, ccRCC tumors express PAX8, CA9 box like pattern, and CD10 but are generally negative for AMACR (35% positive) and KRT7 (15% positive). The prognosis of ccRCC is largely determined by its TNM stage, ISUP/WHO nucleolar grade, and the presence of specific aggressive features. This review article delves into the detailed gross, microscopic, molecular, and clinical features of ccRCC, offering comprehensive insights into its diagnosis, management, and prognosis.

The Clinicopathological Characteristics and Prognosis of 55 Patients With TFE3-Rearranged Renal Cell Carcinomas

ObjectiveTo explore the clinicopathological features and prognosis of TFE3-rearranged renal cell carcinomas (TFE3-rRCC). MethodsIn this retrospective observational study, the data of patients with TFE3-rRCC admitted to Xijing Hospital from January 2010 to October 2023 were collected, encompassing the general information, pathological diagnosis, immunohistochemistry, and the results of FISH detection. The treatment information and survival data of the patients were recorded during the follow-up. ResultsA total of 55 patients with TFE3-rRCC were enrolled, among whom 25 were males and 30 were females. TFE3 FISH assay suggested the disruption of the TFE3 gene. Fifty-four patients underwent surgical resection of kidney lesions, while 1 patient did not. By the end of follow-up in December 2023, 3 patients were lost to follow-up, 28 patients remained alive, and 24 patients had died. Among the 52 patients followed up, 31 developed metastases, involving lymph nodes, liver, bone, lung, peritoneum, pleura, adrenal gland, and brain. The 1-year and 5-year survival rates of the patients were 84.6% and 50.6%, respectively. In this study, there were 31 patients with TFE3-rRCC recurrence or metastasis. Median PFS was 7 and 13 months in the VEGFR-TKI and VEGFR-TKI+ ICI groups, respectively. The median OS was 12 months in the VEGFR-TKI treatment group. The median OS data of VEGFR-TKI+ ICI group has not been reached. The ORR and DCR was 25%, 66.7% in the VEGFR-TKI group. The ORR and DCR was 33.3%, 77.8% in the VEGFR-TKI+ ICI group. ConclusionTFE3-rRCC is a rare subtype of malignant renal tumor. The diagnosis mainly relies on pathological morphology, immunohistochemistry, and the detection of TFE3 gene disruption by FISH. In terms of treatment, surgery is the primary approach, and lymph nodes, liver, and bone are the main metastatic sites. VEGFR-TKI+ICI treatment might be an option of recurrent or metastatic TFE3-rRCC.

High-resolution and highly accelerated MRI T2 mapping as a tool to characterise renal tumour subtypes and grades

BackgroundClinical imaging tools to probe aggressiveness of renal masses are lacking, and T2-weighted imaging as an integral part of magnetic resonance imaging protocol only provides qualitative information. We developed high-resolution and accelerated T2 mapping methods based on echo merging and using k-t undersampling and reduced flip angles (TEMPURA) and tested their potential to quantify differences between renal tumour subtypes and grades.MethodsTwenty-four patients with treatment-naïve renal tumours were imaged: seven renal oncocytomas (RO); one eosinophilic/oncocytic renal cell carcinoma; two chromophobe RCCs (chRCC); three papillary RCCs (pRCC); and twelve clear cell RCCs (ccRCC). Median, kurtosis, and skewness of T2 were quantified in tumours and in the normal-adjacent kidney cortex and were compared across renal tumour subtypes and between ccRCC grades.ResultsHigh-resolution TEMPURA depicted the tumour structure at improved resolution compared to conventional T2-weighted imaging. The lowest median T2 values were present in pRCC (high-resolution, 51 ms; accelerated, 45 ms), which was significantly lower than RO (high-resolution; accelerated, p = 0.012) and ccRCC (high-resolution, p = 0.019; accelerated, p = 0.008). ROs showed the lowest kurtosis (high-resolution, 3.4; accelerated, 4.0), suggestive of low intratumoural heterogeneity. Lower T2 values were observed in higher compared to lower grade ccRCCs (grades 2, 3 and 4 on high-resolution, 209 ms, 151 ms, and 106 ms; on accelerated, 172 ms, 160 ms, and 102 ms, respectively), with accelerated TEMPURA showing statistical significance in comparison (p = 0.037).ConclusionsBoth high-resolution and accelerated TEMPURA showed marked potential to quantify differences across renal tumour subtypes and between ccRCC grades.Trial registrationClinicalTrials.gov, NCT03741426. Registered on 13 November 2018.Relevance statementThe newly developed T2 mapping methods have improved resolution, shorter acquisition times, and promising quantifiable readouts to characterise incidental renal masses.Graphical

Paraoxonase-2 shRNA-mediated gene silencing suppresses proliferation and migration, while promotes chemosensitivity in clear cell renal cell carcinoma cell lines.

Clear cell renal cell carcinoma (ccRCC) represents the most common subtype of renal tumor. Despite recent advances in identifying novel target molecules, the prognosis of patients with ccRCC continues to be poor, mainly due to the lack of sensitivity to chemo- and radiotherapy and because of one-third of renal cell carcinoma patients displays metastatic disease at diagnosis. Thus, identifying new molecules for early detection and for developing effective targeted therapies is mandatory. In this work, we focused on paraoxonase-2 (PON2), an intracellular membrane-bound enzyme ubiquitously expressed in human tissues, whose upregulation has been reported in a variety of malignancies, thus suggesting its possible role in cancer cell survival and proliferation. To investigate PON2 involvement in tumor cell metabolism, human ccRCC cell lines were transfected with plasmid vectors coding short harpin RNAs targeting PON2 transcript and the impact of PON2 silencing on cell viability, migration, and response to chemotherapeutic treatment was then explored. Our results showed that PON2 downregulation was able to trigger a decrease in proliferation and migration of ccRCC cells, as well as an enhancement of cell sensitivity to chemotherapy. Thus, taken together, data reported in this study suggest that the enzyme may represent an interesting therapeutic target for ccRCC.

Characterizing the tumor suppressor activity of FLCN in Birt-Hogg-Dubé syndrome through transcriptiomic and proteomic analysis.

Birt-Hogg-Dubé (BHD) syndrome patients are uniquely susceptible to all renal tumour subtypes. The underlying mechanism of carcinogenesis is unclear. To study cancer development in BHD, we used human proximal kidney (HK2) cells and found that long-term folliculin (FLCN) knockdown was required to increase their tumorigenic potential, forming larger spheroids in non-adherent conditions. Transcriptomic and proteomic analysis uncovered links between FLCN, cell cycle control and the DNA damage response (DDR) machinery. HK2 cells lacking FLCN had an altered transcriptome profile with cell cycle control gene enrichment. G1/S cell cycle checkpoint signaling was compromised with heightened protein levels of cyclin D1 (CCND1) and hyperphosphorylation of retinoblastoma 1 (RB1). A FLCN interactome screen uncovered FLCN binding to DNA-dependent protein kinase (DNA-PK). This novel interaction was reversed in an irradiation-responsive manner. Knockdown of FLCN in HK2 cells caused a marked elevation of γH2AX and RB1 phosphorylation. Both CCND1 and RB1 phosphorylation remained raised during DNA damage, showing an association with defective cell cycle control with FLCN knockdown. Furthermore, Flcn-knockdown C. elegans were defective in cell cycle arrest by DNA damage. This work implicates that long-term FLCN loss and associated cell cycle defects in BHD patients could contribute to their increased risk of cancer.

Establishment and validation of a novel disulfidptosis-related immune checkpoint gene signature in clear cell renal cell carcinoma

BackgroundClear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of renal tumors and is associated with a unfavorable prognosis. Disulfidptosis is a recently identified form of cell death mediated by disulfide bonds. Numerous studies have highlighted the significance of immune checkpoint genes (ICGs) in ccRCC. Nevertheless, the involvement of disulfidptosis-related immune checkpoint genes (DRICGs) in ccRCC remains poorly understood.MethodsThe mRNA expression profiles and clinicopathological data of ccRCC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases. The associations between disulfidptosis-related genes (DRGs) and immune checkpoint genes (ICGs) were assessed to identify DRICGs. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were conducted to construct a risk signature.ResultsA total of 39 differentially expressed immune-related candidate genes were identified. A prognostic signature was constructed utilizing nine DRICGs (CD276, CD80, CD86, HLA-E, LAG3, PDCD1LG2, PVR, TIGIT, and TNFRSF4) and validated using GEO data. The risk model functioned as an independent prognostic indicator for ccRCC, while the associated nomogram provided a reliable scoring system for ccRCC. Gene set enrichment analysis indicated enrichment of phospholipase D, antigen processing and presentation, and ascorbate and aldarate metabolism-related signaling pathways in the high-risk group. Furthermore, the DRICGs exhibited correlations with the infiltration of various immune cells. It is noteworthy that patients with ccRCC categorized into distinct risk groups based on this model displayed varying sensitivities to potential therapeutic agents.ConclusionsThe novel DRICG-based risk signature is a reliable indicator for the prognosis of ccRCC patients. Moreover, it also aids in drug selection and correlates with the tumour immune microenvironment in ccRCC.

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

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

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

ObjectivesTo distinguish histological subtypes of renal tumors using radiomic features and machine learning (ML) based on multiphase computed tomography (CT).Material and methodsPatients who underwent surgical treatment for renal tumors at two tertiary centers from 2012 to 2022 were included retrospectively. Preoperative arterial (corticomedullary) and venous (nephrogenic) phase CT scans from these centers, as well as from external imaging facilities, were manually segmented, and standardized radiomic features were extracted. Following preprocessing and addressing the class imbalance, a ML algorithm based on extreme gradient boosting trees (XGB) was employed to predict renal tumor subtypes using 10-fold cross-validation. The evaluation was conducted using the multiclass area under the receiver operating characteristic curve (AUC). Algorithms were trained on data from one center and independently tested on data from the other center.ResultsThe training cohort comprised n = 297 patients (64.3% clear cell renal cell cancer [RCC], 13.5% papillary renal cell carcinoma (pRCC), 7.4% chromophobe RCC, 9.4% oncocytomas, and 5.4% angiomyolipomas (AML)), and the testing cohort n = 121 patients (56.2%/16.5%/3.3%/21.5%/2.5%).The XGB algorithm demonstrated a diagnostic performance of AUC = 0.81/0.64/0.8 for venous/arterial/combined contrast phase CT in the training cohort, and AUC = 0.75/0.67/0.75 in the independent testing cohort. In pairwise comparisons, the lowest diagnostic accuracy was evident for the identification of oncocytomas (AUC = 0.57–0.69), and the highest for the identification of AMLs (AUC = 0.9–0.94)ConclusionRadiomic feature analyses can distinguish renal tumor subtypes on routinely acquired CTs, with oncocytomas being the hardest subtype to identify.Clinical relevance statementRadiomic feature analyses yield robust results for renal tumor assessment on routine CTs. Although radiologists routinely rely on arterial phase CT for renal tumor assessment and operative planning, radiomic features derived from arterial phase did not improve the accuracy of renal tumor subtype identification in our cohort.

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

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). 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. 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. 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.

Papillary renal neoplasm with reverse polarity: an observational study of histology, immunophenotypes, and molecular variation.

Papillary renal neoplasm with reverse polarity (PRNRP) is a novel entity with unique clinicopathological characteristics, and only a small number of patients with PRNRP have been described. We retrospectively analyzed the data for nine patients with PRNRP and evaluated differences in the clinical, histomorphological, immunohistochemical, and molecular features; prognosis; and differential diagnosis of PRNRP from other renal tumors with papillary structure. There were six males and three females aged 36 to 74 years (mean: 62.33 years; median: 68 years). All the tumors were solitary and ranged from 1 to 3.7 cm (mean: 2.17 cm; median: 2 cm), with three and six tumors arose in the left and right renal tract, respectively. Pathologically, PRNRP is a small, well-circumscribed neoplasm with predominant papillary formations. The lining epithelium is composed of a monolayer of cuboidal to low-columnar cells with low-grade nuclei arranged against the apical pole of the tumor cells. Edema, mucinous degeneration, and hyaline degeneration are found in the fibrovascular cores. Foamy macrophages, psammoma bodies, hemosiderin deposition, and infiltrative tumor boundaries were present in some patients. Immunohistochemically, all tumors showed diffuse positive staining for GATA3. Sanger sequencing confirmed the presence of KRAS mutation in seven patients. All patients had a good prognosis after surgery and were relapse free. Positive staining for GATA3 and negative staining for vimentin were the most significant markers for differentiating PRNRP from other renal tumors with analogous structure. These findings suggested that PRNRP is a distinctive subtype of renal tumor with specific pathological features and indolent behaviors that should be distinguished from other renal tumors, especially papillary renal cell carcinoma. A monolayer of tumor cells with an inverted nuclear pattern, positive staining for GATA3, and KRAS mutation are essential for pathological diagnosis. Owing to its satisfactory prognosis, the surveillance and follow-up of patients with PRNRP should be additionally formulated.

The Expression of Alamandine Receptor MrgD in Clear Cell Renal Cell Carcinoma Is Associated with a Worse Prognosis and Unfavorable Response to Antiangiogenic Therapy.

Renal cell carcinoma (RCC) ranks among the most prevalent malignancies in Western countries, marked by its notable heterogeneity, which contributes to an unpredictable clinical trajectory. The insufficiency of dependable biomarkers adds complexity to assessing this tumor progression. Imbalances of several components of the intrarenal renin-angiotensin system (iRAS) significantly impact patient prognoses and responses to first-line immunotherapies. In this study, we analyzed the immunohistochemical expression of the Mas-related G-protein-coupled receptor D (MrgD), which recognizes the novel RAS peptide alamandine (ALA), in a series of 87 clear cell renal cell (CCRCCs), 19 papillary (PRCC), 7 chromophobe (ChRCC) renal cell carcinomas, and 11 renal oncocytomas (RO). MrgD was expressed in all the renal tumor subtypes, with a higher mean staining intensity in the PRCCs, ChRCCs, and ROs. A high expression of MrgD at the tumor center and at the infiltrative front of CCRCC tissues was significantly associated with a high histological grade, large tumor diameter, local invasion, and locoregional node and distant metastasis. Patients with worse 5-year cancer-specific survival and a poorer response to antiangiogenic tyrosine-kinase inhibitors (TKIs) showed higher MrgD expression at the center of their primary tumors. These findings suggest a possible role of MrgD in renal carcinogenetic processes. Further studies are necessary to unveil its potential as a novel biomarker for CCRCC prognosis and response to frontline therapies.

Role of NF-κB pathway in kidney renal clear cell carcinoma and its potential therapeutic implications.

Kidney renal clear cell carcinoma (KIRC), a common malignant tumor of the urinary system, is the most aggressive renal tumor subtype. Since the discovery of nuclear factor kappa B (NF-κB) in 1986, many studies have demonstrated abnormal NF-κB signaling is associated with the development of various cancers, including kidney renal clear cell carcinoma. In this study, the relationship between NF-κB and kidney renal clear cell carcinoma was confirmed using bioinformatics analysis. First, we explored the differential expression of copy number variation (CNV), single nucleotide variant (SNV), and messenger RNA (mRNA) in NF-κB-related genes in different types of cancer, as well as the impact on cancer prognosis and sensitivity to common chemotherapy drugs. Then, we divided the mRNA expression levels of NF-κB-related genes in KIRC patients into three groups through GSVA cluster analysis and explored the correlation between the NF-κB pathway and clinical data of KIRC patients, classical cancer-related genes, common anticancer drug responsiveness, and immune cell infiltration. Finally, 11 tumor-related genes were screened using least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model. In addition, we used the UALCAN and HPA databases to verify the protein levels of three key NF-κB-related genes (CHUK, IKGGB, and IKBKG) in KIRC. In conclusion, our study established a prognostic survival model based on NF-κB-related genes, which can be used to predict the prognosis of patients with KIRC.

Stimulated Raman histology as a method to determine the adequacy of renal mass biopsy and identify malignant subtypes of renal cell carcinoma.

Renal tumor biopsy requires adequate tissue sampling to aid in the investigation of small renal masses. In some centers the contemporary nondiagnostic renal mass biopsy rate may be as high as 22% and may be as high as 42% in challenging cases. Stimulated Raman Histology (SRH) is a novel microscopic technique which has created the possibility for rapid, label-free, high-resolution images of unprocessed tissue which may beviewed onstandard radiology viewing platforms. The application of SRH to renal biopsy may provide the benefits of routine pathologic evaluation during the procedure, thereby reducing nondiagnostic results. We conducted a pilot feasibility study, to assess if renal cell carcinoma (RCC) subtypes may be imaged and to see if high-quality hematoxylin and eosin (H&E) could subsequently be generated. An 18-gauge core needle biopsy was taken from a series of 25 ex vivo radical or partial nephrectomy specimens. Histologic images of the fresh, unstained biopsy samples were obtained using a SRH microscope using 2 Raman shifts: 2,845 cm-1and 2,930 cm-1. The cores were then processed as per routine pathologic protocols. The SRH images and hematoxylin and eosin (H&E) slides were then viewed by a genitourinary pathologist. The SRH microscope took 8 to 11 minutes to produce high-quality images of the renal biopsies. Total of 25 renal tumors including 1 oncocytoma, 3 chromophobe RCC, 16 clear cells RCC, 4 papillary RCC, and 1 medullary RCC were included. All renal tumor subtypes were captured, and the SRH images were easily differentiated from adjacent normal renal parenchyma. High quality H&E slides were produced from each of the renal biopsies after SRH was completed. Immunostains were performed on selected cases and the staining was not affected by the SRH image process. SRH produces high quality images of all renal cell subtypes that can be rapidly produced and easily interpreted to determine renal mass biopsy adequacy, and on occasion, may allow renal tumor subtype identification. Renal biopsies remained available to produce high quality H&E slides and immunostains for confirmation of diagnosis. Procedural application has promise to decrease the known rate of renal mass nondiagnostic biopsies, and application of convolutional neural network methodology may further improve diagnostic capability and increase utilization of renal mass biopsy among urologists.

Dissecting the molecular signatures underlying hybrid oncocytic tumors in Birt-Hogg-Dubé syndrome.

707 Background: Birt-Hogg-Dubé (BHD) syndrome is associated with an increased risk of renal tumors including the hybrid oncocytic tumors (HOT), renal oncocytoma (RO) and chromophobe renal cell carcinoma (CHRCC). HOT are known to exhibit morphological features overlapping between RO and CHRCC. The molecular underpinnings of HOT and the corresponding morphologic correlates remain poorly understood. Herein we aimed to understand the distinct molecular features underlying HOT and how the genomic phenotype differs from CHRCC. Methods: We performed next generation sequencing and single cell sequencing analysis to understand the clinico-pathologic aspects and tumor biology of HOT and CHRCC, including tumor development. This was followed by spatial transcriptome analysis of the distinct HOT tumor compartments, using nanoString GeoMX digital spatial profiler. The candidate biomarker expression was confirmed by immunohistochemistry (IHC) and RNA in situ hybridization (RNA-ISH), a unique gene expression visualization molecular technology. Results: We discovered unique gene expression signatures underlying the two distinct morphological patterns seen in HOT. FOXI1 and L1CAM show a striking mutually exclusive expression pattern in the two cellular populations seen within this tumor. The signature genes of RO like cells seen within HOT resembles the principal cells of the distal nephron seen in normal kidney; and the signature genes of the CHRCC like cells resembles the intercalated cells of the distal nephron. This suggests distal nephron as the cellular site of origin for HOT. We also nominated tumor specific biomarkers for HOT. Conclusions: We identified unique gene expression signatures which are definitional for HOT and nominated candidate diagnostic biomarkers to help distinguish HOT from other renal tumor subtypes with overlapping morphology. This finding would improve the diagnostic accuracy, assist familial genetic monitoring, and facilitate disease management.

Diagnostic Efficacy of Multiparametric Magnetic Resonance Imaging Parameters in Differentiating Common Subtypes of Renal Cortical Tumors

Diagnostic Efficacy of Multiparametric Magnetic Resonance Imaging Parameters in Differentiating Common Subtypes of Renal Cortical Tumors

The Role of CT Imaging in Characterization of Small Renal Masses.

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