Malignant Renal Tumors Research Articles

Bioinformatical analysis of the key differentially expressed genes for screening potential biomarkers in Wilms tumor

Wilms tumor (WT) is the most common pediatric renal malignant tumor in the world. Overall, the prognosis of Wilms tumor is very good. However, the prognosis of patients with anaplastic tumor histology or disease relapse is still poor, and their recurrence rate, metastasis rate and mortality are significantly increased compared with others. Currently, the combination of histopathological examination and molecular biology is essential to predict prognosis and guide the treatment. However, the molecular mechanism has not been well studied. Genetic profiling may be helpful in some way. Hence, we sought to identify novel promising biomarkers of WT by integrating bioinformatics analysis and to identify genes associated with the pathogenesis of WT. In the presented study, the NCBI Gene Expression Omnibus was used to download two datasets of gene expression profiles related to WT patients for the purpose of detecting overlapped differentially expressed genes (DEGs). The DEGs were then uploaded to DAVID database for enrichment analysis. In addition, the functional interactions between proteins were evaluated by simulating the protein–protein interaction (PPI) network of DEGs. The impact of selected hub genes on survival in WT patients was analyzed by using the online tool R2: Genomics Analysis and Visualization Platform. The correlation between gene expression and the degree of immune infiltration was assessed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using the Expression (ESTIMATE) algorithm and the single sample GSEA. Top 12 genes were identified for further study after constructing a PPI network and screening hub gene modules. Kinesin family member 2C (KIF2C) was identified as the most significant gene predicting the overall survival of WT patients. The expression of KIF2C in WT was further verified by quantitative real-time polymerase chain reaction and immunohistochemistry. Furthermore, we found that KIF2C was significantly correlated with immune cell infiltration in WT. Our present study demonstrated that altered expression of KIF2C may be involved in WT and serve as a potential prognostic biomarker for WT patients.

Impact of Visceral Fat Area on Intraoperative Complexity and Surgical Approach Decision for Robot-Assisted Partial Nephrectomy: A Comparative Analysis with BMI.

BACKGROUND Optimizing surgical approaches for robot-assisted partial nephrectomy (RAPN) is vital for better patient outcomes. This retrospective study aimed to examine how visceral fat area (VFA) and body mass index (BMI) correlate with intraoperative complexities, thereby guiding the selection of surgical techniques for RAPN. MATERIAL AND METHODS The study analyzed the medical records of 213 Chinese patients diagnosed with a range of benign and malignant renal neoplasms and treated with RAPN in 2020. Visceral fat area was quantified using computed tomography (CT) scans taken at the umbilical level. Various perioperative indicators, such as demographic details, clinicopathological parameters, operation time, estimated blood loss (EBL), warm ischemic time (WIT), and intraoperative complications, were assessed. RESULTS For the retroperitoneal approach, patients with either visceral obesity or general obesity had longer operation times (P<0.001 and P=0.004) and had a tendency for higher EBL (P=0.003 and P=0.001) compared to non-obese patients. In the transperitoneal approach, those with visceral obesity had significantly longer operation times (P=0.008) than their non-viscerally obese counterparts; however, general obesity showed no impact on operation time (P=0.251). Estimated blood loss was higher for patients with visceral obesity (P=0.004), but no significant difference was noted among those with general obesity (P=0.980). CONCLUSIONS VFA appears to offer predictive advantages over BMI in assessing intraoperative complexities for transperitoneal RAPN. When used in conjunction with BMI, it could serve as a valuable tool in selecting the most appropriate surgical approach for RAPN.

Renal Abscess Complicated by Renal Vein Thrombosis Mimicking Clinical Stage T3a Renal Cell Carcinoma

Renal abscesses accompanied by a renal vein thrombosis (RVT) may have a similar appearance to clinical stage T3a renal cell carcinoma (RCC) on computed tomography (CT) imaging. RVTs are rare clinical findings that more commonly occur in adults with malignant renal tumors or nephrotic syndrome. A 71-year-old woman with risk factors for both renal infection and RCC was found to have a heterogeneous mass with irregular borders and areas of contrast enhancement on CT imaging. A two-week course of antibiotic and anti-coagulant treatment was prescribed with close follow-up. Urine culture eventually grew greater than 100,000 colonies of Escherichia coli. Repeat imaging after six weeks revealed resolution of the mass and the RVT. This patient's case emphasizes that although the findings of RVT and renal mass more commonly point to the diagnosis of malignancy, infections many present in a similar manner but require substantially different treatment. Close follow-up with repeat imaging is imperative to differentiate malignancy from infection or other causes.

Homolateral and synchronic clear cell and papillary type of renal cell carcinoma Diagnostic hints and review of literature

Commonest solid mass of kidney is renal cell carcinoma, constituting 2-3% of all adult cancers. Synchronous malignant and benign renal tumours bilaterally have been described in a few studies. However, homolateral and synchronic appearance of malignant renal masses is extremely rare and there have been only a few case reports that had different subtypes of malignant renal neoplasms of the same kidney on histopathology. In this report, we present a 66-year-old male patient who had two different histological variants, ie, papillary RCC and clear cell RCC in his right kidney, and underwent radical nephrectomy. Our aim is to provide radiological diagnostic hints and review the literature relevant to this rare case, as the awareness of its occurrence can help in early diagnosis and deciding an appropriate management course.

Böbreğin Berrak Hücreli Sarkomu: Oldukça Nadir Bir Olgu Sunumu

Clear cell sarcoma of the kidney (CCSK) is one of the most common malignant renal neoplasms in childhood, although it is quite rare. Its incidence peaks around the age of 3 and it is twice as common in males. We present a rare case of CCSK showing a rare histopathological pattern in terms of age and gender. A 7-month-old female patient presented with complaints of vomiting, abdominal swelling, and diarrhea. Imaging analysis revealed a mass in the right kidney, and right radical nephrectomy was performed, considering Wilms tumor (WT). There was a solid gray-white mass that completely filled the kidney, with a maximum dimension of 8.5 cm, macroscopically. The samples obtained showed intratubular structures within the tumor and kidney parenchyma in limited areas at the periphery. The tumor had a normochromic monotonous nucleus with occasional clear cytoplasm and mostly exhibited a palisade-like arrangement pattern. In the differential diagnosis, with blastemal WT and Ewing sarcoma being the primary considerations, the positivity of CyclinD1 markers, negativity of other markers, and morphological characteristics were evaluated in favor of CCSK with a palisaded Schwannian pattern. The patient was put on an intensive chemotherapy process, but was lost after relaps at weet 24. CCSK is seen in a similar age group as WT but is distinguished by its rarity and relatively worse prognosis. Histopathologically, it most commonly presents in a myxoid pattern and least commonly in anaplastic pattern. The palisaded schwannian type seen in our case is recorded at a rate of 11%. No spesific diagnostic marker has been identified immunohistochemically, but the overexpression of markers such as CyclinD1, BCOR, and EZH2 is reported to be helpful in diagnosis. Due to its high metastatic potential and limited treatment options, further research is needed to understand the molecular nature of the disease.

A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia

Differentiating benign renal oncocytic tumors and malignant renal cell carcinoma (RCC) on imaging and histopathology is a critical problem that presents an everyday clinical challenge. This manuscript aims to demonstrate a novel methodology integrating metabolomics with radiomics features (RF) to differentiate between benign oncocytic neoplasia and malignant renal tumors. For this purpose, thirty-three renal tumors (14 renal oncocytic tumors and 19 RCC) were prospectively collected and histopathologically characterised. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) was used to extract metabolomics data, while RF were extracted from CT scans of the same tumors. Statistical integration was used to generate multilevel network communities of -omics features. Metabolites and RF critical for the differentiation between the two groups (delta centrality > 0.1) were used for pathway enrichment analysis and machine learning classifier (XGboost) development. Receiver operating characteristics (ROC) curves and areas under the curve (AUC) were used to assess classifier performance. Radiometabolomics analysis demonstrated differential network node configuration between benign and malignant renal tumors. Fourteen nodes (6 RF and 8 metabolites) were crucial in distinguishing between the two groups. The combined radiometabolomics model achieved an AUC of 86.4%, whereas metabolomics-only and radiomics-only classifiers achieved AUC of 72.7% and 68.2%, respectively. Analysis of significant metabolite nodes identified three distinct tumour clusters (malignant, benign, and mixed) and differentially enriched metabolic pathways. In conclusion, radiometabolomics integration has been presented as an approach to evaluate disease entities. In our case study, the method identified RF and metabolites important in differentiating between benign oncocytic neoplasia and malignant renal tumors, highlighting pathways differentially expressed between the two groups. Key metabolites and RF identified by radiometabolomics can be used to improve the identification and differentiation between renal neoplasms.

A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75).

Differentiating benign from malignant renal tumors is important for patient management, and it may be improved by quantitative CT features analysis including radiomic. This study aimed to compare performances of machine learning models using bio-clinical, conventional radiologic and 3D-radiomic features for the differentiation of benign and malignant solid renal tumors using pre-operative multiphasic contrast-enhanced CT examinations. A unicentric retrospective analysis of prospectively acquired data from a national kidney cancer database was conducted between January 2016 and December 2020. Histologic findings were obtained by robotic-assisted partial nephrectomy. Lesion images were semi-automatically segmented, allowing for a 3D-radiomic features extraction in the nephrographic phase. Conventional radiologic parameters such as shape, content and enhancement were combined in the analysis. Biological and clinical features were obtained from the national database. Eight machine learning (ML) models were trained and validated using a ten-fold cross-validation. Predictive performances were evaluated comparing sensitivity, specificity, accuracy and AUC. A total of 122 patients with 132 renal lesions, including 111 renal cell carcinomas (RCCs) (111/132, 84%) and 21 benign tumors (21/132, 16%), were evaluated (58 +/- 14 years, men 74%). Unilaterality (100/111, 90% vs. 13/21, 62%; p = 0.02), necrosis (81/111, 73% vs. 8/21, 38%; p = 0.02), lower values of tumor/cortex ratio at portal time (0.61 vs. 0.74, p = 0.01) and higher variation of tumor/cortex ratio between arterial and portal times (0.22 vs. 0.05, p = 0.008) were associated with malignancy. A total of 35 radiomics features were selected, and "intensity mean value" was associated with RCCs in multivariate analysis (OR = 0.99). After ten-fold cross-validation, a C5.0Tree model was retained for its predictive performances, yielding a sensitivity of 95%, specificity of 42%, accuracy of 87% and AUC of 0.74. Our machine learning-based model combining clinical, radiologic and radiomics features from multiphasic contrast-enhanced CT scans may help differentiate benign from malignant solid renal tumors.

SLUG and SNAIL as Potential Immunohistochemical Biomarkers for Renal Cancer Staging and Survival.

Renal cell carcinoma (RCC) is the deadliest urological neoplasm. Up to date, no validated biomarkers are included in clinical guidelines for the screening and follow up of patients suffering from RCC. Slug (Snail2) and Snail (Snail1) belong to the Snail superfamily of zinc finger transcriptional factors that take part in the epithelial-mesenchymal transition, a process important during embryogenesis but also involved in tumor progression. We examined Slug and Snail immunohistochemical expression in patients with different stages of renal cell carcinomas with the aim to investigate their potential role as staging and prognostic factors. A total of 166 samples of malignant renal cell neoplasms were analyzed using tissue microarray and immunohistochemistry. Slug and Snail expressions were evaluated qualitatively (presence or absence), in nuclear and cytoplasmic cell compartments and compared in relation to clinical parameters. The Kaplan-Meier survival analysis showed the impact of the sarcomatoid component and Slug expression on the survival longevity. Cox regression analysis separated Slug as the only independent prognostic factor (p = 0.046). The expression of Snail was associated with higher stages of the disease (p = 0.004), especially observing nuclear Snail expression (p < 0.001). All of the tumors that had metastasized showed nuclear immunoreactivity (p < 0.001). In clear cell RCC, we showed a significant relationship between a high nuclear grade and nuclear Snail expression (p = 0.039). Our results suggest that Slug and Snail could be useful immunohistochemical markers for staging and prognosis in patients suffering from various RCCs, representing potential targets for further therapy strategies of renal cancer.

Classification of Benign and Malignant Renal Tumors Based on CT Scans and Clinical Data Using Machine Learning Methods

Up to 20% of renal masses ≤4 cm is found to be benign at the time of surgical excision, raising concern for overtreatment. However, the risk of malignancy is currently unable to be accurately predicted prior to surgery using imaging alone. The objective of this study is to propose a machine learning (ML) framework for pre-operative renal tumor classification using readily available clinical and CT imaging data. We tested both traditional ML methods (i.e., XGBoost, random forest (RF)) and deep learning (DL) methods (i.e., multilayer perceptron (MLP), 3D convolutional neural network (3DCNN)) to build the classification model. We discovered that the combination of clinical and radiomics features produced the best results (i.e., AUC [95% CI] of 0.719 [0.712–0.726], a precision [95% CI] of 0.976 [0.975–0.978], a recall [95% CI] of 0.683 [0.675–0.691], and a specificity [95% CI] of 0.827 [0.817–0.837]). Our analysis revealed that employing ML models with CT scans and clinical data holds promise for classifying the risk of renal malignancy. Future work should focus on externally validating the proposed model and features to better support clinical decision-making in renal cancer diagnosis.

A radiomics nomogram for preoperative prediction of nephron-sparing surgery in patients with bilateral Wilms tumor.

Bilateral Wilms tumor (BWT) is a relatively rare malignant renal tumor in children. Nephron-sparing surgery (NSS) is the preferred surgical approach for treating BWT, but lacks uniform surgical indications worldwide. This study aimed to summarize the clinical and imaging features of BWT children, establish a radiomics nomogram, and predict the feasibility of NSS for improving outcomes. A 12-year retrospective single-center review was conducted on clinical data and preoperative imaging features of BWT patients. The tumor kidneys were divided into NSS and non-NSS groups. Logistic regression analysis was performed to identify independent predictors and develop a prediction model of the feasibility of NSS in BWT patients. A radiomics nomogram was constructed and internally validated by the parametric bootstrapping method. A total of 58 BWT patients (115 renal units) were included in this study. After evaluations based on preoperative imaging and clinical data, 94 renal units underwent NSS with negative resection margins and were included in the NSS group, whereas 16 renal units with positive resection margins, macroscopic residual, or total nephrectomies were included in the non-NSS group. Tumor size [odds ratio (OR): 0.540, 95% confidence interval (CI): 0.308-0.945], relationship with the collecting system (OR: 0.013, 95% CI: 0.0004-0.370), and remaining renal parenchyma (RRP) proportion (OR: 71.23, 95% CI: 1.632-3108.8) were identified as independent predictors for NSS. A nomogram was constructed based on these factors, which demonstrated great consistency between the predicted and observed feasibility of NSS. The model presented with good discriminative ability [area under the curve (AUC), 0.982]. The decision curve analysis (DCA) revealed the clinical usefulness of the model. This study analyzed the clinical and preoperative imaging data of BWT patients and identified three independent predictors for the feasibility of NSS, including tumor size, relationship with the collecting system, and residual renal parenchyma proportion. The radiomics nomogram established in this study can provide individualized predictions to assist clinicians in making better decisions and improving patient outcomes.

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

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

MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach.

Between 1983 and 2002, the incidence of solid renal tumors increased from 7.1 to 10.8 cases per 100,000. This is in large part due to the increase in the volume of ultrasound and cross-sectional imaging, although a majority of solid renal tumors are still found incidentally. Ultrasound and computed tomography (CT) have been the mainstay of renal mass screening and diagnosis but recent advances in magnetic resonance (MR) technology have made this the optimal choice when diagnosing and staging renal tumors. Our purpose in writing this review is to survey the modern MR imaging approach to benign and malignant solid renal tumors, consolidate the various imaging findings into an easy-to-read reference, and provide an imaging-based, algorithmic approach to renal mass characterization for clinicians. MR is at the forefront of renal mass characterization, surpassing ultrasound and CT in its ability to describe multiple tissue parameters and predict tumor biology. Cutting-edge MR protocols and the integration of diagnostic algorithms can improve patient outcomes, allowing the imager to narrow the differential and better guide oncologic and surgical management.

Coexistence of multiple clear cell papillary renal cell carcinoma with renal oncocytoma: a case report.

The authors report a case of a female 65-year-old suffering from end-stage kidney failure for 10 years, who presented with a double left renal tumor, composed by an oncocytoma associated to multiple CCPRCC, a very rare entity. A radical left nephrectomy was realized by lumbotomy, with an uneventful postoperative course. Histological examination was challenging. Immunohistological examination showed diffuse positivity of cytokertain 7. No local recurrence nor metastatic progression were found during the 12 months of follow-up. CCPRCC, is a new entity, previously known as the unclassified rena cell carcinoma, is a malignant renal tumor, initially reported in patients at end-stage kidney failure. Oncocytoma is a well-known rare benign renal tumor. The association of both is rare, and should be kept in mind, especially when scanoguided diagnosis biopsy is realized. Histopathological confirmation may be challenging, given the recent identification of CCPRCC. The nuclei disposal toward the luminal surface is a characteristic pathological landmark of CCPRCC. Immunohistopathological examination is of great help, showing a distinctive profile: diffuse staining for cytokertain 7 and carbonic anhydrase IX. CCPRCC is a new malignant pathological entity in renal tumors. It can be associated with other benign renal lesions. This should be taken into consideration while histopathological examination, mainly of scanoguided biopsy cores.

PROSPECTIVE OBSERVATIONAL STUDY COMPARING OUTCOMES AND COMPLICATIONS OF OPEN VS LAPAROSCOPIC NEPHRECTOMY

Introduction: Nephrectomy refers to surgical removal of the kidney and is further divided into simple, radical, partial and donor nephrectomy. Simple nephrectomy is conducted in irreversible kidney damage; radical nephrectomy in malignant or benign renal tumours while partial nephrectomy involves removal of part of kidney. Nephrectomy may be performed via open or laparoscopic approaches; although there is evidence favouring laparoscopy, supplementary evidence is necessary to establish it.2 This study aims to compare outcomes, complications and other parameters for laparoscopic and open surgical nephrectomy at a tertiary care centre in western India. To compare outcomes and Aim: complications for laparoscopic and open nephrectomy. Objectives: To compare: 1. Immediate and late complications. 2. A] Requirement of analgesia. B] Return of bowel functions. 3. Mean length of hospitalization and return to regular normal activity. Materials And Methods: A prospective observational single centre study was conducted at a tertiary care centre in western India. Total sample size was 49 of which 25 patients underwent laparoscopic nephrectomy while 24 patients underwent open nephrectomy. Intraoperative anaesthesia, trocar access, duration of surgery, blood loss, repeat haemoglobin and haematocrit value at post operative day 1, requirement of blood transfusion, duration of analgesics requirement, post-operative stay; histopathology report and time to resume normal activity at immediate follow up, follow up at 3 months and further follow up after surgery were noted down. Current study found statist Results: ically signicant relationship of higher intra-operative blood transfusion requirement and higher post-op requirement of Analgesia in case of open surgery. Moreover, among laparoscopy group there was signicantly lower blood loss during surgery, early time to oral diet, low pain VAS score, lower doses of Tramadol and early return to normal activity was found as compared to Open surgery group (p&lt;0.05). Laparoscopic nephrectom Conclusion: y results in less blood loss, less requirement of post operative analgesia and more rapid post operative recovery in comparison to open surgery

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.

Enhancing Renal Tumor Detection: Leveraging Artificial Neural Networks in Computed Tomography Analysis.

BACKGROUND Renal cell carcinoma is one of the most common cancers in Europe, with a total incidence rate of 18.4 cases per 100 000 population. There is currently significant overdiagnosis (11% to 30.9%) at times of planned surgery based on radiological studies. The purpose of this study was to create an artificial neural network (ANN) solution based on computed tomography (CT) images as an additional tool to improve the differentiation of malignant and benign renal tumors and to aid active surveillance. MATERIAL AND METHODS A retrospective study based on CT images was conducted. Axial CT images of 357 renal tumor cases were collected. There were 265 (74.2%) cases histologically proven to be malignant, while 34 (9.5%) cases were benign. Radiologists diagnosed 58 (16.3%) cases as angiomyolipoma (AML), based on characteristic appearance, not confirmed histopathologically. For ANN training, the arterial CT phase images were used. A total of 7207 arterial-phase images were collected, then cropped and added to the database with the associated diagnosis. For the test dataset (ANN validation), 38 cases (10 benign, 28 malignant) were chosen by subgroup randomization to correspond to statistical tumor type distribution. The VGG-16 ANN architecture was used in this study. RESULTS Trained ANN correctly classified 23 out of 28 malignant tumors and 8 out of 10 benign tumors. Accuracy was 81.6% (95% confidence interval, 65.7-92.3%), sensitivity was 82.1% (63.1-93.9%), specificity was 80.0% (44.4-97.5%), and F1 score was 86.8% (74.7-94.5%). CONCLUSIONS The created ANN achieved promising accuracy in differentiating benign vs malignant renal tumors.

PD08-04 VIRTUAL RENAL MASS BIOPSY: PREDICTING RENAL TUMOR HISTOLOGY ON ABDOMINAL CT IMAGES USING MACHINE LEARNING

You have accessJournal of UrologyCME1 Apr 2023PD08-04 VIRTUAL RENAL MASS BIOPSY: PREDICTING RENAL TUMOR HISTOLOGY ON ABDOMINAL CT IMAGES USING MACHINE LEARNING Abhinav Khanna, Vidit Sharma, Adriana Gregory, H. Chase Gottlich, Cole J. Cook, Jason Klug, Christine Lohse, Theodora Potretzke, Aaron Potretzke, Stephen A. Boorjian, R. Houston Thompson, Naoki Takahashi, Bradley Erickson, John Cheville, Timothy Kline, and Bradley Leibovich Abhinav KhannaAbhinav Khanna More articles by this author , Vidit SharmaVidit Sharma More articles by this author , Adriana GregoryAdriana Gregory More articles by this author , H. Chase GottlichH. Chase Gottlich More articles by this author , Cole J. CookCole J. Cook More articles by this author , Jason KlugJason Klug More articles by this author , Christine LohseChristine Lohse More articles by this author , Theodora PotretzkeTheodora Potretzke More articles by this author , Aaron PotretzkeAaron Potretzke More articles by this author , Stephen A. BoorjianStephen A. Boorjian More articles by this author , R. Houston ThompsonR. Houston Thompson More articles by this author , Naoki TakahashiNaoki Takahashi More articles by this author , Bradley EricksonBradley Erickson More articles by this author , John ChevilleJohn Cheville More articles by this author , Timothy KlineTimothy Kline More articles by this author , and Bradley LeibovichBradley Leibovich More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003239.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Up to 20% of renal tumors are benign and may not require treatment. However, benign versus malignant renal tumors cannot be distinguished using cross-sectional imaging. Renal mass biopsy is a possible solution, but biopsy is invasive and has notable non-diagnostic and false negative rates. As a result, many patients proceed directly to treatment, including some who undergo extirpative surgery for benign tumors. We aim to develop a radiomics and machine learning model for distinguishing between oncocytoma versus malignant renal neoplasms based on abdominal CT images. METHODS: Our institutional registry was queried for patients who underwent surgical treatment of renal tumors from 2000-2018. All surgical specimens underwent pathology re-review by an expert genitourinary pathologist. A total of 843 images from 609 patients (434 images of oncocytoma and 409 images of malignant renal tumor) were included. A previously developed artificial intelligence algorithm for segmentation of kidney, cyst, and tumor area was applied. Images were preprocessed by resampling via linear interpolation to 0.8 mm x 0.8 mm x 5 mm, window/level=440/40, and intensity normalized [0 255]. Features were extracted for the tumor region using PyRadiomics with a fixed bin width of 16. Both unsupervised (PCA, tSNE) and supervised (LR, SVM) machine learning approaches were explored. Data was split 80:20, and cross validation was used in the training/validation set parameters. The best performing model based on F1 macro was then calibrated using an isotonic calibration approach. RESULTS: Dimensionality reduction showed adequate class separation (Figure 1A). The top 10 radiomic features showed a mixture of first and second order features (Figure 1B). The final model reached an accuracy of 0.90 (Figure 1C). Of the 168 images in the testing set, only 2 benign cases were predicted to be malignant, and 15 malignant cases were predicted to be benign. The AUC for oncocytoma versus malignant histology prediction was 0.96 with CI [0.93 0.98] (Figure 1D). CONCLUSIONS: We developed a machine learning model for accurately distinguishing benign oncocytoma versus malignant renal tumors based on CT images alone. This may provide an opportunity for non-invasive risk stratification of solid renal neoplasms, which could potentially reduce overtreatment of renal tumors. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e233 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Abhinav Khanna More articles by this author Vidit Sharma More articles by this author Adriana Gregory More articles by this author H. Chase Gottlich More articles by this author Cole J. Cook More articles by this author Jason Klug More articles by this author Christine Lohse More articles by this author Theodora Potretzke More articles by this author Aaron Potretzke More articles by this author Stephen A. Boorjian More articles by this author R. Houston Thompson More articles by this author Naoki Takahashi More articles by this author Bradley Erickson More articles by this author John Cheville More articles by this author Timothy Kline More articles by this author Bradley Leibovich More articles by this author Expand All Advertisement PDF downloadLoading ...

Low expression of SLC34A1 is associated with poor prognosis in clear cell renal cell carcinoma

ObjectiveClear cell renal cell carcinoma (ccRCC) is a malignant renal tumor that is highly prone to metastasis and recurrence. The exact pathogenesis of this cancer is still not well understood. This study aimed to identify novel hub genes in renal clear cell carcinoma and determine their diagnostic and prognostic value.MethodsIntersection genes were obtained from multiple databases, and protein–protein interaction analysis and functional enrichment analysis were performed to identify key pathways related to the intersection genes. Hub genes were identified using the cytoHubba plugin in Cytoscape. GEPIA and UALCAN were utilized to observe differences in mRNA and protein expression of hub genes between KIRC and adjacent normal tissues. The Wilcoxon rank sum test was used to analyze hub gene levels between paired KIRC and matched non-cancer samples. IHC results were obtained from the HPA online database, and according to the median gene expression level, they were divided into a high-expression group and a low-expression group. The correlation of these groups with the prognosis of KIRC patients was analyzed. Logistic regression and the Wilcoxon rank sum test were used to test the relationship between SLC34A1 level and clinicopathological features. The diagnostic value of SLC34A1 was evaluated by drawing the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). Cox regression analysis was used to analyze the relationship between clinicopathological features, SLC34A1 expression, and KIRC survival rate. LinkedOmics was used to obtain the genes most related to SLC34A1 and their functional enrichment. Genetic mutations and methylation levels of SLC34A1 in KIRC were obtained from the cBioPortal website and the MethSurv website, respectively.ResultsFifty-eight ccRCC differential genes were identified from six datasets, and they were mainly enriched in 10 functional items and 4 pathways. A total of 5 hub genes were identified. According to the GEPIA database analysis, low expression of SLC34A1, CASR, and ALDOB in tumors led to poor prognosis. Low expression of SLC34A1 mRNA was found to be related to clinicopathological features of patients. SLC34A1 expression in normal tissues could accurately identify tumors (AUC 0.776). SLC34A1 was also found to be an independent predictor of ccRCC in univariate and multivariate Cox analyses. The mutation rate of the SLC34A1 gene was 13%. Eight of the 10 DNA methylated CpG sites were associated with the prognosis of ccRCC. SLC34A1 expression in ccRCC was positively correlated with B cells, eosinophils, neutrophils, T cells, TFH, and Th17 cells, and negatively correlated with Tem, Tgd, and Th2 cells.ConclusionThe expression level of SLC34A1 in KIRC samples was found to be decreased, which predicted a decreased survival rate of KIRC. SLC34A1 may serve as a molecular prognostic marker and therapeutic target for KIRC patients.

Diagnostic value of pseudocapsule ring hyperenhancement in contrast-enhanced ultrasound in renal focal lesions.

To investigate the diagnostic value of pseudocapsule ring hyperenhancement in contrast-enhanced ultrasound (CEUS) of focal renal lesions. Ninety eligible patients admitted were selected as the study subjects. The incidence rate of annular hyperenhancement in different benign and malignant focal lesions during angiography and the shape of annular enhancement were counted. The correlation between the occurrence of benign and malignant renal tumors and annular hyperenhancement and unenhanced areas was also analyzed. Capsule enhancement was observed in 56 cases, including 50 cases of malignant tumors (89.3%, 50/56) and 6 cases of benign tumors (10.7%, 6/56). Pearson correlation analysis showed that renal malignancy was positively associated with the occurrence of pseudocapsule ring hyperenhancement (γ = 0.489, P < 0.001). Benign and malignant renal tumors were positively correlated with the occurrence of non-enhancing areas (γ = 0.215, P = 0.042). The thickness and peak intensity of pseudocapsule ring enhancement in renal malignant tumors were significantly higher than those in benign tumors (all P < 0.001), while the time to peak was significantly lower than that in benign tumors (P < 0.001). The results of receiver operating characteristic (ROC) curve showed that the ring enhancement thickness, time to peak, and area under ROC curve of peak intensity of tumor reached more than 0.9. In CEUS, the pseudocapsule of focal renal lesions is a characteristic feature of renal malignant tumors, which can be used as a differential basis for benign and malignant tumors to improve the accuracy of benign and malignant renal tumors.

Extended Lymph Node Sampling During Surgery for Pediatric Renal Tumors Concerning for Malignancy Does Not Increase Postoperative Complication Rates.

Although Children's Oncology Group renal tumor protocols mandate lymph node sampling during extirpative surgery for pediatric renal tumors, lymph node sampling is often omitted or low yield. Concerns over morbidity associated with extended lymph node sampling have led to hesitancy in adopting a formal lymph node sampling template. We hypothesized that complications in children undergoing lymph node sampling for renal tumors would be rare, and not associated with the number of lymph nodes sampled. A single-institution, retrospective review of patients aged 0-18 years undergoing extirpative renal surgery with lymph node sampling for a suspected malignancy between 2005 and 2019 was performed. Patients with 0 or an unknown number of lymph nodes sampled or <150 days of follow-up were excluded. A "clinically significant" complication was defined as any Clavien complication ≥III, small-bowel obstruction, chylous ascites, organ injury, or wound infection. The number of lymph nodes sampled and its influence on the odds of experiencing a clinically significant complication was examined. A total of 144 patients met inclusion criteria. Median patient age was 38 months. Twenty-one patients (15%) had a clinically significant complication, the most common of which was ileus/small-bowel obstruction (n=16). In a multivariable analysis, increased lymph node yield was not found to influence the odds of experiencing a clinically significant complication (P = .6). In this cohort, there was no statistically significant difference in clinically significant complications in patients who underwent more extensive lymph node sampling during surgery for a suspected malignant pediatric renal tumor. Future studies on protocol adherence, staging accuracy, and survival trends using a lymph node sampling template in these patients should be performed.