Kidney Tumors Research Articles

Comparison of the results of laparoscopic retroperitoneal and transperitoneal partial nephrectomy in anterior kidney tumors: a two-center matched-pair analysis.

To compare the intraoperative, postoperative and pathologic results of transperitoneal (TLPN) and retroperitoneal (RLPN) laparoscopic partial nephrectomy for anterior renal tumors. Data of the 401 patients in two different centers, who had LPN operations due to anteriorly placed renal tumor with RLPN and TLPN approaches, were analyzed retrospectively. Demographic, tumor characteristics, intraoperative, postoperative and pathologic data of all patients were evaluated. 74 TLPN cases were matched with 74 RLPN cases with respect to age, body mass index, tumor size and PADUA nephrometry score (matched 1:1). The groups were compared according to the parameters above. The two groups were similar in terms of clinical data and tumor characteristics, including matching parameters. The median operative time was similar between two groups (115 vs. 110min, p = 0.235; 20 vs. 22.5min, p = 0.283 in the RLPN group than in the TLPN group, respectively). Intraoperative complication rates were similar between the groups (6.8% in RLPN group vs. 10.8% in TLPN group, p = 0.384). The median amount of bleeding in the TLPN group was statistically higher than in the RLPN group (150 vs. 50cc, p < 0.001). There were no difference in terms of postoperative complication rates and the rate of residual tumor presence (9.5% in RLPN group vs. 12.2% in TLPN group, p = 0.144; 6.8% in the RLPN group, 5.4% in the TLPN group, p = 0.731,respectively). In high-volume centers, both transperitoneal and retroperitoneal procedures yield comparably favourable outcomes for anterior kidney tumors. Thus, tumor location should not be regarded as a deterrent to considering RLPN.

Paediatric Renal Tumors: A State-of-the-Art Review.

Pediatric renal tumors comprise a wide range of conditions, both malignant and benign, that affect children and require a multidisciplinary approach for optimal diagnosis and treatment. This review offers an in-depth analysis of the epidemiology, diagnosis, treatment options, outcomes, and survival of major pediatric renal tumors. Wilms tumor, or nephroblastoma, is the most common form of renal tumor in children, characterized by growth from primitive renal cells. Standard treatment involves a combination of surgery, chemotherapy and, in some cases, radiation therapy, with the aim of removing the tumor, preventing recurrence and maximizing the chances of long-term recovery. Less common pediatric renal tumors, such as renal clear cell sarcoma, renal cell carcinoma, mesoblastic nephroma, and malignant rhabdoid tumor, require similarly careful and individualized management. Therapeutic strategies, which depend on the characteristics of the tumor, the stage of the disease and the individual response to therapy, may include surgery, chemotherapy, radiotherapy and, in some cases, molecular targeted therapies, immunotherapies and genetic and epigenetic therapies. The management of pediatric kidney tumors requires the involvement of a multidisciplinary team of specialists to ensure accurate evaluation, optimal treatments and long-term follow-up. The aim is to maximize the prospects for recovery and improve the quality of life of patients and their families. Advances in innovative, personalized therapies represent an important opportunity to further improve clinical outcomes in these patients.

Transabdominal Robotic-Assisted Partial Nephrectomy and CT-Guided Percutaneous Cryoablation for the Treatment of De Novo Kidney Tumors After Liver Transplantation

Transabdominal Robotic-Assisted Partial Nephrectomy and CT-Guided Percutaneous Cryoablation for the Treatment of De Novo Kidney Tumors After Liver Transplantation

Evaluation of Role of FNAC in Pediatric Kidney Tumors in a Tertiary Care Hospital

Evaluation of Role of FNAC in Pediatric Kidney Tumors in a Tertiary Care Hospital

When benign turns aggressive: renal angiomyolipoma with renal vein invasion

Angiomyolipoma (AML) is the most common benign kidney tumor, made up of fat, smooth muscle, and abnormal blood vessels. It represents 0.3-3% of kidney tumors and is more common in women. AMLs are typically diagnosed via computed tomography (CT) or magnetic resonance imaging (MRI), with most being asymptomatic and requiring no treatment. However, larger or symptomatic AMLs can cause hematuria, flank pain, or bleeding, necessitating treatments like selective arterial embolization, partial nephrectomy, or active surveillance. Recent studies question the traditional 4 cm intervention threshold, suggesting that larger AMLs may be safely monitored based on individual risk factors. This case report discusses a 47-year-old woman with a 4.1×2.1 cm AML with renal vein invasion, treated successfully with laparoscopic nephrectomy. The findings highlight the need for personalized treatment strategies considering tumor characteristics and patient factors, and emphasize the importance of ongoing renal function monitoring.

Development of a CT radiomics prognostic model for post renal tumor resection overall survival based on transformer enhanced K-means clustering.

Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making. This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making. This study was based on a publicly available C4KC-KiTS-2019 dataset from the TCIA database, including preoperative computed tomography (CT) images and survival time data of 210 patients. Initially, the radiomics features of the kidney tumor area were extracted using the 3D slicer software. Feature selection was then conducted using ICC, mRMR algorithms, and LASSO regression to calculate radiomics scores. Subsequently, the selected features were input into a pre-trained Transformer model for feature transformation to obtain a higher-dimensional feature set. Then, K-means clustering was performed using this feature set, and the model was evaluated using receiver operating characteristic (ROC) and Kaplan-Meier curves. Finally, the SHAP interpretability algorithm was used for the feature importance analysis of the K-means clustering results. Eleven important features were selected from 851 radiomics features. The K-means clustering model after Transformer feature transformation showed AUCs of 0.889, 0.841, and 0.926 for predicting 1-, 3-, and 5-year overall survival rates, respectively, thereby outperforming both the K-means model with original feature inputs and the radiomics score method. A clustering analysis revealed survival prognosis differences among different patient groups, and a SHAP analysis provided insights into the features that had the most significant impacts on the model predictions. The K-means clustering algorithm enhanced by the Transformer feature transformation proposed in this study demonstrates promising accuracy and interpretability in predicting the overall survival rate after kidney tumor resection. This method provides a valuable tool for clinical decision-making and contributes to improved management and treatment strategies for patients with kidney tumors.

MAPK1IP1L::TFE3-rearranged renal cell carcinoma: a novel fusion adding to the differential diagnosis of oncocytic renal neoplasms.

Beyond the more common TFE3 fusion partners PRCC, ASPSCR1, and SFPQ, additional less common fusion partners of TFE3-rearranged renal cell carcinoma (RCC) have been described. Herein, we present an example of TFE3-rearranged renal cell carcinoma harboring fusion partner MAPK1IP1L, a rare rearrangement with only one other reported tumor found in the literature. The currently reported TFE3-rearranged RCC demonstrates unique histological features compared to the previously reported tumor including dense eosinophilic cytoplasm and nuclear pseudoinclusions (corroborated by electron microscopic evaluation), with features not typically seen in other TFE3-rearranged RCCs. Recognizing this novel fusion may be important in the identification, classification, and development of potential therapeutic agents of kidney tumors in the future.

Profile and methodology of ancillary protective measures employed during percutaneous renal cryoablation in a single high-volume centre.

To evaluate the at-risk organs that require protection during percutaneous cryoablation (PCA) of renal tumours and the correlation with patient and target lesion characteristics, type of protective measure used and postoperative outcomes. Single-centre retrospective review of patients with renal tumours who underwent PCA between 2008 and 2020. Final analysis included 374 tumours. Patient, tumour, and procedure technical details were extracted and analysed. At-risk organs were classified according to tumour location relative to kidney side, pyelic axis, and polar lines. There were 171 (46.0%) tumours in the left kidney, and 194 (52.0%) in the right. Cryoprotection was required for 272 (272/374; 73.0%) tumours, with hydrodissection (216/374; 58.0%) being the most common technique. Protective measures were used for 82 (82/93; 88.0%) tumours in under/normal-weight patients and 143 (143/196; 73.0%) in overweight/obese ones (P = 0.004). In the left kidney, colon was the most common at-risk organ (63/171; 37.0%), followed by spleen (21/171; 12.3%), small bowel (21/171; 12.3%), ureter (19/171; 11.1%), abdominal wall (15/171; 8.8%), psoas muscle (10/171; 5.8%), and pancreas (9/171; 5.3%). In the right kidney, common at-risk organs were the colon (67/194; 35.0%), liver (50/194; 25.7%), ureter (15/194; 15.5%), diaphragm (16/194; 8.2%), abdominal wall (14/194; 7.2%), and duodenum (12/194; 6.1%). No cryoinjuries to at-risk organs occurred. Hydrodissection is the most common cryoprotective measure used for renal tumour PCA. Under/normal-weight patients are more likely to require cryoprotection. The colon is the most common adjacent at-risk organ requiring protection for both right- and left-sided tumours.

Artificial intelligence in oncourology: integrated deep learning technologies in the tasks of segmentation of three-dimensional images of kidney tumors

In order to improve the accuracy of cancer diagnosis, a new convolutional neural network architecture is presented, which provides automatic segmentation and detection of kidney tumors on three-dimensional images obtained by computed tomography. The proposed approach is based on the integration of three complementary technologies: multilevel convolutional processing, residual connections and U-Net architectural principles. This approach ensures efficient processing of voluminous medical data. An original neural network system for segmentation of kidney images obtained by computed tomography and detection of kidney tumors has been built. To validate the system, a comprehensive experiment was conducted using the publicly available KiTS19 dataset (Kidney Tumor Segmentation 2019) provided by the University of Minnesota Clinic through the Grand Challenge platform. The dataset includes 300 labeled images of kidneys obtained by computed tomography, with confirmed diagnoses. The experiment consisted of the following stages: dataset preprocessing, including normalization and augmentation; system training for 210 cases; validation on an independent sample of 90 cases. The results of the experiment demonstrate the high diagnostic efficiency of the system: the accuracy of automatic segmentation of anatomical structures of the kidneys was 96 % (according to the Dice coefficient); the accuracy of detection and segmentation of tumor formations has reached 91 % (according to the Dice coefficient). The results obtained can be applied in the following areas of clinical practice: preoperative planning and navigation during organ – preserving operations; automated screening of studies using computed tomography for early detection of kidney tumors; quantitative assessment of the dynamics of tumor growth in monitoring the course of the disease; support for clinical decision-making in oncourology.

Conserved patterns of transcriptional dysregulation, heterogeneity, and cell states in clear cell kidney cancer.

Clear cell kidney cancers are characterized both by conserved oncogenic driver events and by marked intratumor genetic and phenotypic heterogeneity, which help drive tumor progression, metastasis, and resistance to therapy. How these are reflected in transcriptional programs within the cancer and stromal cell components remains an important question with the potential to drive novel therapeutic approaches to treating cancer. To better understand these programs, we perform single-cell transcriptomics on 75 multi-regional biopsies from kidney tumors and normal kidney. We identify conserved patterns of transcriptional dysregulation and their upstream regulators within the tumor and associated vasculature. We describe recurrent subclonal transcriptional consequences of Chr14q loss linked to metastatic potential. We identify prognostically significant conserved patterns of intratumor transcriptional heterogeneity. These reflect co-existing cell states found in both cancer cells and normal kidney cells, indicating that rather than arising from genetic heterogeneity they are a consequence of lineage plasticity.

A refined kidney tumor nephrometry system employed to screen pediatric patients who are eligible for nephron sparing surgery.

Comprehension of the anatomical characteristics of pediatric kidney tumors is crucial for making surgical decisions. Previous kidney tumor nephrometry systems failed to incorporate two significant factors: tumor thrombus and multifocality. We develop a refined nephrometry system based on a comprehensive understanding of the characteristics exhibited by pediatric kidney tumors. The TUMORS nephrometry scoring system comprises 6 indicators, including tumor (T)hrombus, (U)rinary collecting system involvement, (M)ultiple tumors, (O)utward property, (R)adius, and (S)ite relative to the polar lines. Each renal unit was assessed and scored independently. The complexity characteristics of kidney tumors were summarized, and the correlation was compared with RENAL nephrometry system. Furthermore, the complexity of kidney tumors was compared across different surgical procedures. A total of 43 patients were enrolled, involving 70 kidney units. Radical nephrectomy (RN) was performed on 13 kidneys, while the remaining 57 kidneys underwent nephron sparing surgery. In the NSS group, tumors in 37 kidneys were resected in vivo, whereas 20 kidneys underwent tumor resection ex vivo followed by kidney autotransplantation. According to the TUMORS nephrometry scoring system, there were 13, 34 and 23 kidney units classified as low, moderate and high complexity, respectively. Tumors that underwent RN or ex vivo removal exhibited higher complexity. The complications and positive margins of NSS were not statistically significant in relation to tumor complexity. The TUMORS nephrometry scoring system holds significant guidance for the decision of surgical protocol and can be applied to the preoperative evaluation.

Kidney cancer in a 14-year-old child: a rare clinical case description

Kidney tumours are among the most common solid malignant pathologies in children, accounting for 6 % of all childhood malignancies. About 89 % of these tumors are nephroblastomas (Wilms' tumors). Renal cell carcinoma in children is extremely rare, representing less than 1 % of all renal tumor pathologies in childhood and adolescence. Given the rarity of this pathology in children and the small size of neoplasms in the renal parenchyma, differential diagnosis between renal cancer and cystic renal lesions can be challenging. This clinical case describes a successful treatment case of a 14-year-old patient with a detected renal parenchymal mass, subsequently diagnosed as renal cell carcinoma.

Dual-Stage AI Model for Enhanced CT Imaging: Precision Segmentation of Kidney and Tumors.

Accurate kidney and tumor segmentation of computed tomography (CT) scans is vital for diagnosis and treatment, but manual methods are time-consuming and inconsistent, highlighting the value of AI automation. This study develops a fully automated AI model using vision transformers (ViTs) and convolutional neural networks (CNNs) to detect and segment kidneys and kidney tumors in Contrast-Enhanced (CECT) scans, with a focus on improving sensitivity for small, indistinct tumors. The segmentation framework employs a ViT-based model for the kidney organ, followed by a 3D UNet model with enhanced connections and attention mechanisms for tumor detection and segmentation. Two CECT datasets were used: a public dataset (KiTS23: 489 scans) and a private institutional dataset (Private: 592 scans). The AI model was trained on 389 public scans, with validation performed on the remaining 100 scans and external validation performed on all 592 private scans. Tumors were categorized by TNM staging as small (≤4 cm) (KiTS23: 54%, Private: 41%), medium (>4 cm to ≤7 cm) (KiTS23: 24%, Private: 35%), and large (>7 cm) (KiTS23: 22%, Private: 24%) for detailed evaluation. Kidney and kidney tumor segmentations were evaluated against manual annotations as the reference standard. The model achieved a Dice score of 0.97 ± 0.02 for kidney organ segmentation. For tumor detection and segmentation on the KiTS23 dataset, the sensitivities and average false-positive rates per patient were as follows: 0.90 and 0.23 for small tumors, 1.0 and 0.08 for medium tumors, and 0.96 and 0.04 for large tumors. The corresponding Dice scores were 0.84 ± 0.11, 0.89 ± 0.07, and 0.91 ± 0.06, respectively. External validation on the private data confirmed the model's effectiveness, achieving the following sensitivities and average false-positive rates per patient: 0.89 and 0.15 for small tumors, 0.99 and 0.03 for medium tumors, and 1.0 and 0.01 for large tumors. The corresponding Dice scores were 0.84 ± 0.08, 0.89 ± 0.08, and 0.92 ± 0.06. The proposed model demonstrates consistent and robust performance in segmenting kidneys and kidney tumors of various sizes, with effective generalization to unseen data. This underscores the model's significant potential for clinical integration, offering enhanced diagnostic precision and reliability in radiological assessments.

Utilization of cardiopulmonary bypass at radical nephrectomy for renal cell carcinoma with tumour thrombus.

The objective of this study is to evaluate preoperative factors associated with cardiopulmonary bypass (CPB) utilization and outcomes for patients with renal cell carcinoma (RCC) and tumour thrombus (TT). Radical nephrectomy with thrombectomy is a standard treatment for patients with RCC and associated TT. Morbidity and mortality rates tend to correlate with aggressiveness of tumour and TT level. Patients undergoing radical nephrectomy with thrombectomy (2006-2023) were retrospectively identified. Inclusion criteria included RCC histology and preoperative imaging available for thrombus-level categorization based on the Mayo Clinic grading system. Logistic regression assessed predictors for utilizing CPB, and Cox regression identified factors associated with survival. A total of 72 patients with RCC and associated TT were identified. The median age was 67 years. RCC-related symptoms were present in 83%, and 28% had Levels 3 and 4 thrombi. Eleven patients (15.3%) had undergone neoadjuvant therapy, and 81% had clear-cell RCC. CPB was utilized in eight (11.1%) cases. The median tumour size was 10.5cm. Metastatic disease was greater in the CPB cohort (75% vs. 28%, p = 0.008). All cases performed on CPB were Levels 3 and 4 thrombi (100% vs. 19% in the non-CPB group, p < 0.001). CPB cases had significantly longer operative time, and hospital stays and rates of Clavien ≥ 3 complications. On multivariate analysis, metastatic disease was a predictor of CPB utilization. Median survival was 74 and 25 months in the non-CPB and CPB cohorts, respectively (p = 0.01). Pulmonary disease and metastatic disease with CPB utilization were significantly associated with worse survival on multivariate analysis. Surgical extirpation of kidney tumours with associated TT remains the standard of care among patients with locally advanced RCC. CPB can be utilized to increase the feasibility of resection for high-level thrombi. Preoperative planning and cooperation among surgical teams are key given the perioperative morbidity and mortality.

Optimized MALDI2-Mass Spectrometry Imaging for Stable Isotope Tracing of Tissue-Specific Metabolic Pathways in Mice.

Spatial stable isotope tracing metabolic imaging is a cutting-edge technique designed to investigate tissue-specific metabolic functions and heterogeneity. Traditional matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) techniques often struggle with low coverage of low-molecular-weight (LMW) metabolites, which are often crucial for spatial metabolic studies. To address this, we developed a high-coverage spatial isotope tracing metabolic method that incorporates optimized matrix selection, sample preparation protocols, and enhanced post-ionization (MALDI2) techniques. We employed this approach to mouse kidney, brain, and breast tumors to visualize the spatial dynamics of metabolic flow. Our results revealed diverse regional distributions of nine labeled intermediates derived from 13C6-glucose across glycolysis, glycogen metabolism, and the tricarboxylic acid (TCA) cycle in kidney tissues. In brain sections, we successfully mapped six intermediates from the TCA cycle and glutamate-glutamine (Glu-Gln) cycle simultaneously in distinct neurological regions. Furthermore, in breast cancer tumor tissues, our approach facilitated the mapping of nine metabolic intermediates in multiple pathways, including glycolysis, the pentose phosphate pathway (PPP), and the TCA cycle, illustrating metabolic heterogeneity within the tumor microenvironment. This methodology enhances metabolite coverage, enabling more comprehensive imaging of isotope-labeled metabolites and opening new avenues for exploring the metabolic landscape in various biological contexts.

Mutations in tumor suppressor genes Vhl and Rassf1a cause DNA damage, chromosomal instability and induce gene expression changes characteristic of clear cell renal cell carcinoma.

RASSF1A is frequently biallelically inactivated in clear cell renal cell carcinoma (ccRCC) due to loss of chromosome 3p and promoter hypermethylation. Here we investigated the cellular and molecular consequences of single and combined deletion of the Rassf1a and Vhl tumor suppressor genes to model the common ccRCC genotype of combined loss of function of RASSF1A and VHL. In mouse embryonic fibroblasts and in primary kidney epithelial cells, double deletion of Rassf1a and Vhl caused chromosomal segregation defects and increased formation of micronuclei, demonstrating that pVHL and RASSF1A function to maintain genomic integrity. Combined Rassf1a and Vhl deletion in kidney epithelial cells in vivo increased proliferation and caused mild tubular disorganization, but did not lead to the development of kidney tumors. Single cell RNA-sequencing unexpectedly revealed that Rassf1a or Vhl deletion both induce the expression of an overlapping set of genes in a sub-population of proximal tubule cells. Many of these genes are also upregulated in the Vhl/Trp53/Rb1 deficient mouse model of ccRCC. In other subsets of proximal tubule cells, combined Vhl/Rassf1a deletion induced the expression of additional genes that were not upregulated in each of the single knockouts. The expression of the human homologues of Rassf1a-regulated genes correlate negatively with RASSF1 expression levels in human ccRCC. Our results suggest that the loss of RASSF1A function establishes a ccRCC-characteristic gene expression pattern.

KidneyNet: A Novel CNN-Based Technique for the Automated Diagnosis of Chronic Kidney Diseases from CT Scans

This study presents KidneyNet, an innovative computer-aided diagnosis (CAD) system designed to identify chronic kidney diseases (CKDs), such as kidney stones, cysts, and tumors, in CT scans. KidneyNet utilizes a convolutional neural network (CNN) structure consisting of eight convolutional layers, three pooling layers, a flattening layer, and two fully connected layers. Small filters enhance computational efficiency by reducing the number of parameters and minimizing the risk of overfitting compared to larger filters. The model captures more complex and abstract features as data move through the layers. The initial layers identify basic patterns, while the deeper layers focus on more intricate representations. KidneyNet aims to enhance the efficiency and accuracy of kidney disease diagnosis. Additionally, the model incorporates the gradient-weighted class activation mapping (Grad-CAM) algorithm, which helps to pinpoint affected areas in the scans. This feature improves interpretability, allowing clinicians to identify which regions the model deemed significant for detecting abnormalities such as tumors, cysts, or stones. Through extensive testing on a CT kidney dataset, KidneyNet demonstrated impressive performance metrics, with 99.88% accuracy, 99.92% specificity, 99.76% sensitivity, 99.58% precision, and an F1 score of 99.67%, outperforming existing models. This approach alleviates the diagnostic burden on radiologists and promotes early detection, potentially saving lives. This study highlights the critical role of advanced imaging analysis in addressing kidney conditions and emphasizes KidneyNet’s capability to deliver precise and cost-effective diagnoses.

GLI1::FOXO4-rearranged kidney tumors: a potentially distinct renal subtype within the spectrum of GLI1-altered tumors?

Pathogenic alterations, namely, fusions and amplifications, of the GLI1 gene have been identified in various mesenchymal tumors, including pericytoma with t(7;12), plexiform fibromyxoma, gastroblastoma, and other malignant mesenchymal neoplasms arising in the soft tissues, as well as in various visceral organs. However, only three cases of GLI1-rearranged renal tumors have been reported to date, comprising two low-grade spindle cell tumors with GLI1::FOXO4 fusion along with one GLI1-rearranged case with an unknown fusion partner. In this study, we analyzed three cases with GLI1::FOXO4 fusion and overlapping morphology. One of the cases was reported previously, but an extended clinical and immunohistochemical information is provided. The studied cases occurred in 2 female and 1 male patients aged 35, 55, and 62years (mean 51years). All three tumors affected the renal parenchyma and grew as unencapsulated but well-circumscribed solid masses containing occasional entrapped and dilated renal tubules. The tumor cells were organized in cords, nests, or fascicles, had a round to spindled shape, and exhibited only mild nuclear atypia and minimal mitotic activity. They had a sparse eosinophilic to clear cytoplasm and were embedded in myxocollagenous stroma. Immunohistochemically, all cases expressed GLI1(albeit with variable intensity) and harbored GLI1::FOXO4 fusion. All three patients were treated solely by complete surgical excision. Case 1 was alive with unknown disease status, case 2 was alive without evidence of disease, and case 3 died of unrelated causes. Our study doubles the number of reported cases with GLI1::FOXO4 fusion. The so far absolute predilection of this fusion for renal tumors, coupled with the absence of reports of other GLI1 fusions in tumors of the kidney, might indicate the potential existence of a distinct renal subtype with morphological features similar to other GLI1-altered tumors. All four reported cases had an uneventful follow-up which, together with their low-grade morphological features, suggests that these tumors might have a favorable prognosis.

Correlation Between BMI and Kidney Tumor Lateralization: Insights into Survival and Risk Factors.

Background: Kidney cancer accounts for approximately 2% of all diagnosed cancers and fatalities worldwide, and a notable increase in its incidence has been observed in recent years. Previous studies have identified various risk factors for renal cell carcinoma (RCC), including age, gender, smoking, hypertension, overweight, and obesity. However, limited information is available regarding the correlation between RCC risk factors and tumor lateralization. Objectives: To investigate the relationship between body mass index (BMI) and the lateralization of kidney tumors in patients undergoing surgery for renal cell carcinoma. Moreover, we aim to evaluate the impact of lateralization of malignant kidney tumors on overall survival (OS) and cancer-specific survival (CSS). Materials and Methods: This single-center study included 287 patients who underwent surgical treatment for kidney tumors between January 2016 and December 2019. The patients were allocated into the following groups based on their BMI: normal (18.5-24.99 kg/m2), overweight (25-29.99 kg/m2), or obese (≥30 kg/m2). The study collected demographic and histopathological data, as well as patient history, including risk factors such as smoking and hypertension. Results: Right-sided kidney tumors occurred more frequently (55.05%, n = 158) than left-sided ones (44.95%, n = 129). A statistically significant relationship (p = 0.04731) was observed between BMI and the frequency of right-sided kidney tumors in the overweight group (70 vs. 43 cases). There was no correlation between BMI and the occurrence of kidney tumors in the normal BMI and obesity groups. Furthermore, the analysis revealed no association between the lateralization of kidney tumors, smoking, and hypertension. The 5-year survival rate was 62%, with a mean follow-up duration of 104 months (approximately 8.5 years). No statistically significant difference was observed between the right- and left-sided cancer groups, with survival rates of 58% and 66%, respectively (p = 0.652). Conclusions: This study highlighted that right-sided kidney tumors occurred significantly more frequently in overweight individuals in our cohort of patients. No association was observed between lateralization of kidney cancer and overall survival (OS) or cancer-specific survival (CSS).

Characterization of malignant kidney tumors in childhood by 18F-FDG PET/CT.

To evaluate the role of semiquantitative parameters derived from 18F-FDG PET/CT in characterizing pediatric malignant kidney tumors. 54 children with pathologically confirmed renal malignancies who underwent pretreatment 18F-FDG PET/CT were retrospectively reviewed. Association between PET-derived semiquantitative parameters, including maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV) and total lesion glycolysis (TLG), and tumor histology, disease stage, and loss of heterozygosity were analyzed. Of the 54 patients, 43 had Wilms tumor (WT), 4 had clear cell sarcoma of the kidney (CCSK), 3 had rhabdoid tumor of kidney, 2 had renal cell carcinoma, and 2 had congenital mesoblastic nephroma. 18F-FDG PET/CT detected all primary renal tumors, with SUVmax ranging from 1.2 to 14.5. PET-derived parameters showed no significant differences among different tumor types, except for CCSK, which exhibited lower FDG uptake compared to WT (SUVmax 3.60 ± 1.02 vs. 6.10 ± 2.25, p = 0.034). Higher MTV, TLG, and lactate dehydrogenase were associated with stage IV disease. ROC analysis showed that TLG had the highest AUC (0.931) for predicting stage IV disease in the whole cohort, while SUVmax had the highest AUC (0.867) for predicting stage IV and V in WT. Furthermore, Children with 1q gain exhibited higher MTV in WT (447 ± 354 vs. 207 ± 189, p = 0.041). PET-derived semiquantitative parameters highly associated with disease stage both in the overall cohort of renal malignancies and specifically in the WT group. Additionally, volumetric metabolic indices might provide further insights into loss of heterozygosity in children with WT.