Renal tumor segmentation, visualization, and segmentation confidence using ensembles of neural networks in patients undergoing surgical resection.

OBJECTIVES: To develop an automatic segmentation model for solid renal tumors on contrast-enhanced CTs and to visualize segmentation with associated confidence to promote clinical applicability. MATERIALS AND METHODS: The training dataset included solid renal tumor patients from two tertiary centers undergoing surgical resection and receiving CT in the corticomedullary or nephrogenic contrast media (CM) phase. Manual tumor segmentation was performed on all axial CT slices serving as reference standard for automatic segmentations. Independent testing was performed on the publicly available KiTS 2019 dataset. Ensembles of neural networks (ENN, DeepLabV3) were used for automatic renal tumor segmentation, and their performance was quantified with DICE score. ENN average foreground entropy measured segmentation confidence (binary: successful segmentation with DICE score > 0.8 versus inadequate segmentation ≤ 0.8). RESULTS: N = 639/n = 210 patients were included in the training and independent test dataset. Datasets were comparable regarding age and sex (p > 0.05), while renal tumors in the training dataset were larger and more frequently benign (p < 0.01). In the internal test dataset, the ENN model yielded a median DICE score = 0.84 (IQR: 0.62-0.97, corticomedullary) and 0.86 (IQR: 0.77-0.96, nephrogenic CM phase), and the segmentation confidence an AUC = 0.89 (sensitivity = 0.86; specificity = 0.77). In the independent test dataset, the ENN model achieved a median DICE score = 0.84 (IQR: 0.71-0.97, corticomedullary CM phase); and segmentation confidence an accuracy = 0.84 (sensitivity = 0.86 and specificity = 0.81). ENN segmentations were visualized with color-coded voxelwise tumor probabilities and thresholds superimposed on clinical CT images. CONCLUSIONS: ENN-based renal tumor segmentation robustly performs in external test data and might aid in renal tumor classification and treatment planning. CLINICAL RELEVANCE STATEMENT: Ensembles of neural networks (ENN) models could automatically segment renal tumors on routine CTs, enabling and standardizing downstream image analyses and treatment planning. Providing confidence measures and segmentation overlays on images can lower the threshold for clinical ENN implementation. KEY POINTS: Ensembles of neural networks (ENN) segmentation is visualized by color-coded voxelwise tumor probabilities and thresholds. ENN provided a high segmentation accuracy in internal testing and in an independent external test dataset. ENN models provide measures of segmentation confidence which can robustly discriminate between successful and inadequate segmentations.

Clinical Importance of Focal Adhesion Kinase (FAK)-Src and Paxillin Expression in Renal Cell Carcinoma.

BACKGROUND AND OBJECTIVE: The complex focal adhesion kinase (FAK)/Src and paxillin seem to play a key role in the pathogenesis and progression of cancer. The aim of this study is to evaluate the expression of these proteins in renal cell carcinomas (RCCs), considering the immunoreactive score (IRS), the positivity and the intensity, and to find any association with patients' clinical characteristics, histologic type and other pathological features that imply a possible pathophysiological or prognostic role of FAK/Src and paxillin in RCC. METHODS: Patients with RCC who had undergone partial or radical nephrectomy from January 2009 to September 2010 were eligible for this retrospective cross-sectional study. The immunohistochemical expression of FAK, Src and paxillin proteins in formalin-fixed paraffin-embedded tumour tissue was analysed in association with various clinicopathological features. RESULTS: Out of ninety patients, 58 had clear cell renal carcinoma, 15 had papillary, 11 had chromophobe and six had unclassified RCC. FAK, Src and paxillin were expressed in 55.6%, 32.2% and 18.9% of all cases, respectively. In univariate analysis, FAK positivity and IRS were more likely in patients with papillary and chromophobe histologic type versus clear cell RCC (p<0.005), Src positivity and IRS presented more frequently in stage T3 versus T1 (p<0.005) and paxillin positivity was more likely in patients with stage T3 versus T2 (p=0.021) and grades 3-4 versus grade 2 (p=0.013). Paxillin-IRS was not associated with any clinicopathological features. The same associations were also reproduced in the multifactorial analysis for the FAK and Src positivity and IRS, while it was found that paxillin positivity and IRS were associated with the female gender (p=0.052, p=0.024), and were higher in grades 3-4 versus grade 2 (p=0.022, p=0.020). CONCLUSIONS: Our study suggests that RCC shows immunohistochemical expression of FAK, Src and paxillin proteins, and this expression varies in relation to the histologic type, the stage and the stage/grade/gender, respectively. These findings imply a possible involvement of the FAK/Src signalling pathway in the pathogenesis and progression of cancer in RCC, providing future perspectives for targeted therapies with inhibitors.

Tumor morphology evaluation using 3D-morphometric features of renal masses.

OBJECTIVE: To assess the correlation between the general (gender, age, and maximum tumor size) and 3D morphotopometric features of the renal tumor node, following the MSCT data post-processing, and the tumor histological structure; to propose an equation allowing for kidney malignancy assessment based on general and morphometric features. MATERIALS AND METHODS: In total, 304 patients with unilateral solitary renal neoplasms underwent laparoscopic (retroperitoneoscopic) or robotic partial or radical nephrectomy. Before the procedure, kidney contrast-enhanced MSCT followed by the tumor 3D-modeling was performed. 3D model of the kidney tumor, and its morphotopometric features, and histological structure were analyzed. The morphotopometric ones include the side of the lesion, location by segments, the surface where the tumor, the depth of the tumor invasion into the kidney, and the shape of tumor. RESULTS: Out of 304 patients, 254 (83.6%) had malignant kidney tumors and 50 (16.4%) benign kidney tumors. In total, 231 patients, out of 254 (90.9%) were assessed for the degree of malignant tumor differentiation. Malignant tumors were more frequent in men than in women (p < 0.001). Mushroom-shaped tumors were the most common shapes among benign renal masses (35.2%). The most common malignant kidney tumors had spherical with a partially uneven surface (27.6%), multinodular (tuberous (27.2%)), and spherical with a conical base (24.8%) shapes. Logistic regression model enabled the development of prognostic equation for tumor malignancy prediction ("low" or "high"). The univariate analysis revealed the correlation only between high differentiation (G1) and a spherical tumor with a conical base (p = 0.029). CONCLUSION: The resulting logistic model, based on the analysis of such predictors as gender and form of kidney lesions, demonstrated a large share (87.6%) of correct predictions of the kidney tumor malignancy.

A Comparative Analysis of the Novel Conditional Deep Convolutional Neural Network Model, Using Conditional Deep Convolutional Generative Adversarial Network-Generated Synthetic and Augmented Brain Tumor Datasets for Image Classification.

Disease prediction is greatly challenged by the scarcity of datasets and privacy concerns associated with real medical data. An approach that stands out to circumvent this hurdle is the use of synthetic data generated using Generative Adversarial Networks (GANs). GANs can increase data volume while generating synthetic datasets that have no direct link to personal information. This study pioneers the use of GANs to create synthetic datasets and datasets augmented using traditional augmentation techniques for our binary classification task. The primary aim of this research was to evaluate the performance of our novel Conditional Deep Convolutional Neural Network (C-DCNN) model in classifying brain tumors by leveraging these augmented and synthetic datasets. We utilized advanced GAN models, including Conditional Deep Convolutional Generative Adversarial Network (DCGAN), to produce synthetic data that retained essential characteristics of the original datasets while ensuring privacy protection. Our C-DCNN model was trained on both augmented and synthetic datasets, and its performance was benchmarked against state-of-the-art models such as ResNet50, VGG16, VGG19, and InceptionV3. The evaluation metrics demonstrated that our C-DCNN model achieved accuracy, precision, recall, and F1 scores of 99% on both synthetic and augmented images, outperforming the comparative models. The findings of this study highlight the potential of using GAN-generated synthetic data in enhancing the training of machine learning models for medical image classification, particularly in scenarios with limited data available. This approach not only improves model accuracy but also addresses privacy concerns, making it a viable solution for real-world clinical applications in disease prediction and diagnosis.

Current surgical approaches to pediatric renal tumors.

Pediatric renal tumors are among the most common pediatric solid malignancies. Surgical resection is a key component in the multidisciplinary therapy for children with kidney tumors. Therefore, it is imperative that surgeons caring for children with renal tumors fully understand the current standards of care in order to provide appropriate surgical expertise within this multimodal framework. Fortunately, the last 60 years of international, multidisciplinary pediatric cancer cooperative group studies have enabled high rates of cure for these patients. This review will highlight the international surgical approaches to pediatric patients with kidney cancer to help surgeons understand the key differences and similarities between the European (International Society of Pediatric Oncology) and North American (Children's Oncology Group) recommendations.

Initial Experiences of Laparoscopic Nephrectomy in a Tertiary Oncology Center: An Analysis of 142 Cases.

INTRODUCTION: Kidney tumors have an important place among urological malignancies. The increased utilization of imaging methods has led to a rise in renal cell carcinoma (RCC) diagnoses, albeit with declining mortality rates, particularly in developed countries. Radical nephrectomy remains the gold standard treatment. The aim of this study was to share a tertiary oncology hospital's initial experiences with laparoscopic nephrectomy. MATERIALS AND METHODS: This retrospective study analyzes data from patients who underwent laparoscopic nephrectomy, focusing on demographic characteristics, tumor features, and operative outcomes. Information regarding age, gender, tumor size, operative details, and pathology results was collected and analyzed. RESULTS: One hundred forty-two patients were included in the study; 69 (48.60%) were female and 73 (51.40%) were male. The mean age of the patients was 57.11 ± 12.6 years, with tumors primarily located on the left kidney (52.80%). The mean tumor size was 53.01 ± 24.01 mm. Intraoperative complications included the need for conversion to open surgery in five cases and vascular, pneumothorax, or duodenal injuries in a subset of patients. However, postoperative complications, such as sepsis or mortality, were not observed. DISCUSSION: Despite an initial learning curve associated with longer operation times, laparoscopic techniques offer benefits, including reduced blood loss, faster recovery, and improved cosmetic outcomes. Histologically, clear cell RCC was the most common tumor type encountered. This study underscores the safety and efficacy of laparoscopic radical nephrectomy, advocating for its widespread adoption while emphasizing the importance of surgeon experience and patient selection in optimizing outcomes.

The effect of 11th rib excision on perioperative outcomes in patients undergoing partial nephrectomy for upper pole renal tumors.

INTRODUCTION: We aimed to evaluate the effect of eleven11th rib resection.on the perioperative period TRIFECTA criteria in patients who underwent retroperitoneal partial nephrectomy (PN) with the diagnosis of upper pole kidney tumors. MATERIALS AND METHODS: We conducted a retrospective analysis of the data of the patients who underwent Open PN for upper pole renal masses between 2018 and 2023. The patients were divided into two groups: PN with rib resection and PN without rib resection. The demographic characteristics, tumor sizes, PADUA scores, warm-cold renal ischemia times, mass excision and tumor bed suturing times, histopathological tumor type and surgical margin positivity of the patients were examined. Both groups were evaluated comparatively based on this data. RESULTS: The renal nephrometry scores of the two groups were similar. The total renal ischemia time was significantly shorter in the patients who underwent a rib resection than in those who did not (p < 0.001). Both the tumor excision and tumor bed suturing times were significantly shorter in the group that underwent a rib resection than in the group that did not (p < 0.001). The Clavien-Dindo complication grades were statistically similar between the two groups. CONCLUSION: Complex in nature and high-risk renal masses located in the upper pole of the kidney, partial nephrectomy performed with an 11th rib resection can be considered a reliable surgical option with a shorter ischemia time, supporting the preservation of long-term renal function.

Kidney Tumor Classification on CT images using Self-supervised Learning.

One of the most common diseases affecting society around the world is kidney tumor. The risk of kidney disease increases due to reasons such as consumption of ready-made food and bad habits. Early diagnosis of kidney tumors is essential for effective treatment, reducing side effects, and reducing the number of deaths. With the development of computer-aided diagnostic methods, the need for accurate renal tumor classification is also increasing. Because traditional methods based on manual detection are time-consuming, boring, and costly, high-accuracy tests can be performed faster and at a lower cost with deep learning (DL) methods in kidney tumor detection (KTD). Among the current challenges regarding artificial intelligence-assisted KTD, obtaining more precise programming information and the capacity to group with high accuracy make clinical determination more vital and bring it to an important point for current treatment in KTD prediction. This encourages us to propose a more effective DL model that can effectively assist specialist physicians in the diagnosis of kidney tumors. In this way, the workload of radiologists can be alleviated and errors in clinical diagnoses that may occur due to the complex structure of the kidney can be prevented. A large amount of data is needed during the training of the developed methods. Although various studies have been conducted to reduce the amount of data with feature selection techniques, these techniques provide little improvement in the classification accuracy rate. In this paper, a masked autoencoder (MAE) is proposed for KTD, which can produce effective results on datasets containing some samples and can be directly fine-tuned and pre-trained. Self-supervised learning (SSL) is achieved through self-distillation (SD), which can be reintroduced into the configuration loss calculation using masked patches. The SD loss on the decoder and encoder outputs' latent representation is calculated operating SSLSD-KTD. The encoder obtains local attention, while the decoder transfers its global attention to calculate losses. The SSLSD-KTD method reached 98.04 % classification accuracy on the KAUH-kidney dataset, including 8400 samples, and 82.14 % on the CT-kidney dataset, containing 840 samples. By adding more external information to the SSLSD-KTD method with transfer learning, accuracy results of 99.82 % and 95.24 % were obtained on the same datasets. Experimental results have shown that the SSLSD-KTD method can effectively extract kidney tumor features with limited data and can be an aid or even an alternative for radiologists in decision-making in the diagnosis of the disease.

Intrarenal neurofibroma: unveiling a diagnostic challenge-a case report and literature review.

A 53-year-old male patient presented with an incidental finding of a left kidney mass after being evaluated for elevated serum creatinine without having any symptoms. The left kidney mass was confirmed by ultrasound, computed tomography 'CT' scan and magnetic resonance imaging 'MRI'. A left radical nephrectomy was done, and histopathology confirmed the presence of intrarenal neurofibroma with no evidence of malignancy.

Tumor Supplying Artery Injection of Liposome@Sunitinib Could Effectively Inhibit the Progression of Kidney Tumor.

Renal cell carcinoma (RCC) is one of the most common malignancies in the urinary system and is not sensitive to chemotherapy or radiotherapy in its advanced stages. Sunitinib is recommended as a first-line target drug for unresectable and metastatic RCC by targeting tyrosine kinase-related signaling pathways, but its therapeutic effect is unsatisfactory. Recently, nanomaterials have shown great prospects in the medical field because of their unique physicochemical properties. Particularly, liposomes are considered as ideal drug delivery systems due to their biodegradability, biocompatibility, and ideal drug-loading efficiency. Considering that tumor supplying artery injection can directly distribute drugs into tumor tissues, in this study, liposomes were employed to encapsulate water-insoluble sunitinib to construct the liposome@sunitinib (Lipo@Suni) complex, so that the drug could directly target and distribute into tumor tissue, and effectively trapped in tumor tissues after tumor supplying artery injection for the advantage of the physicochemical properties of liposomes, thereby achieving a better therapeutic effect on advanced RCC. Here, we found that compared with the peripheral intravenous administration, trans-renal arterial administration increases the content and prolongs the retention time of liposomes in tumor tissues; accordingly, more sunitinib is dispersed and retained in tumor tissues. Ultimately, trans-renal arterial administration of Lipo@Suni exerts a better suppressive effect on RCC progression than peripheral intravenous administration, even better than the conventional oral administration of sunitinib.

Ewing's sarcoma of kidney extending into IVC and RA: resection using modified cannulation strategy for CPB.

Ewing's sarcoma of the kidney is a rare tumor. Although renal carcinomas are known to involve the inferior cava, extension of the tumor up to the right atrium is not common. In the majority of cases when the tumor extends into the infrahepatic part of the inferior vena cava, it can be removed from the abdominal approach. Few patients require the use of cardiopulmonary bypass for removal of the tumor in the inferior vena cava and right atrium. The management of patients requiring resection of kidney tumors and right atrial mass is more complicated and requires a team approach consisting of oncosurgeons, cardiac surgeons, and cardiac anesthetists. The resection of the kidney tumor with a mass in the right atrium is usually done concomitantly. The cardiopulmonary bypass cannulation strategy needs to be modified in such cases.

Cytokine Signaling in Pediatric Kidney Tumor Cell Lines WT-CLS1, WT-3ab and G-401.

Renal tumors comprise ~7% of all malignant pediatric tumors. Approximately 90% of pediatric kidney tumors comprise Wilms tumors, and the remaining 10% include clear cell sarcoma of the kidney, malignant rhabdoid tumor of the kidney, renal cell carcinoma and other rare renal tumors. Over the last 30 years, the role of cytokines and their receptors has been considerably investigated in both cancer progression and anti-cancer therapy. However, more effective immunotherapies require the cytokine profiling of each tumor type and comprehensive understanding of tumor biology. In this study, we aimed to investigate the activation of signaling pathways in response to cytokines in three pediatric kidney tumor cell lines, in WT-CLS1 and WT-3ab cells (both are Wilms tumors), and in G-401 cells (a rhabdoid kidney tumor, formerly classified as Wilms tumor). We observed that interferon-alpha (IFN-α) and interferon-gamma (IFN-γ) very strongly induced the activation of the STAT1 protein, whereas IL-6 and IFN-α activated STAT3 and IL-4 activated STAT6 in all examined tumor cell lines. STAT protein activation was examined by flow cytometry and Western blot using phospho-specific anti-STAT antibodies which recognize only activated (phosphorylated) STAT proteins. Nuclear translocation of phospho-STAT proteins upon activation with specific cytokines was furthermore confirmed by immunofluorescence. Our results also showed that both IFN-α and IFN-γ caused upregulation of major histocompatibility complex (MHC) class I proteins, however, these cytokines did not have any effect on the expression of MHC class II proteins. We also observed that pediatric kidney tumor cell lines exhibit the functional expression of an additional cytokine signaling pathway, the tumor necrosis factor (TNF)-α-mediated activation of nuclear factor kappa B (NF-κB). In summary, our data show that human pediatric renal tumor cell lines are responsive to stimulation with various human cytokines and could be used as in vitro models for profiling cytokine signaling pathways.

Deep learning-assisted survival prognosis in renal cancer: A CT scan-based personalized approach.

This paper presents a deep learning (DL) approach for predicting survival probabilities of renal cancer patients based solely on preoperative CT imaging. The proposed approach consists of two networks: a classifier- and a survival- network. The classifier attempts to extract features from 3D CT scans to predict the ISUP grade of Renal cell carcinoma (RCC) tumors, as defined by the International Society of Urological Pathology (ISUP). Our classifier is a 3D convolutional neural network to avoid losing crucial information on the interconnection of slides in 3D images. We employ multiple procedures, including image augmentation, preprocessing, and concatenation, to improve the performance of the classifier. Given the strong correlation between ISUP grading and renal cancer prognosis in the clinical context, we use the ISUP grading features extracted by the classifier as the input to the survival network. By leveraging this clinical association and the classifier network, we are able to model our survival analysis using a simple DL-based network. We adopt a discrete LogisticHazard-based loss to extract intrinsic survival characteristics of RCC tumors from CT images. This allows us to build a completely parametric survival model that varies with patients' tumor characteristics and predicts non-proportional survival probability curves for different patients. Our results demonstrated that the proposed method could predict the future course of renal cancer with reasonable accuracy from the CT scans. The proposed method obtained an average concordance index of 0.72, an integrated Brier score of 0.15, and an area under the curve value of 0.71 on the test cohorts.

Multi-Scale and Spatial Information Extraction for Kidney Tumor Segmentation: A Contextual Deformable Attention and Edge-Enhanced U-Net.

Kidney tumor segmentation is a difficult task because of the complex spatial and volumetric information present in medical images. Recent advances in deep convolutional neural networks (DCNNs) have improved tumor segmentation accuracy. However, the practical usability of current CNN-based networks is constrained by their high computational complexity. Additionally, these techniques often struggle to make adaptive modifications based on the structure of the tumors, which can lead to blurred edges in segmentation results. A lightweight architecture called the contextual deformable attention and edge-enhanced U-Net (CDA2E-Net) for high-accuracy pixel-level kidney tumor segmentation is proposed to address these challenges. Rather than using complex deep encoders, the approach includes a lightweight depthwise dilated ShuffleNetV2 (LDS-Net) encoder integrated into the CDA2E-Net framework. The proposed method also contains a multiscale attention feature pyramid pooling (MAF2P) module that improves the ability of multiscale features to adapt to various tumor shapes. Finally, an edge-enhanced loss function is introduced to guide the CDA2E-Net to concentrate on tumor edge information. The CDA2E-Net is evaluated on the KiTS19 and KiTS21 datasets, and the results demonstrate its superiority over existing approaches in terms of Hausdorff distance (HD), intersection over union (IoU), and dice coefficient (DSC) metrics.

Actual encounters of the kidney kind: Exploring 48 cases of renal collision tumors through the lens of literature.

Multiple tumors of different lineages merging into a single mass, termed collision tumors, are considered a rare phenomenon in the kidney. Tumor components, or partners, may be malignant (including metastatic disease), borderline, or benign. We report the largest cohort to date of 48 cases. The cases were identified from the archives of three institutions in the last 16 years, including 43 (90%) with 2 tumor partners (dyad) and 5 (10%) with 3 partners (triad), totaling 101 individual neoplasms. The majority of cases involved immunohistochemical workup, and 5 underwent FISH or molecular studies. Forty (83%) cases featured a malignant entity, including all triads. Twenty dyads and two triads were composed entirely of malignant tumors. The most common malignant partner was clear cell renal cell carcinoma (RCC) (N = 19) followed by papillary RCC (N = 17). Nine (19%) cases featured borderline entities, including 5 multilocular cystic neoplasms of low malignant potential and 6 clear cell papillary renal cell tumors. Twenty one (44%) cases contained a benign partner, including 6 benign dyads. Papillary adenoma (N = 13) and oncocytoma (N = 8) were most common. Epithelial tumors were present in all 48 cases, and non-epithelial neoplasms in 9 cases (19%). Our cohort includes many novel combinations and collision partners with rare entities such as SDH-deficient RCC, TFE3-rearranged RCC, eosinophilic solid and cystic RCC, and acquired cystic disease associated RCC. A comprehensive literature review and analysis of collision tumor phenomenon in kidney placed these cases in context suggesting that collision tumors of the kidney are more common than previously recognized.

Exosomal long non-coding RNA MALAT1: a candidate of liquid biopsy in monitoring of Wilms' tumor.

PURPOSE: Wilms' tumor (WT) is a rare kidney cancer that primarily affects children. Exosomes are extracellular vesicles that cargo nucleic acids, proteins,etc. for cellular communication. Long non-coding RNAs (lncRNAs) have utility as biomarkers for cancer diagnosis, prognosis, and disease monitoring. We hypothesize that expression of lncRNA, metastasis-associated lung adenocarcinoma transcript-1(MALAT1), is dysregulated and possibly trafficked within exosomes to influence the tissue microenvironment for metastasis and recurrence of WT. METHODS: We investigated the expression of MALAT1 in thirty WT samples by qPCR. Exosomes were isolated using a precipitated and affinity-binding-based kit, and characterized using TEM, NTA, and DLS. RESULTS: Mean number of exosomes was 9.01×108/mL in primary culture, 1.64×108/mL in urine, and 4.65×108/plasma:400µl. Average yield of total RNA was 1.28µg (primary-culture supernatant:1ml), 1.47µg (Urine:1ml), 1.65µg (Plasma:400 µL). We quantified MALAT1 in exosomes derived from these sources in patients of WT. Expression of MALAT1 was significantly downregulated (p=0.008) in WT samples. CONCLUSION: This is the first study that demonstrated the presence of lncRNA MALAT1 in various invasive and non-invasive samples of patients with WT(primary tissue culture, urine, and plasma samples).

ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation.

Early intervention in tumors can greatly improve human survival rates. With the development of deep learning technology, automatic image segmentation has taken a prominent role in the field of medical image analysis. Manually segmenting kidneys on CT images is a tedious task, and due to the diversity of these images and varying technical skills of professionals, segmentation results can be inconsistent. To address this problem, a novel ASD-Net network is proposed in this paper for kidney and kidney tumor segmentation tasks. First, the proposed network employs newly designed Adaptive Spatial-channel Convolution Optimization (ASCO) blocks to capture anisotropic information in the images. Then, other newly designed blocks, i.e., Dense Dilated Enhancement Convolution (DDEC) blocks, are utilized to enhance feature propagation and reuse it across the network, thereby improving its segmentation accuracy. To allow the network to segment complex and small kidney tumors more effectively, the Atrous Spatial Pyramid Pooling (ASPP) module is incorporated in its middle layer. With its generalized pyramid feature, this module enables the network to better capture and understand context information at various scales within the images. In addition to this, the concurrent spatial and channel squeeze & excitation (scSE) attention mechanism is adopted to better comprehend and manage context information in the images. Additional encoding layers are also added to the base (U-Net) and connected to the original encoding layer through skip connections. The resultant enhanced U-Net structure allows for better extraction and merging of high-level and low-level features, further boosting the network's ability to restore segmentation details. In addition, the combined Binary Cross Entropy (BCE)-Dice loss is utilized as the network's loss function. Experiments, conducted on the KiTS19 dataset, demonstrate that the proposed ASD-Net network outperforms the existing segmentation networks according to all evaluation metrics used, except for recall in the case of kidney tumor segmentation, where it takes the second place after Attention-UNet.

Atrophic Kidney-Like Lesion-Case Report and Review of the Literature.

Atrophic kidney-like lesion (AKLL) is a rare kidney lesion, which was recently suggested by the Genitourinary Pathology Society as a provisional entity. As of now, 16 examples of AKLL have been described in the literature. Here we report a new tumor which shows similar clinicopathologic characteristics with those previously reported in AKLL. Immunohistochemical (IHC) studies in the current lesion identified a biphasic staining pattern consisting of a mixture of WT1+/KRT7-/PAX8- large dilated cysts and WT-/KRT7+/PAX8+ small atrophic cysts. Histomorphologic features of AKLL overlap with several neoplastic and non-neoplastic entities which can lead to mischaracterization. Awareness of the differentiating features is likely important when evaluating these lesions.

Self-Assembled Hyperbranched Gold Nanoarrays Decode Serum United Urine Metabolic Fingerprints for Kidney Tumor Diagnosis.

Serum united urine metabolic analysis comprehensively reveals the disease status for kidney diseases in particular. Thus, the precise and convenient acquisition of metabolic molecular information from united biofluids is vitally important for clinical disease diagnosis and biomarker discovery. Laser desorption/ionization mass spectrometry (LDI-MS) presents various advantages in metabolic analysis; however, there remain challenges in ionization efficiency and MS signal reproducibility. Herein, we constructed a self-assembled hyperbranched black gold nanoarray (HyBrAuNA) assisted LDI-MS platform to profile serum united urine metabolic fingerprints (S-UMFs) for diagnosis of early stage renal cell carcinoma (RCC). The closely packed HyBrAuNA afforded strong electromagnetic field enhancement and high photothermal conversion efficacy, enabling effective ionization of low abundant metabolites for S-UMF collection. With a uniform nanoarray, the platform presented excellent reproducibility to ensure the accuracy of S-UMFs obtained in seconds. When it was combined with automated machine learning analysis of S-UMFs, early stage RCC patients were discriminated from the healthy controls with an area under the curve (AUC) > 0.99. Furthermore, we screened out a panel of 9 metabolites (4 from serum and 5 from urine) and related pathways toward early stage kidney tumor. In view of its high-throughput, fast analytical speed, and low sample consumption, our platform possesses potential in metabolic profiling of united biofluids for disease diagnosis and pathogenic mechanism exploration.