Kidney Tumors Research Articles

Integrating neural networks with advanced optimization techniques for accurate kidney disease diagnosis

Kidney diseases pose a significant global health challenge, requiring precise diagnostic tools to improve patient outcomes. This study addresses this need by investigating three main categories of renal diseases: kidney stones, cysts, and tumors. Utilizing a comprehensive dataset of 12,446 CT whole abdomen and urogram images, this study developed an advanced AI-driven diagnostic system specifically tailored for kidney disease classification. The innovative approach of this study combines the strengths of traditional convolutional neural network architecture (AlexNet) with modern advancements in ConvNeXt architectures. By integrating AlexNet’s robust feature extraction capabilities with ConvNeXt’s advanced attention mechanisms, the paper achieved an exceptional classification accuracy of 99.85%. A key advancement in this study’s methodology lies in the strategic amalgamation of features from both networks. This paper concatenated hierarchical spatial information and incorporated self-attention mechanisms to enhance classification performance. Furthermore, the study introduced a custom optimization technique inspired by the Adam optimizer, which dynamically adjusts the step size based on gradient norms. This tailored optimizer facilitated faster convergence and more effective weight updates, imporving model performance. The model of this study demonstrated outstanding performance across various metrics, with an average precision of 99.89%, recall of 99.95%, and specificity of 99.83%. These results highlight the efficacy of the hybrid architecture and optimization strategy in accurately diagnosing kidney diseases. Additionally, the methodology of this paper emphasizes interpretability and explainability, which are crucial for the clinical deployment of deep learning models.

TUMOR IN HORSESHOE KIDNEYS: A CASE REPORT AND LITERATURE REVIEW

Objective: This study aims to report a renal tumor in a horseshoe kidney successfully treated with an open left nephrectomy. Case(s) Presentation: A 3 years old girl with a lump in the left abdomen and intermittent hematuria was referred to our hospital. Whole abdominal contrast CT scan showed the presence of a left renal mass in a horseshoe kidney that infiltrates the left renal artery and is attached to the spleen. The patient underwent preoperative chemotherapy followed by surgical resection. Discussion: In a horseshoe kidney, renal tumors are uncommon. The limited kidney mobilization and access to the renal hilum and the abnormal and highly variable vasculature make surgical treatment of a tumor in a horseshoe kidney extremely difficult. Conclusion: Open surgery continues to be the treatment of choice for horseshoe kidney tumors due to their anatomical complexity, particularly in cases where the tumor is difficult to eradicate. Keywords: Renal tumor, horseshoe kidney, surgical management.

Automated detection of kidney masses lesions using a deep learning approach

<p>Deep learning has emerged as a potent tool for various tasks, such as image classification. However, in the medical domain, there exists a scarcity of data, which poses a challenge in obtaining a well-balanced and high-quality dataset. Commonly seen issues in the realm of renal health include conditions such as kidney stones, cysts, and tumors. This study is centered on the examination of deep learning models for the purpose of classifying renal computed tomography (CT)-scan pictures. State-of-the-art classification models, such as convolutional neural network (CNN) approaches, are employed to boost model performance and improve accuracy. The algorithm is comprised of six convolutional layers that progressively increase in complexity. Every layer in the network utilizes a uniform 3x3 kernel size and applies the rectified linear unit (ReLU) activation function. This is followed by a max-pooling layer that downsamples the feature maps using a 2x2 pool size. Following this, a flatten layer was implemented in order to preprocess the data for the fully linked layers. The consistent utilization of uniform kernel sizes and activation functions throughout all layers of the model facilitated the smooth extraction of complex features, thereby enhancing the model’s ability to accurately identify different kidney conditions. As a result, we achieved a high accuracy rate of 99.8%, precision is 99.8%, and F1 score of approximately 99.7%.</p>

Comparison of polyclonal and monoclonal antibody assays for serum amyloid A in cats: a study based on an automated turbidimetric immunoassay in a primary care veterinary hospital.

Comparing the utility of the anti-human serum amyloid A (SAA)-specific monoclonal and polyclonal antibodies assays (LZ-SAA) with the pure monoclonal anti-human antibody assays (VET-SAA) during clinical practice in primary care hospital populations by measuring SAA measurement in healthy and diseased domestic cats. 52 healthy and 185 diseased client-owned cats. SAA concentration was measured using different LZ-SAA and VET-SAA measurements for healthy and various diseased cats. Sensitivity, specificity, and accuracy were calculated for each disease. VET-SAA has higher sensitivity than LZ-SAA for the most common diseases presenting to primary care veterinary hospitals, including chronic kidney disease, tumors, and gingivostomatitis. Our results reveal the capability of detecting low SAA concentrations in healthy and diseased cats using VET-SAA in contrast to LZ-SAA, which found elevations of SAA concentrations only in diseased cats. Our findings indicate that switching to the new VET-SAA instead of the conventional LZ-SAA will likely enhance the diagnostic performance in primary care veterinary hospitals.

Can incorporating image resolution into neural networks improve kidney tumor classification performance in ultrasound images?

Deep learning has been widely used in ultrasound image analysis, and it also benefits kidney ultrasound interpretation and diagnosis. However, the importance of ultrasound image resolution often goes overlooked within deep learning methodologies. In this study, we integrate the ultrasound image resolution into a convolutional neural network and explore the effect of the resolution on diagnosis of kidney tumors. In the process of integrating the image resolution information, we propose two different approaches to narrow the semantic gap between the features extracted by the neural network and the resolution features. In the first approach, the resolution is directly concatenated with the features extracted by the neural network. In the second approach, the features extracted by the neural network are first dimensionally reduced and then combined with the resolution features to form new composite features. We compare these two approaches incorporating the resolution with the method without incorporating the resolution on a kidney tumor dataset of 926 images consisting of 211 images of benign kidney tumors and 715 images of malignant kidney tumors. The area under the receiver operating characteristic curve (AUC) of the method without incorporating the resolution is 0.8665, and the AUCs of the two approaches incorporating the resolution are 0.8926 (P < 0.0001) and 0.9135 (P < 0.0001) respectively. This study has established end-to-end kidney tumor classification systems and has demonstrated the benefits of integrating image resolution, showing that incorporating image resolution into neural networks can more accurately distinguish between malignant and benign kidney tumors in ultrasound images.

Kidney Tumor Segmentation Based on DWR-SegFormer

Kidney cancer is a malignant tumor with a high mortality rate. The accurate segmentation of tumors from computed tomography (CT) scans can assist physicians in clinical diagnosis. We introduced a new segmentation network called DWR-SegFormer to address the challenge of accurately segmenting kidney tumors in CT images. The method involved binarizing the label maps of clear cell renal cell carcinoma and papillary renal cell carcinoma CT images for identification, and the cancer lesion area was obtained by the label so that the model could accurately identify the area and enhance the feature extraction ability. Secondly, an optimized segmentation model combining a DWR attention mechanism and SegFormer network was constructed. MiT-B0 was used as the encoder of the model to establish long-distance feature dependencies and effectively extract feature information at different resolutions. The decoder with a multi-branch DWR module was implemented to utilize multi-scale feature information effectively and enhance segmentation accuracy. Comparing the experimental results with other existing models shows that the model significantly outperformed the comparison methods in all evaluation metrics on the CT image dataset of clear cell renal cancer. Furthermore, the experimental findings highlight the robustness of the proposed model across other datasets.

Comparison of Outcomes Between Partial and Radical Laparoscopic Nephrectomy for Localized Renal Tumors Larger Than Four Centimeters: A Systematic Review and Meta-Analysis.

Earlier studies have juxtaposed different laparoscopic methods for treating renal tumors; however, extensive evidence with a particular focus on large kidney tumors remains lacking. The objective of this meta-analysis was to assess the perioperative outcomes, kidney performance, and cancer-related results of laparoscopic partial nephrectomy (LPN) versus laparoscopic radical nephrectomy (LRN) for treating extensive, localized, non-metastatic kidney tumors (cT1b-cT2N0M0). We systematically searched multiple databases from database inception until December 2023 for relevant studies. Selected data were analyzed with the Cochrane Collaboration's Review Manager 5.4 software using a random-effects model. Outcomes were expressed as odds ratios and weighted mean differences with 95% confidence intervals, considering a P value of < 0.05 as significant. Data from nine studies encompassing 1,303 patients (529 LPN, 774 LRN) revealed that LPN was associated with lengthier surgeries and increased blood loss compared to LRN. While LPN exhibited higher postoperative complication rates, the disparity did not reach statistical significance. LPN led to improved postoperative renal function, manifesting as a reduced estimated glomerular filtration rate (eGFR) decline and fewer incidents of new chronic kidney disease cases. Both groups demonstrated comparable tumor recurrence and overall mortality rates, but LPN exhibited significantly lower cancer-specific mortality rates. LPN, despite longer operative times and greater intraoperative blood loss, was found to be superior to LRN in preserving postoperative renal function. Oncologically, LPN and LRN have comparable overall mortality rates, but LPN showed a significant advantage in terms of lower cancer-specific mortality rates.

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

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. 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. 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). 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.

Genotype Phenotype Correlation of Renal Tumors in the Cancer Genome Atlas Database.

Morphological features are critical in evaluation of renal tumors and directing molecular workup. The objective of this study was to review histomorphology of renal tumors with molecular alterations of known subtypes. Renal tumors in The Cancer Genome Atlas were reviewed to identify tumors with defining molecular alterations. Single representative digital slides and pathology reports were reviewed and morphologic features recorded. Sixty tumors were identified with molecular alterations in genes characteristic of defined renal cell carcinoma (RCC) subtypes. Findings included the presence of both low- and high-grade histology in TFE3 rearranged RCCs, TFEB amplified RCCs, succinate dehydrogenase (SDH) mutated RCCs and RCCs with mutations in mismatch repair genes. Three ELOC mutated RCCs were identified, one of which demonstrated infiltrative features. Pseudostratification of nuclei in fumarate hydratase mutated RCCs and nuclear grooves in SDH mutated RCCs were intriguing findings not previously reported. Mucinous features were noted in NF2, KRAS, and SDH mutated and ALK rearranged tumors. Significant morphologic overlap was noted across most categories with limited clues for subclassification. Whereas the number of diagnostic entities for kidney tumors continues to increase, many of these have overlapping features, highlighting the significant role molecular characterization currently plays and will continue to play in the future.

Mutagenicity evaluation of methyl tertiary- butyl ether in multiple tissues of transgenic rats following whole body inhalation exposure.

Methyl tertiary-butyl ether (MTBE) is used as a component of motor vehicle fuel to enhance combustion efficiency and to reduce emissions of carbon monoxide and nitrogen oxides. Although MTBE was largely negative in the in vitro and in vivo genotoxicity studies, isolated reports of positive findings along with the observation of tumors in the rat cancer bioassays raised concern for its in vivo mutagenic potential. To investigate this, transgenic male Big Blue Fischer 344 rats were exposed to 0 (negative control), 400, 1000, and 3000 ppm MTBE via whole body inhalation for 28 consecutive days, 6 h/day. Mutant frequencies (MF) at the cII locus of the transgene in the nasal epithelium (portal of entry tissue), liver (site of primary metabolism), bone marrow (rapidly proliferating tissue), and kidney (tumor target) were analyzed (5 rats/exposure group) following a 3-day post-exposure manifestation period. MTBE did not induce a mutagenic response in any of the tissues investigated. The adequacy of the experimental conditions to detect induced mutations was confirmed by utilizing tissue samples from animals treated with the known mutagen ethyl nitrosourea. These data provide support to the conclusion that MTBE is not an in vivo mutagen and male rat kidney tumors are not likely the result of a mutagenic mode of action.

DEEP LEARNING BASED KIDNEY DISEASE CLASSIFICATION SYSTEM

Kidney disease is a significant global health issue that requires timely and accurate diagnosis to improve patient outcomes. This project presents a deep learning-based kidney classification system designed to assist in the early detection and classification of various kidney conditions using medical imaging data. The proposed system leverages advanced convolutional neural networks (CNNs) to analyze and classify kidney ultrasound and MRI images, distinguishing between healthy kidneys and those affected by diseases such as kidney stones, cysts, and tumors. This methodology enhances the model's ability to learn intricate patterns and features associated with different kidney conditions, ensuring high classification accuracy and robustness. Keywords: Deep Learning, Kidney Classification, Medical Imaging, Convolutional Neural Networks (CNNs)

Bilateral simultaneous partial nephrectomy

Introduction. Partial nephrectomy is the treatment of choice for small renal tumors. There are other indications include tumors in a solitary kidney, multiple and bilateral tumors.Сase presentation. A 67-year-old male presented with left flank pain and lower urinary tract symptoms. Computed tomography for abdominal and pelvis showed bilateral renal masses. After doing the essential laboratory tests and investigations, he underwent bilateral open simultaneous partial nephrectomies. After two days, he was discharged with no complains. Follow-up after three months showed no recurrence and acceptable renal function.Discussion. Partial nephrectomy is increasingly used for the management of renal masses. The preservation of renal function with reduced morbidity and equivalent oncologic outcomes led to a paradigm shift away from radical nephrectomy.Conclusion. Bilateral partial nephrectomy is feasible with both clinical and oncological good results.

Convolutional Neural Network‐Based CT Image Segmentation of Kidney Tumours

ABSTRACTKidney tumours are one of the most common tumours in humans and the main current treatment is surgical removal. The CT images are usually manually segmented by a specialist for pre‐operative planning, but this can be influenced by the surgeon's experience and skill and can be time‐consuming. Due to the complex lesions and different morphologies of kidney tumours that make segmentation difficult, this article proposes a convolutional neural network‐based automatic segmentation method for CT images of kidney tumours to address the most common problems of boundary blurring and false positives in tumour segmentation images. The method is highly accurate and reliable, and is used to assist doctors in surgical planning as well as diagnostic treatment, relieving medical pressure to a certain extent. The EfficientNetV2‐UNet segmentation model proposed in this article includes three main parts: feature extractor, reconstruction network and Bayesian decision algorithm. Firstly, for the phenomenon of tumour false positives, the EfficientNetV2 feature extractor, which has high training accuracy and efficiency, is selected as the backbone network, which extracts shallow features such as tumour location, morphology and texture in the CT image by downsampling. Secondly, on the basis of the backbone network, the reconstruction network is designed, which mainly consists of conversion block, deconvolution block, convolution block and output block. Then, the up‐sampling architecture is constructed to gradually recover the spatial resolution of the feature map, fully identify the contextual information and form a complete encoding–decoding structure. Multi‐scale feature fusion is achieved by superimposing all levels of feature map channels on the left and right sides of the network, preventing the loss of details and performing accurate tumour segmentation. Finally, a Bayesian decision algorithm is designed for the edge blurring phenomenon of segmented tumours and cascaded over the output of the reconstruction network, combining the edge features of the original CT image and the segmented image for probability estimation, which is used to improve the accuracy of the model edge segmentation. Medical images in NII special format were converted to Numpy matrix format using python, and then more than 2000 CT images containing only kidney tumours were selected from the KiTS19 dataset as the dataset for the model, and the dimensions were standardised to 128 × 128, and the experimental results show that the model outperforms many other advanced models with good segmentation performance.

21 Decoding Kidney Tumours: A Comparative Study of Immunohistochemical Markers in Renal Oncocytoma and Chromophobe Renal Cell Carcinoma

21 Decoding Kidney Tumours: A Comparative Study of Immunohistochemical Markers in Renal Oncocytoma and Chromophobe Renal Cell Carcinoma

The role of tissue persistent organic pollutants and genetic polymorphisms in patients with benign and malignant kidney tumors

This study aimed to explore whether there is an association between environmental exposure to POPs and kidney tumor induction, and whether blood POP concentrations reflect kidney tissue concentrations. POP derivatives were determined in blood, tumor tissue, tumor surrounding tissue, and perirenal fat tissue samples taken from patients who underwent surgery for renal tumors. A voluntary control group was recruited for blood and urine samples as well. Urinary excretions of o,o′-dityrosine, chlorotyrosine, nitrotyrosine, and 8-OHdG were measured in the same patients. The possible role of genetic polymorphisms in CYP1A1, GST isozymes P, M, and T, and hOGG1 genes on the predisposition to renal cancer was investigated. Some POPs have been found to be associated with kidney cancer, as evidenced by their significantly high ORs. 8-OHdG levels were significantly higher compared to the control group. The GSTT1 null polymorphism can be a risk factor for malignant but not for benign kidney tumors.

QOL-31. NEUROPSYCHOLOGICAL FUNCTIONING AND QUALITY OF LIFE IN INFANT AT/RT SURVIVORS: FOCUS ON FLUID INTELLIGENCE AND VISUAL PROCESSING

Abstract BACKGROUND Understanding the long-term cognitive sequelae in infant brain tumor survivors remains incomplete, particularly regarding the impact of tumor type, multimodal treatment, and other patient-related factors. This retrospective analysis explores neuropsychological and quality of survival (QoS) outcomes in survivors of atypical teratoid/rhabdoid tumors (AT/RT) and extracranial malignant rhabdoid tumors of soft tissues (eMRT) and kidneys (RTK), all treated within the same framework. Neuropsychological data from children with AT/RT were compared to data from children with non-irradiated low-grade glioma (LGG). METHODS Patients (0 - 36 months at diagnosis) underwent various treatments, including radio-chemotherapy for AT/RT (n = 13) and eMRT/RTK (n = 7), chemotherapy only for LGG (n = 4) and eMRT/RTK (n = 1), or observation for LGG (n = 11). Neuropsychological evaluations were conducted at a median of 6.8 years (AT/RT), 6.6 years (eMRT/RTK), and 5.2 years (LGG) post-diagnosis. RESULTS Impairments were observed for all tumour types. Patients with AT/RT exhibited impairments in fluid intelligence (p =.041; d = 1.11) and visual processing (p =.001; d = 2.09) when compared to LGG-patients. Both groups demonstrated deficits in psychomotor speed and attention abilities (p &amp;lt;.001–.019; d = 0.79–1.90). Diagnosis significantly predicted cognitive outcomes, whereas gender and age-related variables did not. QoS outcomes for all rhabdoid patients indicated lower scores in psychosocial functioning (p =.023; d = 0.78) and quality of life (p =.006; d = 0.79) compared to healthy controls. CONCLUSIONS Infant rhabdoid tumor survivors experience cognitive and quality-of-life sequelae. Patients with AT/RT are especially vulnerable to impairments in fluid intelligence and visual processing, while infant LGG-patients without radiotherapy demonstrated comparable deficits in psychomotor and attention abilities. Close monitoring of neuropsychological and quality of life outcomes is crucial for early onset and multimodal treatment.

An automated two-stage approach to kidney and tumor segmentation in CT imaging.

The incidence of kidney tumors is progressively increasing each year. The precision of segmentation for kidney tumors is crucial for diagnosis and treatment. To enhance accuracy and reduce manual involvement, propose a deep learning-based method for the automatic segmentation of kidneys and kidney tumors in CT images. The proposed method comprises two parts: object detection and segmentation. We first use a model to detect the position of the kidney, then narrow the segmentation range, and finally use an attentional recurrent residual convolutional network for segmentation. Our model achieved a kidney dice score of 0.951 and a tumor dice score of 0.895 on the KiTS19 dataset. Experimental results show that our model significantly improves the accuracy of kidney and kidney tumor segmentation and outperforms other advanced methods. The proposed method provides an efficient and automatic solution for accurately segmenting kidneys and renal tumors on CT images. Additionally, this study can assist radiologists in assessing patients' conditions and making informed treatment decisions.

MDSK‐Net: Multi‐scale dynamic segmentation kernel network for renal tumour endoscopic image segmentation

AbstractAutomatic segmentation of renal tumours during renal cell carcinoma surgery can help doctors accurately locate the tumour region, protect the tissues and organs around the kidneys, enhance surgical efficiency, and reduce the possibility of leakage and misdiagnosis. However, since general polyp endoscopic image segmentation models have many problems when facing the task of renal tumour segmentation, there needs to be more research on the segmentation of endoscopic images of renal tumours. This paper proposes a multi‐scale dynamic segmentation kernel network for endoscopic image segmentation of kidney tumours. First, a spatial receptive field module is proposed to augment the feature information and improve the performance of the whole network. Second, an enhanced cross‐attention module is offered to attenuate the effect of a high‐similarity segmentation background. Finally, a multi‐scale dynamic segmentation kernel module is introduced to gradually refine the segmentation results from small to large sizes to obtain more accurate tumour boundaries. Extensive experiments on the established kidney tumour endoscopic dataset and publicly available endoscopic datasets show that this method exhibits enhanced performance and generalization capabilities compared to existing techniques. On this renal tumour dataset, MDSK‐Net achieved excellent results of 94.1% and 90.1% on mDice and mIoU.

Cytotoxic function of human natural killer cells is transiently inhibited by transforming growth factor beta (TGFβ).

e14557 Background: Natural killer (NK) cells can eliminate early neoplastic cells, however, their efficacy as an adoptive therapy for solid tumors is limited. We have shown that NK cells recovered from human kidney tumors are CD56pos/CD16dim/neg, poorly cytotoxic, and produce vascular endothelial growth factor (VEGF). These features are analogous to human decidual NK (dNK) cells that support the development and growth of the placenta during implantation. Transforming growth factor beta (TGFβ) is found at high levels in the decidua and kidney tumor environment, and is known to convert peripheral blood NK (pNK) cells to dNK-like cells in vitro. We hypothesized that TGFβ-mediated inhibition of NK cell proliferation and function is transient and therefore reversible. Methods: pNK cells were isolated from healthy donors (HD) by negative selection or purchased from ATCC (NK-92; CRL-2407). RT-qPCR and flow cytometry were used to assess NK markers: CD56, CD16 NKp46, CD49a, CD69; TGFβ receptors R1, R2, R3; VEGF; and cytokines IFNγ, TNFα. NK cells were cultured for 4-5 days with proliferative cytokines IL2 or IL15 alone and in combination, and without or with TGFβ (2-5 ng/mL). Cytotoxic function of NK cells (effectors) was quantified by lysis of human K562 cells engineered to express firefly luciferase (targets). Statistics (ANOVA/t-test) were performed using GraphPad Prism with p≤0.05 considered significant. Results: pNK cells from HD were CD56pos/CD16pos and NK-92 cells were CD56pos/CD16neg as reported. Both expressed all three TGFβ receptors, required IL2 or IL15 to survive in culture and were potently cytotoxic against K562 targets. TGFβ treatment of NK cells cultured with IL2 or IL2 plus IL15 suppressed proliferation by 20% and potently inhibited cytotoxic ability (85% reduction; p≤0.05). TGFβ caused significant changes in CD49a (35% increase), CD69 (45% increase) and IFNγ (50% decrease) but had no effect on NKp46, VEGF, or TNFα. TGFβ-mediated changes in NK cell phenotype and function reverted to normal when NK cells were returned to standard culture conditions for 4-5 days. Conclusions: Despite differences in CD16 expression, pNK cells and NK-92 cells are potently cytotoxic and represent an effective anti-cancer therapy. TGFβ limits this potential by blocking the proliferative effects of IL2 and IL15 and quelling cytotoxic function. TGFβ caused increased expression of integrin CD49a, a marker of tissue resident immune cells, and the early lymphocyte activation marker CD69. Augmentation of these surface markers was concomitant with diminution of IFN-γ, a cytokine critical for cell-mediated responses to tumors. These collective changes are akin to dNK cells that are not cytotoxic and instead support the implant. Importantly, all alterations were restored if NK cells were no longer exposed to TGFβ. Thus, inhibition of TGFβ signaling could preserve NK function in the tumor environment.

Emerging Novel Functional Imaging and Immunotherapy in Renal Cell Carcinoma and Current Treatment Sequencing Strategies After Immunotherapy.

The management of renal cell carcinoma (RCC) has advanced significantly in the past two decades. Many promising functional imaging modalities such as radiolabeled tracer targeting carbonic anhydrase IX and prostate-specific membrane antigen are under development to detect primary kidney tumors, stage systemic disease, and assess treatment response in RCC. Immune checkpoint inhibitors targeting PD-1 and cytotoxic T-cell lymphocyte-4 have changed the treatment paradigm in advanced RCC. Trials investigating novel mechanisms such as LAG-3 immune checkpoint inhibition, chimeric antigen receptor T-cell therapies, and T-cell engagers targeting RCC-associated antigens are currently ongoing. With the rapidly changing treatment landscape of RCC, the treatment sequence strategies will continue to evolve. Familiarity with the toxicities associated with the therapeutic agents and how to manage them are essential to achieve optimal patient outcomes. This review summarizes the recent developments of functional imaging and immunotherapy strategies in RCC, and the evidence supports treatment sequencing.