The kidney imaging surveillance scoring system (KISSS): using qualitative MRI features to predict growth rate of renal tumors in patients with von-Hippel Lindau (VHL) syndrome.

OBJECTIVE: To determine the reliability of an MRI-based qualitative kidney imaging surveillance scoring system (KISSS) and assess which imaging features predict growth rate (GR) of renal tumors in patients with VHL. MATERIALS AND METHODS: We identified 55 patients with VHL with 128 renal tumors who underwent intervention from 2015 to 2020 at the National Cancer Institute. All patients had 2 preoperative MRIs at least 3 months apart. Two fellowship-trained radiologists scored each tumor on location and MR-sequence-specific imaging parameters from the earlier MRI. Weighted kappa was used to determine the degree of agreement between radiologists for each parameter. GR was calculated as the difference in maximum tumor dimension over time (cm/year). Differences in mean growth rate (MGR) within categories of each imaging variable were assessed by ANOVA. RESULTS: Apart from tumor margin and renal sinus, reliability was at least moderate (K > 0.40) for imaging parameters. Median initial tumor size was 2.1 cm, with average follow-up of 1.2 years. Tumor MGR was 0.42 cm/year. T2 hypointense, mixed/predominantly solid, and high restricted diffusion tumors grew faster. When comparing different combinations of these variables, the model with the lowest mean error among both radiologists utilized only solid/cystic and restricted diffusion features. CONCLUSIONS: We demonstrate a novel MR-based scoring system (KISSS) that has good precision with minimal training and can be applied to other qualitative radiology studies. A subset of imaging variables (T2 intensity; restricted diffusion; and solid/cystic) were independently associated with growth rate in VHL renal tumors, with the combination of the latter two most optimal. Additional validation, including in sporadic RCC population, is warranted.

Preoperative Renal Parenchyma Volume as a Predictor of Kidney Function Following Nephrectomy of Complex Renal Masses.

Background: The von Hippel-Lindau disease (VHL) is a hereditary cancer syndrome with multifocal, bilateral cysts and solid tumors of the kidney. Surgical management may include multiple extirpative surgeries, which ultimately results in parenchymal volume loss and subsequent renal function decline. Recent studies have utilized parenchyma volume as an estimate of renal function prior to surgery for renal cell carcinoma; however, it is not yet validated for surgically altered kidneys with multifocal masses and complex cysts such as are present in VHL. Objective: We sought to validate a magnetic resonance imaging (MRI)-based volumetric analysis with mercaptoacetyltriglycine (MAG-3) renogram and postoperative renal function. Design setting and participants: We identified patients undergoing renal surgery at the National Cancer Institute from 2015 to 2020 with preoperative MRI. Renal tumors, cysts, and parenchyma of the operated kidney were segmented manually using ITK-SNAP software. Outcome measurements and statistical analysis: Serum creatinine and urinalysis were assessed preoperatively, and at 3- and 12-mo follow-up time points. Estimated glomerular filtration rate (eGFR) was calculated using serum creatinine-based CKD-EPI 2021 equation. A statistical analysis was conducted on R Studio version 4.1.1. Results and limitations: Preoperative MRI scans of 113 VHL patients (56% male, median age 48 yr) were evaluated between 2015 and 2021. Twelve (10.6%) patients had a solitary kidney at the time of surgery; 59 (52%) patients had at least one previous partial nephrectomy on the renal unit. Patients had a median of three (interquartile range [IQR]: 2-5) tumors and five (IQR: 0-13) cysts per kidney on imaging. The median preoperative GFR was 70 ml/min/1.73 m2 (IQR: 58-89). Preoperative split renal function derived from MAG-3 studies and MRI split renal volume were significantly correlated (r = 0.848, p < 0.001). On the multivariable analysis, total preoperative parenchymal volume, solitary kidney, and preoperative eGFR were significant independent predictors of 12-mo eGFR. When only considering patients with two kidneys undergoing partial nephrectomy, preoperative parenchymal volume and eGFR remained significant predictors of 12-mo eGFR. Conclusions: A parenchyma volume analysis on preoperative MRI correlates well with renogram split function and can predict long-term renal function with added benefit of anatomic detail and ease of application. Patient summary: Prior to kidney surgery, it is important to understand the contribution of each kidney to overall kidney function. Nuclear medicine scans are currently used to measure split kidney function. We demonstrated that kidney volumes on preoperative magnetic resonance imaging can also be used to estimate split kidney function before surgery, while also providing essential details of tumor and kidney anatomy.

Outcomes of and indications for renal transplantation in patients with von Hippel Lindau disease.

INTRODUCTION AND OBJECTIVE: Von Hippel-Lindau (VHL) is a hereditary cancer syndrome characterized by bilateral, multifocal renal masses. The cumulative impact of extirpative surgery can depreciate renal function and render patients anephric. In the larger end-stage renal disease population, renal transplant offers both excellent quality of life and functional renal replacement. This case control study aims to examine and compare oncologic and functional outcomes of patients who have undergone renal transplant as renal replacement therapy (RRT) to those who remain anephric. METHODS: Patient charts were retrospectively reviewed of patients with germline testing confirmed VHL between 1980 and 2022 for transplant, all prior surgical history (within and outside the NCI), renal function and graft outcomes. Overall survival (OS) was determined from years after radical nephrectomy, and graft time was defined as years of graft function from initial transplant until failure or patient death. Graft survival was determined as time between transplant(s) to last follow up. Kaplan-Meier analysis was conducted to compare graft times of anephric VHL patients to those with transplanted kidneys. RESULTS: A total of 23 VHLD patients were identified as either anephric or candidates for transplant. Out of this cohort, 11 total VHLD received 12 total kidney grafts. Median wait time from nephrectomy to transplant was 22.6 months (IQR: 1.02-40.25 months). Median age at transplant was 32 years (IQR: 23-54 years). OS at 5 and 10 years of anephric patients who did not receive a transplant was 33% and 16.7%, respectively. OS rates of the transplant cohort at 10, 15, and 20 years were 91%, 78%, and 58% years, respectively. Median graft time was 161 months (IQR: 56-214 months). Graft survival at 10, 15, and 20 years was 69.8%, 69.8%, and 26.2%, respectively. CONCLUSIONS: We demonstrate that transplant recipients have decreased mortality with no difference in cancer recurrence compared to those who do not receive renal transplant for RRT. This data can aid in informing providers of the optimal window for early RRT planning in VHL, while also improving patient counseling.

Automated Society of Fetal Urology (SFU) grading of hydronephrosis on ultrasound imaging using a convolutional neural network.

INTRODUCTION: Grading of hydronephrosis severity on postnatal renal ultrasound guides management decisions in antenatal hydronephrosis (ANH). Multiple systems exist to help standardize hydronephrosis grading, yet poor inter-observer reliability persists. Machine learning methods may provide tools to improve the efficiency and accuracy of hydronephrosis grading. OBJECTIVE: To develop an automated convolutional neural network (CNN) model to classify hydronephrosis on renal ultrasound imaging according to the Society of Fetal Urology (SFU) system as potential clinical adjunct. STUDY DESIGN: A cross-sectional, single-institution cohort of postnatal renal ultrasounds with radiologist SFU grading from pediatric patients with and without hydronephrosis of stable severity was obtained. Imaging labels were used to automatedly select sagittal and transverse grey-scale renal images from all available studies from each patient. A VGG16 pre-trained ImageNet CNN model analyzed these preprocessed images. Three-fold stratified cross-validation was used to build and evaluate the model that was used to classify renal ultrasounds on a per patient basis into five classes based on the SFU system (normal, SFU I, SFU II, SFU III, or SFU IV). These predictions were compared to radiologist grading. Confusion matrices evaluated model performance. Gradient class activation mapping demonstrated imaging features driving model predictions. RESULTS: We identified 710 patients with 4659 postnatal renal ultrasound series. Per radiologist grading, 183 were normal, 157 were SFU I, 132 were SFU II, 100 were SFU III, and 138 were SFU IV. The machine learning model predicted hydronephrosis grade with 82.0% (95% CI: 75-83%) overall accuracy and classified 97.6% (95% CI: 95-98%) of the patients correctly or within one grade of the radiologist grade. The model classified 92.3% (95% CI: 86-95%) normal, 73.2% (95% CI: 69-76%) SFU I, 73.5% (95% CI: 67-75%) SFU II, 79.0% (95% CI: 73-82%) SFU III, and 88.4% (95% CI: 85-92%) SFU IV patients accurately. Gradient class activation mapping demonstrated that the ultrasound appearance of the renal collecting system drove the model's predictions. DISCUSSION: The CNN-based model classified hydronephrosis on renal ultrasounds automatically and accurately based on the expected imaging features in the SFU system. Compared to prior studies, the model functioned more automatically with greater accuracy. Limitations include the retrospective, relatively small cohort, and averaging across multiple imaging studies per patient. CONCLUSIONS: An automated CNN-based system classified hydronephrosis on renal ultrasounds according to the SFU system with promising accuracy based on appropriate imaging features. These findings suggest a possible adjunctive role for machine learning systems in the grading of ANH.

Deep Learning of Videourodynamics to Classify Bladder Dysfunction Severity in Patients With Spina Bifida.

PURPOSE: Urologists rely heavily on videourodynamics to identify patients with neurogenic bladders who are at risk of upper tract injury, but their interpretation has high interobserver variability. Our objective was to develop deep learning models of videourodynamics studies to categorize severity of bladder dysfunction. MATERIALS AND METHODS: We performed a cross-sectional study of patients aged 2 months to 28 years with spina bifida who underwent videourodynamics at a single institution between 2019 and 2021. The outcome was degree of bladder dysfunction, defined as none/mild, moderate, and severe, defined by a panel of 5 expert reviewers. Reviewers considered factors that increase the risk of upper tract injury, such as poor compliance, elevated detrusor leak point pressure, and detrusor sphincter dyssynergia, in determining bladder dysfunction severity. We built 4 models to predict severity of bladder dysfunction: (1) a random forest clinical model using prospectively collected clinical data from videourodynamics studies, (2) a deep learning convolutional neural network of raw data from the volume-pressure recordings, (3) a deep learning imaging model of fluoroscopic images, (4) an ensemble model averaging the risk probabilities of the volume-pressure and fluoroscopic models. RESULTS: Among 306 videourodynamics studies, the accuracy and weighted kappa of the ensemble model classification of bladder dysfunction when at least 75% expected bladder capacity was reached were 70% (95% CI 66%,76%) and 0.54 (moderate agreement), respectively. The performance of the clinical model built from data extracted by pediatric urologists was the poorest with an accuracy of 61% (55%, 66%) and a weighted kappa of 0.37. CONCLUSIONS: Our models built from urodynamic pressure-volume tracings and fluoroscopic images were able to automatically classify bladder dysfunction with moderately high accuracy.

Adjuvant Therapy for High-Risk Localized Renal Cell Carcinoma: Current Landscape and Future Direction.

Locally and regionally advanced renal cell carcinoma (RCC) can recur at high rates even after visually complete resection of primary disease. Both targeted therapies and immunotherapies represent potential agents that might help reduce recurrence of RCC in these patients. This paper reviews the current body of evidence defining their potential impact and examines the large Phase III randomized clinical trials that have been performed to assess the safety and efficacy of these systemic therapies in the adjuvant setting. Given that the findings from these trials have been predominantly negative, this paper also explores the role of other potential adjuvant agents, including single and combination agent targeted therapies and immunotherapies, whose use is currently limited to metastatic RCC. Finally, the use of radiation therapy and the use of advanced imaging modalities in RCC are also considered.

Comparison of Race-Based and Non-Race-Based Glomerular Filtration Rate Equations for the Assessment of Renal Functional Risk Before Nephrectomy.

OBJECTIVE: To compare the performance of race-based and race-neutral estimated glomerular filtration rate (eGFR) calculators in patients undergoing kidney surgery. METHODS: Analysis of institutional kidneys surgeries from 2006-2021 was conducted. Demographics, serum creatinine (SCr), protein dipstick, and creatinine clearance (CrCl) were assessed within 1 week prior to surgery. SCr was used to calculate eGFR using 3 models: Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI 2009), and CKD-EPI 2021. Patients were classified based on Kidney Disease: Improving Global Outcomes (KDIGO) criteria for CKD staging and prognosis, with urine CrCl treated as benchmark for analysis. Receiver operating characteristic (ROC) analysis evaluated accuracy of eGFR calculators' binary discrimination of eGFR less than 60 mL/min. CKD stage agreement between eGFR and urine CrCl was assessed. RESULTS: A total of 554 kidney surgeries in 336 patients had necessary laboratory data for analysis. The cohort was 62% male, with a median age of 47. Within this cohort, 8.1% (n=45) were Black, and 80% (n=441) were White. glomerular filtration rate (GFR) from 24-hour urine CrCl normalized by BSA did not vary significantly from eGFR by SCr based calculators. The proportion of patients with eGFR <60 significantly differed when using Modification of Diet in Renal Disease, CKD-EPI 2009, and CKD-EPI 2021 equations when compared to CrCl (P<.001). Still, they performed equivalently in the staging of CKD, as well as in predicting GFR of less than 60, and classifying CKD prognosis of "moderately increased or higher". CONCLUSION: A race-neutral eGFR calculator can perform equivalently to established eGFR calculators, with the added benefit of mitigating biases that account for racial disparities in nephrectomy decision making.

Real-Time Cellular Thermal Shift Assay to Monitor Target Engagement.

Determining a molecule's mechanism of action is paramount during chemical probe development and drug discovery. The cellular thermal shift assay (CETSA) is a valuable tool to confirm target engagement in cells for a small molecule that demonstrates a pharmacological effect. CETSA directly detects biophysical interactions between ligands and protein targets, which can alter a protein's unfolding and aggregation properties in response to thermal challenge. In traditional CETSA experiments, each temperature requires an individual sample, which restricts throughput and requires substantial optimization. To capture the full aggregation profile of a protein from a single sample, we developed a prototype real-time CETSA (RT-CETSA) platform by coupling a real-time PCR instrument with a CCD camera to detect luminescence. A thermally stable Nanoluciferase variant (ThermLuc) was bioengineered to withstand unfolding at temperatures greater than 90 °C and was compatible with monitoring target engagement events when fused to diverse targets. Utilizing well-characterized inhibitors of lactate dehydrogenase alpha, RT-CETSA showed significant correlation with enzymatic, biophysical, and other cell-based assays. A data analysis pipeline was developed to enhance the sensitivity of RT-CETSA to detect on-target binding. RT-CETSA technology advances capabilities of the CETSA method and facilitates the identification of ligand-target engagement in cells, a critical step in assessing the mechanism of action of a small molecule.

Hyperspectral z-scan: Measurement of spectrally resolved nonlinear optical properties.

Broadband hyperspectral z-scan using a supercontinuum light source is a convenient technique to obtain spectrally resolved nonlinear optical properties of the materials under investigation. Post-processing and segregation of the data obtained from the supercontinuum based hyperspectral z-scan measurement aids in determining the nonlinear optical properties with high spectral resolution. However, few data models exist to store and represent the large amount of information acquired from the hyperspectral z-scan measurement. In this paper, a 3D data model for representing the data obtained from broadband z-scan measurements and analysis is presented. This method would help in the quick characterization of spectrally resolved nonlinear optical properties of materials from a single z-scan measurement. The proposed model is used for obtaining the spectrally resolved nonlinear optical properties of rhodamine 6G.

Micro RNA 181c-5p: A promising target for post-stroke recovery in socially isolated mice.

Dysregulation of microRNAs (miRNAs) has been tied to several neurological disorders, including ischemic stroke. It has also been established that social environments can modulate miRNA profiles. We have previously shown that post-stroke social isolation (SI) is linked to poor stroke outcomes and that miR-181c-5p emerged as one of few lead miRNAs that was downregulated in both stroke and SI. Therefore, in this study we examined the potential role of miR-181c-5p mimic in reversing the detrimental effects of post-stroke SI. Two to three-month-old C57BL/6 male mice were pair-housed (PH) for at least two weeks. After two weeks, mice underwent stroke survival surgery using middle cerebral artery occlusion (MCAO) and were randomly assigned to one of two housing conditions: stroke isolation (ST-ISO) or stroke pair-housing with a healthy partner (ST-PH). ST-ISO mice were randomized to receive either miR-181c-5p mimic or a scrambled RNA (7 mg/kg i.v./day×drug) control at 24 h and 48 h after stroke. The effects of miR-181c-5p mimic treatment were evaluated at 1, 3, and 7 days after stroke at histological, behavioral, and biochemical levels. Target genes of miR-181c-5p were then analyzed by qPCR using an RT2 Profiler qPCR Array of pre-coated miR-181c gene targets. Temporal profile expression data suggested that miR-181c-5p was significantly downregulated (p < 0.05 vs ST-PH) up to 7 days after post-stroke SI. MiR-181c-5p mimic treatment significantly increased miR-181c-5p expression in brain tissue and showed partial swift recovery in sensorimotor deficit. Target gene analysis identified downregulation of several calcium signaling-related genes, e.g., Cpne2 and Gria 1 & 2 after miR-181c-5p mimic treatment. In summary, present data suggests that miR-181c-5p is a potential target for post-stroke SI. Data also suggests that genes related to calcium and glutamate signaling might be involved in the beneficial effect of the miR-181c-5p mimic.