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Rationale & Objective: In this pilot study, we hypothesized that autosomal dominant polycystic kidney disease (ADPKD) is characterized by impaired kidney oxidative metabolism that associates with kidney size and cyst burden., Study Design: Cross-sectional study., Setting & Participants: Twenty adults with ADPKD (age, 31±6 years; 65% women; body mass index [BMI], 26.8 [22.7-30.4] kg/m 2 ; estimated glomerular filtration rate [eGFR, 2021 CKD-EPI creatinine], 103±18mL/min/1.73m 2 ; height-adjusted total kidney volume [HTKV], 731±370mL/m; Mayo classifications 1B [5%], 1C [42%], 1D [21%], and 1E [32%]) and 11 controls in normal weight category (NWC) (age, 25±3 years; 45% women; BMI, 22.5 [21.7-24.2] kg/m2; eGFR, 113±15mL/min/1.73m 2 ; HTKV, 159±31mL/m) at the University of Colorado Anschutz Medical Campus., Predictors: ADPKD status (yes/no) and severity (Mayo classifications)., Outcome: HTKV and cyst burden by magnetic resonance imaging, kidney oxidative metabolism, and perfusion by 11 C-acetate positron emission tomography/computed tomography, insulin sensitivity by hyperinsulinemic-euglycemic clamps (presented as ratio of M-value of steady state insulin concentration [M/I])., Analytical Approach: For categorical variables, χ 2 /Fisher's exact tests, and for continuous variables t tests/Mann-Whitney U tests. Pearson correlation was used to estimate the relationships between variables., Results: Compared with NWC individuals, the participants with ADPKD exhibited lower mean±SD M/I ratio (0.586±0.205 vs 0.424±0.171 [mg/kg lean/min]/(μIU/mL), P=0.04), lower median cortical perfusion (1.93 [IQR, 1.80-2.09] vs 0.68 [IQR, 0.47-1.04] mL/min/g, P<0.001) and lower median total kidney oxidative metabolism (0.17 [IQR, 0.16-0.19] vs. 0.14 [IQR, 0.12-0.15] min -1 , P=0.001) in voxel-wise models excluding cysts. HTKV correlated inversely with cortical perfusion (r: -0.83, P < 0.001), total kidney oxidative metabolism (r: -0.61, P<0.001) and M/I (r: -0.41, P = 0.03)., Limitations: Small sample size and cross-sectional design., Conclusions: Adults with ADPKD and preserved kidney function exhibited impaired renal perfusion and kidney oxidative metabolism across a wide range of cysts and kidney enlargements., Funding: Grants from government (National Institutes of Health, Centers for Disease Control and Prevention) and not-for-profit (JDRF) entities., Trial Registration: Registered at ClinicalTrials.gov with study numbers NCT04407481 and NCT04074668., Plain-Language Summary: In our study, we explored how a common genetic kidney condition, autosomal dominant polycystic kidney disease (ADPKD), relates to kidney metabolism. ADPKD leads to the growth of numerous cysts in the kidneys, which can impact their ability to work properly. We wanted to understand the kidneys' ability to process oxygen and blood flow in ADPKD. Our approach involved using advanced imaging techniques to observe kidney metabolism and blood flow in people with ADPKD compared with healthy individuals. We discovered that those with ADPKD had significant changes in kidney oxygen metabolism even when their kidney function was still normal. These findings are crucial as they provide deeper insights into ADPKD, potentially guiding future treatments to target these changes., (Copyright © 2024 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.)

Rationale & Objective: Body mass index (BMI) is an independent predictor of kidney disease progression in individuals with autosomal dominant polycystic kidney disease (ADPKD). Adipocytes do not simply act as a fat reservoir but are active endocrine organs. We hypothesized that greater visceral abdominal adiposity would associate with more rapid kidney growth in ADPKD and influence the efficacy of tolvaptan., Study Design: A retrospective cohort study., Setting & Participants: 1,053 patients enrolled in the TEMPO 3:4 tolvaptan trial with ADPKD and at high risk of rapid disease progression., Predictor: Estimates of visceral adiposity extracted from coronal plane magnetic resonance imaging (MRI) scans using deep learning., Outcome: Annual change in total kidney volume (TKV) and effect of tolvaptan on kidney growth., Analytical Approach: Multinomial logistic regression and linear mixed models., Results: In fully adjusted models, the highest tertile of visceral adiposity was associated with greater odds of annual change in TKV of≥7% versus<5% (odds ratio [OR], 4.78 [95% CI, 3.03-7.47]). The association was stronger in women than men (interaction P<0.01). In linear mixed models with an outcome of percent change in TKV per year, tolvaptan efficacy (% change in TKV) was reduced with higher visceral adiposity (3-way interaction of treatment ∗ time ∗ visceral adiposity, P=0.002). Visceral adiposity significantly improved classification performance of predicting rapid annual percent change in TKV for individuals with a normal BMI (DeLong's test z score: -2.03; P=0.04). Greater visceral adiposity was not associated with estimated glomerular filtration rate (eGFR) slope in the overall cohort; however, visceral adiposity was associated with more rapid decline in eGFR slope (below the median) in women (fully adjusted OR, 1.06 [95% CI, 1.01-1.11] per 10 unit increase in visceral adiposity) but not men (OR, 0.98 [95% CI, 0.95-1.02])., Limitations: Retrospective; rapid progressors; computational demand of deep learning., Conclusions: Visceral adiposity that can be quantified by MRI in the coronal plane using a deep learning segmentation model independently associates with more rapid kidney growth and improves classification of rapid progression in individuals with a normal BMI. Tolvaptan efficacy decreases with increasing visceral adiposity., Plain-Language Summary: We analyzed images from a previous study with the drug tolvaptan conducted in patients with autosomal dominant polycystic kidney disease (ADPKD) to measure the amount of fat tissue surrounding the kidneys (visceral fat). We had previously shown body mass index can predict kidney growth in this population; now we determined whether visceral fat was an important factor associated with kidney growth. Using a machine learning tool to automate measurement of fat in images, we observed that visceral fat was independently associated with kidney growth, that it was a better predictor of faster kidney growth in lean patients than body mass index, and that having more visceral fat made treatment of ADPKD with tolvaptan less effective., (Copyright © 2024 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.)

Background: 3D imaging and histology are critical tools for assessing polycystic kidney disease ( PKD ) in patients and animal models. Magnetic resonance ( MR ) imaging provides micron resolution, but is time consuming, expensive, and access to equipment and expertise is limiting. Robotic ultrasound ( US ) imaging has lower spatial resolution but is faster, more cost effective, and accessible. Similarly, Picrosirius red ( PSR ) staining and brightfield microscopy is commonly used to assess fibrosis; however, alternative methods have been shown in non-kidney tissues to provide greater sensitivity and more detailed structural characterization., Methods: In this study, we evaluated the utility of robotic US and alternative methods of quantifying PSR staining for PKD research. We compared longitudinal total kidney volume ( TKV ) measurements using US and MR. We additionally compared PSR imaging and quantification using standard brightfield with that by circularly polarized light with hue analysis, and fluorescence imaging analyzed using CT-FIRE software for automatic detection of individual collagen fibers., Results: Increased TKV was detected by US in Pkd1RC/RC vs wild type ( WT ) at timepoints spanning early to established disease. US inter-observer variability was greater but allowed scanning in 2-5 minutes/mouse while MR required 20-30 minutes/mouse. While no change in fibrotic index was detected in this cohort of relatively mild disease using brightfield, polarized light showed fibers skewed thinner in Pkd1RC/RC vs WT. Fluorescence imaging showed a higher density of collagen fibers in Pkd1RC/RC vs WT, and fibers were thinner and curvier with no change in length. Additionally, fiber density was higher in both glomeruli and tubules in Pkd1RC/RC , and glomeruli had a higher fiber density than tubules in Pkd1RC/RC , and trended higher in WT., Conclusions: These studies show robotic ultrasound is a rigorous imaging tool for pre-clinical PKD research. Additionally, they demonstrate the increased sensitivity of polarized and fluorescence analysis of PSR-stained collagen., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Society of Nephrology.)

Objectives: Transvaginal ultrasound is typically the initial diagnostic approach in patients with postmenopausal bleeding for detecting endometrial atypical hyperplasia/cancer. Although transvaginal ultrasound demonstrates notable sensitivity, its specificity remains limited. The objective of this study was to enhance the diagnostic accuracy of transvaginal ultrasound through the integration of artificial intelligence. By using transvaginal ultrasound images, we aimed to develop an artificial intelligence based automated segmentation model and an artificial intelligence based classifier model., Methods: Patients with postmenopausal bleeding undergoing transvaginal ultrasound and endometrial sampling at Mayo Clinic between 2016 and 2021 were retrospectively included. Manual segmentation of images was performed by four physicians (readers). Patients were classified into cohort A (atypical hyperplasia/cancer) and cohort B (benign) based on the pathologic report of endometrial sampling. A fully automated segmentation model was developed, and the performance of the model in correctly identifying the endometrium was compared with physician made segmentation using similarity metrics. To develop the classifier model, radiomic features were calculated from the manually segmented regions-of-interest. These features were used to train a wide range of machine learning based classifiers. The top performing machine learning classifier was evaluated using a threefold approach, and diagnostic accuracy was assessed through the F1 score and area under the receiver operating characteristic curve (AUC-ROC)., Results: 302 patients were included. Automated segmentation-reader agreement was 0.79±0.21 using the Dice coefficient. For the classification task, 92 radiomic features related to pixel texture/shape/intensity were found to be significantly different between cohort A and B. The threefold evaluation of the top performing classifier model showed an AUC-ROC of 0.90 (range 0.88-0.92) on the validation set and 0.88 (range 0.86-0.91) on the hold-out test set. Sensitivity and specificity were 0.87 (range 0.77-0.94) and 0.86 (range 0.81-0.94), respectively., Conclusions: We trained an artificial intelligence based algorithm to differentiate endometrial atypical hyperplasia/cancer from benign conditions on transvaginal ultrasound images in a population of patients with postmenopausal bleeding., Competing Interests: Competing interests: None declared., (© IGCS and ESGO 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: This study aimed to investigate if a deep learning model trained with a single institution's data has comparable accuracy to that trained with multi-institutional data for segmenting kidney and cyst regions in magnetic resonance (MR) images of patients affected by autosomal dominant polycystic kidney disease (ADPKD)., Methods: We used TensorFlow with a Keras custom UNet on 2D slices of 756 MRI images of kidneys with ADPKD obtained from four institutions in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study. The ground truth was determined via a manual plus global thresholding method. Five models were trained with 80 % of all institutional data (n = 604) and each institutional data (n = 232, 172, 148, or 52), respectively, and validated with 10 % and tested on an unseen 10 % of the data. The model's performance was evaluated using the Dice Similarity Coefficient (DSC)., Results: The DSCs by the model trained with all institutional data ranged from 0.92 to 0.95 for kidney image segmentation, only 1-2 % higher than those by the models trained with single institutional data (0.90-0.93).In cyst segmentation, however, the DSCs by the model trained with all institutional data ranged from 0.83 to 0.89, which were 2-20 % higher than those by the models trained with single institutional data (0.66-0.86)., Conclusion: The UNet performance, when trained with a single institutional dataset, exhibited similar accuracy to the model trained on a multi-institutional dataset. Segmentation accuracy increases with models trained on larger sample sizes, especially in more complex cyst segmentation., Competing Interests: Declaration of competing interest M. M. reports grants and consulting fees outside the submitted work from Otsuka Pharmaceuticals, Sanofi, Palladio Biosciences, Reata, Natera, Chinook Therapeutics, Goldilocks Therapeutics, and Carraway Therapeutics., (Copyright © 2023. Published by Elsevier Inc.)

Significance Statement: Segmentation of multiple structures in cross-sectional imaging is time-consuming and impractical to perform manually, especially if the end goal is clinical implementation. In this study, we developed, validated, and demonstrated the capability of a deep learning algorithm to segment individual medullary pyramids in a rapid, accurate, and reproducible manner. The results demonstrate that cortex volume, medullary volume, number of pyramids, and mean pyramid volume is associated with patient clinical characteristics and microstructural findings and provide insights into the mechanisms that may lead to CKD., Background: The kidney is a lobulated organ, but little is known regarding the clinical importance of the number and size of individual kidney lobes., Methods: After applying a previously validated algorithm to segment the cortex and medulla, a deep-learning algorithm was developed and validated to segment and count individual medullary pyramids on contrast-enhanced computed tomography images of living kidney donors before donation. The association of cortex volume, medullary volume, number of pyramids, and mean pyramid volume with concurrent clinical characteristics (kidney function and CKD risk factors), kidney biopsy morphology (nephron number, glomerular volume, and nephrosclerosis), and short- and long-term GFR <60 or <45 ml/min per 1.73 m 2 was assessed., Results: Among 2876 living kidney donors, 1132 had short-term follow-up at a median of 3.8 months and 638 had long-term follow-up at a median of 10.0 years. Larger cortex volume was associated with younger age, male sex, larger body size, higher GFR, albuminuria, more nephrons, larger glomeruli, less nephrosclerosis, and lower risk of low GFR at follow-up. Larger pyramids were associated with older age, female sex, larger body size, higher GFR, more nephrons, larger glomerular volume, more nephrosclerosis, and higher risk of low GFR at follow-up. More pyramids were associated with younger age, male sex, greater height, no hypertension, higher GFR, lower uric acid, more nephrons, less nephrosclerosis, and a lower risk of low GFR at follow-up., Conclusions: Cortex volume and medullary pyramid volume and count reflect underlying variation in nephron number and nephron size as well as merging of pyramids because of age-related nephrosclerosis, with loss of detectable cortical columns separating pyramids., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Society of Nephrology.)

Introduction: Methods that automatically flag poor performing predictions are drastically needed to safely implement machine learning workflows into clinical practice as well as to identify difficult cases during model training., Methods: Disagreement between the fivefold cross-validation sub-models was quantified using dice scores between folds and summarized as a surrogate for model confidence. The summarized Interfold Dices were compared with thresholds informed by human interobserver values to determine whether final ensemble model performance should be manually reviewed., Results: The method on all tasks efficiently flagged poor segmented images without consulting a reference standard. Using the median Interfold Dice for comparison, substantial dice score improvements after excluding flagged images was noted for the in-domain CT (0.85 ± 0.20 to 0.91 ± 0.08, 8/50 images flagged) and MR (0.76 ± 0.27 to 0.85 ± 0.09, 8/50 images flagged). Most impressively, there were dramatic dice score improvements in the simulated out-of-distribution task where the model was trained on a radical nephrectomy dataset with different contrast phases predicting a partial nephrectomy all cortico-medullary phase dataset (0.67 ± 0.36 to 0.89 ± 0.10, 122/300 images flagged)., Discussion: Comparing interfold sub-model disagreement against human interobserver values is an effective and efficient way to assess automated predictions when a reference standard is not available. This functionality provides a necessary safeguard to patient care important to safely implement automated medical image segmentation workflows., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Gottlich, Korfiatis, Gregory and Kline.)

The aim of this study is to investigate the use of an exponential-plateau model to determine the required training dataset size that yields the maximum medical image segmentation performance. CT and MR images of patients with renal tumors acquired between 1997 and 2017 were retrospectively collected from our nephrectomy registry. Modality-based datasets of 50, 100, 150, 200, 250, and 300 images were assembled to train models with an 80-20 training-validation split evaluated against 50 randomly held out test set images. A third experiment using the KiTS21 dataset was also used to explore the effects of different model architectures. Exponential-plateau models were used to establish the relationship of dataset size to model generalizability performance. For segmenting non-neoplastic kidney regions on CT and MR imaging, our model yielded test Dice score plateaus of [Formula: see text] and [Formula: see text] with the number of training-validation images needed to reach the plateaus of 54 and 122, respectively. For segmenting CT and MR tumor regions, we modeled a test Dice score plateau of [Formula: see text] and [Formula: see text], with 125 and 389 training-validation images needed to reach the plateaus. For the KiTS21 dataset, the best Dice score plateaus for nn-UNet 2D and 3D architectures were [Formula: see text] and [Formula: see text] with number to reach performance plateau of 177 and 440. Our research validates that differing imaging modalities, target structures, and model architectures all affect the amount of training images required to reach a performance plateau. The modeling approach we developed will help future researchers determine for their experiments when additional training-validation images will likely not further improve model performance., (© 2023. The Author(s).)

New image-derived biomarkers for patients affected by autosomal dominant polycystic kidney disease are needed to improve current clinical management. The measurement of total kidney volume (TKV) provides critical information for clinicians to drive care decisions. However, patients with similar TKV may present with very different phenotypes, often requiring subjective decisions based on other factors (e.g., appearance of healthy kidney parenchyma, a few cysts contributing significantly to overall TKV, etc.). In this study, we describe a new technique to individually segment cysts and quantify biometric parameters including cyst volume, cyst number, parenchyma volume, and cyst parenchyma surface area. Using data from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study the utility of these new parameters was explored, both quantitatively as well as visually. Total cyst number and cyst parenchyma surface area showed superior prediction of the slope of estimated glomerular filtration rate decline, kidney failure and chronic kidney disease stages 3A, 3B, and 4, compared to TKV. In addition, presentations such as a few large cysts contributing significantly to overall kidney volume were shown to be much better stratified in terms of outcome predictions. Thus, these new image biomarkers, which can be obtained automatically, will have great utility in future studies and clinical care for patients affected by autosomal dominant polycystic kidney disease., (Copyright © 2023 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.)

Background: Autosomal dominant polycystic kidney disease (ADPKD) presents with variable disease severity and progression. Advanced imaging biomarkers may provide insights into cystic and non-cystic processes leading to kidney failure in different age groups., Methods: This pilot study included 39 ADPKD patients with kidney failure, stratified into three age groups (<46, 46-56, >56 years old). Advanced imaging biomarkers were assessed using an automated instance cyst segmentation tool. The biomarkers were compared with an age- and sex-matched ADPKD cohort in early chronic kidney disease (CKD)., Results: Ht-total parenchymal volume correlated negatively with age at kidney failure. The median Ht-total parenchymal volume was significantly lower in patients older than 56 years. Cystic burden was significantly higher at time of kidney failure, especially in patients who reached it before age 46 years. The cyst index at kidney failure was comparable across age groups and Mayo Imaging Classes. Advanced imaging biomarkers showed higher correlation with Ht-total kidney volume in early CKD than at kidney failure. Cyst index and parenchymal index were relatively stable over 5 years prior to kidney failure, whereas Ht-total cyst volume and cyst parenchymal surface area increased significantly., Conclusion: Age-related differences in advanced imaging biomarkers suggest variable pathophysiological mechanisms in ADPKD patients with kidney failure. Further studies are needed to validate the utility of these biomarkers in predicting disease progression and guiding treatment strategies., Competing Interests: V.E.T. receives grants for preclinical research and clinical trials from Palladio Biosciences, Mironid, Blueprint Medicines, Tribune, Sanofi, and Reata and Regulus. He reports consultancy agreements with uResearch Technology and MFMER for imaging analytics for PCKD and repurposing of probenecid to treat PCKD. He reports royalties for system and method of classifying ADPKD. He is a member of the International Society of Nephrology Kaplan award committee, the American Society of Nephrology editorial board and the PKD Foundation advisory board. P.C.H. reports receiving grants and/or research reagents from Amgen, Inc., Bayer AG, Genzyme Corporation, GlaxoSmithKline, Mitobridge Inc., Otsuka Pharmaceuticals, Palladio Biosciences, Regulus Therapeutics and Vertex Pharmaceuticals, all outside of the submitted work. P.C.H. also reports a position on the Clinical Advisory Board of Mironid, honoraria from Otsuka Pharmaceuticals and Vertex Pharmaceuticals, and other fees from Otsuka Pharmaceuticals. F.T.C. receives research funding from Natera Inc. and Otsuka Pharmaceuticals. He is Chair of the educational advisory panel at the PKD foundation in the USA. All others authors declared no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of the ERA.)

Objective: To evaluate the performance of an internally developed and previously validated artificial intelligence (AI) algorithm for magnetic resonance (MR)-derived total kidney volume (TKV) in autosomal dominant polycystic kidney disease (ADPKD) when implemented in clinical practice., Patients and Methods: The study included adult patients with ADPKD seen by a nephrologist at our institution between November 2019 and January 2021 and undergoing an MR imaging examination as part of standard clinical care. Thirty-three nephrologists ordered MR imaging, requesting AI-based TKV calculation for 170 cases in these 161 unique patients. We tracked implementation and performance of the algorithm over 1 year. A radiologist and a radiology technologist reviewed all cases (N=170) for quality and accuracy. Manual editing of algorithm output occurred at radiology or radiology technologist discretion. Performance was assessed by comparing AI-based and manually edited segmentations via measures of similarity and dissimilarity to ensure expected performance. We analyzed ADPKD severity class assignment of algorithm-derived vs manually edited TKV to assess impact., Results: Clinical implementation was successful. Artificial intelligence algorithm-based segmentation showed high levels of agreement and was noninferior to interobserver variability and other methods for determining TKV. Of manually edited cases (n=84), the AI-algorithm TKV output showed a small mean volume difference of -3.3%. Agreement for disease class between AI-based and manually edited segmentation was high (five cases differed)., Conclusion: Performance of an AI algorithm in real-life clinical practice can be preserved if there is careful development and validation and if the implementation environment closely matches the development conditions., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

ADPKD is caused by pathogenic variants in PKD1 or PKD2 , encoding polycystin-1 and -2 proteins. Polycystins are expressed in osteoblasts and chondrocytes in animal models, and loss of function is associated with low bone mineral density (BMD) and volume. However, it is unclear whether these variants impact bone strength in ADPKD patients. Here, we examined BMD in ADPKD after kidney transplantation (KTx). This retrospective observational study retrieved data from adult patients who received a KTx over the past 15 years. Patients with available dual-energy X-ray absorptiometry (DXA) of the hip and/or lumbar spine (LS) post-transplant were included. ADPKD patients (n = 340) were matched 1:1 by age (±2 years) at KTx and sex with non-diabetic non-ADPKD patients (n = 340). Patients with ADPKD had slightly higher BMD and T-scores at the right total hip (TH) as compared to non-ADPKD patients [BMD: 0.951 vs. 0.897, p < 0.001; T-score: -0.62 vs. -0.99, p < 0.001] and at left TH [BMD: 0.960 vs. 0.893, p < 0.001; T-score: -0.60 vs. -1.08, p < 0.001], respectively. Similar results were found at the right femoral neck (FN) between ADPKD and non-ADPKD [BMD: 0.887 vs. 0.848, p = 0.001; T-score: -1.20 vs. -1.41, p = 0.01] and at left FN [BMD: 0.885 vs. 0.840, p < 0.001; T-score: -1.16 vs. -1.46, p = 0.001]. At the LS level, ADPKD had a similar BMD and lower T-score compared to non-ADPKD [BMD: 1.120 vs. 1.126, p = 0.93; T-score: -0.66 vs. -0.23, p = 0.008]. After adjusting for preemptive KTx, ADPKD patients continued to have higher BMD T-scores in TH and FN. Our findings indicate that BMD by DXA is higher in patients with ADPKD compared to non-ADPKD patients after transplantation in sites where cortical but not trabecular bone is predominant. The clinical benefit of the preserved cortical bone BMD in patients with ADPKD needs to be explored in future studies., Competing Interests: PCH reports receiving grants and/or research reagents from Amgen, Inc., Bayer AG, Genzyme Corporation, GlaxoSmithKline, Mitobridge Inc., Otsuka Pharmaceuticals, Palladio Biosciences, Regulus Therapeutics, and Vertex Pharmaceuticals, all outside of the submitted work. PCH also reports a position on the Clinical Advisory Board of Mironid, honoraria from Otsuka Pharmaceuticals and Vertex Pharmaceuticals, and other fees from Otsuka Pharmaceuticals. VET reports grants and/or other fees from Blueprint Medicines, Mironid, Otsuka Pharmaceuticals, Palladio Biosciences, Sanofi Genzyme, and Regulus Therapeutics, all outside the submitted work. ZMZ reports grants from Kadmon Inc. and consulting fees from Chronisense Medical, LTD. FTC reports research grant support from Otsuka Pharmaceuticals. All the other authors declared no competing interests., (© 2023 The Authors.)

Purpose: Total kidney volume (TKV) is the most important imaging biomarker for quantifying the severity of autosomal-dominant polycystic kidney disease (ADPKD). 3D ultrasound (US) can accurately measure kidney volume compared to 2D US; however, manual segmentation is tedious and requires expert annotators. We investigated a deep learning-based approach for automated segmentation of TKV from 3D US in ADPKD patients., Method: We used axially acquired 3D US-kidney images in 22 ADPKD patients where each patient and each kidney were scanned three times, resulting in 132 scans that were manually segmented. We trained a convolutional neural network to segment the whole kidney and measure TKV. All patients were subsequently imaged with MRI for measurement comparison., Results: Our method automatically segmented polycystic kidneys in 3D US images obtaining an average Dice coefficient of 0.80 on the test dataset. The kidney volume measurement compared with linear regression coefficient and bias from human tracing were R 2  = 0.81, and - 4.42%, and between AI and reference standard were R 2  = 0.93, and - 4.12%, respectively. MRI and US measured kidney volumes had R 2  = 0.84 and a bias of 7.47%., Conclusion: This is the first study applying deep learning to 3D US in ADPKD. Our method shows promising performance for auto-segmentation of kidneys using 3D US to measure TKV, close to human tracing and MRI measurement. This imaging and analysis method may be useful in a number of settings, including pediatric imaging, clinical studies, and longitudinal tracking of patient disease progression., (© 2022. The Author(s).)

Background: In kidney transplantation, a contrast CT scan is obtained in the donor candidate to detect subclinical pathology in the kidney. Recent work from the Aging Kidney Anatomy study has characterized kidney, cortex, and medulla volumes using a manual image-processing tool. However, this technique is time consuming and impractical for clinical care, and thus, these measurements are not obtained during donor evaluations. This study proposes a fully automated segmentation approach for measuring kidney, cortex, and medulla volumes., Methods: A total of 1930 contrast-enhanced CT exams with reference standard manual segmentations from one institution were used to develop the algorithm. A convolutional neural network model was trained ( n =1238) and validated ( n =306), and then evaluated in a hold-out test set of reference standard segmentations ( n =386). After the initial evaluation, the algorithm was further tested on datasets originating from two external sites ( n =1226)., Results: The automated model was found to perform on par with manual segmentation, with errors similar to interobserver variability with manual segmentation. Compared with the reference standard, the automated approach achieved a Dice similarity metric of 0.94 (right cortex), 0.90 (right medulla), 0.94 (left cortex), and 0.90 (left medulla) in the test set. Similar performance was observed when the algorithm was applied on the two external datasets., Conclusions: A fully automated approach for measuring cortex and medullary volumes in CT images of the kidneys has been established. This method may prove useful for a wide range of clinical applications., (Copyright © 2022 by the American Society of Nephrology.)

Autosomal dominant polycystic kidney disease (ADPKD), characterized by progressive cyst formation/expansion, results in enlarged kidneys and often end stage kidney disease. ADPKD is genetically heterogeneous; PKD1 and PKD2 are the common loci (∼78% and ∼15% of families) and GANAB, DNAJB11, and ALG9 are minor genes. PKD is a ciliary-associated disease, a ciliopathy, and many syndromic ciliopathies have a PKD phenotype. In a multi-cohort/-site collaboration, we screened ADPKD-diagnosed families that were naive to genetic testing (n = 834) or for whom no PKD1 and PKD2 pathogenic variants had been identified (n = 381) with a PKD targeted next-generation sequencing panel (tNGS; n = 1,186) or whole-exome sequencing (WES; n = 29). We identified monoallelic IFT140 loss-of-function (LoF) variants in 12 multiplex families and 26 singletons (1.9% of naive families). IFT140 is a core component of the intraflagellar transport-complex A, responsible for retrograde ciliary trafficking and ciliary entry of membrane proteins; bi-allelic IFT140 variants cause the syndromic ciliopathy, short-rib thoracic dysplasia (SRTD9). The distinctive monoallelic phenotype is mild PKD with large cysts, limited kidney insufficiency, and few liver cysts. Analyses of the cystic kidney disease probands of Genomics England 100K showed that 2.1% had IFT140 LoF variants. Analysis of the UK Biobank cystic kidney disease group showed probands with IFT140 LoF variants as the third most common group, after PKD1 and PKD2. The proximity of IFT140 to PKD1 (∼0.5 Mb) in 16p13.3 can cause diagnostic confusion, and PKD1 variants could modify the IFT140 phenotype. Importantly, our studies link a ciliary structural protein to the ADPKD spectrum., Competing Interests: Declaration of interests M.M. reports grants and consulting fees outside the submitted work from Otsuka Pharmaceuticals, Sanofi, Chinook, Goldilocks, Natera, and Palladio. R.D.P. reports clinical trial support from Reata, Kadmon, Sanofi-Genzyme, US Department of Defense; consultant/advisory fees from Otsuka and Sanofi-Genzyme; and is section editor Renal Cystic Disease: UpToDate. J.A.S. has received honorarium from consulting positions from Otsuka Pharmaceuticals, Sanofi, and Takeda. V.E.T. reports grants and/or other fees from Mironid, Blueprint Medicines, Otsuka Pharmaceuticals, Palladio Biosciences, Sanofi Genzyme, Reata, and Regulus Therapeutics, all outside the submitted work., (Copyright © 2021 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

BACKGROUND: Arginine vasopressin promotes kidney cyst growth in autosomal dominant polycystic kidney disease (ADPKD). Increased water intake reduces arginine vasopressin and urine osmolality and may slow kidney cyst growth. METHODS: In this randomized controlled 3-year clinical trial, we randomly assigned adults with ADPKD who had a height-corrected total kidney volume in Mayo imaging subclass categories 1B to 1E and an estimated glomerular filtration rate of 30 ml/min/1.73 m2 or greater to (1) water intake prescribed to reduce 24-hour urine osmolality to 270 mOsmol/kg or less or (2) ad libitum water intake irrespective of 24-hour urine osmolality. The primary end point was the percentage annualized rate of change in height-corrected total kidney volume. RESULTS: A total of 184 patients participated in either the ad libitum water intake group (n=92) or the prescribed water intake group (n=92). Over 3 years, there was no difference in the annualized rate of change in height-corrected total kidney volume between the ad libitum (7.8% per year; 95% confidence interval [CI], 6.6 to 9.0) and prescribed (6.8% per year; 95% CI, 5.8 to 7.7) water intake groups (mean difference, −0.97% per year; 95% CI, −2.37 to 0.44; P=0.18). The difference in mean 24-hour urine osmolality between the ad libitum and prescribed water intake groups was −91 mOsmol/kg (95% CI, −127 to −54 mOsmol/kg), with 52.3% of patients achieving adherence to the target 24-hour urine osmolality and no reduction in serum copeptin over 3 years. The frequency of adverse events was similar between groups. CONCLUSIONS: For patients with ADPKD, prescribed water intake was not associated with excess adverse events and achieved the target 24-hour urine osmolality for half of the patients but did not reduce copeptin or slow the growth of total kidney volume over 3 years compared with ad libitum water intake. (Funded by the National Health and Medical Research Council of Australia [grant GNT1138533], Danone Research, PKD Australia, the University of Sydney, and the Westmead Medical Research Foundation; Australian New Zealand Clinical Trials Registry number, ACTRN12614001216606).

Background: Autosomal dominant polycystic kidney disease (ADPKD) has phenotypic variability only partially explained by established biomarkers that do not readily assess pathologically important factors of inflammation and kidney fibrosis. We evaluated asymptomatic pyuria (AP), a surrogate marker of inflammation, as a biomarker for disease progression., Methods: We performed a retrospective cohort study of adult patients with ADPKD. Patients were divided into AP and no pyuria (NP) groups. We evaluated the effect of pyuria on kidney function and kidney volume. Longitudinal models evaluating kidney function and kidney volume rate of change with respect to incidences of AP were created., Results: There were 687 included patients (347 AP, 340 NP). The AP group had more women (65% versus 49%). Median ages at kidney failure were 86 and 80 years in the NP and AP groups (log rank, P =0.49), respectively, for patients in Mayo Imaging Class (MIC) 1A-1B as compared with 59 and 55 years for patients in MIC 1C-1D-1E (log rank, P =0.02), respectively. Compared with the NP group, the rate of kidney function (ml/min per 1.73 m 2 per year) decline shifted significantly after detection of AP in the models, including all patients (-1.48; P <0.001), patients in MIC 1A-1B (-1.79; P <0.001), patients in MIC 1C-1D-1E (-1.18; P <0.001), and patients with PKD1 (-1.04; P <0.001). Models evaluating kidney volume rate of growth showed no change after incidence of AP as compared with the NP group., Conclusions: AP is associated with kidney failure and faster kidney function decline irrespective of the ADPKD gene, cystic burden, and cystic growth. These results support AP as an enriching prognostic biomarker for the rate of disease progression., Competing Interests: C. Hanna reports consultancy agreements with Horizon Therapeutics and honoraria from Horizon Therapeutics. P.C. Harris reports consultancy agreements with Mitobridge, Otsuka, Regulus, and Vertex; research funding from Acceleron, Jemincare, Navitor, Otsuka Pharmaceuticals, and Palladio Biosciences; patents and inventions with Amgen, Bayer, Genzyme, GlaxoSmithKline, Millipore, Mitobridge, and Vertex; honoraria from Otsuka Pharmaceuticals and Vertex Pharmaceuticals; and serving on the clinical advisory board of Mironid. P.J. Schulte reports other interests/relationships with OxThera. S.R. Senum reports ownership interest with Marvell Technology Inc. V.E. Torres reports consultancy agreements with Acceleron Pharma, Blueprint Medicines, Mironid, Otsuka Pharmaceuticals, Palladio, Reata, and Sanofi; research funding from Blueprint Medicines, Mironid, Otsuka Pharmaceuticals, Palladio Biosciences, Reata, and Sanofi Genzyme (all preclinical trials, preclinical research, or clinical trials); honoraria from Otsuka Pharmaceuticals (to the institution); and scientific advisor or membership with Mironid, Otsuka Pharmaceuticals, and Sanofi. Z.M. Zoghby reports research funding from Kadmon Corporation. All remaining authors have nothing to disclose., (Copyright © 2022 by the American Society of Nephrology.)

Common modalities for in vivo imaging of rodents include positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US). Each method has limitations and advantages, including availability, ease of use, cost, size, and the use of ionizing radiation or magnetic fields. This protocol describes the use of 3D robotic US for in vivo imaging of rodent kidneys and heart, subsequent data analysis, and possible research applications. Practical applications of robotic US are the quantification of total kidney volume (TKV), as well as the measurement of cysts, tumors, and vasculature. Although the resolution is not as high as other modalities, robotic US allows for more practical high throughput data collection. Furthermore, using US M-mode imaging, cardiac function may be quantified. Since the kidneys receive 20%-25% of the cardiac output, assessing cardiac function is critical to the understanding of kidney physiology and pathophysiology.

Total kidney volume (TKV) is the main imaging biomarker used to monitor disease progression and to classify patients affected by autosomal dominant polycystic kidney disease (ADPKD) for clinical trials. However, patients with similar TKVs may have drastically different cystic presentations and phenotypes. In an effort to quantify these cystic differences, we developed the first 3D semantic instance cyst segmentation algorithm for kidneys in MR images. We have reformulated both the object detection/localization task and the instance-based segmentation task into a semantic segmentation task. This allowed us to solve this unique imaging problem efficiently, even for patients with thousands of cysts. To do this, a convolutional neural network (CNN) was trained to learn cyst edges and cyst cores. Images were converted from instance cyst segmentations to semantic edge-core segmentations by applying a 3D erosion morphology operator to up-sampled versions of the images. The reduced cysts were labeled as core; the eroded areas were dilated in 2D and labeled as edge. The network was trained on 30 MR images and validated on 10 MR images using a fourfold cross-validation procedure. The final ensemble model was tested on 20 MR images not seen during the initial training/validation. The results from the test set were compared to segmentations from two readers. The presented model achieved an averaged R 2 value of 0.94 for cyst count, 1.00 for total cyst volume, 0.94 for cystic index, and an averaged Dice coefficient of 0.85. These results demonstrate the feasibility of performing cyst segmentations automatically in ADPKD patients., (© 2021. The Author(s).)

Background: Little is known on how greenspace redevelopment-creating or improving existing parks and trails-targeted for low-income and/or majority Black neighborhoods could amplify existing social environmental stressors, increase residents' susceptibility to displacement, and impact their sleep quality., Objective: To examine the relationship between social environmental stressors associated with displacement and sleep quality among Black adults., Methods: Linear regression models were employed on survey data to investigate the association between social environmental stressors, independently and combined, on sleep quality among Black adults residing in block groups targeted for greenspace redevelopment (i.e., exposed) and matched with block groups that were not (i.e., unexposed)., Results: The independent associations between everyday discrimination, heightened vigilance, housing unaffordability, and subjective sleep quality were not modified by greenspace redevelopment, controlling for other factors. The association between financial strain and subjective sleep quality was different for exposed and unexposed participants with exposed participants having a poorer sleep quality. The combined model revealed that the association between financial strain and sleep quality persisted. However, for different financial strain categories exposed participants slept poorer and/or better than unexposed participants., Significance: Our findings suggest a nuanced relationship between social environmental stressors, pressure of displacement related to greenspace redevelopment, and sleep quality among Black adults.

Background: Early prediction of tumor response to neoadjuvant chemotherapy (NACT) is crucial for optimal treatment and improved outcome in breast cancer patients. The purpose of this study is to investigate the role of shear wave elastography (SWE) for early assessment of response to NACT in patients with invasive breast cancer., Methods: In a prospective study, 62 patients with biopsy-proven invasive breast cancer were enrolled. Three SWE studies were conducted on each patient: before, at mid-course, and after NACT but before surgery. A new parameter, mass characteristic frequency (f mass ), along with SWE measurements and mass size was obtained from each SWE study visit. The clinical biomarkers were acquired from the pre-NACT core-needle biopsy. The efficacy of different models, generated with the leave-one-out cross-validation, in predicting response to NACT was shown by the area under the receiver operating characteristic curve and the corresponding sensitivity and specificity., Results: A significant difference was found for SWE parameters measured before, at mid-course, and after NACT between the responders and non-responders. The combination of E mean2 and mass size (s 2 ) gave an AUC of 0.75 (0.95 CI 0.62-0.88). For the ER+ tumors, the combination of E mean&#95;ratio1 , s 1 , and Ki-67 index gave an improved AUC of 0.84 (0.95 CI 0.65-0.96). For responders, f mass was significantly higher during the third visit., Conclusions: Our study findings highlight the value of SWE estimation in the mid-course of NACT for the early prediction of treatment response. For ER+ tumors, the addition of Ki-67improves the predictive power of SWE. Moreover, f mass is presented as a new marker in predicting the endpoint of NACT in responders.

Ultrasound bladder vibrometry (UBV) parameters have been shown in previous studies to strongly correlate with measurements from urodynamic studies. Just like urodynamic studies, UBV can be performed in supine and sitting positions. The objective of this study is to compare UBV parameters obtained in the two different positions using statistical methods. We recruited eight volunteers with healthy bladders for this purpose. The elasticity, group velocity squared and thickness of the bladder were the UBV parameters of interest, and their values were recorded at different bladder volumes for each volunteer. The results presented indicate that the measurements made in the two positions are in agreement using the Bland-Altman method and a parameter q which compares the values at each bladder volume for each volunteer. UBV parameters were also repeatable for measurements recorded in the supine and sitting positions., Competing Interests: Conflict of interest disclosure M.F. is an inventor of US patent 9,345,448 on the technology used in this study. However, M.F. and the other authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article, and they affirm that they do not have any potential financial interest related to the technology referenced in this article., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

The objective of this study was to validate a fully automated total kidney volume measurement method for pre-clinical rodent trials that is fast, accurate, reproducible, and to provide these resources to the research community. Rodent studies that involve imaging are crucial for monitoring treatment efficacy in diseases such as polycystic kidney disease. Previous studies utilize manual or semi-automated segmentations, which are time consuming and potentially biased. To develop our automated system, a total of 150 axial magnetic resonance images (MRI) from a variety of mouse models were manually segmented and used to train/validate an automated algorithm. To test the longitudinal application of the model, four mutant and four wild-type mice were imaged sequentially over three to twelve weeks via MRI. Segmentations of the kidneys (excluding the renal pelvis) were generated by the automated method and two different readers, with one reader repeating the measurements. Similarity metrics and longitudinal analysis were calculated to assess the performance of the automated compared to the manual methods. The automated approach required no user input, besides a final visual quality control step. Similarity metrics of the automated method versus the manual segmentations were on par with inter- and intra-reader comparisons. Thus, our fully automated approach described here can be safely used in longitudinal, pre-clinical trials that involve the segmentation of rodent kidneys in T2-weighted MRIs., (Copyright © 2020 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.)

Purpose: For patients affected by autosomal-dominant polycystic kidney disease (ADPKD), successful differentiation of cysts is useful for automatic classification of patient phenotypes, clinical decision-making, and disease progression. The objective was to develop and evaluate a fully automated semantic segmentation method to differentiate and analyze renal cysts in patients with ADPKD., Methods: An automated deep learning approach using a convolutional neural network was trained, validated, and tested on a set of 60 MR T2-weighted images. A three-fold cross-validation approach was used to train three models on distinct training and validation sets (n = 40). An ensemble model was then built and tested on the hold out cases (n = 20), with each of the cases compared to manual segmentations performed by two readers. Segmentation agreement between readers and the automated method was assessed., Results: The automated approach was found to perform at the level of interobserver variability. The automated approach had a Dice coefficient (mean ± standard deviation) of 0.86 ± 0.10 vs Reader-1 and 0.84 ± 0.11 vs. Reader-2. Interobserver Dice was 0.86 ± 0.08. In terms of total cyst volume (TCV), the automated approach had a percent difference of 3.9 ± 19.1% vs Reader-1 and 8.0 ± 24.1% vs Reader-2, whereas interobserver variability was - 2.0 ± 16.4%., Conclusion: This study developed and validated a fully automated approach for performing semantic segmentation of kidney cysts in MR images of patients affected by ADPKD. This approach will be useful for exploring additional imaging biomarkers of ADPKD and automatically classifying phenotypes.

We applied sub-Hertz analysis of viscoelasticity (SAVE) to differentiate breast masses in pre-biopsy patients. Tissue response during external ramp-and-hold stress was ultrasonically detected. Displacements were used to acquire tissue viscoelastic parameters. The fast instantaneous response and slow creep-like deformations were modeled as the response of a linear standard solid from which viscoelastic parameters were estimated. These parameters were used in a multi-variable classification framework to differentiate malignant from benign masses identified by pathology. When employing all viscoelasticity parameters, SAVE resulted in 71.43% accuracy in differentiating lesions. When combined with ultrasound features and lesion size, accuracy was 82.24%. Adding a quality metric based on uniaxial motion increased the accuracy to 81.25%. When all three were combined with SAVE, accuracy was 91.3%. These results confirm the utility of SAVE as a robust ultrasound-based diagnostic tool for non-invasive differentiation of breast masses when used as stand-alone biomarkers or in conjunction with ultrasonic features., Competing Interests: Conflict of interest disclosure The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article and the authors affirm that they do not have any potential financial interest related to the technology referenced in this paper., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Polycystic kidney disease (PKD) is an inherited disorder characterized by renal cyst formation and enlargement of the kidney. PKD severity can be staged noninvasively by measuring total kidney volume (TKV), a promising biomarker that has recently received regulatory qualification. In preclinical mouse models, where the disease is studied and potential therapeutics are evaluated, the most popular noninvasive method of measuring TKV is magnetic resonance imaging (MRI). Although MRI provides excellent 3D resolution and contrast, these systems are expensive to operate, have long acquisition times, and, consequently, are not heavily used in preclinical PKD research. In this study, a new imaging instrument, based on robotic ultrasound (US), was evaluated as a complementary approach for assessing PKD in rodent models. The objective was to determine the extent to which TKV measurements on the robotic US scanner correlated with both in vivo and ex vivo reference standards (MRI and Vernier calipers, respectively). A cross-sectional study design was implemented that included both PKD-affected mice and healthy wild types, spanning sex and age for a wide range of kidney volumes. It was found that US-derived TKV measurements and kidney lengths were strongly associated with both in vivo MRI and ex vivo Vernier caliper measurements ( R 2 =0.94 and 0.90, respectively). In addition to measuring TKV, renal vascular density was assessed using acoustic angiography (AA), a novel contrast-enhanced US methodology. AA image intensity, indicative of volumetric vascularity, was seen to have a strong negative correlation with TKV ( R 2 =0.82), suggesting impaired renal vascular function in mice with larger kidneys. These studies demonstrate that robotic US can provide a rapid and accurate approach for noninvasively evaluating PKD in rodent models.

Background & Aims: Isolated autosomal-dominant polycystic liver disease (ADPLD) is generally considered a rare disease. However, the frequency of truncating mutations to ADPLD genes in large, population sequencing databases is 1:496. With the increasing use of abdominal imaging, incidental detection of hepatic cysts and ADPLD has become more frequent. The present study was performed to ascertain the incidence and point prevalence of ADPLD in Olmsted County, MN, USA, and how these are impacted by the increasing utilisation of abdominal imaging., Methods: The Rochester Epidemiology Project and radiology databases of Mayo Clinic and Olmsted Medical Center were searched to identify all subjects meeting diagnostic criteria for definite, likely, or possible ADPLD. Annual incidence rates were calculated using incident cases during 1980-2016 as numerator, and age- and sex-specific estimates of the population of Olmsted County as denominator. Point prevalence was calculated using prevalence cases as numerator, and age- and sex-specific estimates of the population of Olmsted County on 1 January 2010 as denominator., Results: The incidence rate and point prevalence of combined definite and likely ADPLD were 1.01 per 100,000 person-years and 9.5 per 100,000 population, respectively. Only 15 of 35 definite and likely incident ADPLD cases had received a diagnostic code, and only 8 had clinically significant hepatomegaly. The incidence rates were much higher when adding possible cases, mainly identified through radiology databases, particularly in recent years and in older patients because of the increased utilisation of imaging studies., Conclusions: Clinically significant isolated ADPLD is a rare disease with a prevalence <1:10,000 population. The overall prevalence of ADPLD, however, to a large extent not clinically significant, is likely much higher and closer to the reported genetic prevalence., Lay Summary: Isolated autosomal-dominant polycystic liver disease (ADPLD) is generally considered a rare disease. However, we demonstrate that it is a relatively common disease, which is rarely (<1:10,000 population) clinically significant., Competing Interests: The authors declare no conflicts of interest related to this work. Please refer to the accompanying ICMJE disclosure forms for further details., (© 2020 Published by Elsevier B.V. on behalf of European Association for the Study of the Liver (EASL).)

Using morpholino antisense oligonucleotide (MO) technology, we blocked leptin A or leptin receptor expression in embryonic zebrafish, and analyzed consequences of leptin A knock-down on fish development. Embryos injected with leptin A or leptin receptor MOs (leptin A or leptin receptor morphants) had smaller bodies and eyes, undeveloped inner ear, enlarged pericardial cavity, curved body and/or tail and larger yolk compared to control embryos of the same stages. The defects persisted in 6-9 days old larvae. We found that blocking leptin A function had little effect on the development of early brain (1 day old), but differentiation of both the morphant dorsal brain and retinal cells was severely disrupted in older (2 days old) embryos. Despite the enlarged pericardial cavity, differentiation of cardiac cells appeared to be similar to control embryos. Formation of the morphants' inner ear is also severely disrupted, which corroborates existing reports of leptin receptor expression in inner ear of both zebrafish and mammals. Co-injection of leptin A MO and recombinant leptin results in partial rescue of the wild-type phenotype. Our results suggest that leptin A plays distinct roles in zebrafish development., (Copyright © 2012 Elsevier Inc. All rights reserved.)