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.

BackgroundArginine 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.MethodsIn 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.ResultsA 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.ConclusionsFor 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). The effect of increased water intake on kidney cyst growth in patients with polycystic kidney disease was compared for two groups randomly assigned to either prescribed or ad libitum water intake. Over 3 years, there was no difference in height-corrected total kidney volume between the groups.

Significance Statement Volumetric measurements are needed to characterize kidney structural findings on CT images to evaluate and test their potential utility in clinical decision making. Deep learning can enable this task in a scalable and reliable manner. Although automated kidney segmentation has been previously explored, methods for distinguishing cortex from medulla have never been done before. In addition, automated methods are typically evaluated at a single institution, without testing generalizability and robustness across different institutions. The tool developed in this study performs at the level of human readers and could enable large diverse population studies to evaluate how kidney, cortex, and medulla volumes can be used in various clinical settings, and establish normative values at large scale. 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.

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. 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. 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 R2 = 0.81, and − 4.42%, and between AI and reference standard were R2 = 0.93, and − 4.12%, respectively. MRI and US measured kidney volumes had R2 = 0.84 and a bias of 7.47%. 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.

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 R2 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.

Background: Autosomal dominant polycystic kidney disease (ADPKD) is one of the most common monogenetic disorders in humans and is characterized by numerous fluid-filled cysts that grow slowly, resulting in end-stage renal disease in the majority of patients. Preclinical studies have indicated that treatment with low-dose thiazolidinediones, such as pioglitazone, decrease cyst growth in rodent models of PKD. Methods: This Phase 1b cross-over study compared the safety of treatment with a low dose (15mg) of the peroxisome proliferator-activated receptor-gamma (PPAR-gamma) agonist pioglitazone or placebo in PKD patients, with each treatment given for 1year. The study monitored known side effects of PPAR-gamma agonist treatment, including fluid retention and edema. Liver enzymes and risk of hypoglycemia were assessed throughout the study. As a secondary objective, the efficacy of low-dose pioglitazone was followed using a primary assessment of total kidney volume (TKV), blood pressure (BP) and kidney function. Results: Eighteen patients were randomized and 15 completed both arms. Compared with placebo, allocation to pioglitazone resulted in a significant decrease in total body water as assessed by bioimpedance analysis {mean difference 0.16 Omega [95% confidence interval(CI)0.24-2.96], P=0.024} and no differences in episodes of heart failure, clinical edema or change in echocardiography. Allocation to pioglitazone led to no difference in the percent change in TKV of -3.5% (95% CI -8.4-1.4, P=0.14), diastolic BP and microalbumin:creatinine ratio. Conclusions :In this small pilot trial in people with ADPKD but without diabetes, pioglitazone 15mg was found to be as safe as placebo. Larger and longer-term randomized trials powered to assess effects on TKV are needed.

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.

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. 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. 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%. 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.

To evaluate the performance of trained technologists vis-à-vis radiologists for volumetric pancreas segmentation and to assess the impact of supplementary training on their performance. In this IRB-approved study, 22 technologists were trained in pancreas segmentation on portal venous phase CT through radiologist-led interactive videoconferencing sessions based on an image-rich curriculum. Technologists segmented pancreas in 188 CTs using freehand tools on custom image-viewing software. Subsequent supplementary training included multimedia videos focused on common errors, which were followed by second batch of 159 segmentations. Two radiologists reviewed all cases and corrected inaccurate segmentations. Technologists’ segmentations were compared against radiologists’ segmentations using Dice-Sorenson coefficient (DSC), Jaccard coefficient (JC), and Bland–Altman analysis. Corrections were made in 71 (38%) cases from first batch [26 (37%) oversegmentations and 45 (63%) undersegmentations] and in 77 (48%) cases from second batch [12 (16%) oversegmentations and 65 (84%) undersegmentations]. DSC, JC, false positive (FP), and false negative (FN) [mean (SD)] in first versus second batches were 0.63 (0.15) versus 0.63 (0.16), 0.48 (0.15) versus 0.48 (0.15), 0.29 (0.21) versus 0.21 (0.10), and 0.36 (0.20) versus 0.43 (0.19), respectively. Differences were not significant (p > 0.05). However, range of mean pancreatic volume difference reduced in the second batch [− 2.74 cc (min − 92.96 cc, max 87.47 cc) versus − 23.57 cc (min − 77.32, max 30.19)]. Trained technologists could perform volumetric pancreas segmentation with reasonable accuracy despite its complexity. Supplementary training further reduced range of volume difference in segmentations. Investment into training technologists could augment and accelerate development of body imaging datasets for AI applications.

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.

Introduction: Cystic expansion damaging the parenchyma is thought to lead to end-stage kidney disease (ESKD) in autosomal dominant polycystic kidney disease (ADPKD). Here we characterized genotypic and phenotypic attributes of ADPKD at time of ESKD. Methods: This is a retrospective cross-sectional study of patients with ADPKD with ESKD evaluated at Mayo Clinic with available abdominal computed tomography (CT) or magnetic resonance imaging (MRI). Kidney volumes were measured (total kidney volume adjusted for height [HtTKV]), Mayo Image Class (MIC) calculated, ADPKD genotype determined, and clinical and laboratory features obtained from medical records. Results: Differences in HtTKV at ESKD were associated with patient age and sex; older patients and women had smaller HtTKV at ESKD. HtTKV at ESKD was observed to be 12.3% smaller with each decade of age (P < 0.01); but significant only in women (17.8%, P < 0.01; men 6.9%, P 1/4 0.06). Patients with onset of ESKD at <47, 47-61, or >61 years had different characteristics, with a shift from youngest to oldest in male to female enrichment, MIC from 1D/1E to 1B/1C, likely fully penetrant PKD1 mutations from 95% to 42%, and presence of macrovascular disease from 8% to 40%. Macrovascular disease was associated with smaller kidneys in female patients. Conclusion: HtTKV at ESKD was smaller with advancing age in patients with ADPKD, particularly in women. These novel findings provide insight into possible underlying mechanisms leading to ESKD, which differ between younger and older individuals. Cystic growth is the predominant mechanism in younger patients with ESKD, whereas aging-related factors, including vascular disease, becomes poten-tially important as patients age.

Background: Polycystic liver disease can cause severe symptomatic hepatomegaly. Combined partial hepatectomy and cyst fenestration can be performed to reduce liver volume and symptom burden. We aimed to assess change in symptom relief and quality of life 6 months after partial hepatectomy and cyst fenestration in polycystic liver disease patients. Method: We established a prospective cohort between 2014 and 2018 at a referral center in the United States. Patients who underwent partial hepatectomy and cyst fenestration for volume-related symptoms were included. Primary outcome was change in polycystic liver disease-related symptoms, measured with Polycystic Liver Disease Questionnaire. Secondary outcomes were change in liver volume (computed tomography/ magnetic resonance imaging) and change in quality of life, measured with the 12-Item Short Form Survey and the EuroQoL Visual Analogue Scale. Questionnaire scores range from 0 to 100 and were assessed before and 6 months after partial hepatectomy and cyst fenestration. Surgical complications were scored according to Clavien-Dindo (grade 1 to 5). Results: We included 18 patients (mean age 52 years, 82% female). Partial hepatectomy and cyst fenestration reduced median liver volume (4,917 to 2,120 mL). Symptoms, measured with Polycystic Liver Disease Questionnaire, decreased (76.9 to 34.8 points; P < .001) 6 months after surgery; 15/16 symptoms declined after treatment, with the most impact seen on early satiety and dyspnea. Quality of life also improved after surgery: median physical and mental component scales of the 12-Item Short Form Survey and EuroQoL Visual Analog Scale increased (24.9 to 45.7, P = .004; 40.5 to 55.4, P = .02; and 40.0 to 72.5, P = .003). Major complications (grade 4) occurred in 2 patients. There was no procedure-related mortality. Conclusion: Partial hepatectomy and cyst fenestration substantially improves symptom burden and quality of life in highly symptomatic polycystic liver disease patients. (C) 2020 Elsevier Inc. All rights reserved.

Abstract Introduction Cystic expansion damaging the parenchyma is thought to lead to end-stage kidney disease (ESKD) in autosomal dominant polycystic kidney disease (ADPKD). Here we characterized genotypic and phenotypic attributes of ADPKD at time of ESKD. Methods This is a retrospective cross-sectional study of ADPKD patients with ESKD evaluated at Mayo Clinic with available abdominal CT or MR imaging. Kidney volumes were measured (HtTKV), Mayo Image Class (MIC) calculated, ADPKD genotype determined, and clinical and laboratory features obtained from medical records. Results Differences in HtTKV at ESKD were associated with patient age and sex; older patients and women had smaller HtTKV at ESKD. HtTKV at ESKD was observed to be 12.3% smaller with each decade of age (p 61 years had different characteristics, with a shift from youngest to oldest in: male to female enrichment, MIC from 1D/1E to 1B/1C, likely fully penetrant PKD1 mutations from 95% to 42%, and presence of macrovascular disease from 8% to 40%. Macrovascular disease was associated with smaller kidneys in females. Conclusion HtTKV at ESKD was smaller with advancing age in patients with ADPKD, particularly in women. These novel findings provide insight into possible underlying mechanisms leading to ESKD, which differ between younger and older individuals. Cystic growth is the predominant mechanism in younger patients with ESKD, whereas aging-related factors, including vascular disease, becomes potentially important as patients age.

Background and objectives We assessed safety and efficacy of another somatostatin receptor analog, pasireotide long-acting release, in severe polycystic liver disease and autosomal dominant polycystic kidney disease. Pasireotide long-acting release, with its broader binding profile and higher affinity to known somatostatin receptors, has potential for greater efficacy. Design, setting, participants, & measurements Individuals with severe polycystic liver disease were assigned in a 2:1 ratio in a 1-year, double-blind, randomized trial to receive pasireotide long-acting release or placebo. Primary outcome was change in total liver volume; secondary outcomes were change in total kidney volume, eGFR, and quality of life. Results Of 48 subjects randomized, 41 completed total liver volume measurements ( n =29 pasireotide long-acting release and n =12 placebo). From baseline, there were −99±189 ml/m absolute and −3%±7% change in annualized change in height-adjusted total liver volume (from 2582±1381 to 2479±1317 ml/m) in the pasireotide long-acting release group compared with 136±117 ml/m absolute and 6%±7% increase (from 2387±759 to 2533±770 ml/m) in placebo ( P <0.001 for both). Total kidney volumes decreased by −12±34 ml/m and −1%±4% in pasireotide long-acting release compared with 21±21 ml/m and 4%±5% increase in the placebo group ( P =0.05 for both). Changes in eGFR were similar between groups. Among the n =48 randomized, adverse events included hyperglycemia (26 of 33 [79%] in pasireotide long-acting release versus four of 15 [27%] in the placebo group; P <0.001), and among the 47 without diabetes at baseline, 19 of 32 (59%) in the pasireotide long-acting release group versus one of 15 (7%) in the placebo group developed diabetes ( P =0.001). Conclusions Another somatostatin analog, pasireotide long-acting release, slowed progressive increase in both total liver volume/total kidney volume growth rates without affecting GFR decline. Participants experienced higher frequency of adverse events (hyperglycemia and diabetes). Clinical Trial registry name and registration number Pasireotide LAR in Severe Polycystic Liver Disease, NCT01670110 Podcast This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2020_08_28_CJN13661119.mp3

Objectives Pancreatic lesions in autosomal dominant polycystic kidney disease (ADPKD) are primarily cysts. They are increasingly recognized, with isolated reports of intraductal papillary mucinous neoplasia (IPMN). Methods Retrospective study to determine prevalence, number, size, and location of pancreatic abnormalities using abdominal magnetic resonance imaging (MRI) of genotyped ADPKD patients (seen February 1998 to October 2013) and compared with age- and sex-matched non-ADPKD controls. We evaluated presentation, investigation, and management of all IPMNs among individuals with ADPKD (January 1997 to December 2016). Results Abdominal MRIs were examined for 271 genotyped ADPKD patients. A pancreatic cyst lesion (PCL) was detected in 52 patients (19%; 95% confidence interval, 15%–23%). Thirty-seven (71%) had a solitary PCL; 15 (28%) had multiple. Pancreatic cyst lesion prevalence did not differ by genotype. Intraductal papillary mucinous neoplasia was detected in 1% of ADPKD cases. Among 12 IPMN patients (7 branch duct; 5 main duct or mixed type) monitored for about 140 months, 2 with main duct IPMNs required Whipple resection, and 1 patient died of complications from small-bowel obstruction after declining surgical intervention. Conclusions With MRI, PCLs were detected in 19% and IPMNs in 1% of 271 ADPKD patients with proven mutations, without difference across genotypes. Pancreatic cyst lesions were asymptomatic and remained stable in size.

BackgroundApproximately 30% of Persian cats have a c.10063C>A variant in polycystin 1 (PKD1) homolog causing autosomal dominant polycystic kidney disease (ADPKD). The variant is lethal in utero when in the homozygous state and is the only ADPKD variant known in cats. Affected cats have a wide range of progression and disease severity. However, cats are an overlooked biomedical model and have not been used to test therapeutics and diets that may support human clinical trials. To reinvigorate the cat as a large animal model for ADPKD, the efficacy of imaging modalities was evaluated and estimates of kidney and fractional cystic volumes (FCV) determined.MethodsThree imaging modalities, ultrasonography, computed tomography (CT), and magnetic resonance imaging examined variation in disease presentation and disease progression in 11 felines with ADPKD. Imaging data was compared to well-known biomarkers for chronic kidney disease and glomerular filtration rate. Total kidney volume, total cystic volume, and FCV were determined for the first time in ADPKD cats. Two cats had follow-up examinations to evaluate progression.ResultsFCV measurements were feasible in cats. CT was a rapid and an efficient modality for evaluating therapeutic effects that cause alterations in kidney volume and/or FCV. Biomarkers, including glomerular filtration rate and creatinine, were not predictive for disease progression in feline ADPKD. The wide variation in cystic presentation suggested genetic modifiers likely influence disease progression in cats. All imaging modalities had comparable resolutions to those acquired for humans, and software used for kidney and cystic volume estimates in humans proved useful for cats.ConclusionsRoutine imaging protocols used in veterinary medicine are as robust and efficient for evaluating ADPKD in cats as those used in human medicine. Cats can be identified as fast and slow progressors, thus, could assist with genetic modifier discovery. Software to measure kidney and cystic volume in human ADPKD kidney studies is applicable and efficient in cats. The longer life and larger kidney size span than rodents, similar genetics, disease presentation and progression as humans suggest cats are an efficient biomedical model for evaluation of ADPKD therapeutics.

BACKGROUND:The formation and growth of cysts in kidneys, and often liver, in autosomal dominant polycystic kidney disease (ADPKD) cause progressive increases in total kidney volume (TKV) and liver volume (TLV). Laborious and time-consuming manual tracing of kidneys and liver is the current gold standard. We developed a fully automated segmentation method for TKV and TLV measurement that uses a deep learning network optimized to perform semantic segmentation of kidneys and liver.METHODS:We used 80% of a set of 440 abdominal magnetic resonance images (T2-weighted HASTE coronal sequences) from patients with ADPKD to train the network and the remaining 20% for validation. Both kidneys and liver were also segmented manually. To evaluate the method's performance, we used an additional test set of images from 100 patients, 45 of whom were also involved in longitudinal analyses.RESULTS:TKV and TLV measured by the automated approach correlated highly with manually traced TKV and TLV (intraclass correlation coefficients, 0.998 and 0.996, respectively), with low bias and high precision (<0.1%±2.7% for TKV and -1.6%±3.1% for TLV); this was comparable with inter-reader variability of manual tracing (<0.1%±3.5% for TKV and -1.5%±4.8% for TLV). For longitudinal analysis, bias and precision were <0.1%±3.2% for TKV and 1.4%±2.9% for TLV growth.CONCLUSIONS:These findings demonstrate a fully automated segmentation method that measures TKV, TLV, and changes in these parameters as accurately as manual tracing. This technique may facilitate future studies in which automated and reproducible TKV and TLV measurements are needed to assess disease severity, disease progression, and treatment response.

ObjectiveTo describe first episodes of bacterial cholangitis complicating autosomal dominant polycystic kidney disease (ADPKD) and autosomal dominant polycystic liver disease (ADPLD) and to identify risk factors for cholangitis episodes among patients with ADPKD-associated polycystic liver disease (PLD).Patients and MethodsWe searched the electronic medical records at our tertiary referral center for episodes of cholangitis in patients with ADPKD or ADPLD from January 1, 1996, through June 30, 2017. Cases were categorized as suspected or definite cholangitis by expert review. Clinical, laboratory, and radiologic data were manually abstracted. A nested case-control study was conducted to investigate risk factors for cholangitis in patients with ADPKD.ResultsWe identified 29 cases of definite or suspected cholangitis complicating PLD (24 with ADPKD-associated PLD and 5 with ADPLD). Among patients with definite cholangitis in ADPKD-associated PLD (n=19) vs ADPLD (n=4), the mean ± SD age was 62.4±12.2 vs 55.1±8.6 years, and 9 (47.4%) vs 0 (0%), respectively, were male. The odds of gallstones (odds ratio [OR], 21.6; 95% CI, 3.17-927; P<.001), prior cholecystectomy (OR, 12.2; 95% CI, 1.59-552; P=.008), duodenal diverticulum (OR, 13.5; 95% CI, 2.44 to not estimable; P=.004), type 2 diabetes mellitus (OR, 6.41; 95% CI, 1.01 to not estimable; P=.05), prior endoscopic retrograde cholangiopancreatography (OR, 14.0; 95% CI, 1.80-631; P=.005), and prior kidney transplant (OR, 8.06; 95% CI, 1.72-76.0; P=.004) were higher in patients with ADPKD-associated PLD with definite cholangitis compared to controls.ConclusionGallstones, prior cholecystectomy, duodenal diverticulosis, type 2 diabetes mellitus, prior endoscopic retrograde cholangiopancreatography, and prior kidney transplant constituted risk factors for cholangitis among patients with ADPKD-associated PLD.