Ultra high frequency ultrasound enables real-time visualization of blood supply from chorioallantoic membrane to human autosomal dominant polycystic kidney tissue.

Ultra high frequency (UHF) ultrasound enables the visualization of very small structures that cannot be detected by conventional ultrasound. The utilization of UHF imaging as a new imaging technique for the 3D-in-vivo chorioallantoic membrane (CAM) model can facilitate new insights into tissue perfusion and survival. Therefore, human renal cystic tissue was grafted onto the CAM and examined using UHF ultrasound imaging. Due to the unprecedented resolution of UHF ultrasound, it was possible to visualize microvessels, their development, and the formation of anastomoses. This enabled the observation of anastomoses between human and chicken vessels only 12 h after transplantation. These observations were validated by 3D reconstructions from a light sheet microscopy image stack, indocyanine green angiography, and histological analysis. Contrary to the assumption that the nutrient supply of the human cystic tissue and the gas exchange happens through diffusion from CAM vessels, this study shows that the vasculature of the human cystic tissue is directly connected to the blood vessels of the CAM and perfusion is established within a short period. Therefore, this in-vivo model combined with UHF imaging appears to be the ideal platform for studying the effects of intravenously applied therapeutics to inhibit renal cyst growth.

Laparoscopic Nephrectomy Via the Retroperitoneal Approach for Autosomal Dominant Polycystic Kidney Disease After Renal Transplant: A Case Report.

We report a case of laparoscopic nephrectomy via the retroperitoneal approach for autosomal dominant polycystic kidney disease after renal transplant. A 54-year-old male patient with end-stage renal failure because of autosomal dominant polycystic kidney disease underwent a living donor renal transplant and right nephrectomy via open surgery through a median abdominal incision 5 years previously. However, the left kidney gradually became enlarged. We performed laparoscopic left nephrectomy via the retroperitoneal approach. After dissecting the renal vessels, we performed cyst puncture and aspiration to decrease the kidney volume. The patient's symptoms improved after operation. Laparoscopic nephrectomy for enlarged kidneys with multiple cysts can be safely performed, and the retroperitoneal approach can be preferred if the patient has a history of abdominal surgery or an enlarged polycystic kidney.

nnUNet for Automatic Kidney and Cyst Segmentation in Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND: Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a genetic disorder that causes uncontrolled kidney cyst growth, leading to kidney volume enlargement and renal function loss over time. Total kidney volume (TKV) and cyst burdens have been used as prognostic imaging biomarkers for ADPKD. OBJECTIVE: This study aimed to evaluate nnUNet for automatic kidney and cyst segmentation in T2-weighted (T2W) MRI images of ADPKD patients. METHODS: 756 kidney images were retrieved from 95 patients in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) cohort (95 patients × 2 kidneys × 4 follow-up scans). The nnUNet model was trained, validated, and tested on 604, 76, and 76 images, respectively. In contrast, all images of each patient were exclusively assigned to either the training, validation, or test sets to minimize evaluation bias. The kidney and cyst regions defined using a semi-automatic method were employed as ground truth. The model performance was assessed using the Dice Similarity Coefficient (DSC), the intersection over union (IoU) score, and the Hausdorff distance (HD). RESULTS: The test DSC values were 0.96±0.01 (mean±SD) and 0.90±0.05 for kidney and cysts, respectively. Similarly, the IoU scores were 0.91± 0.09 and 0.81±0.06, and the HD values were 12.49±8.71 mm and 12.04±10.41 mm, respectively, for kidney and cyst segmentation. CONCLUSION: The nnUNet model is a reliable tool to automatically determine kidney and cyst volumes in T2W MRI images for ADPKD prognosis and therapy monitoring.

Dual-task kidney MR segmentation with transformers in autosomal-dominant polycystic kidney disease.

Autosomal-dominant polycystic kidney disease is a prevalent genetic disorder characterized by the development of renal cysts, leading to kidney enlargement and renal failure. Accurate measurement of total kidney volume through polycystic kidney segmentation is crucial to assess disease severity, predict progression and evaluate treatment effects. Traditional manual segmentation suffers from intra- and inter-expert variability, prompting the exploration of automated approaches. In recent years, convolutional neural networks have been employed for polycystic kidney segmentation from magnetic resonance images. However, the use of Transformer-based models, which have shown remarkable performance in a wide range of computer vision and medical image analysis tasks, remains unexplored in this area. With their self-attention mechanism, Transformers excel in capturing global context information, which is crucial for accurate organ delineations. In this paper, we evaluate and compare various convolutional-based, Transformers-based, and hybrid convolutional/Transformers-based networks for polycystic kidney segmentation. Additionally, we propose a dual-task learning scheme, where a common feature extractor is followed by per-kidney decoders, towards better generalizability and efficiency. We extensively evaluate various architectures and learning schemes on a heterogeneous magnetic resonance imaging dataset collected from 112 patients with polycystic kidney disease. Our results highlight the effectiveness of Transformer-based models for polycystic kidney segmentation and the relevancy of exploiting dual-task learning to improve segmentation accuracy and mitigate data scarcity issues. A promising ability in accurately delineating polycystic kidneys is especially shown in the presence of heterogeneous cyst distributions and adjacent cyst-containing organs. This work contribute to the advancement of reliable delineation methods in nephrology, paving the way for a broad spectrum of clinical applications.

Validation of the Mayo Imaging Classification System for Predicting Kidney Outcomes in ADPKD.

BACKGROUND: The Mayo Imaging Classification was developed to predict the rate of disease progression in patients with autosomal dominant polycystic kidney disease. This study aimed to validate its ability to predict kidney outcomes in a large multicenter autosomal dominant polycystic kidney disease cohort. METHODS: Included were patients with ≥1 height-adjusted total kidney volume (HtTKV) measurement and ≥3 eGFR values during ≥1-year follow-up. Mayo HtTKV class stability, kidney growth rates, and eGFR decline rates were calculated. The observed eGFR decline was compared with predictions from the Mayo Clinic future eGFR equation. The future eGFR prediction equation was also tested for nonlinear eGFR decline. Kaplan-Meier survival analysis and Cox regression models were used to assess time to kidney failure using Mayo HtTKV class as a predictor variable. RESULTS: We analyzed 618 patients with a mean age of 47±11 years and mean eGFR of 64±25 ml/min per 1.73 m 2 at baseline. Most patients (82%) remained in their baseline Mayo HtTKV class. During a mean follow-up of 5.1±2.2 years, the mean total kidney volume growth rates and eGFR decline were 5.33%±3.90%/yr and -3.31±2.53 ml/min per 1.73 m 2 per year, respectively. Kidney growth and eGFR decline showed considerable overlap between the classes. The observed annual eGFR decline was not significantly different from the predicted values for classes 1A, 1B, 1C, and 1D but significantly slower for class 1E. This was also observed in patients aged younger than 40 years and older than 60 years and those with PKD2 mutations. A polynomial model allowing nonlinear eGFR decline provided more accurate slope predictions. Ninety-seven patients (16%) developed kidney failure during follow-up. The classification predicted the development of kidney failure, although the sensitivity and positive predictive values were limited. CONCLUSIONS: The Mayo Imaging Classification demonstrated acceptable stability and generally predicted kidney failure and eGFR decline rate. However, there was marked interindividual variability in the rate of disease progression within each class.

Autosomal dominant polycystic kidney disease with ectopic unilateral multicystic kidney: a case report.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary renal disorder and the fourth cause of death of end-stage renal disease. The disease has a prevalence of 1:400-1:1000 accounting for 10% of patients on dialysis. In most ADPKD patients, bilateral kidneys are similarly affected, with numerous fluid-filled cysts arising from different nephron segments. Only a few cases of ADPKD with ectopic unilateral multicystic kidney have been reported. It has been observed that the deterioration of their kidney function seemed to be quicker than their age- and sex-matched controls and siblings especially when the ectopic kidney is dysplastic. CASE PRESENTATION: We report a case of a 46-year-old Ghanaian male patient who presented with left flank pain and hematuria with high BP and deranged renal function. Abdominal ultrasonography showed both kidneys to be larger than normal and had multiple cysts of varying sizes with the right kidney located in the right iliac fossa. Follow up Abdominopelvic computer tomographic scan (CT-Scan) without contrast showed enlarged kidneys with the renal parenchyma replaced by innumerable cyst of varying sizes. The right kidney was ectopically located in the right aspect of the pelvis. A diagnosis of ADPKD with right pelvic ectopic multicystic kidney was made. He was put on antihypertensives, analgesia for the left flank pain and to have follow up at the urology and nephrology departments. CONCLUSION: In most ADPKD patients, bilateral kidneys are similarly affected. Only a few cases of ADPKD with ectopic unilateral multicystic kidney have been reported. It has been observed that the deterioration of their kidney function seemed to be quicker than their age- and sex-matched controls and siblings especially when the ectopic kidney is dysplastic.

Metastatic Renal Cell Carcinoma in a Patient With Autosomal Dominant Polycystic Kidney Disease.

ABSTRACT: Autosomal dominant polycystic kidney disease (ADPKD) manifests as multiple cysts in the kidneys and liver but can also present with musculoskeletal and cardiovascular abnormalities. ADPKD patients are at increased risk for renal cell carcinoma development. We show the FDG PET/CT findings in a patient with renal cell carcinoma secondary to ADPKD and complicated by worsening pulmonary metastasis. The primary renal tumor shows intense FDG uptake despite no suspicious features with contrast CT.

Test Retest Reproducibility of Organ Volume Measurements in ADPKD Using 3D Multimodality Deep Learning.

RATIONALE AND OBJECTIVES: Following autosomal dominant polycystic kidney disease (ADPKD) progression by measuring organ volumes requires low measurement variability. The objective of this study is to reduce organ volume measurement variability on MRI of ADPKD patients by utilizing all pulse sequences to obtain multiple measurements which allows outlier analysis to find errors and averaging to reduce variability. MATERIALS AND METHODS: In order to make measurements on multiple pulse sequences practical, a 3D multi-modality multi-class segmentation model based on nnU-net was trained/validated using T1, T2, SSFP, DWI and CT from 413 subjects. Reproducibility was assessed with test-re-test methodology on ADPKD subjects (n = 19) scanned twice within a 3-week interval correcting outliers and averaging the measurements across all sequences. Absolute percent differences in organ volumes were compared to paired students t-test. RESULTS: Dice similarlity coefficient > 97%, Jaccard Index > 0.94, mean surface distance < 1 mm and mean Hausdorff Distance < 2 cm for all three organs and all five sequences were found on internal (n = 25), external (n = 37) and test-re-test reproducibility assessment (38 scans in 19 subjects). When averaging volumes measured from five MRI sequences, the model automatically segmented kidneys with test-re-test reproducibility (percent absolute difference between exam 1 and exam 2) of 1.3% which was better than all five expert observers. It reliably stratified ADPKD into Mayo Imaging Classification (area under the curve=100%) compared to radiologist. CONCLUSION: 3D deep learning measures organ volumes on five MRI sequences leveraging the power of outlier analysis and averaging to achieve 1.3% total kidney test-re-test reproducibility.

Statin therapy in patients with early-stage autosomal dominant polycystic kidney disease: Design and baseline characteristics.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is characterized by the development and continued growth of multiple cysts in the kidneys leading to ultimate loss of kidney function in most patients. Currently, tolvaptan is the only agency approved therapy to slow kidney disease advancement in patients with faster progressing disease underscoring the need for additional ADPKD therapies suitable for all patients. We previously showed that pravastatin slowed kidney disease progression in children and young adults with ADPKD. However, the intervention has not been tested in an adult cohort. AIMS: The aim of the study is to conduct a single center, randomized, placebo-controlled double-blinded clinical trial to determine the efficacy of pravastatin on slowing kidney disease progression in adult patients with early stage ADPKD. METHODS: One hundred and fifty adult patients with ADPKD and eGFR ≥60 ml/min/1.73m2 will be enrolled in the study and randomized to receive 40 mg/day pravastatin or placebo for a period of 2-years. OUTCOMES: The primary outcome of the trial is change in total kidney volume assessed by magnetic resonance imaging (MRI). Secondary outcomes include change in kidney function by iothalamate GFR and renal blood flow and markers of inflammation and oxidative stress. CONCLUSION: This study will assess the kidney therapeutic benefits of pravastatin in adult patients with ADPKD. The recruitment goal of 150 subjects was attained and the study is ongoing. REGISTRATION: This study is registered on Clinicaltrials.gov # NCT03273413.

Deep learning-based automated kidney and cyst segmentation of autosomal dominant polycystic kidney disease using single vs. multi-institutional data.

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.

Non-contrast low-dose CT can be used for volumetry of ADPKD.

BACKGROUND: Kidney volume provides important information for the diagnosis and prognosis of autosomal dominant polycystic kidney disease (ADPKD), as well as for the evaluation of the effects of drugs such as tolvaptan. Non-contrast computed tomography (CT) is commonly used for volumetry, and this study examined the correspondence and correlation of kidney volume measured by standard-dose or low-dose CT. METHODS: Axial standard-dose and low-dose CT images with 1-mm slices were obtained from 24 ADPKD patients. The kidney was segmented in the Synapse 3D software and the kidney volume was calculated using stereology. The kidney volume was compared between the two sets of images using R2, Bland-Altman plots, coefficient of variation, and intra-class correlation coefficients (ICCs). RESULTS: The mean age of the 24 patients was 48.4 ± 10.9 years, and 45.8% were men (n = 11). The mean total kidney volume on standard-dose CT was 1501 ± 838.2 mL. The R2 of volume between standard-dose and low-dose CT was 0.995. In the Bland-Altman plot, except for one case with a large kidney volume, the two measurements were consistent, and the coefficient of variation and ICC were also good (0.02, 0.998). The CT radiation dose (dose-length product) was 229 ± 68 mGy·cm for standard-dose CT and 50 ± 19 mGy·cm for low-dose CT. A comparable volume was obtained with 20% of the radiation dose of standard-dose CT. CONCLUSIONS: Standard-dose and low-dose CT showed comparable kidney volume in ADPKD. Therefore, low-dose CT can substitute for ADPKD volumetry while minimizing radiation exposure.

Function follows form: the quest for the best prognostic imaging biomarker in ADPKD.

Total kidney volume represents the most solid prognostic biomarker for autosomal dominant polycystic kidney disease, because it mirrors cyst growth that precedes kidney function decline. Considerable variability of glomerular filtration rate trajectories, however, remains unexplained by total kidney volume, and its calculation is time-consuming. Using deep learning algorithms, Gregory et al. determined total kidney volume and other, novel, imaging-based biomarkers. They achieved automation and improved prognostic accuracy for long-term kidney function loss, yet the study leaves some open questions and room for further improvement.

Feasibility of artificial intelligence-based decision supporting system in tolvaptan prescription for autosomal dominant polycystic kidney disease.

PURPOSE: Total kidney volume (TKV) measurement is crucial for selecting treatment candidates in autosomal dominant polycystic kidney disease (ADPKD). We developed and investigated the performance of fully-automated 3D-volumetry model and applied it to software as a service (SaaS) for clinical support on tolvaptan prescription in ADPKD patients. MATERIALS AND METHODS: Computed tomography scans of ADPKD patients taken between January 2000 and June 2022 were acquired from seven institutions. The quality of the images was manually reviewed in advance. The acquired dataset was split into training, validation, and test datasets at a ratio of 8.5:1:0.5. Convolutional, neural network-based automatic segmentation model was trained to obtain 3D segment mask for TKV measurement. The algorithm consisted of three steps: data preprocessing, ADPKD area extraction, and post-processing. After performance validation with the Dice score, 3D-volumetry model was applied to SaaS which is based on Mayo imaging classification for ADPKD. RESULTS: A total of 753 cases with 95,117 slices were included. The differences between the ground-truth ADPKD kidney mask and the predicted ADPKD kidney mask were negligible, with intersection over union >0.95. The post-process filter successfully removed false alarms. The test-set performance was homogeneously equal and the Dice score of the model was 0.971; after post-processing, it improved to 0.979. The SaaS calculated TKV from uploaded Digital Imaging and Communications in Medicine images and classified patients according to height-adjusted TKV for age. CONCLUSIONS: Our artificial intelligence-3D volumetry model exhibited effective, feasible, and non-inferior performance compared with that of human experts and successfully predicted the rapid ADPKD progressor.

[Stanford Type A Acute Aortic Dissection Associated with Polycystic Kidney Disease].

Whereas cerebral aneurysm is a well-known consequence of autosomal dominant polycystic kidney disease (ADPKD), acute aortic dissection has been rarely reported. A patient was a 44-year-old male with a diagnosis of ADPKD, who had previously undergone transcatheter arterial embolization for a renal cyst hemorrhage. He presented with sudden onset of back pain, which got worse at emergency service. Contrast-enhanced computed tomography (CT) revealed Stanford type A acute aortic dissection. The patient subsequently underwent partial aortic arch replacement with a vascular graft under circulatory arrest. His postoperative course was complicated by pneumonia and required ventilation support for a week. Peak creatinine level was 3.28 mg/dl, but hemodialysis was not required. Patients with ADPKD should be considered a high-risk cohort of aortic dissection.

Xanthogranulomatous pyelonephritis with polycystic kidney disease as a mimic of cystic renal cell carcinoma: a case report.

BACKGROUND: Xanthogranulomatous pyelonephritis (XGP) is a rare chronic pyelonephritis that often mimics other renal diseases, when combined with autosomal dominant polycystic kidney disease(ADPKD), preoperative diagnosis is exceedingly difficult. It is important for clinicians to be aware of an XGP with ADPKD since a misdiagnosis can lead to unnecessary surgical intervention. CASE PRESENTATION: Here, we report a case of a 66-year-old female with a history of bilateral ADPKD and urinary tract infection admitted to our hospital due to right flank pain, feeble, and low-grade fever. Contrast-enhanced ultrasound revealed a malignant mass of the right kidney suspected to be a cystic renal cell carcinoma with polycystic kidney disease. In addition, contrast-enhanced computed tomography (CT) and fluorine 18 fluorodeoxyglucose PET/CT (18F FDG PET/CT) showed similar results. Subsequently, the patient underwent a right radical nephrectomy, but histopathological examination revealed XGP with ADPKD. On the follow-up, the patient's symptoms were relieved. CONCLUSIONS: XGP should be kept in mind during the differential diagnosis of renal masses with ADPKD even in the absence of characteristic clinical symptoms and imaging manifestations.

Diffusion magnetic resonance imaging for kidney cyst volume quantification and non-cystic tissue characterisation in ADPKD.

OBJECTIVES: Beyond total kidney and cyst volume (TCV), non-cystic tissue plays an important role in autosomal dominant polycystic kidney disease (ADPKD) progression. This study aims at presenting and preliminarily validating a diffusion MRI (DWI)-based TCV quantification method and providing evidence of DWI potential in characterising non-cystic tissue microstructure. METHODS: T2-weighted MRI and DWI scans (b = 0, 15, 50, 100, 200, 350, 500, 700, 1000; 3 directions) were acquired from 35 ADPKD patients with CKD stage 1 to 3a and 15 healthy volunteers on a 1.5 T scanner. ADPKD classification was performed using the Mayo model. DWI scans were processed by mono- and segmented bi-exponential models. TCV was quantified on T2-weighted MRI by the reference semi-automatic method and automatically computed by thresholding the pure diffusivity (D) histogram. The agreement between reference and DWI-based TCV values and the differences in DWI-based parameters between healthy and ADPKD tissue components were assessed. RESULTS: There was strong correlation between DWI-based and reference TCV (rho = 0.994, p < 0.001). Non-cystic ADPKD tissue had significantly higher D, and lower pseudo-diffusion and flowing fraction than healthy tissue (p < 0.001). Moreover, apparent diffusion coefficient and D values significantly differed by Mayo imaging class, both in the whole kidney (Wilcoxon p = 0.007 and p = 0.004) and non-cystic tissue (p = 0.024 and p = 0.007). CONCLUSIONS: DWI shows potential in ADPKD to quantify TCV and characterise non-cystic kidney tissue microstructure, indicating the presence of microcysts and peritubular interstitial fibrosis. DWI could complement existing biomarkers for non-invasively staging, monitoring, and predicting ADPKD progression and evaluating the impact of novel therapies, possibly targeting damaged non-cystic tissue besides cyst expansion. CLINICAL RELEVANCE STATEMENT: This study shows diffusion-weighted MRI (DWI) potential to quantify total cyst volume and characterise non-cystic kidney tissue microstructure in ADPKD. DWI could complement existing biomarkers for non-invasively staging, monitoring, and predicting ADPKD progression and evaluating the impact of novel therapies, possibly targeting damaged non-cystic tissue besides cyst expansion. KEY POINTS: • Diffusion magnetic resonance imaging shows potential to quantify total cyst volume in ADPKD. • Diffusion magnetic resonance imaging might allow to non-invasively characterise non-cystic kidney tissue microstructure. • Diffusion magnetic resonance imaging-based biomarkers significantly differ by Mayo imaging class, suggesting their possible prognostic value.

Clinical Implementation of an Artificial Intelligence Algorithm for Magnetic Resonance-Derived Measurement of Total Kidney Volume.

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.