Segmentation of individual renal cysts from MR images in patients with autosomal dominant polycystic kidney disease

Update on the Application of Ultrasonography in Understanding Autosomal Dominant Polycystic Kidney Disease

With an estimated prevalence of 1 in 1000 individuals globally, autosomal dominant polycystic kidney disease (ADPKD) stands as the most prevalent inherited renal disorder. Ultrasonography (US) is the most widely used imaging modality in the diagnosis and monitoring of ADPKD. This review discusses the role of US in the evaluation of ADPKD, including its diagnostic accuracy, limitations, and recent advances. An overview of the pathophysiology and clinical manifestations of ADPKD has also been provided. Furthermore, the potential of US as a noninvasive tool for the assessment of disease progression and treatment response is examined. Overall, US remains an essential tool for the management of ADPKD, and ongoing research efforts are aimed at improving its diagnostic and prognostic capabilities.

Abdominal Imaging in ADPKD: Beyond Total Kidney Volume

In the context of autosomal dominant polycystic kidney disease (ADPKD), measurement of the total kidney volume (TKV) is crucial. It acts as a marker for tracking disease progression, and evaluating the effectiveness of treatment strategies. The TKV has also been recognized as an enrichment biomarker and a possible surrogate endpoint in clinical trials. Several imaging modalities and methods are available to calculate the TKV, and the choice depends on the purpose of use. Technological advancements have made it possible to accurately assess the cyst burden, which can be crucial to assessing the disease state and helping to identify rapid progressors. Moreover, the development of automated algorithms has increased the efficiency of total kidney and cyst volume measurements. Beyond these measurements, the quantification and characterization of non-cystic kidney tissue shows potential for stratifying ADPKD patients early on, monitoring disease progression, and possibly predicting renal function loss. A broad spectrum of radiological imaging techniques are available to characterize the kidney tissue, showing promise when it comes to non-invasively picking up the early signs of ADPKD progression. Radiomics have been used to extract textural features from ADPKD images, providing valuable information about the heterogeneity of the cystic and non-cystic components. This review provides an overview of ADPKD imaging biomarkers, focusing on the quantification methods, potential, and necessary steps toward a successful translation to clinical practice.

A Deep Learning Approach for Automated Segmentation of Kidneys and Exophytic Cysts in Individuals with Autosomal Dominant Polycystic Kidney Disease

BACKGROUND

Total kidney volume (TKV) is an important imaging biomarker in autosomal dominant polycystic kidney disease (ADPKD). Manual computation of TKV, particularly with the exclusion of exophytic cysts, is laborious and time consuming.

METHODS

We developed a fully automated segmentation method for TKV using a deep learning network to selectively segment kidney regions while excluding exophytic cysts. We used abdominal T₂ -weighted magnetic resonance images from 210 individuals with ADPKD who were divided into two groups: one group of 157 to train the network and a second group of 53 to test it. With a 3D U-Net architecture using dataset fingerprints, the network was trained by K-fold cross-validation, in that 80% of 157 cases were for training and the remaining 20% were for validation. We used Dice similarity coefficient, intraclass correlation coefficient, and Bland-Altman analysis to assess the performance of the automated segmentation method compared with the manual method.

RESULTS

The automated and manual reference methods exhibited excellent geometric concordance (Dice similarity coefficient: mean±SD, 0.962±0.018) on the test datasets, with kidney volumes ranging from 178.9 to 2776.0 ml (mean±SD, 1058.5±706.8 ml) and exophytic cysts ranging from 113.4 to 2497.6 ml (mean±SD, 549.0±559.1 ml). The intraclass correlation coefficient was 0.9994 (95% confidence interval, 0.9991 to 0.9996; P<0.001) with a minimum bias of -2.424 ml (95% limits of agreement, -49.80 to 44.95).

CONCLUSIONS

We developed a fully automated segmentation method to measure TKV that excludes exophytic cysts and has an accuracy similar to that of a human expert. This technique may be useful in clinical studies that require automated computation of TKV to evaluate progression of ADPKD and response to treatment.

Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images

Recent breakthroughs of deep learning algorithms in medical imaging, automated detection, and segmentation techniques for renal (kidney) in abdominal computed tomography (CT) images have been limited. Radiomics and machine learning analyses of renal diseases rely on the automatic segmentation of kidneys in CT images. Inspired by this, our primary aim is to utilize deep semantic segmentation learning models with a proposed training scheme to achieve precise and accurate segmentation outcomes. Moreover, this work aims to provide the community with an open-source, unenhanced abdominal CT dataset for training and testing the deep learning segmentation networks to segment kidneys and detect kidney stones. Five variations of deep segmentation networks are trained and tested both dependently (based on the proposed training scheme) and independently. Upon comparison, the models trained with the proposed training scheme enable the highly accurate 2D and 3D segmentation of kidneys and kidney stones. We believe this work is a fundamental step toward AI-driven diagnostic strategies, which can be an essential component of personalized patient care and improved decision-making in treating kidney diseases.

Consensus document on autosomal dominant polycystic kindey disease from the Spanish Working Group on Inherited Kindey Diseases. Review 2020

Autosomal dominant polycystic kidney disease (ADPKD) is the most frequent cause of genetic renal disease and accounts for 6-10% of patients on kidney replacement therapy (KRT). Very few prospective, randomized trials or clinical studies address the diagnosis and management of this relatively frequent disorder. No clinical guidelines are available to date. This is a revised consensus statement from the previous 2014 version, presenting the recommendations of the Spanish Working Group on Inherited Kidney Diseases, which were agreed to following a literature search and discussions. Levels of evidence mostly are C and D according to the Centre for Evidence-Based Medicine (University of Oxford). The recommendations relate to, among other topics, the use of imaging and genetic diagnosis, management of hypertension, pain, cyst infections and bleeding, extra-renal involvement including polycystic liver disease and cranial aneurysms, management of chronic kidney disease (CKD) and KRT and management of children with ADPKD. Recommendations on specific ADPKD therapies are provided as well as the recommendation to assess rapid progression.

Artificial intelligence optimized image segmentation techniques for renal cyst detection

The vast number of image modalities available nowadays has given rise and access to a number of medical images. These images perhaps suffer issues such as low contrast, noise, ill-defined boundaries and poor visualisation. Therefore, a need for effective segmentation arises. Medical image segmentation plays a significant role in identifying a disorder, treatment planning, routine follow ups and computer-guided surgery respectively. The paper presents automatic medical image segmentation to overcome the imaging concerns and demarcate each notch & boundary in an image. The proposed algorithm identifies the existing kidney cyst precisely as they may be related to extreme disorders that may affect kidney function. The algorithm has been further tested on automatic segmentation using Genetic Algorithm, Ant Colony Optimisation and Fuzzy C Means Clustering. In terms of visualisation of valuable pathology, GA stands out and further helps in better assessment of the extent of the disease providing with better representation of the kidney cysts thereby giving a better diagnostic assurance and understanding of the nature of any disorder helping the medical practitioners as well as the patients. Experimental results on segmentation of kidney CT images conclusively demonstrate that the Genetic Algorithm is much more effective and robust.

Semi-Automated 3D Volumetric Renal Measurements in Polycystic Kidney Disease Using b0-Images-A Feasibility Study

Autosomal dominant polycystic kidney disease (ADPKD) eventually leads to end stage renal disease (ESRD) with an increase in size and number of cysts over time. Progression to ESRD has previously been shown to correlate with total kidney volume (TKV). An accurate and relatively simple method to perform measurement of TKV has been difficult to develop. We propose a semi-automated approach of calculating TKV inclusive of all cysts in ADPKD patients based on b0 images relatively quickly without requiring any calculations or additional MRI time. Our purpose is to evaluate the reliability and reproducibility of our method by raters of various training levels within the environment of an advanced 3D viewer. Thirty patients were retrospectively identified who had DWI performed as part of 1.5T MRI renal examination. Right and left TKVs were calculated by five radiologists of various training levels. Interrater reliability (IRR) was estimated by computing the intraclass correlation (ICC) for all raters. ICC values calculated for TKV measurements between the five raters were 0.989 (95% CI = (0.981, 0.994), p < 0.01) for the right and 0.961 (95% CI = (0.936, 0.979), p < 0.01) for the left. Our method shows excellent intraclass correlation between raters, allowing for excellent interrater reliability.

PSP net-based automatic segmentation network model for prostate magnetic resonance imaging

PURPOSE

Prostate cancer is a common cancer. To improve the accuracy of early diagnosis, we propose a prostate Magnetic Resonance Imaging (MRI) segmentation model based on Pyramid Scene Parsing Network (PSP Net).

METHOD

A total of 270 prostate MRI images were collected, and the data set was divided. Contrast limited adaptive histogram equalization (CLAHE) was enhanced in this study. We use the prostate MRI segmentation model based on PSP net, and use segmentation accuracy, under segmentation rate, over segmentation rate and receiver operating characteristic (ROC) curve evaluation index to compare the segmentation effect based on FCN and U-Net.

RESULTS

PSP net has the highest segmentation accuracy of 0.9865, over segmentation rate of 0.0023, under segmentation rate of 0.1111, which is less than FCN and U-Net. The ROC curve of PSP net is closest to the upper left corner, AUC is 0.9427, larger than FCN and U-Net.

CONCLUSION

This paper proves through a large number of experimental results that the prostate MRI automatic segmentation network model based on PSP Net is able to improve the accuracy of segmentation, relieve the workload of doctors, and is worthy of further clinical promotion.

Semantic Instance Segmentation of Kidney Cysts in MR Images: A Fully Automated 3D Approach Developed Through Active Learning

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.

Automatic semantic segmentation of kidney cysts in MR images of patients affected by autosomal-dominant polycystic kidney disease

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.

Management of Autosomal Dominant Polycystic Kidney Disease (ADPKD) During Pregnancy: Risks and Challenges

Autosomal dominant polycystic kidney disease (ADPKD) affects up to 1 in 1000 people. The disease is characterized by the progressive development of cysts throughout the renal parenchyma due to inherited pathogenic variants in genes including PKD1 or PKD2 and eventually leads to gradual loss of renal function, along with manifestations in other organ systems such as hepatic cysts and intracranial aneurysms. ADPKD management has advanced considerably in recent years due to genetic testing availability, pre-implantation genetic diagnosis technology and new therapeutic agents. Renal disease in pregnancy is recognised as an important risk factor for adverse maternal and fetal outcome. Women with ADPKD and health professionals face multiple challenges in optimising outcomes during the pre-pregnancy, pregnancy and post-partum periods.

Targeted Therapies for Autosomal Dominant Polycystic Kidney Disease

BACKGROUND

Autosomal dominant polycystic kidney disease (ADPKD) is the most common life-threatening genetic disease in humans, affecting approximately 1 in 500 people. ADPKD is characterized by cyst growth in the kidney leading to progressive parenchymal damage and is the underlying pathology in approximately 10% of patients requiring hemodialysis or transplantation for end-stage kidney disease. The two proteins that are mutated in ADPKD, polycystin-1 and polycystin-2, form a complex located on the primary cilium and the plasma membrane to facilitate calcium ion release in the cell. There is currently no Food and Drug Administration (FDA)-approved therapy to cure or slow the progression of the disease. Rodent ADPKD models do not completely mimic the human disease, and therefore preclinical results have not always successfully translated to the clinic. Moreover, the toxicity of many of these potential therapies has led to patient withdrawals from clinical trials.

RESULTS

Here, we review compounds in clinical trial for treating ADPKD, and we examine the feasibility of using a kidney-targeted approach, with potential for broadening the therapeutic window, decreasing treatment-associated toxicity and increasing the efficacy of agents that have demonstrated activity in animal models. We make recommendations for integrating kidney- targeted therapies with current treatment regimes, to achieve a combined approach to treating ADPKD.

CONCLUSION

Many compounds are currently in clinical trial for ADPKD yet, to date, none are FDA-approved for treating this disease. Patients could benefit from efficacious pharmacotherapy, especially if it can be kidney-targeted, and intensive efforts continue to be focused on this goal.

A rapid high-performance semi-automated tool to measure total kidney volume from MRI in autosomal dominant polycystic kidney disease

OBJECTIVES

To develop a high-performance, rapid semi-automated method (Sheffield TKV Tool) for measuring total kidney volume (TKV) from magnetic resonance images (MRI) in patients with autosomal dominant polycystic kidney disease (ADPKD).

METHODS

TKV was initially measured in 61 patients with ADPKD using the Sheffield TKV Tool and its performance compared to manual segmentation and other published methods (ellipsoidal, mid-slice, MIROS). It was then validated using an external dataset of MRI scans from 65 patients with ADPKD.

RESULTS

Sixty-one patients (mean age 45 ± 14 years, baseline eGFR 76 ± 32 ml/min/1.73 m2) with ADPKD had a wide range of TKV (258-3680 ml) measured manually. The Sheffield TKV Tool was highly accurate (mean volume error 0.5 ± 5.3% for right kidney, - 0.7 ± 5.5% for left kidney), reproducible (intra-operator variability - 0.2 ± 1.3%; inter-operator variability 1.1 ± 2.9%) and outperformed published methods. It took less than 6 min to execute and performed consistently with high accuracy in an external MRI dataset of T2-weighted sequences with TKV acquired using three different scanners and measured using a different segmentation methodology (mean volume error was 3.45 ± 3.96%, n = 65).

CONCLUSIONS

The Sheffield TKV Tool is operator friendly, requiring minimal user interaction to rapidly, accurately and reproducibly measure TKV in this, the largest reported unselected European patient cohort with ADPKD. It is more accurate than estimating equations and its accuracy is maintained at larger kidney volumes than previously reported with other semi-automated methods. It is free to use, can run as an independent executable and will accelerate the application of TKV as a prognostic biomarker for ADPKD into clinical practice.

KEY POINTS

• This new semi-automated method (Sheffield TKV Tool) to measure total kidney volume (TKV) will facilitate the routine clinical assessment of patients with ADPKD. • Measuring TKV manually is time consuming and laborious. • TKV is a prognostic indicator in ADPKD and the only imaging biomarker approved by the FDA and EMA.

Unique interstitial miRNA signature drives fibrosis in a murine model of autosomal dominant polycystic kidney disease

AIM

To delineate changes in miRNA expression localized to the peri-cystic local microenvironment (PLM) in an orthologous mouse model of autosomal dominant polycystic kidney disease (ADPKD) (mcwPkd1^(nl/nl)).

METHODS

We profiled miRNA expression in the whole kidney and laser captured microdissection (LCM) samples from PLM in mcwPkd1^(nl/nl) kidneys with Qiagen miScript 384 HC miRNA PCR arrays. The three times points used are: (1) post-natal (PN) day 21, before the development of trichrome-positive areas; (2) PN28, the earliest sign of trichrome staining; and (3) PN42 following the development of progressive fibrosis. PN21 served as appropriate controls and as the reference time point for comparison of miRNA expression profiles.

RESULTS

LCM samples revealed three temporally upregulated miRNAs [2 to 2.75-fold at PN28 and 2.5 to 4-fold (P ≤ 0.05) at PN42] and four temporally downregulated miRNAs [2 to 2.75 fold at PN28 and 2.75 to 5-fold (P ≤ 0.05) at PN42]. Expression of twenty-six miRNAs showed no change until PN42 [six decreased (2.25 to 3.5-fold) (P ≤ 0.05) and 20 increased (2 to 4-fold) (P ≤ 0.05)]. Many critical miRNA changes seen in the LCM samples from PLM were not seen in the contralateral whole kidney.

CONCLUSION

Precise sampling with LCM identifies miRNA changes that occur with the initiation and progression of renal interstitial fibrosis (RIF). Identification of the target proteins regulated by these miRNAs will provide new insight into the process of fibrosis and identify unique therapeutic targets to prevent or slow the development and progression of RIF in ADPKD.

Magnetic resonance imaging biomarkers for chronic kidney disease: a position paper from the European Cooperation in Science and Technology Action PARENCHIMA

Functional renal magnetic resonance imaging (MRI) has seen a number of recent advances, and techniques are now available that can generate quantitative imaging biomarkers with the potential to improve the management of kidney disease. Such biomarkers are sensitive to changes in renal blood flow, tissue perfusion, oxygenation and microstructure (including inflammation and fibrosis), processes that are important in a range of renal diseases including chronic kidney disease. However, several challenges remain to move these techniques towards clinical adoption, from technical validation through biological and clinical validation, to demonstration of cost-effectiveness and regulatory qualification. To address these challenges, the European Cooperation in Science and Technology Action PARENCHIMA was initiated in early 2017. PARENCHIMA is a multidisciplinary pan-European network with an overarching aim of eliminating the main barriers to the broader evaluation, commercial exploitation and clinical use of renal MRI biomarkers. This position paper lays out PARENCHIMA's vision on key clinical questions that MRI must address to become more widely used in patients with kidney disease, first within research settings and ultimately in clinical practice. We then present a series of practical recommendations to accelerate the study and translation of these techniques.

Standardizing total kidney volume measurements for clinical trials of autosomal dominant polycystic kidney disease

Background

The ability of unstandardized methods to track kidney growth in clinical trials for autosomal dominant polycystic kidney disease (ADPKD) has not been critically evaluated.

Methods

The Tolvaptan Efficacy and Safety Management of ADPKD and its Outcomes (TEMPO) 3:4 study involved baseline and annual magnetic resonance follow-up imaging yearly for 3 years. Total kidney volume (TKV) measurements were performed on these four time points in addition to the baseline imaging in TEMPO 4:4, initially by Perceptive Informatics (Waltham, MA, USA) using planimetry (original dataset) and for this study by the Mayo Translational PKD Center using semiautomated and complementary automated methods (sequential dataset). In the original dataset, the same reader was assigned to all scans of individual patients in TEMPO 3:4, but readers were reassigned in TEMPO 4:4. Two placebo-treated cohorts were included. In the first (n = 158), intervals between the end of TEMPO 3:4 and the start of TEMPO 4:4 scan visits ranged from 12 to 403 days; in the second (n = 95), the same scan (measured twice) visit was used for both.

Results

Growth rates in TEMPO 3:4 were similar in the original and sequential datasets (5.5 and 5.9%/year). Growth rates during the TEMPO 3:4 to TEMPO 4:4 interval were higher in the original (13.7%/year) but were not different in the sequential dataset (4.0%/year). Comparing volumes from the same images, TKVs showed a bias of 2.2% [95% confidence interval (CI) −5.2–9.7] in the original and −0.16% (95% CI −1.91–1.58) in the sequential dataset.

Conclusions

Despite using the same software, TKV and growth rate changes were present, likely due to reader differences in the transition from TEMPO 3:4 to TEMPO 4:4 in the original but not in the sequential dataset. Robust, standardized methods are essential in ADPKD trials to minimize errors in serial TKV measurements.

Novel semi-automated kidney volume measurements in autosomal dominant polycystic kidney disease

BACKGROUND

We assessed the effectiveness and convenience of a novel semi-automatic kidney volume (KV) measuring high-speed 3D-image analysis system SYNAPSE VINCENT^® (Fuji Medical Systems, Tokyo, Japan) for autosomal dominant polycystic kidney disease (ADPKD) patients.

METHODS

We developed a novel semi-automated KV measurement software for patients with ADPKD to be included in the imaging analysis software SYNAPSE VINCENT^®. The software extracts renal regions using image recognition software and measures KV (VINCENT KV). The algorithm was designed to work with the manual designation of a long axis of a kidney including cysts. After using the software to assess the predictive accuracy of the VINCENT method, we performed an external validation study and compared accurate KV and ellipsoid KV based on geometric modeling by linear regression analysis and Bland-Altman analysis.

RESULTS

Median eGFR was 46.9 ml/min/1.73 m². Median accurate KV, Vincent KV and ellipsoid KV were 627.7, 619.4 ml (IQR 431.5-947.0) and 694.0 ml (IQR 488.1-1107.4), respectively. Compared with ellipsoid KV (r = 0.9504), Vincent KV correlated strongly with accurate KV (r = 0.9968), without systematic underestimation or overestimation (ellipsoid KV; 14.2 ± 22.0%, Vincent KV; - 0.6 ± 6.0%). There were no significant slice thickness-specific differences (p = 0.2980).

CONCLUSIONS

The VINCENT method is an accurate and convenient semi-automatic method to measure KV in patients with ADPKD compared with the conventional ellipsoid method.

Comparison of MRI segmentation techniques for measuring liver cyst volumes in autosomal dominant polycystic kidney disease

PURPOSE

To compare MRI segmentation methods for measuring liver cyst volumes in autosomal dominant polycystic kidney disease (ADPKD).

METHODS

Liver cyst volumes in 42 ADPKD patients were measured using region growing, thresholding and cyst diameter techniques. Manual segmentation was the reference standard.

RESULTS

Root mean square deviation was 113, 155, and 500 for cyst diameter, thresholding and region growing respectively. Thresholding error for cyst volumes below 500ml was 550% vs 17% for cyst volumes above 500ml (p<0.001).

CONCLUSION

For measuring volume of a small number of cysts, cyst diameter and manual segmentation methods are recommended. For severe disease with numerous, large hepatic cysts, thresholding is an acceptable alternative.

Image texture features predict renal function decline in patients with autosomal dominant polycystic kidney disease

Magnetic resonance imaging (MRI) examinations provide high-resolution information about the anatomic structure of the kidneys and are used to measure total kidney volume (TKV) in patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD). Height-adjusted TKV (HtTKV) has become the gold-standard imaging biomarker for ADPKD progression at early stages of the disease when estimated glomerular filtration rate (eGFR) is still normal. However, HtTKV does not take advantage of the wealth of information provided by MRI. Here we tested whether image texture features provide additional insights into the ADPKD kidney that may be used as complementary information to existing biomarkers. A retrospective cohort of 122 patients from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study was identified who had T2-weighted MRIs and eGFR values over 70 mL/min/1.73m² at the time of their baseline scan. We computed nine distinct image texture features for each patient. The ability of each feature to predict subsequent progression to CKD stage 3A, 3B, and 30% reduction in eGFR at eight-year follow-up was assessed. A multiple linear regression model was developed incorporating age, baseline eGFR, HtTKV, and three image texture features identified by stability feature selection (Entropy, Correlation, and Energy). Including texture in a multiple linear regression model (predicting percent change in eGFR) improved Pearson correlation coefficient from -0.51 (using age, eGFR, and HtTKV) to -0.70 (adding texture). Thus, texture analysis offers an approach to refine ADPKD prognosis and should be further explored for its utility in individualized clinical decision making and outcome prediction.

Automated segmentation of liver and liver cysts from bounded abdominal MR images in patients with autosomal dominant polycystic kidney disease

Liver and liver cyst volume measurements are important quantitative imaging biomarkers for assessment of disease progression in autosomal dominant polycystic kidney disease (ADPKD) and polycystic liver disease (PLD). To date, no study has presented automated segmentation and volumetric computation of liver and liver cysts in these populations. In this paper, we proposed an automated segmentation framework for liver and liver cysts from bounded abdominal MR images in patients with ADPKD. To model the shape and variations in ADPKD livers, the spatial prior probability map (SPPM) of liver location and the tissue prior probability maps (TPPMs) of liver parenchymal tissue intensity and cyst morphology were generated. Formulated within a three-dimensional level set framework, the TPPMs successfully captured liver parenchymal tissues and cysts, while the SPPM globally constrained the initial surfaces of the liver into the desired boundary. Liver cysts were extracted by combined operations of the TPPMs, thresholding, and false positive reduction based on spatial prior knowledge of kidney cysts and distance map. With cross-validation for the liver segmentation, the agreement between the radiology expert and the proposed method was 84% for shape congruence and 91% for volume measurement assessed by the intra-class correlation coefficient (ICC). For the liver cyst segmentation, the agreement between the reference method and the proposed method was ICC  =  0.91 for cyst volumes and ICC  =  0.94 for % cyst-to-liver volume.

Quantification of angiotensin II-regulated proteins in urine of patients with polycystic and other chronic kidney diseases by selected reaction monitoring

Background

Angiotensin-II (Ang II) mediates progression of autosomal-dominant polycystic kidney disease (ADPKD) and other chronic kidney diseases (CKD). However, markers of kidney Ang II activity are lacking. We previously defined 83 Ang II-regulated proteins in vitro, which reflected kidney Ang II activity in vivo.

Methods

In this study, we developed selected reaction monitoring (SRM) assays for quantification of Ang II-regulated proteins in urine of ADPKD and CKD patients. We demonstrated that 47 of 83 Ang II-regulated transcripts were differentially expressed in cystic compared to normal kidney tissue. We then developed SRM assays for 18 Ang II-regulated proteins overexpressed in cysts and/or secreted in urine. Methods that yielded CV ≤ 6 % for control proteins, and recovery ~100 % were selected. Heavy-labeled peptides corresponding to 13 identified Ang II-regulated peptides were spiked into urine samples of 17 ADPKD patients, 9 patients with CKD predicted to have high kidney Ang II activity and 11 healthy subjects. Samples were then digested and analyzed on triple-quadrupole mass spectrometer in duplicates.

Resluts

Calibration curves demonstrated linearity (R² > 0.99) and within-run CVs < 9 % in the concentration range of 7/13 peptides. Peptide concentrations were normalized by urine creatinine. Deamidated peptide forms were monitored, and accounted for <15 % of the final concentrations. Urine excretion rates of proteins BST1, LAMB2, LYPA1, RHOB and TSP1 were significantly different (p < 0.05, one-way ANOVA) between patients with CKD, those with ADPKD and healthy controls. Urine protein excretion rates were highest in CKD patients and lowest in ADPKD patients. Univariate analysis demonstrated significant association between urine protein excretion rates of most proteins and disease group (p < 0.05, ANOVA) as well as sex (p < 0.05, unpaired t test). Multivariate analysis across protein concentration, age and sex demonstrated good separation between ADPKD and CKD patients.

Conclusions

We have optimized methods for quantification of Ang II-regulated proteins, and we demonstrated that they reflected differences in underlying kidney disease in this pilot study. High urine excretion of Ang II-regulated proteins in CKD patients likely reflects high kidney Ang II activity. Low excretion in ADPKD appears related to lack of communication between cysts and tubules. Future studies will determine whether urine excretion rate of Ang II-regulated proteins correlates with kidney Ang II activity in larger cohorts of chronic kidney disease patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-016-9117-x) contains supplementary material, which is available to authorized users.

Automated Segmentation of Kidneys from MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease

BACKGROUND AND OBJECTIVES

Our study developed a fully automated method for segmentation and volumetric measurements of kidneys from magnetic resonance images in patients with autosomal dominant polycystic kidney disease and assessed the performance of the automated method with the reference manual segmentation method.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Study patients were selected from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease. At the enrollment of the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease Study in 2000, patients with autosomal dominant polycystic kidney disease were between 15 and 46 years of age with relatively preserved GFRs. Our fully automated segmentation method was on the basis of a spatial prior probability map of the location of kidneys in abdominal magnetic resonance images and regional mapping with total variation regularization and propagated shape constraints that were formulated into a level set framework. T2-weighted magnetic resonance image sets of 120 kidneys were selected from 60 patients with autosomal dominant polycystic kidney disease and divided into the training and test datasets. The performance of the automated method in reference to the manual method was assessed by means of two metrics: Dice similarity coefficient and intraclass correlation coefficient of segmented kidney volume. The training and test sets were swapped for crossvalidation and reanalyzed.

RESULTS

Successful segmentation of kidneys was performed with the automated method in all test patients. The segmented kidney volumes ranged from 177.2 to 2634 ml (mean, 885.4±569.7 ml). The mean Dice similarity coefficient ±SD between the automated and manual methods was 0.88±0.08. The mean correlation coefficient between the two segmentation methods for the segmented volume measurements was 0.97 (P<0.001 for each crossvalidation set). The results from the crossvalidation sets were highly comparable.

CONCLUSIONS

We have developed a fully automated method for segmentation of kidneys from abdominal magnetic resonance images in patients with autosomal dominant polycystic kidney disease with varying kidney volumes. The performance of the automated method was in good agreement with that of manual method.

Clinical Correlates of Mass Effect in Autosomal Dominant Polycystic Kidney Disease

Mass effect from polycystic kidney and liver enlargement can result in significant clinical complications and symptoms in autosomal dominant polycystic kidney disease (ADPKD). In this single-center study, we examined the correlation of height-adjusted total liver volume (htTLV) and total kidney volume (htTKV) by CT imaging with hepatic complications (n = 461) and abdominal symptoms (n = 253) in patients with ADPKD. “Mass-effect” complications were assessed by review of medical records and abdominal symptoms, by a standardized research questionnaire. Overall, 91.8% of patients had 4 or more liver cysts on CT scans. Polycystic liver disease (PLD) was classified as none or mild (htTLV < 1,600 mL/m); moderate (1,600 ≤ htTLV <3,200 mL/m); and severe (htTLV ≥ 3,200 mL/m). The prevalence of moderate and severe PLD in our patient cohort was 11.7% (n = 54/461) and 4.8% (n = 22/461), respectively, with a female predominance in both the moderate (61.1%) and severe (95.5%) PLD groups. Pressure-related complications such as leg edema (20.4%), ascites (16.6%), and hernia (3.6%) were common, and patients with moderate to severe PLD exhibited a 6-fold increased risk (compared to no or mild PLD) for these complications in multivariate analysis. Similarly, abdominal symptoms including back pain (58.8%), flank pain (53.1%), abdominal fullness (46.5%), and dyspnea/chest-discomfort (44.3%) were very common, and patients with moderate to severe PLD exhibited a 5-fold increased risk for these symptoms. Moderate to severe PLD is a common and clinically important problem in ~16% of patients with ADPKD who may benefit from referral to specialized centers for further management.

Reliability of Total Renal Volume Computation in Polycystic Kidney Disease From Magnetic Resonance Imaging

RATIONALE AND OBJECTIVES

Total renal volume (TRV) is an important quantitative indicator of the progression of autosomal dominant polycystic kidney disease (ADPKD). The Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease proposes a method for TRV computation based on manual tracing and geometric modeling. Alternative approaches for TRV computation are represented by the application of advanced image processing techniques. In this study, we aimed to compare TRV estimates derived from these two different approaches.

MATERIALS AND METHODS

The nearly automated technique for the analysis of magnetic resonance (MR) images was tested on 30 ADPKD patients. TRV was computed from both axial (KVax) and coronal (KVcor) acquisitions and compared to measurements based on geometric modeling (KVap) by linear regression and Bland-Altman analysis. In addition, to assess reproducibility, intraobserver and interobserver variabilities were computed.

RESULTS

Linear regression analysis between KVax and KVcor resulted in an excellent correlation (KVax = 1KVcor - 0.78; r(2) = 0.997). Bland-Altman analysis showed a negligible bias and narrow limits of agreement (bias: -11.7 mL; SD: 54.3 mL). Similar results were obtained by comparison of volumes obtained applying the nearly automated method and the one based on geometric modeling (y = 0.98x + 75.9; r(2) = 0.99; bias: -53.7 mL; SD: 108.1 mL). Importantly, geometric modeling does not provide reliable TRV estimates in huge kidney affected by regional deformation. Intraobserver and interobserver variability resulted in very small percentage error <2%.

CONCLUSIONS

The results of this study provide the feasibility of using a nearly automated approach for accurate and fast evaluation of TRV also in markedly enlarged ADPKD kidneys including exophytic cysts.

A semi-automated "blanket" method for renal segmentation from non-contrast T1-weighted MR images

OBJECTIVE

To investigate the precision and accuracy of a new semi-automated method for kidney segmentation from single-breath-hold non-contrast MRI.

MATERIALS AND METHODS

The user draws approximate kidney contours on every tenth slice, focusing on separating adjacent organs from the kidney. The program then performs a sequence of fully automatic steps: contour filling, interpolation, non-uniformity correction, sampling of representative parenchyma signal, and 3D binary morphology. Three independent observers applied the method to images of 40 kidneys ranging in volume from 94.6 to 254.5 cm(3). Manually constructed reference masks were used to assess accuracy.

RESULTS

The volume errors for the three readers were: 4.4% ± 3.0%, 2.9% ± 2.3%, and 3.1% ± 2.7%. The relative discrepancy across readers was 2.5% ± 2.1%. The interactive processing time on average was 1.5 min per kidney.

CONCLUSIONS

Pending further validation, the semi-automated method could be applied for monitoring of renal status using non-contrast MRI.

Kidney: polycystic kidney disease

Polycystic kidney disease (PKD) is a life-threatening genetic disorder characterized by the presence of fluid-filled cysts primarily in the kidneys. PKD can be inherited as autosomal recessive (ARPKD) or autosomal dominant (ADPKD) traits. Mutations in either the PKD1 or PKD2 genes, which encode polycystin 1 and polycystin 2, are the underlying cause of ADPKD. Progressive cyst formation and renal enlargement lead to renal insufficiency in these patients, which need to be managed by lifelong dialysis or renal transplantation. While characteristic features of PKD are abnormalities in epithelial cell proliferation, fluid secretion, extracellular matrix and differentiation, the molecular mechanisms underlying these events are not understood. Here we review the progress that has been made in defining the function of the polycystins, and how disruption of these functions may be involved in cystogenesis.

Polycystic kidney disease - where gene dosage counts

Gene dosage effects have emerged as playing a central role in the pathogenesis of polycystic kidney disease. Yet, how gene dosage can ultimately have an impact on the formation of kidney cysts remains unknown. In this commentary we review the evidence for the role of gene dosage effects versus the “2-hit” mutation model in polycystic kidney disease (PKD), and also discuss how gene networks may potentially make intertwined contributions to PKD.

Novel methodology to evaluate renal cysts in polycystic kidney disease

AIM

To develop and assess a semiautomated method for segmenting and counting individual renal cysts from mid-slice MR images in patients with autosomal dominant polycystic kidney disease (ADPKD).

METHODS

A semiautomated method was developed to segment and count individual renal cysts from mid-slice MR images in 241 subjects with ADPKD from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease. For each subject, a mid-slice MR image was selected from each set of coronal T2-weighted MR images covering the entire kidney. The selected mid-slice image was processed with the semiautomated method to segment and count individual renal cysts. The number of cysts from the mid-slice image of each kidney was also measured by manual counting. The level of agreement between the semiautomated and manual cyst counts was compared using intraclass correlation (ICC) and a Bland-Altman plot.

RESULTS

Individual renal cysts were successfully segmented using the semiautomated method in all 241 cases. The number of cysts in each kidney measured with the semiautomated and manual counting methods correlated well (ICC = 0.96 for the right or left kidney), with a small average difference (-0.52, with higher semiautomated counts, for the right kidney, and 0.13, with higher manual counts, for the left kidney) in the semiautomated method. However, there was substantial variation in a small number of subjects; 6 of 241 participants (2.5%) had a difference in the total cyst count of more than 15.

CONCLUSION

We have developed a semiautomated method to segment individual renal cysts from mid-slice MR images in ADPKD kidneys as a quantitative indicator of characterization and disease progression of ADPKD.