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

Improved predictions of total kidney volume growth rate in ADPKD using two-parameter least squares fitting

Mayo Imaging Classification (MIC) for predicting future kidney growth in autosomal dominant polycystic kidney disease (ADPKD) patients is calculated from a single MRI/CT scan assuming exponential kidney volume growth and height-adjusted total kidney volume at birth to be 150 mL/m. However, when multiple scans are available, how this information should be combined to improve prediction accuracy is unclear. Herein, we studied ADPKD subjects ( n = 36 ) with 8+ years imaging follow-up (mean = 11 years) to establish ground truth kidney growth trajectory. MIC annual kidney growth rate predictions were compared to ground truth as well as 1- and 2-parameter least squares fitting. The annualized mean absolute error in MIC for predicting total kidney volume growth rate was 2.1 % ± 2 % compared to 1.1 % ± 1 % ( p = 0.002 ) for a 2-parameter fit to the same exponential growth curve used for MIC when 4 measurements were available or 1.4 % ± 1 % ( p = 0.01 ) with 3 measurements averaging together with MIC. On univariate analysis, male sex ( p = 0.05 ) and PKD2 mutation ( p = 0.04 ) were associated with poorer MIC performance. In ADPKD patients with 3 or more CT/MRI scans, 2-parameter least squares fitting predicted kidney volume growth rate better than MIC, especially in males and with PKD2 mutations where MIC was less accurate.

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.

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.

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.

Validation of automatically measured T1 map cortico-medullary difference (ΔT1) for eGFR and fibrosis assessment in allograft kidneys

MRI T1-mapping is an important non-invasive tool for renal diagnosis. Previous work shows that ΔT1 (cortex-medullary difference in T1) has significant correlation with interstitial fibrosis in chronic kidney disease (CKD) allograft patients. However, measuring cortico-medullary values by manually drawing ROIs over cortex and medulla (a gold standard method) is challenging, time-consuming, subjective and requires human training. Moreover, such subjective ROI placement may also affect the work reproducibility. This work proposes a deep learning-based 2D U-Net (RCM U-Net) to auto-segment the renal cortex and medulla of CKD allograft kidney T1 maps. Furthermore, this study presents a correlation of automatically measured ΔT1 values with eGFR and percentage fibrosis in allograft kidneys. Also, the RCM U-Net correlation results are compared with the manual ROI correlation analysis. The RCM U-Net has been trained and validated on T1 maps from 40 patients (n = 2400 augmented images) and tested on 10 patients (n = 600 augmented images). The RCM U-Net segmentation results are compared with the standard VGG16, VGG19, ResNet34 and ResNet50 networks with U-Net as backbone. For clinical validation of the RCM U-Net segmentation, another set of 114 allograft kidneys patient's cortex and medulla were automatically segmented to measure the ΔT1 values and correlated with eGFR and fibrosis. Overall, the RCM U-Net showed 50% less Mean Absolute Error (MAE), 16% better Dice Coefficient (DC) score and 12% improved results in terms of Sensitivity (SE) over conventional CNNs (i.e. VGG16, VGG19, ResNet34 and ResNet50) while the Specificity (SP) and Accuracy (ACC) did not show significant improvement (i.e. 0.5% improvement) for both cortex and medulla segmentation. For eGFR and fibrosis assessment, the proposed RCM U-Net correlation coefficient (r) and R-square (R2) was better correlated (r = -0.2, R2 = 0.041 with p = 0.039) to eGFR than manual ROI values (r = -0.19, R2 = 0.037 with p = 0.051). Similarly, the proposed RCM U-Net had noticeably better r and R2 values (r = 0.25, R2 = 0.065 with p = 0.007) for the correlation with the renal percentage fibrosis than the Manual ROI results (r = 0.3, R2 = 0.091 and p = 0.0013). Using a linear mixed model, T1 was significantly higher in the medulla than in the cortex (p<0.0001) and significantly lower in patients with cellular rejection when compared to both patients without rejection and those with humoral rejection (p<0.001). There was no significant difference in T1 between patients with and without humoral rejection (p = 0.43), nor between the types of T1 measurements (Gold standard manual versus automated RCM U-Net) (p = 0.7). The cortico-medullary area ratio measured by the RCM U-Net was significantly increased in case of cellular rejection by comparison to humoral rejection (1.6 +/- 0.39 versus 0.99 +/- 0.32, p = 0.019). In conclusion, the proposed RCM U-Net provides more robust auto-segmented cortex and medulla than the other standard CNNs allowing a good correlation of ΔT1 with eGFR and fibrosis as reported in literature as well as the differentiation of cellular and humoral transplant rejection. Therefore, the proposed approach is a promising alternative to the gold standard manual ROI method to measure T1 values without user interaction, which helps to reduce analysis time and improves reproducibility.

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 Learning Automation of Kidney, Liver, and Spleen Segmentation for Organ Volume Measurements in Autosomal Dominant Polycystic Kidney Disease

Organ volume measurements are a key metric for managing ADPKD (the most common inherited renal disease). However, measuring organ volumes is tedious and involves manually contouring organ outlines on multiple cross-sectional MRI or CT images. The automation of kidney contouring using deep learning has been proposed, as it has small errors compared to manual contouring. Here, a deployed open-source deep learning ADPKD kidney segmentation pipeline is extended to also measure liver and spleen volumes, which are also important. This 2D U-net deep learning approach was developed with radiologist labeled T2-weighted images from 215 ADPKD subjects (70% training = 151, 30% validation = 64). Additional ADPKD subjects were utilized for prospective (n = 30) and external (n = 30) validations for a total of 275 subjects. Image cropping previously optimized for kidneys was included in training but removed for the validation and inference to accommodate the liver which is closer to the image border. An effective algorithm was developed to adjudicate overlap voxels that are labeled as more than one organ. Left kidney, right kidney, liver and spleen labels had average errors of 3%, 7%, 3%, and 1%, respectively, on external validation and 5%, 6%, 5%, and 1% on prospective validation. Dice scores also showed that the deep learning model was close to the radiologist contouring, measuring 0.98, 0.96, 0.97 and 0.96 on external validation and 0.96, 0.96, 0.96 and 0.95 on prospective validation for left kidney, right kidney, liver and spleen, respectively. The time required for manual correction of deep learning segmentation errors was only 19:17 min compared to 33:04 min for manual segmentations, a 42% time saving (p = 0.004). Standard deviation of model assisted segmentations was reduced to 7, 5, 11, 5 mL for right kidney, left kidney, liver and spleen respectively from 14, 10, 55 and 14 mL for manual segmentations. Thus, deep learning reduces the radiologist time required to perform multiorgan segmentations in ADPKD and reduces measurement variability.

MRI-based kidney radiomic analysis during chronic lithium treatment

BACKGROUND

Lithium therapy during bipolar disorder is associated with an increased risk of chronic kidney disease (CKD) that is slowly progressive and undetectable at early stages. We aimed at identifying kidney image texture features as possible imaging biomarkers of decreased measured glomerular filtration rate (mGFR) using radiomics of T2-weighted magnetic resonance imaging (MRI).

METHODS

One hundred and eight patients treated with lithium were evaluated including mGFR and kidney MRI, with T2-weighted sequence single-shot fast spin-echo. Computed radiomic analysis was performed after kidney segmentation. Significant features were selected to build a radiomic signature using multivariable Cox analysis to detect an mGFR <60 ml/min/1.73 m². The texture index was validated using a training and a validation cohort.

RESULTS

Texture analysis index was able to detect an mGFR decrease, with an AUC of 0.85 in the training cohort and 0.71 in the validation cohort. Patients with a texture index below the median were older (59 [42-66] vs. 46 [34-54] years, p = .001), with longer treatment duration (10 [3-22] vs. 6 [2-10] years, p = .02) and a lower mGFR (66 [46-84] vs. 83 [71-94] ml/min/1.73m², p < .001). Texture analysis index was independently and negatively associated with age (β = -.004 ± 0.001, p < .001), serum vasopressin (-0.005 ± 0.002, p = .02) and lithium treatment duration (-0.01 ± 0.003, p = .001), with a significant interaction between lithium treatment duration and mGFR (p = .02).

CONCLUSIONS

A renal texture index was developed among patients treated with lithium associated with a decreased mGFR. This index might be relevant in the diagnosis of lithium-induced renal toxicity.

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.

Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network

PURPOSE

Total kidney volume (TKV) is an important measure in renal disease detection and monitoring. We developed a fully automated method to segment the kidneys from T₂ -weighted MRI to calculate TKV of healthy control (HC) and chronic kidney disease (CKD) patients.

METHODS

This automated method uses machine learning, specifically a 2D convolutional neural network (CNN), to accurately segment the left and right kidneys from T₂ -weighted MRI data. The data set consisted of 30 HC subjects and 30 CKD patients. The model was trained on 50 manually defined HC and CKD kidney segmentations. The model was subsequently evaluated on 50 test data sets, comprising data from 5 HCs and 5 CKD patients each scanned 5 times in a scan session to enable comparison of the precision of the CNN and manual segmentation of kidneys.

RESULTS

The unseen test data processed by the 2D CNN had a mean Dice score of 0.93 ± 0.01. The difference between manual and automatically computed TKV was 1.2 ± 16.2 mL with a mean surface distance of 0.65 ± 0.21 mm. The variance in TKV measurements from repeat acquisitions on the same subject was significantly lower using the automated method compared to manual segmentation of the kidneys.

CONCLUSION

The 2D CNN method provides fully automated segmentation of the left and right kidney and calculation of TKV in <10 s on a standard office computer, allowing high data throughput and is a freely available executable.

Quantitative assessment of renal allograft pathologic changes: comparisons of mono-exponential and bi-exponential models using diffusion-weighted imaging

Background

Diffusion-weighted imaging (DWI) can noninvasively assess renal allograft pathologic changes that provide useful information for clinical management and prognostication. However, it is still unknown whether the bi-exponential model analysis of DWI signals is superior to that of the mono-exponential model.

Methods

Pathologic and DWI data from a total of 47 allografts were prospectively collected and analyzed. Kidney transplant interstitial fibrosis was quantified digitally. The severity of acute and chronic pathologic changes was semi-quantified by calculating the acute composite scores (ACS) and chronic composite score (CCS). Mono-exponential total apparent diffusion coefficient (ADCT), and the bi-exponential parameters of true diffusion (D) and perfusion fraction (fp) were acquired. The diagnostic performances of both mono-exponential and bi-exponential parameters were assessed and compared by calculating the area under the curve (AUC) from receiver-operating characteristic (ROC) curve analysis.

Results

ADCT, D, and fp were all significantly correlated with interstitial fibrosis, ACS, and CCS. Cortical fp discriminated mild from moderate and severe ACS with the largest AUC of 0.89 [95% confidence interval (CI), 0.77-0.96]. Noticeably, only cortical fp could differentiate severe ACS from mild-to-moderate ACS (P<0.001) with an AUC of 0.80 (95% CI, 0.65-0.90) and a sensitivity of 100% (95% CI, 66.4-100%). Strikingly, the joint use of D and fp in either the cortex or the medulla could achieve a sensitivity of 100% for identifying either mild or severe interstitial fibrosis. Meanwhile, the serial use of cortical D and cortical fp showed the largest specificity for identifying both mild [88.9% (95% CI, 70.8-97.6%)] and severe [84.4% (95% CI, 67.2-94.7%)] interstitial fibrosis. For identifying mild CCS, the AUC of medullary ADCT (0.90, 95% CI, 0.78-0.97) was similar to that of cortical D (0.81, 95% CI, 0.67-0.91) and fp (0.86, 95% CI, 0.73-0.94), but statistically larger than that of medullary D (P=0.005) and fp (P=0.01). Furthermore, the parallel use of cortical D and cortical fp could increase the sensitivity to 95.0% (95% CI, 75.1-99.9%), whereas serial use of medullary D and medullary fp could increase the specificity to 100% (95% CI, 87.2-100%). The AUCs for differentiating severe from mild and moderate CCS were statistically insignificant among all parameters in the cortex and medulla (P≥0.15).

Conclusions

Cortical fp was superior to the ADCT for identifying both mild and severe acute pathologic changes. Nevertheless, ADCT was equal to or better than single D or fp for evaluating chronic pathologic changes. Thus, both monoexponential and bi-exponential analysis of DWI images are complementary for evaluating kidney allograft pathologic changes, and the combined use of D and fp can increase the sensitivity and specificity for discriminating allograft pathologic changes severity.

A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images

Background

The automatic segmentation of kidneys in medical images is not a trivial task when the subjects undergoing the medical examination are affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). Several works dealing with the segmentation of Computed Tomography images from pathological subjects were proposed, showing high invasiveness of the examination or requiring interaction by the user for performing the segmentation of the images. In this work, we propose a fully-automated approach for the segmentation of Magnetic Resonance images, both reducing the invasiveness of the acquisition device and not requiring any interaction by the users for the segmentation of the images.

Methods

Two different approaches are proposed based on Deep Learning architectures using Convolutional Neural Networks (CNN) for the semantic segmentation of images, without needing to extract any hand-crafted features. In details, the first approach performs the automatic segmentation of images without any procedure for pre-processing the input. Conversely, the second approach performs a two-steps classification strategy: a first CNN automatically detects Regions Of Interest (ROIs); a subsequent classifier performs the semantic segmentation on the ROIs previously extracted.

Results

Results show that even though the detection of ROIs shows an overall high number of false positives, the subsequent semantic segmentation on the extracted ROIs allows achieving high performance in terms of mean Accuracy. However, the segmentation of the entire images input to the network remains the most accurate and reliable approach showing better performance than the previous approach.

Conclusion

The obtained results show that both the investigated approaches are reliable for the semantic segmentation of polycystic kidneys since both the strategies reach an Accuracy higher than 85%. Also, both the investigated methodologies show performances comparable and consistent with other approaches found in literature working on images from different sources, reducing both the invasiveness of the analyses and the interaction needed by the users for performing the segmentation task.

Comparing the interobserver reproducibility of different regions of interest on multi-parametric renal magnetic resonance imaging in healthy volunteers, patients with heart failure and renal transplant recipients

OBJECTIVE

To assess interobserver reproducibility of different regions of interest (ROIs) on multi-parametric renal MRI using commercially available software.

MATERIALS AND METHODS

Healthy volunteers (HV), patients with heart failure (HF) and renal transplant recipients (Tx) were recruited. Localiser scans, T1 mapping and pseudo-continuous arterial spin labelling (pCASL) were performed. HV and Tx also underwent diffusion-weighted imaging to allow calculation of apparent diffusion coefficient (ADC). For T1, pCASL and ADC, ROIs were drawn for whole kidney (WK), cortex (Cx), user-defined representative cortex (rep-Cx) and medulla. Intraclass correlation coefficient (ICC) and coefficient of variation (CoV) were assessed.

RESULTS

Forty participants were included (10 HV, 10 HF and 20 Tx). The ICC for renal volume was 0.97 and CoV 6.5%. For T1 and ADC, WK, Cx, and rep-Cx were highly reproducible with ICC ≥ 0.76 and CoV < 5%. However, cortical pCASL results were more variable (ICC > 0.86, but CoV up to 14.2%). While reproducible, WK values were derived from a wide spread of data (ROI standard deviation 17% to 55% of the mean value for ADC and pCASL, respectively). Renal volume differed between groups (p < 0.001), while mean cortical T1 values were greater in Tx compared to HV (p = 0.009) and HF (p = 0.02). Medullary T1 values were also higher in Tx than HV (p = 0.03), while medullary pCASL values were significantly lower in Tx compared to HV and HF (p = 0.03 for both).

DISCUSSION

Kidney volume calculated by manually contouring a localiser scan was highly reproducible between observers and detected significant differences across patient groups. For T1, pCASL and ADC, Cx and rep-Cx ROIs are generally reproducible with advantages over WK values.

European ADPKD Forum multidisciplinary position statement on autosomal dominant polycystic kidney disease care: European ADPKD Forum and Multispecialist Roundtable participants

Autosomal dominant polycystic kidney disease (ADPKD) is a chronic, progressive condition characterized by the development and growth of cysts in the kidneys and other organs and by additional systemic manifestations. Individuals with ADPKD should have access to lifelong, multidisciplinary, specialist and patient-centred care involving: (i) a holistic and comprehensive assessment of the manifestations, complications, prognosis and impact of the disease (in physical, psychological and social terms) on the patient and their family; (ii) access to treatment to relieve symptoms, manage complications, preserve kidney function, lower the risk of cardiovascular disease and maintain quality of life; and (iii) information and support to help patients and their families act as fully informed and active partners in care, i.e. to maintain self-management approaches, deal with the impact of the condition and participate in decision-making regarding healthcare policies, services and research. Building on discussions at an international roundtable of specialists and patient advocates involved in ADPKD care, this article sets out (i) the principles for a patient-centred, holistic approach to the organization and delivery of ADPKD care in practice, with a focus on multispecialist collaboration and shared-decision making, and (ii) the rationale and knowledge base for a route map for ADPKD care intended to help patients navigate the services available to them and to help stakeholders and decision-makers take practical steps to ensure that all patients with ADPKD can access the comprehensive multispecialist care to which they are entitled. Further multispecialty collaboration is encouraged to design and implement these services, and to work with patient organizations to promote awareness building, education and research.

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.

Electronic supplementary material

The online version of this article (10.1007/s00330-018-5918-9) contains supplementary material, which is available to authorized users.

Updated Canadian Expert Consensus on Assessing Risk of Disease Progression and Pharmacological Management of Autosomal Dominant Polycystic Kidney Disease

PURPOSE

The purpose of this article is to update the previously published consensus recommendations from March 2017 discussing the optimal management of adult patients with autosomal dominant polycystic kidney disease (ADPKD). This document focuses on recent developments in genetic testing, renal imaging, assessment of risk regarding disease progression, and pharmacological treatment options for ADPKD.

SOURCES OF INFORMATION

Published literature was searched in PubMed, the Cochrane Library, and Google Scholar to identify the latest evidence related to the treatment and management of ADPKD.

METHODS

All pertinent articles were reviewed by the authors to determine if a new recommendation was required, or if the previous recommendation needed updating. The consensus recommendations were developed by the authors based on discussion and review of the evidence.

KEY FINDINGS

The genetics of ADPKD are becoming more complex with the identification of new and rarer genetic variants such as GANAB. Magnetic resonance imaging (MRI) and computed tomography (CT) continue to be the main imaging modalities used to evaluate ADPKD. Total kidney volume (TKV) continues to be the most validated and most used measure to assess disease progression. Since the publication of the previous consensus recommendations, the use of the Mayo Clinic Classification for prognostication purposes has been validated in patients with class 1 ADPKD. Recent evidence supports the benefits of a low-osmolar diet and dietary sodium restriction in patients with ADPKD. Evidence from the Replicating Evidence of Preserved Renal Function: an Investigation of Tolvaptan Safety and Efficacy in ADPKD (REPRISE) trial supports the use of ADH (antidiuretic hormone) receptor antagonism in patients with ADPKD 18 to 55 years of age with eGFR (estimated glomerular filtration rate) of 25 to 65 mL/min/1.73 m2 or 56 to 65 years of age with eGFR of 25 to 44 mL/min/1.73 m2 with historical evidence of a decline in eGFR >2.0 mL/min/1.73 m2/year.

LIMITATIONS

Available literature was limited to English language publications and to publications indexed in PubMed, the Cochrane Library, and Google Scholar.

IMPLICATIONS

Advances in the assessment of the risk of disease progression include the validation of the Mayo Clinic Classification for patients with class 1 ADPKD. Advances in the pharmacological management of ADPKD include the expansion of the use of ADH receptor antagonism in patients 18 to 55 years of age with eGFR of 25 to 65 mL/min/1.73 m2 or 56 to 65 years of age with eGFR of 25 to 44 mL/min/1.73 m2 with historical evidence of a decline in eGFR >2.0 mL/min/1.73 m2/year, as per the results of the REPRISE study.

Hepatic Production of Fibroblast Growth Factor 23 in Autosomal Dominant Polycystic Kidney Disease

CONTEXT

The bone-derived hormone fibroblast growth factor (FGF) 23 controls phosphate homeostasis and urinary phosphate excretion. FGF23 plasma levels increase in the early stage of renal insufficiency to prevent hyperphosphatemia. Recent evidence suggests that this increase has effects on cardiac and immune cells that compromise patients' health. Patients with autosomal dominant polycystic kidney disease (ADPKD) have been reported to have higher FGF23 concentrations than other patients with similar renal function. The significance of this finding has remained unknown.

METHODS AND RESULTS

Analyzing the FGF23 plasma levels in 434 patients with ADPKD and 355 control subjects with a measured glomerular filtration rate (mGFR) between 60 and 120 mL/min per 1.73 m2, we confirmed that patients with ADPKD had higher FGF23 plasma concentrations than controls. Remarkably, this difference did not translate into renal phosphate leakage. Using different assays for FGF23, we found that this discrepancy was explained by a predominant increase in the cleaved C-terminal fragment of FGF23, which lacks phosphaturic activity. We found that FGF23 plasma concentration independently correlated with the severity of cystic liver disease in ADPKD. We observed that, in contrast to control liver tissues, the cystic liver from patients with ADPKD markedly expressed FGF23 messenger RNA and protein. In line with this finding, the surgical reduction of polycystic liver mass was associated with a decrease in FGF23 plasma levels independently of any modification in mGFR, phosphate, or iron status.

CONCLUSION

Our findings demonstrate that severely polycystic livers produce FGF23 and increase levels of circulating FGF23 in patients with ADPKD.

3DUS as an alternative to MRI for measuring renal volume in children with autosomal dominant polycystic kidney disease

BACKGROUND

Total kidney volume, measured by magnetic resonance imaging (MRI), is a validated disease progression marker in adults with autosomal dominant polycystic kidney disease (ADPKD). However, in childhood, MRI is burdensome, explaining the need for alternatives.

METHODS

Kidney volume (KV) was evaluated in 30 children with ADPKD, using three-dimensional ultrasound (3DUS), applying the ellipsoid method and manual contouring (KV_{3DUS-ellipsoid,} KV_3DUS-contour respectively); manual contouring on MRI (KV_MRI), and the ellipsoid method on two-dimensional ultrasound (2DUS, KV_2DUS). Correlations and differences were evaluated using Pearson's r and Wilcoxon signed-rank tests, and variability using Bland-Altman plots.

RESULTS

All ultrasound volumetry methods showed significantly lower mean (± SD) KV (mL), compared with MRI-KV_2DUS: 159 (±101); K_{3DUS-ellipsoid}: 169 (±105); KV_3DUS-contour: 185 (±110); KV_MRI: 206 (±130); all p < 0.001. All had a strong correlation with KV_MRI: 2DUS: r = 0.96; 3DUS-ellipsoid: r = 0.89 and 3DUS-contour: r = 0.94. Both before and after correction factor application, Bland-Altman plots showed lower variability and absolute error for KV_3DUS-contour vs KV_2DUS and KV_{3DUS-ellipsoid}.

CONCLUSIONS

Compared with MRI, ultrasound volumetry was prone to underestimation. However, KV_3DUS-contour represents a valuable alternative for MRI in early ADPKD. Although more time-consuming, KV_3DUS-contour is recommended over KV_2DUS for estimation and follow-up of KV in ADPKD children, given its smaller error.

Quantitative magnetic resonance imaging assessments of autosomal recessive polycystic kidney disease progression and response to therapy in an animal model

BackgroundAutosomal recessive polycystic kidney disease (ARPKD) is associated with significant mortality and morbidity, and currently, there are no disease-specific treatments available for ARPKD patients. One major limitation in establishing new therapies for ARPKD is a lack of sensitive measures of kidney disease progression. Magnetic resonance imaging (MRI) can provide multiple quantitative assessments of the disease.MethodsWe applied quantitative image analysis of high-resolution (noncontrast) T2-weighted MRI techniques to study cystic kidney disease progression and response to therapy in the PCK rat model of ARPKD.ResultsSerial imaging over a 2-month period demonstrated that renal cystic burden (RCB, %)=[total cyst volume (TCV)/total kidney volume (TKV) × 100], TCV, and, to a lesser extent, TKV detected cystic kidney disease progression, as well as the therapeutic effect of octreotide, a clinically available medication shown previously to slow both kidney and liver disease progression in this model. All three MRI measures correlated significantly with histologic measures of renal cystic area, although the correlation of RCB and TCV was stronger than that of TKV.ConclusionThese preclinical MRI results provide a basis for applying these quantitative MRI techniques in clinical studies, to stage and measure progression in human ARPKD kidney disease.

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.

The protective effects of ischemic preconditioning on rats with renal ischemia-reperfusion injury and the effects on the expression of Bcl-2 and Bax

The aim of the present study was to investigate the protective effects of ischemic preconditioning on rats with renal ischemia-reperfusion injury and the effects on the expression of Bcl-2 and Bax. Thirty-six SD rats were randomly divided into three groups (n=12) including sham operation (S) group, ischemia-reperfusion group (I/R) group and ischemic preconditioning (IP) group. After anesthesia with intraperitoneal injection of chloral hydrate, bilateral renal pedicles were clipped for 45 min, followed by perfusion for 6 h to establish the I/R model. Both kidneys in rats of S group were separated and exposed for 45 min, but renal pedicles were not clipped. In IP group, bilateral renal pedicles were clipped for 5 min, followed by perfusion for 5 min, this procedure was repeated 3 times. Then bilateral renal pedicles were clipped for 45 min, followed by perfusion for 6 h. Blood samples were collected and rats were sacrificed to collect renal tissue. Levels of serum creatinine (Cr) and blood urea nitrogen (BUN) were measured. Activity of superoxide dismutase (SOD) was measured by xanthine oxidase assay. Degree of renal injury was evaluated by H&E staining. TUNEL kit was used to detect the number of apoptotic cells in renal tissue. Expression levels of Bcl-2 and Bax were detected by semi-quantitative PCR and western blot analysis at mRNA and protein levels, respectively. Results showed that levels of Cr and BUN in I/R and IP groups were significantly higher than those in S group, and levels of Cr and BUN in I/R group were significantly higher than that in IP group (P<0.05). Activity of SOD in I/R group and IP group were significantly lower than those in S group, and activity of SOD in I/R group were significantly lower than those in IP group (P<0.05). H&E staining showed that, compared with S group, renal injury in the I/R and IP groups was more serious than that in the S group, and I/R group was more serious than the IP group (P<0.05). TUNEL apoptosis assay showed that number of apoptotic cells in IP and I/R groups were significantly higher than that in the S group (P<0.01). Semi-quantitative PCR and western blot analysis showed that, compared with the S group, expression levels of Bcl-2 mRNA and protein were significantly decreased, expression levels of Bax mRNA and protein were significantly increased, and the ratio of Bcl-2/Bax was significantly decreased in the IP and I/R groups (P<0.01). Compared with the I/R group, expression level of Bcl-2 was significantly increased, the level of Bax was significantly deceased, and the ratio of Bcl-2/Bax was significantly increased in the IP group (P<0.01). As a result, ischemic preconditioning can protect rats with renal ischemia-reperfusion injury possibly by increasing the expression level of Bcl-2 and decreasing the expression level of Bax.

Kidney volume measurement methods for clinical studies on autosomal dominant polycystic kidney disease

Background

In autosomal dominant polycystic kidney disease (ADPKD), total kidney volume (TKV) is regarded as an important biomarker of disease progression and different methods are available to assess kidney volume. The purpose of this study was to identify the most efficient kidney volume computation method to be used in clinical studies evaluating the effectiveness of treatments on ADPKD progression.

Methods and findings

We measured single kidney volume (SKV) on two series of MR and CT images from clinical studies on ADPKD (experimental dataset) by two independent operators (expert and beginner), twice, using all of the available methods: polyline manual tracing (reference method), free-hand manual tracing, semi-automatic tracing, Stereology, Mid-slice and Ellipsoid method. Additionally, the expert operator also measured the kidney length. We compared different methods for reproducibility, accuracy, precision, and time required. In addition, we performed a validation study to evaluate the sensitivity of these methods to detect the between-treatment group difference in TKV change over one year, using MR images from a previous clinical study. Reproducibility was higher on CT than MR for all methods, being highest for manual and semiautomatic contouring methods (planimetry). On MR, planimetry showed highest accuracy and precision, while on CT accuracy and precision of both planimetry and Stereology methods were comparable. Mid-slice and Ellipsoid method, as well as kidney length were fast but provided only a rough estimate of kidney volume. The results of the validation study indicated that planimetry and Stereology allow using an importantly lower number of patients to detect changes in kidney volume induced by drug treatment as compared to other methods.

Conclusions

Planimetry should be preferred over fast and simplified methods for accurately monitoring ADPKD progression and assessing drug treatment effects. Expert operators, especially on MR images, are required for performing reliable estimation of kidney volume. The use of efficient TKV quantification methods considerably reduces the number of patients to enrol in clinical investigations, making them more feasible and significant.

Automatic Segmentation of Kidneys using Deep Learning for Total Kidney Volume Quantification in Autosomal Dominant Polycystic Kidney Disease

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most common inherited disorder of the kidneys. It is characterized by enlargement of the kidneys caused by progressive development of renal cysts, and thus assessment of total kidney volume (TKV) is crucial for studying disease progression in ADPKD. However, automatic segmentation of polycystic kidneys is a challenging task due to severe alteration in the morphology caused by non-uniform cyst formation and presence of adjacent liver cysts. In this study, an automated segmentation method based on deep learning has been proposed for TKV computation on computed tomography (CT) dataset of ADPKD patients exhibiting mild to moderate or severe renal insufficiency. The proposed method has been trained (n = 165) and tested (n = 79) on a wide range of TKV (321.2-14,670.7 mL) achieving an overall mean Dice Similarity Coefficient of 0.86 ± 0.07 (mean ± SD) between automated and manual segmentations from clinical experts and a mean correlation coefficient (ρ) of 0.98 (p < 0.001) for segmented kidney volume measurements in the entire test set. Our method facilitates fast and reproducible measurements of kidney volumes in agreement with manual segmentations from clinical experts.

Comparison of Total Kidney Volume Quantification Methods in Autosomal Dominant Polycystic Disease for a Comprehensive Disease Assessment

BACKGROUND

In recent times, the scientific community has been showing increasing interest in the treatments aimed at slowing the progression of the autosomal dominant polycystic kidney disease (ADPKD). Therefore, in this paper, we test and evaluate the performance of several available methods for total kidney volume (TKV) computation in ADPKD patients - from echography to MRI - in order to optimize patient classification.

METHODS

Two methods based on geometric assumptions (mid-slice [MS], ellipsoid [EL]) and a third one on true contour detection were tested on 40 ADPKD patients at different disease stage using MRI. The EL method was also tested using ultrasound images in a subset of 14 patients. Their performance was compared against TKVs derived from reference manual segmentation of MR images. Patient clinical classification was also performed based on computed volumes.

RESULTS

Kidney volumes derived from echography significantly underestimated reference volumes. Geometric-based methods applied to MR images had similar acceptable results. The highly automated method showed better performance. Volume assessment was accurate and reproducible. Importantly, classification resulted in 79, 13, 10, and 2.5% of misclassification using kidney volumes obtained from echo and MRI applying the EL, the MS and the highly automated method respectively.

CONCLUSION

Considering the fact that the image-based technique is the only approach providing a 3D patient-specific kidney model and allowing further analysis including cyst volume computation and monitoring disease progression, we suggest that geometric assumption (e.g., EL method) should be avoided. The contour-detection approach should be used for a reproducible and precise morphologic classification of the renal volume of ADPKD patients.

Assessing Risk of Disease Progression and Pharmacological Management of Autosomal Dominant Polycystic Kidney Disease: A Canadian Expert Consensus

Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited renal disorder worldwide. The disease is characterized by renal cysts and progressive renal failure due to progressive enlargement of cysts and renal fibrosis. An estimated 45% to 70% of patients with ADPKD progress to end-stage renal disease by age 65 years. Although both targeted and nontargeted therapies have been tested in patients with ADPKD, tolvaptan is currently the only pharmacological therapy approved in Canada for the treatment of ADPKD. The purpose of this consensus recommendation is to develop an evidence-informed recommendation for the optimal management of adult patients with ADPKD. This document focuses on the role of genetic testing, the role of renal imaging, predicting the risk of disease progression, and pharmacological treatment options for ADPKD. These areas of focus were derived from 2 national surveys that were disseminated to nephrologists and patients with ADPKD with the aim of identifying unmet needs in the management of ADPKD in Canada. Specific recommendations are provided for the treatment of ADPKD with tolvaptan.

Accuracy and efficiency of computer-aided anatomical analysis using 3D visualization software based on semi-automated and automated segmentations

OBJECTIVE

We investigated and compared the functionality of two 3D visualization software provided by a CT vendor and a third-party vendor, respectively. Using surgical anatomical measurement as baseline, we evaluated the accuracy of 3D visualization and verified their utility in computer-aided anatomical analysis.

METHODS

The study cohort consisted of 50 adult cadavers fixed with the classical formaldehyde method. The computer-aided anatomical analysis was based on CT images (in DICOM format) acquired by helical scan with contrast enhancement, using a CT vendor provided 3D visualization workstation (Syngo) and a third-party 3D visualization software (Mimics) that was installed on a PC. Automated and semi-automated segmentations were utilized in the 3D visualization workstation and software, respectively. The functionality and efficiency of automated and semi-automated segmentation methods were compared. Using surgical anatomical measurement as a baseline, the accuracy of 3D visualization based on automated and semi-automated segmentations was quantitatively compared.

RESULTS

In semi-automated segmentation, the Mimics 3D visualization software outperformed the Syngo 3D visualization workstation. No significant difference was observed in anatomical data measurement by the Syngo 3D visualization workstation and the Mimics 3D visualization software (P>0.05).

CONCLUSIONS

Both the Syngo 3D visualization workstation provided by a CT vendor and the Mimics 3D visualization software by a third-party vendor possessed the needed functionality, efficiency and accuracy for computer-aided anatomical analysis.

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.