T1 vs. T2 weighted magnetic resonance imaging to assess total kidney volume in patients with autosomal dominant polycystic kidney disease

Kidney phosphate wasting predicts poor outcome in polycystic kidney disease

BACKGROUND

Patients with autosomal dominant polycystic kidney disease (ADPKD) have disproportionately high levels of fibroblast growth factor 23 (FGF-23) for their chronic kidney disease stage, however only a subgroup develops kidney phosphate wasting. We assessed factors associated with phosphate wasting and hypothesize that it identifies patients with more severe disease and predicts disease progression.

METHODS

We included 604 patients with ADPKD from a multicenter prospective observational cohort (DIPAK; Developing Intervention Strategies to Halt Progression of Autosomal Dominant Polycystic Kidney Disease) in four university medical centers in the Netherlands. We measured parathyroid hormone (PTH) and total plasma FGF-23 levels, and calculated the ratio of tubular maximum reabsorption rate of phosphate to glomerular filtration rate (TmP/GFR) with <0.8 mmol/L defined as kidney phosphate wasting. We analysed the association of TmP/GFR with estimated GFR (eGFR) decline over time and the risk for a composite kidney outcome (≥30% eGFR decline, kidney failure or kidney replacement therapy).

RESULTS

In our cohort (age 48 ± 12 years, 39% male, eGFR 63 ± 28 mL/min/1.73 m2), 59% of patients had phosphate wasting. Male sex [coefficient -0.2, 95% confidence interval (CI) -0.2; -0.1], eGFR (0.002, 95% CI 0.001; 0.004), FGF-23 (0.1, 95% CI 0.03; 0.2), PTH (-0.2, 95% CI -0.3; -0.06) and copeptin (-0.08, 95% CI -0.1; -0.08) were associated with TmP/GFR. Corrected for PTH, FGF-23 and eGFR, every 0.1 mmol/L decrease in TmP/GFR was associated with a greater eGFR decline of 0.2 mL/min/1.73 m2/year (95% CI 0.01; 0.3) and an increased hazard ratio of 1.09 (95% CI 1.01; 1.18) of the composite kidney outcome.

CONCLUSION

Our study shows that in patients with ADPKD, phosphate wasting is prevalent and associated with more rapid disease progression. Phosphate wasting may be a consequence of early proximal tubular dysfunction and insufficient suppression of PTH.

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.

Prostaglandin E2, Osmoregulation, and Disease Progression in Autosomal Dominant Polycystic Kidney Disease

BACKGROUND

Prostaglandin E2 (PGE2) plays a physiological role in osmoregulation, a process that is affected early in autosomal dominant polycystic kidney disease (ADPKD). PGE2 has also been implicated in the pathogenesis of ADPKD in preclinical models, but human data are limited. Here, we hypothesized that urinary PGE2 excretion is associated with impaired osmoregulation, disease severity, and disease progression in human ADPKD.

METHODS

Urinary excretions of PGE2 and its metabolite (PGEM) were measured in a prospective cohort of patients with ADPKD. The associations between urinary PGE2 and PGEM excretions, markers of osmoregulation, eGFR and height-adjusted total kidney volume were assessed using linear regression models. Cox regression and linear mixed models were used for the longitudinal analysis of the associations between urinary PGE2 and PGEM excretions and disease progression defined as 40% eGFR loss or kidney failure, and change in eGFR over time. In two intervention studies, we quantified the effect of starting tolvaptan and adding hydrochlorothiazide to tolvaptan on urinary PGE2 and PGEM excretions.

RESULTS

In 562 patients with ADPKD (61% female, eGFR 63±28 ml/min per 1.73 m 2 ), higher urinary PGE2 or PGEM excretions were independently associated with higher plasma copeptin, lower urine osmolality, lower eGFR, and greater total kidney volume. Participants with higher baseline urinary PGE2 and PGEM excretions had a higher risk of 40% eGFR loss or kidney failure (hazard ratio, 1.28; 95% confidence interval [CI], 1.13 to 1.46 and hazard ratio, 1.50; 95% CI, 1.26 to 1.80 per two-fold higher urinary PGE2 or PGEM excretions) and a faster change in eGFR over time (-0.39 [95% CI, -0.59 to -0.20] and -0.53 [95% CI, -0.75 to -0.31] ml/min per 1.73 m 2 per year). In the intervention studies, urinary PGEM excretion was higher after starting tolvaptan, while urinary PGE2 excretion was higher after adding hydrochlorothiazide to tolvaptan.

CONCLUSIONS

Higher urinary PGE2 and PGEM excretions in patients with ADPKD are associated with impaired osmoregulation, disease severity, and progression.

The utility of automatic segmentation of kidney MRI in chronic kidney disease using a 3D convolutional neural network

We developed a 3D convolutional neural network (CNN)-based automatic kidney segmentation method for patients with chronic kidney disease (CKD) using MRI Dixon-based T1-weighted in-phase (IP)/opposed-phase (OP)/water-only (WO) images. The dataset comprised 100 participants with renal dysfunction (RD; eGFR < 45 mL/min/1.73 m2) and 70 without (non-RD; eGFR ≥ 45 mL/min/1.73 m2). The model was applied to the right, left, and both kidneys; it was first evaluated on the non-RD group data and subsequently on the combined data of the RD and non-RD groups. For bilateral kidney segmentation of the non-RD group, the best performance was obtained when using IP image, with a Dice score of 0.902 ± 0.034, average surface distance of 1.46 ± 0.75 mm, and a difference of - 27 ± 21 mL between ground-truth and automatically computed volume. Slightly worse results were obtained for the combined data of the RD and non-RD groups and for unilateral kidney segmentation, particularly when segmenting the right kidney from the OP images. Our 3D CNN-assisted automatic segmentation tools can be utilized in future studies on total kidney volume measurements and various image analyses of a large number of patients with CKD.

The association of urinary epidermal growth factors with ADPKD disease severity and progression

BACKGROUND

The epidermal growth factor receptor (EGFR) pathway is involved in kidney tissue repair and growth. Preclinical interventional data and scarce human data have suggested a role for this pathway in the pathophysiology of autosomal dominant polycystic kidney disease (ADPKD), while other data have suggested that its activation is causally linked to repair of damaged kidney tissue. We hypothesize that urinary EGFR ligands, as a reflection of EGFR activity, are associated with kidney function decline in ADPKD in the context of tissue repair following injury, and as the disease progresses as a sign of insufficient repair.

METHODS

In the present study, we measured the EGFR ligands, EGF and heparin binding-EGF (HB-EGF), in 24-h urine samples of 301 ADPKD patients and 72 age- and sex-matched living kidney donors to dissect the role of the EGFR pathway in ADPKD. During a median follow-up of 2.5 years, the association of urinary EGFR ligand excretion with annual change in estimated glomerular filtration rate (eGFR) and height-adjusted total kidney volume in ADPKD patients was analyzed using mixed-models methods, and the expression of three closely related EGFR family receptors in ADPKD kidney tissue was investigated by immunohistochemistry. Additionally, the effect of reducing renal mass (after kidney donation), was assessed to investigate whether urinary EGF matches this reduction and thus reflects the amount of remaining healthy kidney tissue.

RESULTS

At baseline, urinary HB-EGF did not differ between ADPKD patients and healthy controls (P = .6), whereas a lower urinary EGF excretion was observed in ADPKD patients [18.6 (11.8-27.8)] compared with healthy controls [51.0 (34.9-65.4) μg/24 h, P < .001]. Urinary EGF was positively associated with baseline eGFR (R = 0.54, P < .001) and a lower EGF was strongly associated with a more rapid GFR decline, even when adjusted for ADPKD severity markers (β = 1.96, P < .001), whereas HB-EGF was not. Expression of the EGFR, but not other EGFR-related receptors, was observed in renal cysts but was absent in non-ADPKD kidney tissue. Finally, unilateral nephrectomy resulted in a decrease of 46.4 (-63.3 to -17.6) % in urinary EGF excretion, alongside a decrease of 35.2 ± 7.2% in eGFR and 36.8 ± 6.9% in measured GFR (mGFR), whereas maximal mGFR (measured after dopamine induced hyperperfusion) decreased by 46.1 ± 7.8% (all P < .001).

CONCLUSIONS

Our data suggest that lower urinary EGF excretion may be a valuable novel predictor for kidney function decline in patients with ADPKD.

Size Matters: How to Characterize ADPKD Severity by Measuring Total Kidney Volume

Following patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD) has been challenging because serum biomarkers such as creatinine often remain normal until relatively late in the disease [...].

Severity of polycystic kidney disease revealed by multiparametric MRI

PURPOSE

We aimed to compare multiple MRI parameters, including relaxation rates (R 1 $$ {R}_1 $$ ,R 2 $$ {R}_2 $$ , andR 1 ρ $$ {R}_{1\rho } $$ ), ADC from diffusion weighted imaging, pool size ratio (PSR) from quantitative magnetization transfer, and measures of exchange from spin-lock imaging (S ρ $$ {S}_{\rho } $$ ), for assessing and predicting the severity of polycystic kidney disease (PKD) over time.

METHODS

Pcy/Pcy mice with CD1 strain, a mouse model of autosomal dominant PKD, were imaged at 5, 9, and 26 wk of age using a 7T MRI system. Twelve-week normal CD1 mice were used as controls. Post-mortem paraffin tissue sections were stained using hematoxylin and eosin and picrosirius red to identify histological changes.

RESULTS

Histology detected segmental cyst formation in the early stage (week 5) and progression of PKD over time in Pcy kidneys. InT 2 $$ {T}_2 $$ -weighted images, small cysts appeared locally in cystic kidneys in week 5 and gradually extended to the whole cortex and outer stripe of outer medulla region from week 5 to week 26. Regional PSR,R 1 $$ {R}_1 $$ ,R 2 $$ {R}_2 $$ , andR 1 ρ $$ {R}_{1\rho } $$ decreased consistently over time compared to normal kidneys, with significant changes detected in week 5. Among all the MRI measures,R 2 $$ {R}_2 $$ andR 1 ρ $$ {R}_{1\rho } $$ allow highest detectability to PKD, while PSR andR 1 $$ {R}_1 $$ have highest correlation with pathological indices of PKD. Using optimum MRI parameters as regressors, multiple linear regression provides reliable prediction of PKD progression.

CONCLUSION

R 2 $$ {R}_2 $$ ,R 1 $$ {R}_1 $$ , and PSR are sensitive indicators of the presence of PKD. Multiparametric MRI allows a comprehensive analysis of renal changes caused by cyst formation and expansion.

Clinical Quality Control of MRI Total Kidney Volume Measurements in Autosomal Dominant Polycystic Kidney Disease

Total kidney volume measured on MRI is an important biomarker for assessing the progression of autosomal dominant polycystic kidney disease and response to treatment. However, we have noticed that there can be substantial differences in the kidney volume measurements obtained from the various pulse sequences commonly included in an MRI exam. Here we examine kidney volume measurement variability among five commonly acquired MRI pulse sequences in abdominal MRI exams in 105 patients with ADPKD. Right and left kidney volumes were independently measured by three expert observers using model-assisted segmentation for axial T2, coronal T2, axial single-shot fast spin echo (SSFP), coronal SSFP, and axial 3D T1 images obtained on a single MRI from ADPKD patients. Outlier measurements were analyzed for data acquisition errors. Most of the outlier values (88%) were due to breathing during scanning causing slice misregistration with gaps or duplication of imaging slices (n = 35), slice misregistration from using multiple breath holds during acquisition (n = 25), composing of two overlapping acquisitions (n = 17), or kidneys not entirely within the field of view (n = 4). After excluding outlier measurements, the coefficient of variation among the five measurements decreased from 4.6% pre to 3.2%. Compared to the average of all sequences without errors, TKV measured on axial and coronal T2 weighted imaging were 1.2% and 1.8% greater, axial SSFP was 0.4% greater, coronal SSFP was 1.7% lower and axial T1 was 1.5% lower than the mean, indicating intrinsic measurement biases related to the different MRI contrast mechanisms. In conclusion, MRI data acquisition errors are common but can be identified using outlier analysis and excluded to improve organ volume measurement consistency. Bias toward larger volume measurements on T2 sequences and smaller volumes on axial T1 sequences can also be mitigated by averaging data from all error-free sequences acquired.

Automated measurement of total kidney volume from 3D ultrasound images of patients affected by polycystic kidney disease and comparison to MR measurements

PURPOSE

Total kidney volume (TKV) is the most important imaging biomarker for quantifying the severity of autosomal-dominant polycystic kidney disease (ADPKD). 3D ultrasound (US) can accurately measure kidney volume compared to 2D US; however, manual segmentation is tedious and requires expert annotators. We investigated a deep learning-based approach for automated segmentation of TKV from 3D US in ADPKD patients.

METHOD

We used axially acquired 3D US-kidney images in 22 ADPKD patients where each patient and each kidney were scanned three times, resulting in 132 scans that were manually segmented. We trained a convolutional neural network to segment the whole kidney and measure TKV. All patients were subsequently imaged with MRI for measurement comparison.

RESULTS

Our method automatically segmented polycystic kidneys in 3D US images obtaining an average Dice coefficient of 0.80 on the test dataset. The kidney volume measurement compared with linear regression coefficient and bias from human tracing were R² = 0.81, and - 4.42%, and between AI and reference standard were R² = 0.93, and - 4.12%, respectively. MRI and US measured kidney volumes had R² = 0.84 and a bias of 7.47%.

CONCLUSION

This is the first study applying deep learning to 3D US in ADPKD. Our method shows promising performance for auto-segmentation of kidneys using 3D US to measure TKV, close to human tracing and MRI measurement. This imaging and analysis method may be useful in a number of settings, including pediatric imaging, clinical studies, and longitudinal tracking of patient disease progression.

Limited Performance of Estimated Total Kidney Volume for Follow-up of ADPKD

Introduction

Total kidney volume (TKV) is a qualified biomarker for disease progression in autosomal dominant polycystic kidney disease (ADPKD). Recent studies suggest that TKV estimated using ellipsoid formula correlates well with TKV measured by manual planimetry (gold standard). We investigated whether the ellipsoid formula could replace manual planimetry for follow-up of ADPKD patients.

Methods

Abdominal magnetic resonance images of patients with ADPKD performed between January 1, 2013, and June 31, 2019, in Saint-Luc Hospital, Brussels, were used. Two radiologists independently performed manual TKV (mTKV) measures and kidney axial measures necessary for estimating TKV (eTKV) using ellipsoid equation. Repeatability and reproducibility of axial measures, mTKV and eTKV, and agreement between mTKV and eTKV were assessed (Bland-Altman). Intraclass correlation coefficient (ICC) was used to assess agreement on Mayo Clinic Imaging Classification (MCIC) scores.

Results

140 patients were included with mean age 45±13 years, estimated glomerular filtration rate (eGFR) 71±31 ml/min per 1.73 m², and mTKV 1697±1538 ml. Repeatability and reproducibility were superior for mTKV versus eTKV (repeatability coefficient 2.4% vs. 14% in senior reader, and reproducibility coefficient 6.7% vs. 15%). Intertechnique reproducibility coefficient (95% confidence interval [CI]) was 19% (17%, 21%) in senior reader. Intertechnique agreement on derived MCIC scores was very good (ICC = 0.924 [0.884, 0.949]).

Conclusion

TKV estimated using ellipsoid equation demonstrates poor repeatability and reproducibility compared with that of mTKV. Intertechnique agreement is also limited, even when measurements are performed by an experienced radiologist. Estimated TKV, however, accurately determines MCIC score.

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.

Use of the Urine-to-Plasma Urea Ratio to Predict ADPKD Progression

BACKGROUND AND OBJECTIVES

Predicting disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD) poses a challenge, especially in early-stage disease when kidney function is not yet affected. Ongoing growth of cysts causes maximal urine-concentrating capacity to decrease from early on. We therefore hypothesized that the urine-to-plasma urea ratio, as a reflection of the urine-concentrating capacity, can be used as a marker to predict ADPKD progression.

DESIGN

The urine-to-plasma urea ratio was calculated by dividing concentrations of early morning fasting spot urine urea by plasma urea. First, this ratio was validated as surrogate marker in 30 patients with ADPKD who underwent a prolonged water deprivation test. Thereafter, association with kidney outcome was evaluated in 583 patients with ADPKD with a broad range of kidney function. Multivariable mixed-model regression was used to assess association with eGFR slope, and logarithmic regression to identify patients with rapidly progressive disease, using a cutoff of -3.0 ml/min per 1.73 m² per year. The urine-to-plasma urea ratio was compared with established predictors, namely, sex, age, baseline eGFR, Mayo Clinic height-adjusted total kidney volume class, and PKD gene mutation.

RESULTS

The maximal urine-concentrating capacity and urine-to-plasma urea ratio correlated strongly (R=0.90; P<0.001). Next, the urine-to-plasma urea ratio was significantly associated with rate of eGFR decline during a median follow-up of 4.0 (interquartile range, 2.6-5.0) years, both crude and after correction for established predictors (β=0.58; P=0.02). The odds ratio of rapidly progressive disease was 1.35 (95% confidence interval, 1.19 to 1.52; P<0.001) for every 10 units decrease in urine-to-plasma urea ratio, with adjustment for predictors. A combined risk score of the urine-to-plasma urea ratio, Mayo Clinic height-adjusted total kidney volume class, and PKD mutation predicted rapidly progressive disease better than each of the predictors separately.

CONCLUSIONS

The urine-to-plasma urea ratio, which is calculated from routine laboratory measurements, predicts disease progression in ADPKD in addition to other risk markers.

PODCAST

This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_01_27_CJN10470620_final.mp3.

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.

Automatic Measurement of Kidney and Liver Volumes from MR Images of Patients Affected by Autosomal Dominant Polycystic Kidney Disease

BACKGROUND

The formation and growth of cysts in kidneys, and often liver, in autosomal dominant polycystic kidney disease (ADPKD) cause progressive increases in total kidney volume (TKV) and liver volume (TLV). Laborious and time-consuming manual tracing of kidneys and liver is the current gold standard. We developed a fully automated segmentation method for TKV and TLV measurement that uses a deep learning network optimized to perform semantic segmentation of kidneys and liver.

METHODS

We used 80% of a set of 440 abdominal magnetic resonance images (T2-weighted HASTE coronal sequences) from patients with ADPKD to train the network and the remaining 20% for validation. Both kidneys and liver were also segmented manually. To evaluate the method's performance, we used an additional test set of images from 100 patients, 45 of whom were also involved in longitudinal analyses.

RESULTS

TKV and TLV measured by the automated approach correlated highly with manually traced TKV and TLV (intraclass correlation coefficients, 0.998 and 0.996, respectively), with low bias and high precision (<0.1%±2.7% for TKV and -1.6%±3.1% for TLV); this was comparable with inter-reader variability of manual tracing (<0.1%±3.5% for TKV and -1.5%±4.8% for TLV). For longitudinal analysis, bias and precision were <0.1%±3.2% for TKV and 1.4%±2.9% for TLV growth.

CONCLUSIONS

These findings demonstrate a fully automated segmentation method that measures TKV, TLV, and changes in these parameters as accurately as manual tracing. This technique may facilitate future studies in which automated and reproducible TKV and TLV measurements are needed to assess disease severity, disease progression, and treatment response.

Change in kidney volume after kidney transplantation in patients with autosomal polycystic kidney disease

Background

The indication to bilateral nephrectomy in patients with autosomal dominant polycystic kidney scheduled for kidney transplantation is controversial. Indeed, the progressive enlargement of cysts may increase the risk of complications and the need for nephrectomy. However, very few studies investigated the change in kidney volume after kidney transplantation.

Material and methods

In this prospective cohort study, the change in native kidney volume in polycystic patients was evaluated with magnetic resonance imaging. Forty patients were included in the study. Kidney diameters and total kidney volume were evaluated with magnetic resonance imaging in patients who underwent simultaneous nephrectomy and kidney transplantation and in patients with kidney transplant alone, before transplantation and 1 year after transplantation.

Results

There was a significant reduction of kidney volume after transplantation, with a mean degree of kidney diameters reduction varying from 12.24% to 14.43%. Mean total kidney volume of the 55 kidney considered in the analysis significantly reduced from 1617.94 ± 833.42 ml to 1381.42 ± 1005.73 ml (P<0.05), with a mean rate of 16.44% of volume decrease. More than 80% of patients had a volume reduction in both groups.

Conclusions

Polycystic kidneys volume significantly reduces after kidney transplantation, and this would reduce the need for prophylactic bilateral nephrectomy in asymptomatic patients.

Association of plasma somatostatin with disease severity and progression in patients with autosomal dominant polycystic kidney disease

Background

Somatostatin (SST) inhibits intracellular cyclic adenosine monophosphate (cAMP) production and thus may modify cyst formation in autosomal dominant polycystic kidney disease (ADPKD). We investigated whether endogenous plasma SST concentration is associated with disease severity and progression in patients with ADPKD, and whether plasma SST concentrations change during treatment with a vasopressin V2 receptor antagonist or SST analogue.

Methods

In this observational study, fasting concentrations of SST were measured in 127 ADPKD patients (diagnosed upon the revised Ravine criteria) by ELISA. cAMP was measured in 24 h urine by Radio Immuno Assay. Kidney function was measured (mGFR) as ¹²⁵I-iothalamate clearance, and total kidney volume was measured by MRI volumetry and adjusted for height (htTKV). Disease progression was expressed as annual change in mGFR and htTKV. Additionally, baseline versus follow-up SST concentrations were compared in ADPKD patients during vasopressin V2 receptor antagonist (tolvaptan) (n = 27) or SST analogue (lanreotide) treatment (n = 25).

Results

In 127 ADPKD patients, 41 ± 11 years, 44% female, eGFR 73 ± 32 ml/min/1.73m², mGFR 75 ± 32 ml/min/1.73m² and htTKV 826 (521–1297) ml/m, SST concentration was 48.5 (34.3–77.8) pg/ml. At baseline, SST was associated with urinary cAMP, mGFR and htTKV (p = 0.02, p = 0.004 and p = 0.02, respectively), but these associations lost significance after adjustment for age and sex or protein intake (p = 0.09, p = 0.06 and p = 0.15 respectively). Baseline SST was not associated with annual change in mGFR, or htTKV during follow-up (st. β = − 0.02, p = 0.87 and st. β = − 0.07, p = 0.54 respectively). During treatment with tolvaptan SST levels remained stable 38.2 (23.8–70.7) pg/mL vs. 39.8 (31.2–58.5) pg/mL, p = 0.85), whereas SST levels decreased significantly during treatment with lanreotide (42.5 (33.2–55.0) pg/ml vs. 29.3 (24.8–37.6), p = 0.008).

Conclusions

Fasting plasma SST concentration is not associated with disease severity or progression in patients with ADPKD. Treatment with lanreotide caused a decrease in SST concentration. These data suggest that plasma SST cannot be used as a biomarker to assess prognosis in ADPKD, but leave the possibility open that change in SST concentration during lanreotide treatment may reflect therapy efficacy.

Electronic supplementary material

The online version of this article (10.1186/s12882-018-1176-y) contains supplementary material, which is available to authorized users.

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.

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.