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

Magnetic resonance imaging based kidney volume assessment for risk stratification in pediatric autosomal dominant polycystic kidney disease

Introduction

In the pediatric context, most children with autosomal dominant polycystic kidney disease (ADPKD) maintain a normal glomerular filtration rate (GFR) despite underlying structural kidney damage, highlighting the critical need for early intervention and predictive markers. Due to the inverse relationship between kidney volume and kidney function, risk assessments have been presented on the basis of kidney volume. The aim of this study was to use magnetic resonance imaging (MRI)-based kidney volume assessment for risk stratification in pediatric ADPKD and to investigate clinical and genetic differences among risk groups.

Methods

This multicenter, cross-sectional, and case-control study included 75 genetically confirmed pediatric ADPKD patients (5-18 years) and 27 controls. Kidney function was assessed by eGFR calculated from serum creatinine and cystatin C using the CKiD-U25 equation. Blood pressure was assessed by both office and 24-hour ambulatory measurements. Kidney volume was calculated from MRI using the stereological method. Total kidney volume was adjusted for the height (htTKV). Patients were stratified from A to E classes according to the Leuven Imaging Classification (LIC) using MRI-derived htTKV.

Results

Median (Q1-Q3) age of the patients was 6.0 (2.0-10.0) years, 56% were male. There were no differences in sex, age, height-SDS, or GFR between the patient and control groups. Of the patients, 89% had PKD1 and 11% had PKD2 mutations. Non-missense mutations were 73% in PKD1 and 75% in PKD2. Twenty patients (27%) had hypertension based on ABPM. Median htTKV of the patients was significantly higher than controls (141 vs. 117 ml/m, p = 0.0003). LIC stratification revealed Classes A (38.7%), B (28%), C (24%), and D + E (9.3%). All children in class D + E and 94% in class C had PKD1 variants. Class D + E patients had significantly higher blood pressure values and hypertension compared to other classes (p > 0.05 for all).

Discussion

This study distinguishes itself by using MRI-based measurements of kidney volume to stratify pediatric ADPKD patients into specific risk groups. It is important to note that PKD1 mutation and elevated blood pressure were higher in the high-risk groups stratified by age and kidney volume. Our results need to be confirmed in further studies.

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.

Angiotensinogen and interleukin 18 in serum and urine of children with kidney cysts

BACKGROUND

The most common disease associated with the presence of kidney cysts in the population is autosomal dominant polycystic kidney disease (ADPKD), which finally leads to end-stage renal disease.

METHOD

The study evaluated serum and urinary concentration of angiotensinogen (AGT) and interleukin 18 (IL-18) in a group of 39 children with renal cysts of different aetiology.

RESULTS

Serum and urinary AGT concentration in children with renal cysts was higher compared to controls, regardless of the underlying background and gender. Serum IL-18 concentration was lower, in contrast, and the concentration of IL-18 in the urine did not differ between affected and healthy children. Negative correlation between urinary IL-18 concentration and systolic and mean arterial blood pressure was noted.

CONCLUSIONS

Higher AGT levels in serum and urine in children with renal cysts may indicate the activation of the renin-angiotensin-aldosterone system, including its intrarenal part, even before the onset of hypertension. Lower serum concentration of IL-18 in children with kidney cysts may indicate the loss of the protective role of this cytokine with the occurrence of hypertension.

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.

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.

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.

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.