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

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

PURPOSE

This study aimed to investigate if a deep learning model trained with a single institution's data has comparable accuracy to that trained with multi-institutional data for segmenting kidney and cyst regions in magnetic resonance (MR) images of patients affected by autosomal dominant polycystic kidney disease (ADPKD).

METHODS

We used TensorFlow with a Keras custom UNet on 2D slices of 756 MRI images of kidneys with ADPKD obtained from four institutions in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study. The ground truth was determined via a manual plus global thresholding method. Five models were trained with 80 % of all institutional data (n = 604) and each institutional data (n = 232, 172, 148, or 52), respectively, and validated with 10 % and tested on an unseen 10 % of the data. The model's performance was evaluated using the Dice Similarity Coefficient (DSC).

RESULTS

The DSCs by the model trained with all institutional data ranged from 0.92 to 0.95 for kidney image segmentation, only 1-2 % higher than those by the models trained with single institutional data (0.90-0.93).In cyst segmentation, however, the DSCs by the model trained with all institutional data ranged from 0.83 to 0.89, which were 2-20 % higher than those by the models trained with single institutional data (0.66-0.86).

CONCLUSION

The UNet performance, when trained with a single institutional dataset, exhibited similar accuracy to the model trained on a multi-institutional dataset. Segmentation accuracy increases with models trained on larger sample sizes, especially in more complex cyst segmentation.

An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD

Introduction

Accurate tools to inform individual prognosis in patients with autosomal dominant polycystic kidney disease (ADPKD) are lacking. Here, we report an artificial intelligence (AI)-generated method for routinely measuring total kidney volume (TKV).

Methods

An ensemble U-net algorithm was created using the nnUNet approach. The training and internal cross-validation cohort consisted of all 1.5T magnetic resonance imaging (MRI) data acquired using 5 different MRI scanners (454 kidneys, 227 scans) in the CYSTic consortium, which was first manually segmented by a single human operator. As an independent validation cohort, we utilized 48 sequential clinical MRI scans with reference results of manual segmentation acquired by 6 individual analysts at a single center. The tool was then implemented for clinical use and its performance analyzed.

Results

The training or internal validation cohort was younger (mean age 44.0 vs. 51.5 years) and the female-to-male ratio higher (1.2 vs. 0.94) compared to the clinical validation cohort. The majority of CYSTic patients had PKD1 mutations (79%) and typical disease (Mayo Imaging class 1, 86%). The median DICE score on the clinical validation data set between the algorithm and human analysts was 0.96 for left and right kidneys with a median TKV error of -1.8%. The time taken to manually segment kidneys in the CYSTic data set was 56 (±28) minutes, whereas manual corrections of the algorithm output took 8.5 (±9.2) minutes per scan.

Conclusion

Our AI-based algorithm demonstrates performance comparable to manual segmentation. Its rapidity and precision in real-world clinical cases demonstrate its suitability for clinical application.

The normal reference values and estimation formulae of renal structural parameters in Chinese children based on large-sample CT data

Purpose

Our aim was to investigate the normal reference value and to establish an estimation formulae for renal structural parameters (RSPs) based on large-sample CT data of Chinese children, which can provide a data reference for the clinical assessment of kidney development and diseases in Chinese children.

Materials and Methods

A total of 438 children aged 0-17 years with normal renal CT images and basic indices were continuously collected. The bilateral RSP, including renal length (RL), renal width (RW), renal thickness (RT), renal volume (RV), renal cortical thickness (RCT), renal artery diameter (RAD) and renal CT value, were measured. Kendall's rank correlation was used to analyze the correlation between RSP and sex. Pearson's correlation was used to analyze the correlation between RSP and age, height and weight. Differences in the RSP of bilateral kidneys were analyzed via a paired samples t-test. Multiple linear regression was used to analyze the multivariate relationships between RSP and basic indices and establish the estimation formula of RSP.

Results

The RSP of normal kidneys showed a dynamic increasing trend with age, except for the CT values. The reference value ranges (95% confidence interval) of normal RSP for each age group were determined. Pearson correlation analysis demonstrated strong correlations between RSP (RL, RW, RT, RV, RCT and, RAD) and basic indices (age, height and, weight), with height exhibiting the greatest correlation coefficient, followed by age or weight. Kendall's analysis showed that none of the RSPs were correlated with sex. The RL, RW, RV and RAD of the left kidney were larger than those of the right kidney, and the RT and RCT of the right kidney exhibited opposite results. Multiple linear regression analysis demonstrated a significant linear relationship between the RSP (RL, RW, RT, RV and, RCT) and the variables of the basic indices. The estimation formulae for calculating the RSP were established.

Conclusion

This is the first Chinese study to report of the trends, normal reference values and estimation formulae of normal RSP based on large-sample CT data. These results can provide data references for assessing adequate kidney growth or disease damage in Chinese children.

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

PURPOSE

Total kidney volume (TKV) measurement is crucial for selecting treatment candidates in autosomal dominant polycystic kidney disease (ADPKD). We developed and investigated the performance of fully-automated 3D-volumetry model and applied it to software as a service (SaaS) for clinical support on tolvaptan prescription in ADPKD patients.

MATERIALS AND METHODS

Computed tomography scans of ADPKD patients taken between January 2000 and June 2022 were acquired from seven institutions. The quality of the images was manually reviewed in advance. The acquired dataset was split into training, validation, and test datasets at a ratio of 8.5:1:0.5. Convolutional, neural network-based automatic segmentation model was trained to obtain 3D segment mask for TKV measurement. The algorithm consisted of three steps: data preprocessing, ADPKD area extraction, and post-processing. After performance validation with the Dice score, 3D-volumetry model was applied to SaaS which is based on Mayo imaging classification for ADPKD.

RESULTS

A total of 753 cases with 95,117 slices were included. The differences between the ground-truth ADPKD kidney mask and the predicted ADPKD kidney mask were negligible, with intersection over union >0.95. The post-process filter successfully removed false alarms. The test-set performance was homogeneously equal and the Dice score of the model was 0.971; after post-processing, it improved to 0.979. The SaaS calculated TKV from uploaded Digital Imaging and Communications in Medicine images and classified patients according to height-adjusted TKV for age.

CONCLUSIONS

Our artificial intelligence-3D volumetry model exhibited effective, feasible, and non-inferior performance compared with that of human experts and successfully predicted the rapid ADPKD progressor.

Accuracy and processing time of kidney volume measurement methods in rodents polycystic kidney disease models: superiority of semiautomated kidney segmentation

Measurement of total kidney volume (TKV) using magnetic resonance imaging (MRI) is a valuable approach for monitoring disease progression in autosomal dominant polycystic kidney disease (PKD) and is becoming more common in preclinical studies using animal models. Manual contouring of kidney MRI areas [i.e., manual method (MM)] is a conventional, but time-consuming, way to determine TKV. We developed a template-based semiautomatic image segmentation method (SAM) and validated it in three commonly used PKD models: Cys1cpk/cpk mice, Pkd1RC/RC mice, and Pkhd1pck/pck rats (n = 10 per model). We compared SAM-based TKV with that obtained by clinical alternatives including the ellipsoid formula-based method (EM) using three kidney dimensions, the longest kidney length method (LM), and MM, which is considered the gold standard. Both SAM and EM presented high accuracy in TKV assessment in Cys1cpk/cpk mice [interclass correlation coefficient (ICC) ≥ 0.94]. SAM was superior to EM and LM in Pkd1RC/RC mice (ICC = 0.87, 0.74, and <0.10 for SAM, EM, and LM, respectively) and Pkhd1pck/pck rats (ICC = 0.59, <0.10, and <0.10, respectively). Also, SAM outperformed EM in processing time in Cys1cpk/cpk mice (3.6 ± 0.6 vs. 4.4 ± 0.7 min/kidney) and Pkd1RC/RC mice (3.1 ± 0.4 vs. 7.1 ± 2.6 min/kidney, both P < 0.001) but not in Pkhd1PCK/PCK rats (3.7 ± 0.8 vs. 3.2 ± 0.5 min/kidney). LM was the fastest (∼1 min) but correlated most poorly with MM-based TKV in all studied models. Processing times by MM were longer for Cys1cpk/cpk mice, Pkd1RC/RC mice, and Pkhd1pck.pck rats (66.1 ± 7.3, 38.3 ± 7.5, and 29.2 ± 3.5 min). In summary, SAM is a fast and accurate method to determine TKV in mouse and rat PKD models.NEW & NOTEWORTHY Total kidney volume (TKV) is a valuable readout in preclinical studies for autosomal dominant and autosomal recessive polycystic kidney diseases (ADPKD and ARPKD). Since conventional TKV assessment by manual contouring of kidney areas in all images is time-consuming, we developed a template-based semiautomatic image segmentation method (SAM) and validated it in three commonly used ADPKD and ARPKD models. SAM-based TKV measurements were fast, highly reproducible, and accurate across mouse and rat ARPKD and ADPKD models.

Validation of a semi-automatic method to measure total liver volumes in polycystic liver disease on computed tomography - high speed and accuracy

OBJECTIVES

Polycystic liver disease (PLD) is characterized by growth of hepatic cysts, causing hepatomegaly. Disease severity is determined using total liver volume (TLV), which can be measured from computed tomography (CT). The gold standard is manual segmentation which is time-consuming and requires expert knowledge of the anatomy. This study aims to validate the commercially available semi-automatic MMWP (Multimodality Workplace) Volume tool for CT scans of PLD patients.

METHODS

We included adult patients with one (n = 60) or two (n = 46) abdominal CT scans. Semi-automatic contouring was compared with manual segmentation, using comparison of observed volumes (cross-sectional) and growth (longitudinal), correlation coefficients (CC), and Bland-Altman analyses with bias and precision, defined as the mean difference and SD from this difference. Inter- and intra-reader variability were assessed using coefficients of variation (CV) and we assessed the time to perform both procedures.

RESULTS

Median TLV was 5292.2 mL (IQR 3141.4-7862.2 mL) at baseline. Cross-sectional analysis showed high correlation and low bias and precision between both methods (CC 0.998, bias 1.62%, precision 2.75%). Absolute volumes were slightly higher for semi-automatic segmentation (manual 5292.2 (3141.4-7862.2) versus semi-automatic 5432.8 (3071.9-7960.2) mL, difference 2.7%, p < 0.001). Longitudinal analysis demonstrated that semi-automatic segmentation accurately measures liver growth (CC 0.908, bias 0.23%, precision 4.04%). Inter- and intra-reader variability were small (2.19% and 0.66%) and comparable to manual segmentation (1.21% and 0.63%) (p = 0.26 and p = 0.37). Semi-automatic segmentation was faster than manual tracing (19 min versus 50 min, p = 0.009).

CONCLUSIONS

Semi-automatic liver segmentation is a fast and accurate method to determine TLV and liver growth in PLD patients.

KEY POINTS

• Semi-automatic liver segmentation using the commercially available MMWP volume tool accurately determines total liver volume as well as liver growth over time in polycystic liver disease patients. • This method is considerably faster than manual segmentation through the use of Hounsfield unit settings. • We used a real-life CT set for the validation and showed that the semi-automatic tool measures accurately regardless of contrast used for the CT scan or not, presence of polycystic kidneys, liver volume, and previous invasive treatment for polycystic liver disease.

Utility of ultrasonography for predicting indications for tolvaptan in patients with autosomal dominant polycystic kidney disease

PURPOSE

Tolvaptan is the first approved treatment for autosomal dominant polycystic kidney disease (ADPKD) that targets a mechanism directly contributing to the development and growth of renal cysts. We investigated the ability of ultrasonography to predict total kidney volume (TKV) of 750 mL or more, which is an indication for tolvaptan therapy in patients with ADPKD.

METHODS

A total of 46 patients with ADPKD were evaluated. The most statistically appropriate measurement based on ultrasonography for predicting TKV determined by computed tomography (CT) was assessed.

RESULTS

TKV determined by CT was 796.8 (508.8-1,560.3) mL. The median length, anteroposterior distance, and mediolateral distance determined using ultrasonography were 15.7 cm, 7.6 cm, and 7.6 cm in the left kidney, and 13.4 cm, 6.9 cm, and 7.2 cm in the right kidney, respectively. Multivariate regression analysis showed that total kidney length (left and right) [variance inflation factor (VIF), 9.349] and total mediolateral distance (left and right) (VIF, 3.988) were independently associated with TKV. The correlation (r) between the logarithm of TKV determined by CT and total mediolateral distance determined using ultrasonography was 0.915 (p < 0.001). The linear regression equation was log (total kidney volume) = 1.833 + 0.075 × total mediolateral distance (left and right) based on ultrasonography. The area under the receiver operating characteristic curve for total mediolateral distance determined using ultrasonography to predict TKV of 750 mL or more was 0.989. Using the total mediolateral distance cut-off value of 14.2 cm, the sensitivity and specificity were 96.0% and 100.0%, respectively.

CONCLUSION

Total mediolateral distance determined using ultrasonography can predict TKV in patients with ADPKD.

Automated Kidney and Liver Segmentation in MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease: A Multicenter Study

Background

Imaging-based total kidney volume (TKV) and total liver volume (TLV) are major prognostic factors in autosomal dominant polycystic kidney disease (ADPKD) and end points for clinical trials. However, volumetry is time consuming and reader dependent in clinical practice. Our aim was to develop a fully automated method for joint kidney and liver segmentation in magnetic resonance imaging (MRI) and to evaluate its performance in a multisequence, multicenter setting.

Methods

The convolutional neural network was trained on a large multicenter dataset consisting of 992 MRI scans of 327 patients. Manual segmentation delivered ground-truth labels. The model's performance was evaluated in a separate test dataset of 93 patients (350 MRI scans) as well as a heterogeneous external dataset of 831 MRI scans from 323 patients.

Results

The segmentation model yielded excellent performance, achieving a median per study Dice coefficient of 0.92-0.97 for the kidneys and 0.96 for the liver. Automatically computed TKV correlated highly with manual measurements (intraclass correlation coefficient [ICC]: 0.996-0.999) with low bias and high precision (-0.2%±4% for axial images and 0.5%±4% for coronal images). TLV estimation showed an ICC of 0.999 and bias/precision of -0.5%±3%. For the external dataset, the automated TKV demonstrated bias and precision of -1%±7%.

Conclusions

Our deep learning model enabled accurate segmentation of kidneys and liver and objective assessment of TKV and TLV. Importantly, this approach was validated with axial and coronal MRI scans from 40 different scanners, making implementation in clinical routine care feasible.Clinical Trial registry name and registration number: The German ADPKD Tolvaptan Treatment Registry (AD[H]PKD), NCT02497521.

Regional variation in tolvaptan prescribing across England: national data and retrospective evaluation from an expert centre

Background

Tolvaptan, a vasopressin V2 receptor antagonist, was approved in 2015 by the UK National Institute for Health and Care Excellence for use in patients with autosomal dominant polycystic kidney disease (ADPKD) and rapid disease progression. Simultaneous guidance was issued by the UK Kidney Association (UKKA) to facilitate national implementation.

Methods

Data on tolvaptan prescribing in England was obtained through the National Health Service (NHS) Digital, a national survey of all 77 adult kidney units, and the implementation of UKKA guidance was evaluated at an expert PKD centre.

Results

A regional variation of up to 4-fold for tolvaptan prescribing in England was found. Despite most kidney units following UKKA guidance, centre-based estimates of eligible or treated patient numbers were highly variable. Retrospective evaluation at an expert PKD centre revealed that in a cohort demonstrating rapid estimated glomerular filtration rate (eGFR) decline, 14% would not be eligible for tolvaptan by Mayo imaging classification and more than half (57%) would not be eligible by Predicting Renal Outcome in Polycystic Kidney Disease score. The 3-year discontinuation rate was higher than expected (56%), the majority (70%) due to aquaretic symptoms. In patients taking tolvaptan for at least 2 years, 81% showed a reduction in the rate of eGFR decline compared with baseline, with earlier disease associated with positive treatment response.

Conclusion

Real-world data have revealed a much higher regional variation in tolvaptan prescribing for ADPKD in England than expected. We propose further investigation into the factors responsible for this variation.

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.

An update on the use of tolvaptan for ADPKD: Consensus statement on behalf of the ERA Working Group on Inherited Kidney Disorders (WGIKD), the European Rare Kidney Disease Reference Network (ERKNet) and Polycystic Kidney Disease International (PKD-International)

Approval of the vasopressin V2 receptor antagonist tolvaptan—based on the landmark TEMPO 3:4 trial—marked a transformation in the management of autosomal dominant polycystic kidney disease (ADPKD). This development has advanced patient care in ADPKD from general measures to prevent progression of chronic kidney disease to targeting disease-specific mechanisms. However, considering the long-term nature of this treatment, as well as potential side effects, evidence-based approaches to initiate treatment only in patients with rapidly progressing disease are crucial. In 2016, the position statement issued by the European Renal Association (ERA) was the first society-based recommendation on the use of tolvaptan and has served as a widely used decision-making tool for nephrologists. Since then, considerable practical experience regarding the use of tolvaptan in ADPKD has accumulated. More importantly, additional data from REPRISE, a second randomized clinical trial (RCT) examining the use of tolvaptan in later-stage disease, have added important evidence to the field, as have post hoc studies of these RCTs. To incorporate this new knowledge, we provide an updated algorithm to guide patient selection for treatment with tolvaptan and add practical advice for its use.

Reliable kidney size determination by magnetic resonance imaging in pathophysiological settings

AIM

Kidney diseases constitute a major health challenge, which requires noninvasive imaging to complement conventional approaches to diagnosis and monitoring. Several renal pathologies are associated with changes in kidney size, offering an opportunity for magnetic resonance imaging (MRI) biomarkers of disease. This work uses dynamic MRI and an automated bean-shaped model (ABSM) for longitudinal quantification of pathophysiologically relevant changes in kidney size.

METHODS

A geometry-based ABSM was developed for kidney size measurements in rats using parametric MRI (T₂ , T₂ * mapping). The ABSM approach was applied to longitudinal renal size quantification using occlusion of the (a) suprarenal aorta or (b) the renal vein, (c) increase in renal pelvis and intratubular pressure and (d) injection of an X-ray contrast medium into the thoracic aorta to induce pathophysiologically relevant changes in kidney size.

RESULTS

The ABSM yielded renal size measurements with accuracy and precision equivalent to the manual segmentation, with >70-fold time savings. The automated method could detect a ~7% reduction (aortic occlusion) and a ~5%, a ~2% and a ~6% increase in kidney size (venous occlusion, pelvis and intratubular pressure increase and injection of X-ray contrast medium, respectively). These measurements were not affected by reduced image quality following administration of ferumoxytol.

CONCLUSION

Dynamic MRI in conjunction with renal segmentation using an ABSM supports longitudinal quantification of changes in kidney size in pathophysiologically relevant experimental setups mimicking realistic clinical scenarios. This can potentially be instrumental for developing MRI-based diagnostic tools for various kidney disorders and for gaining new insight into mechanisms of renal pathophysiology.

The Future Role of Machine Learning in Clinical Transplantation

The use of artificial intelligence and machine learning (ML) has revolutionized our daily lives and will soon be instrumental in healthcare delivery. The rise of ML is due to multiple factors: increasing access to massive datasets, exponential increases in processing power, and key algorithmic developments that allow ML models to tackle increasingly challenging questions. Progressively more transplantation research is exploring the potential utility of ML models throughout the patient journey, although this has not yet widely transitioned into the clinical domain. In this review, we explore common approaches used in ML in solid organ clinical transplantation and consider opportunities for ML to help clinicians and patients. We discuss ways in which ML can aid leverage of large complex datasets, generate cutting-edge prediction models, perform clinical image analysis, discover novel markers in molecular data, and fuse datasets to generate novel insights in modern transplantation practice. We focus on key areas in transplantation in which ML is driving progress, explore the future potential roles of ML, and discuss the challenges and limitations of these powerful tools.

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.