Search Results (28)

Background/Objectives: Autosomal dominant polycystic kidney disease (ADPKD) is a major cause of end-stage kidney disease (ESKD), accounting for approximately 5–10% of patients receiving dialysis worldwide. The large and numerous cysts in the liver and kidneys cause abdominal distention and poor appetite. Previous studies showed that renal arterial embolization (RAE) reduces total kidney volume (TKV), increases appetite, and improves quality of life. This article aims to evaluate the efficacy of RAE in increasing psoas muscle (PM) and paraspinal muscle (PS) mass in patients with polycystic kidney disease. Methods: A retrospective study was conducted from May 2016 to December 2020. Thirty-five patients with PKD and ESKD who received RAE were enrolled. The clinical data, including age, sex, body weight, abdominal circumference, and laboratory results, including albumin, creatinine, estimated glomerular filtration rate, and dialysis vintage, were collected. TKV was calculated with the ellipsoid formula method, and muscle mass was measured with bilateral PM and PS areas at the third lumbar level. The associated clinical, laboratory, and imaging data were compared before and after RAE. Results: There were 19 females and 16 males with a mean age of 59.9 for the final analysis. There were significant changes between baseline and 3-month, 6-month, 12-month after RAE, such as a decrease in TKV (4684 ± 3361 vs. 4079 ± 3456, 3675 ± 3401, 2459 ± 1706 mL, all p < 0.001), an increase in the PM area (12.6 ± 5.8 vs. 13.3 ± 5.7, 14.7 ± 6.9, 14.3 ± 7.1 cm², all p < 0.05), but no difference in body weight, body mass index, albumin, hemoglobin, creatinine, or estimated glomerular filtration rate. The increase in the PM and PS was more obvious in the sarcopenic group than in the non-sarcopenic group in the 12-month follow-up (p = 0.001 and 0.016 vs. p = 0.205 and 0.259). Conclusions: RAE effectively reduces TKV, increases PM and PS mass, and serves as a candidate to reverse muscle loss in patients with PKD. Full article

Objectives: In patients with autosomal dominant polycystic kidney disease (ADPKD), total kidney volume (TKV) is the gold standard biomarker for assessing the risk of progression and the need for drug therapy. However, it is a time-consuming process. In this systematic review and meta-analysis, we evaluate the current state of deep learning (DL) algorithms for automatic kidney volume segmentation. Methods: All original research, including the search terms ADPKD, diagnostic imaging, DL, and TKV, was identified in PubMed, Embase, and Ovid MEDLINE databases from January 2000 to 13 October 2024. Articles with insufficient information to assess methodological quality were excluded. Quality was assessed using the “Quality Assessment of Diagnostic Accuracy Studies, Version 2” (QUADAS-2) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) tools. We focused on the Dice Similarity Coefficient (DSC), bias differences, and time efficiency as outcomes. Results: Nineteen studies were included, with an overall low risk of bias; however, the mean adherence to the CLAIM checklist was 64%. The pooled DSC under the random-effects model was $0.953$ (95% CI: $0.938–0.969$) with relatively low bias for TKV in 5622 ADPKD patients (mean age, 46.1 years; 45% male) and 9180 scans (79% MRI). The average segmentation time was decreased by 75% compared to the ground truth. Performance differences were evident among imaging modalities, MRI sequences, and 3D vs. 2D models, but not among imaging planes. The between-study heterogeneity was low ($I^2=0\%$), and no statistically significant evidence of small-study effects or publication bias was detected. Conclusions: DL models for TKV in ADPKD patients demonstrated high precision compared to manual segmentation in a large, pooled sample with heterogeneous study designs and methods. While clinical implementation is not yet feasible, the current work demonstrates the technical and diagnostic efficacy of image-based DL segmentation models. Full article

Background: Tolvaptan, a vasopressin V2 receptor antagonist, is the only approved disease-modifying therapy for Autosomal Dominant Polycystic Kidney Disease (ADPKD), yet real-world data on its long-term effectiveness remain limited. Methods: In this single-center retrospective study, we evaluated 30 patients with ADPKD who received tolvaptan therapy for at least three years between 2019 and 2024. All patients met standard inclusion criteria and underwent serial magnetic resonance imaging to assess total kidney volume (TKV), along with longitudinal monitoring of renal function using estimated glomerular filtration rate (eGFR). Results: At the end of follow-up, the median annual TKV growth rate was 4.27% (IQR: 1.39–7.98), which did not differ significantly from the predicted without treatment growth rate of 5.3% (95% CI: −2.75 to 0.69, p = 0.194). Although the impact on TKV was limited, tolvaptan notably slowed the decline in kidney function, with a median eGFR of 65 mL/min/1.73 m² at follow-up, compared to a predicted value of 60.8 mL/min/1.73 m² (95% CI: −14.60 to −6.18, p < 0.001), reflecting a 33.9% relative benefit. In 80% of patients, renal function after three years was better than predicted. Conclusions: These findings suggest that tolvaptan provides significant functional benefit in ADPKD patients in routine clinical practice, even in the absence of marked suppression in TKV growth and support its continued use in carefully selected individuals. Full article

Background: The Mayo imaging classification (MIC) for polycystic kidney disease (PKD) is a crucial basis for clinical treatment decisions; however, the volumetric assessment for its evaluation remains tedious and inaccurate. While the ellipsoid method for measuring the total kidney volume (TKV) in patients with PKD provides a practical TKV estimation using computed tomography (CT), its inconsistency and inaccuracy are limitations, highlighting the need for improved, accessible techniques in real-world clinics. Methods: We compared manual ellipsoid and artificial intelligence (AI)-based kidney volumetry methods using a convolutional neural network-based segmentation model (3D Dynamic U-Net) for measuring the TKV by assessing 32 patients with PKD in a single tertiary hospital. Results: The median age and average TKV were 56 years and 1200.24 mL, respectively. Most of the patients were allocated to Mayo Clinic classifications 1B and 1C using the ellipsoid method, similar to the AI volumetry classification. AI volumetry outperformed the ellipsoid method with highly correlated scores (AI vs. nephrology professor ICC: r = 0.991, 95% confidence interval (CI) = 0.9780–0.9948, p < 0.01; AI vs. trained clinician ICC: r = 0.983, 95% CI = 0.9608–0.9907, p < 0.01). The Bland–Altman plot also showed that the mean differences between professor and AI volumetry were statistically insignificant (mean difference 159.5 mL, 95% CI = 11.8368–330.7817, p = 0.07). Conclusions: AI-based kidney volumetry demonstrates strong agreement with expert manual measurements and offers a reliable, labor-efficient alternative for TKV assessment in clinical practice. It is helpful and essential for managing PKD and optimizing therapeutic outcomes. Full article

Background: Several studies have shown that total kidney volume (TKV) measurements may serve as a non-invasive imaging biomarker for monitoring and predicting the progression of autosomal dominant polycystic kidney disease (ADPKD) in children. Methods: This study aimed to evaluate the relationship between height-adjusted TKV (htTKV), estimated glomerular filtration rate (GFR), and blood pressure, assessed using 24 h ambulatory blood pressure monitoring (ABPM), in children with early-stage ADPKD. The study was conducted with 72 children, mean age 12.46 ± 3.76 (5.42–17.92). Results: Hypertension (HT) was diagnosed in (20) 28% of children. ABPM allowed the identification of previously undiagnosed HT in 12 (16.7%) children. Decreased GFR was demonstrated in 10 (14%) children, and hyperfiltration in 5 (7%) children. Significantly higher htTKV and calculated TKV z-score and more frequent decreases in GFR were observed in hypertensive children (p = 0.018; 0.020 and 0.010, respectively). The study demonstrated a significant inverse correlation between htTKV and GFR (r −0.25; p = 0.032). The TKV z-score showed a very good correlation with all ABPM parameters, except for DBP and DBP z-score during the day. Receiver operating curve (ROC) analysis showed that htTKV and TKV z-score had good diagnostic value for predicting a decline in GFR (AUC 0.808, p < 0.001), but were not useful for predicting the onset of HT (AUC 0.697, p = 0.010). Conclusions: There is a relationship between TKV, GFR, and blood pressure parameters in children with early-stage ADPKD. The TKV z-score can be useful for predicting GFR decline. Children with ADPKD and increasing TKV require careful blood pressure monitoring. Full article

Background/Objectives: Polycystic liver disease (PLD) is the most common extrarenal manifestation of autosomal dominant polycystic kidney disease (ADPKD). PLD is more prevalent in women, and women have larger liver cysts, possibly due to estrogen-related mechanisms. Maternal estrogen levels normally increase during pregnancy. Thus, we investigated the pregnancy-associated increase in liver volume, liver cyst volume, total kidney volume (TKV), and kidney cyst growth rates in ADPKD patients. Methods: Kidney, liver, and cyst volumes were measured in 16 ADPKD patients by magnetic resonance imaging (MRI) at multiple timepoints before and after pregnancy. The log-transformed TKV, liver volume, and cyst volume growth rates during a period with pregnancy were compared to a period without pregnancy. Results: In ADPKD patients, a higher annualized liver cyst growth rate was observed during a period with pregnancy compared to a period without pregnancy (34 ± 16%/yr vs. 23 ± 17%/yr; p-value = 0.005). Liver volume growth was also higher during a period with pregnancy, 6 [2, 7]%/yr vs. 0.3 [−0.4, 2]%/yr (p-value = 0.04). In addition, the mean kidney cyst growth rate was higher (12 ± 11%/yr vs. 4 ± 9%/yr; p-value = 0.05), and there was a trend toward a pregnancy-associated increase in the TKV growth rate (6 [4, 8]%/yr vs. 3 [0.8, 5]%/yr, (p-value = 0.14) during a period with pregnancy. Conclusions: In patients with ADPKD, the liver volume and cyst volume growth rates increased during pregnancy. This supports the hypothesis that the estrogen-mediated stimulation of liver cyst growth may contribute to the severe polycystic liver disease that is more prevalent in women than men with ADPKD. Further studies with larger populations are needed to explore the mechanisms and long-term implications of these findings. Full article

Background/Objectives: Although tolvaptan efficacy in ADPKD has been demonstrated in randomized clinical trials, there is no definitive method for assessing its efficacy in the individual patient in the clinical setting. In this exploratory feasibility study, we report a method to quantify the change in total kidney volume (TKV) growth rate to retrospectively evaluate tolvaptan efficacy for individual patients. Treatment-related changes in estimated glomerular filtration rate (eGFR) are also assessed. Methods: MRI scans covering at least 1 year prior to and during treatment with tolvaptan were performed, with deep learning facilitated kidney segmentation and fitting multiple imaging timepoints to exponential growth in 32 ADPKD patients. Clustering analysis differentiated tolvaptan treatment “responders” and “non-responders” based upon the magnitude of change in TKV growth rate. Differences in rate of eGFR decline, urine osmolality, and other parameters were compared between responders and non-responders. Results: Eighteen (56%) tolvaptan responders (mean age 42 ± 8 years) were identified by k-means clustering, with an absolute reduction in annual TKV growth rate of >2% (mean = −5.1% ± 2.5% per year). Thirteen (44%) non-responders were identified, with <1% absolute reduction in annual TKV growth rate (mean = +2.4% ± 2.7% per year) during tolvaptan treatment. Compared to non-responders, tolvaptan responders had significantly higher mean TKV growth rates prior to tolvaptan treatment (7.1% ± 3.6% per year vs. 3.7% ± 2.4% per year; p = 0.003) and higher median pretreatment spot urine osmolality (Uosm, 393 mOsm/kg vs. 194 mOsm/kg, p = 0.03), confirmed by multivariate analysis. Mean annual rate of eGFR decline was less in responders than in non-responders (−0.25 ± 0.04, CI: [−0.27, −0.23] mL/min/1.73 m² per year vs. −0.40 ± 0.06, CI: [−0.43, −0.37] mL/min/1.73 m² per year, p = 0.036). Conclusions: In this feasibility study designed to assess predictors of tolvaptan treatment efficacy in individual patients with ADPKD, we found that high pretreatment levels of annual TKV growth rate and higher pretreatment spot urine osmolality were associated with a responder phenotype. Full article

Background: Total Kidney Volume (TKV) is widely used globally to predict the progressive loss of renal function in patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD). Typically, TKV is calculated using Computed Tomography (CT) images by manually locating, delineating, and segmenting the ADPKD kidneys. However, manual localization and segmentation are tedious, time-consuming tasks and are prone to human error. Specifically, there is a lack of studies that focus on CT modality variation. Methods: In contrast, our work develops a step-by-step framework, which robustly handles both Non-enhanced Computed Tomography (NCCT) and Contrast-enhanced Computed Tomography (CCT) images, ensuring balanced sample utilization and consistent performance across modalities. To achieve this, Artificial Intelligence (AI)-enabled localization and segmentation models are proposed for estimating TKV, which is designed to work robustly on both NCCT and Contrast-Computed Tomography (CCT) images. These AI-based models incorporate various image preprocessing techniques, including dilation and global thresholding, combined with Deep Learning (DL) approaches such as the adapted Single Shot Detector (SSD), Inception V2, and DeepLab V3+. Results: The experimental results demonstrate that the proposed AI-based models outperform other DL architectures, achieving a mean Average Precision (mAP) of $95\%$ for automatic localization, a mean Intersection over Union (mIoU) of $92\%$ for segmentation, and a mean $R^2$ score of $97\%$ for TKV estimation. Conclusions: These results clearly indicate that the proposed AI-based models can robustly localize and segment ADPKD kidneys and estimate TKV using both NCCT and CCT images. Full article

Background and Objectives: Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most frequent genetic renal disease with a complex physiopathology. More and more studies sustain that inflammation plays a crucial role in ADPKD pathogenesis and progression. We evaluated IL-12 involvement in ADPKD pathophysiology by assessing the serum levels of its monomers and heterodimers. Materials and Methods: A prospective case-control study was developed and included 66 ADPKD subjects and a control group of 40 healthy subjects. The diagnosis of ADPKD was based on familial history clinical and imagistic exams. The study included subjects with eGFR > 60 mL/min/1.73 mp, with no history of hematuria or other renal disorders, with stable blood pressure in the last 6 months. We tested serum levels of monomers IL-12 p40 and IL-12 p35 and heterodimers IL-12 p70, IL-23, IL 35, assessed by ELISA method. Results: IL-12 family programming was abnormal in ADPKD patients. IL-12p70, IL-12p40, and IL-23 secretion increased, while IL-12p35 and IL-35 secretion decreased compared to control. IL-12p70, IL-12p40, and IL-23 had a progressive increase correlated with immune response amplification, a decrease of eGFR, an increase in TKV, and in albuminuria. On the other hand, IL-35 and IL-12p35 were correlated negatively with CRP and albuminuria and positively with eGFR in advanced ADPKD. Conclusions: The present study investigated IL-12 cytokine family members’ involvement in ADPKD pathogenesis, enriching our understanding of inflammation in the most common renal genetic disorder. Full article

Autosomal dominant polycystic kidney disease (ADPKD) is the predominant hereditary factor leading to end-stage renal disease (ESRD) worldwide, affecting individuals across all races with a prevalence of 1 in 400 to 1 in 1000. The disease presents significant challenges in management, particularly with limited options for slowing cyst progression, as well as the use of tolvaptan being restricted to high-risk patients due to potential liver injury. However, determining high-risk status typically requires magnetic resonance imaging (MRI) to calculate total kidney volume (TKV), a time-consuming process demanding specialized expertise. Motivated by these challenges, this study proposes alternative methods for high-risk categorization that do not rely on TKV data. Utilizing historical patient data, we aim to predict rapid kidney enlargement in ADPKD patients to support clinical decision-making. We applied seven machine learning algorithms—Random Forest, Logistic Regression, Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Tree, XGBoost, and Deep Neural Network (DNN)—to data from the Polycystic Kidney Disease Outcomes Consortium (PKDOC) database. The XGBoost model, combined with the Synthetic Minority Oversampling Technique (SMOTE), yielded the best performance. We also leveraged explainable artificial intelligence (XAI) techniques, specifically Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), to visualize and clarify the model’s predictions. Furthermore, we generated text summaries to enhance interpretability. To evaluate the effectiveness of our approach, we proposed new metrics to assess explainability and conducted a survey with 27 doctors to compare models with and without XAI techniques. The results indicated that incorporating XAI and textual summaries significantly improved expert explainability and increased confidence in the model’s ability to support treatment decisions for ADPKD patients. Full article

Background: Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary renal disease leading to end-stage renal disease. Total kidney volume (TKV) measurement has been considered as a surrogate in the evaluation of disease severity and prognostic predictor of ADPKD. However, the traditional manual measurement of TKV by medical professionals is labor-intensive, time-consuming, and human error prone. Materials and methods: In this investigation, we conducted TKV measurements utilizing magnetic resonance imaging (MRI) data. The dataset consisted of 30 patients with ADPKD and 10 healthy individuals. To calculate TKV, we trained models using both coronal- and axial-section MRI images. The process involved extracting images in Digital Imaging and Communications in Medicine (DICOM) format, followed by augmentation and labeling. We employed a U-net model for image segmentation, generating mask images of the target areas. Subsequent post-processing steps and TKV estimation were performed based on the outputs obtained from these mask images. Results: The average TKV, as assessed by medical professionals from the testing dataset, was 1501.84 ± 965.85 mL with axial-section images and 1740.31 ± 1172.21 mL with coronal-section images, respectively (p = 0.73). Utilizing the deep learning model, the mean TKV derived from axial- and coronal-section images was 1536.33 ± 958.68 mL and 1636.25 ± 964.67 mL, respectively (p = 0.85). The discrepancy in mean TKV between medical professionals and the deep learning model was 44.23 ± 58.69 mL with axial-section images (p = 0.8) and 329.12 ± 352.56 mL with coronal-section images (p = 0.9), respectively. The average variability in TKV measurement was 21.6% with the coronal-section model and 3.95% with the axial-section model. The axial-section model demonstrated a mean Dice Similarity Coefficient (DSC) of 0.89 ± 0.27 and an average patient-wise Jaccard coefficient of 0.86 ± 0.27, while the mean DSC and Jaccard coefficient of the coronal-section model were 0.82 ± 0.29 and 0.77 ± 0.31, respectively. Conclusion: The integration of deep learning into image processing and interpretation is becoming increasingly prevalent in clinical practice. In our pilot study, we conducted a comparative analysis of the performance of a deep learning model alongside corresponding axial- and coronal-section models, a comparison that has been less explored in prior research. Our findings suggest that our deep learning model for TKV measurement performs comparably to medical professionals. However, we observed that varying image orientations could introduce measurement bias. Specifically, our AI model exhibited superior performance with axial-section images compared to coronal-section images. Full article

Abdominal imaging of autosomal dominant polycystic kidney disease (ADPKD) has historically focused on detecting complications such as cyst rupture, cyst infection, obstructing renal calculi, and pyelonephritis; discriminating complex cysts from renal cell carcinoma; and identifying sources of abdominal pain. Many imaging features of ADPKD are incompletely evaluated or not deemed to be clinically significant, and because of this, treatment options are limited. However, total kidney volume (TKV) measurement has become important for assessing the risk of disease progression (i.e., Mayo Imaging Classification) and predicting tolvaptan treatment’s efficacy. Deep learning for segmenting the kidneys has improved these measurements’ speed, accuracy, and reproducibility. Deep learning models can also segment other organs and tissues, extracting additional biomarkers to characterize the extent to which extrarenal manifestations complicate ADPKD. In this concept paper, we demonstrate how deep learning may be applied to measure the TKV and how it can be extended to measure additional features of this disease. Full article

Tolvaptan, an oral vasopressin V2 receptor antagonist, reduces renal volume expansion and loss of renal function in patients with autosomal dominant polycystic kidney disease (ADPKD). Data for predictive factors indicating patients more likely to benefit from long-term tolvaptan are lacking. Data were retrospectively collected from 55 patients on tolvaptan for 6 years. Changes in renal function, progression of renal dysfunction (estimated glomerular filtration rate [eGFR], 1-year change in eGFR [ΔeGFR/year]), and renal volume (total kidney volume [TKV], percentage 1-year change in TKV [ΔTKV%/year]) were evaluated at 3-years pre-tolvaptan, at baseline, and at 6 years. In 76.4% of patients, ΔeGFR/year improved at 6 years. The average 6-year ΔeGFR/year (range) minus baseline ΔeGFR/year: 3.024 (−8.77–20.58 mL/min/1.73 m²). The increase in TKV was reduced for the first 3 years. A higher BMI was associated with less of an improvement in ΔeGFR (p = 0.027), and family history was associated with more of an improvement in ΔeGFR (p = 0.044). Hypernatremia was generally mild; 3 patients had moderate-to-severe hyponatremia due to prolonged, excessive water intake in response to water diuresis—a side effect of tolvaptan. Family history of ADPKD and baseline BMI were contributing factors for ΔeGFR/year improvement on tolvaptan. Hyponatremia should be monitored with long-term tolvaptan administration. Full article

Autosomal dominant polycystic renal disease (ADPKD) is the most frequent kidney inheritable disease, characterized by the presence of numerous bilateral renal cysts, causing a progressive increase in total kidney volume (TKV) and a progressive loss of renal function. Several methods can be used to measure TKV by using MRI, and they differ in complexity, accuracy and time consumption. This study was performed to assess the performance of the ellipsoid method and the semi-automatic segmentation method, both for TKV and SKV (single kidney volume) computation. In total, 40 patients were enrolled, and 78 polycystic kidneys analyzed. Two independent operators with different levels of experience evaluated renal volumetry using both methods. Mean error for ellipsoid method for SKV computation was −2.74 ± 11.79% and 3.25 ± 10.02% for the expert and the beginner operator, respectively (p = 0.0008). A Wilcoxon test showed a statistically significant difference between the two operators for both methods (SKV p = 0.0371 and 0.0034; TKV p = 0.0416 and 0.0171 for the expert and the beginner operator, respectively). No inter-operator significant difference was found for the semi-automatic method, in contrast to the ellipsoid method. Both with a Wilcoxon test and Bland–Altman plot, statistically significant differences were found when comparing SKV and TKV measurements obtained with the two methods for both operators, even if the differences are stronger for the beginner operator than for the expert one. The semi-automatic segmentation method showed more inter-observer reproducibility. The ellipsoid method, in contrast, appears to be affected by greater inter-observer variability, especially when performed by operators with limited experience. Full article

Background: Multiparametric renal Magnetic Resonance Imaging (MRI) provides a non-invasive method to assess kidney structure and function, but longitudinal studies are limited. Methods: A total of 22 patients with CKD category G3-4 (estimated glomerular filtration rate (eGFR) 15–59 mL/min/1.73 m²) were recruited. Annual 3T multiparametric renal MRI scans were performed, comprising total kidney volume (TKV), longitudinal relaxation time (T₁), apparent diffusion coefficient (ADC), Arterial Spin Labelling, and Blood Oxygen Level Dependent relaxation time (T₂*), with 15 patients completing a Year 2 scan. CKD progression over 2 years was defined as eGFR_slope ≥ −5 mL/min/1.73 m²/year. Results: At baseline, T₁ was higher (cortex p = 0.05, medulla p = 0.03) and cortex perfusion lower (p = 0.015) in participants with subsequent progression versus stable eGFR. A significant decrease in TKV and ADC and an increase in cortex T₁ occurred in progressors at Year 1 and Year 2, with a significant decrease in perfusion in progressors only at Year 2. The only decline in the stable group was a reduction in TKV. There was no significant change in cortex or medulla T₂* at Year 1 or Year 2 for progressors or stable participants. Conclusion: Lower renal cortex perfusion and higher T₁ in the cortex and medulla may predict CKD progression, while renal cortex T₁, TKV, and ADC may be useful to monitor progression. This study provides pilot data for future large-scale studies. Full article