Hemorrhagic Cysts and Other MR Biomarkers for Predicting Renal Dysfunction Progression in Autosomal Dominant Polycystic Kidney Disease

Kidney growth progression patterns in autosomal dominant polycystic kidney disease

BACKGROUND

Prognosis for autosomal dominant polycystic kidney disease (ADPKD), the main inherited cause of kidney failure, relies on estimating cystic growth using linear formulas derived from height-adjusted total kidney volume (Ht-TKV). However, nonlinear renal growth patterns may occur in typical ADPKD.

AIMS

To determine kidney outcomes of subjects diagnosed with typical ADPKD exhibiting nonlinear, and unpredictable cystic growth during follow-up.

METHODS

Retrospective cohort study. We categorized TKV changes in individuals with typical ADPKD according to observed kidney growth trajectories. Ht-TKV was calculated from consecutive CT or MRI using the ellipsoid method. We compared estimated glomerular filtration rate (eGFR) trajectories with linear mixed models.

RESULTS

We included 83 individuals with ADPKD (67% women; age 47 ± 12 years; follow-up 5.2 years [IQR 2.8-9.0]). Three kidney growth patterns were observed: slow progression (24%, <3%/year linear increase), fast progression (39%, ≥3%/year linear increase), and atypical progression (37%, nonlinear growth). Adjusted ht-TKV change in mL/m/year was +1.4 (IQR -4.5 to +10.0), +40.3 (+16.9 to +89.3), and +32.8 (+15.9 to +85.9) for slow, fast, and atypical progressors, respectively (p < 0.001). Atypical progressors exhibited a significantly greater decline in eGFR in mL/min/m²/year (-7.9, 95% CI -6.5, -3.9) compared to slow (-0.5, 95% CI -3.1 to +0.5) and fast progressors (-3.4, 95% CI -7.9, -2.0; between-group p < 0.001). Atypical progressors had a higher proportion of acute complications, including hemorrhages, infections, and urolithiasis (84%), compared to slow (20%) and fast progressors (31%) (p < 0.001).

CONCLUSION

In typical ADPKD, nonlinear, abrupt, and unpredictable cyst growth occurs frequently, leading to a higher risk of acute complications and kidney function decline.

STING Promotes the Progression of ADPKD by Regulating Mitochondrial Function, Inflammation, Fibrosis, and Apoptosis

Autosomal dominant polycystic kidney disease (ADPKD) is a predominant genetic disease, which is caused by mutations in PKD genes and is associated with DNA damage in cystic cells. The intrinsic stimulator of interferon genes (STING) pathway is crucial for recognizing damaged DNA in the cytosol, triggering the expression of inflammatory cytokines to activate defense mechanisms. However, the precise roles and mechanisms of STING in ADPKD remain elusive. In this study, we show that Pkd1 mutant mouse kidneys show upregulation of STING, which is stimulated by the DNAs of nuclear and mitochondrial origin. The activation of STING promotes cyst growth through increasing (1) the activation of NF-κB in Pkd1 mutant cells and (2) the recruitment of macrophages in the interstitial and peri-cystic regions in Pkd1 mutant mouse kidneys via NF-κB mediating the upregulation of TNF-α and MCP-1. Targeting STING with its specific inhibitor C-176 delays cyst growth in an early-stage aggressive Pkd1 conditional knockout mouse model and a milder long-lasting Pkd1 mutant mouse model. Targeting STING normalizes mitochondrial structure and function, decreases the formation of micronuclei, induces Pkd1 mutant renal epithelial cell death via p53 signaling, and decreases renal fibrosis in Pkd1 mutant mouse kidneys. These results support that STING is a novel therapeutic target for ADPKD treatment.

Quantitative susceptibility mapping for detection of kidney stones, hemorrhage differentiation, and cyst classification in ADPKD

BACKGROUND AND PURPOSE

The objective is to demonstrate feasibility of quantitative susceptibility mapping (QSM) in autosomal dominant polycystic kidney disease (ADPKD) patients and to compare imaging findings with traditional T1/T2w magnetic resonance imaging (MRI).

METHODS

Thirty-three consecutive patients (11 male, 22 female) diagnosed with ADPKD were initially selected. QSM images were reconstructed from the multiecho gradient echo data and compared to co-registered T2w, T1w, and CT images. Complex cysts were identified and classified into distinct subclasses based on their imaging features. Prevalence of each subclass was estimated.

RESULTS

QSM visualized two renal calcifications measuring 9 and 10 mm and three pelvic phleboliths measuring 2 mm but missed 24 calcifications measuring 1 mm or less and 1 larger calcification at the edge of the field of view. A total of 121 complex T1 hyperintense/T2 hypointense renal cysts were detected. 52 (43%) Cysts appeared hyperintense on QSM consistent with hemorrhage; 60 (49%) cysts were isointense with respect to simple cysts and normal kidney parenchyma, while the remaining 9 (7%) were hypointense. The presentation of the latter two complex cyst subtypes is likely indicative of proteinaceous composition without hemorrhage.

CONCLUSION

Our results indicate that QSM of ADPKD kidneys is possible and uniquely suited to detect large renal calculi without ionizing radiation and able to identify properties of complex cysts unattainable with traditional approaches.

Automated prognosis of renal function decline in ADPKD patients using deep learning

An accurate prognosis of renal function decline in Autosomal Dominant Polycystic Kidney Disease (ADPKD) is crucial for early intervention. Current biomarkers used are height-adjusted total kidney volume (HtTKV), estimated glomerular filtration rate (eGFR), and patient age. However, manually measuring kidney volume is time-consuming and subject to observer variability. Additionally, incorporating automatically generated features from kidney MRI images, along with conventional biomarkers, can enhance prognostic improvement. To address these issues, we developed two deep-learning algorithms. Firstly, an automated kidney volume segmentation model accurately calculates HtTKV. Secondly, we utilize segmented kidney volumes, predicted HtTKV, age, and baseline eGFR to predict chronic kidney disease (CKD) stages >=3A, >=3B, and a 30% decline in eGFR after 8 years from the baseline visit. Our approach combines a convolutional neural network (CNN) and a multi-layer perceptron (MLP). Our study included 135 subjects and the AUC scores obtained were 0.96, 0.96, and 0.95 for CKD stages >=3A, >=3B, and a 30% decline in eGFR, respectively. Furthermore, our algorithm achieved a Pearson correlation coefficient of 0.81 between predicted and measured eGFR decline. We extended our approach to predict distinct CKD stages after eight years with an AUC of 0.97. The proposed approach has the potential to enhance monitoring and facilitate prognosis in ADPKD patients, even in the early disease stages.

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.

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 [...].

Abdominal Imaging in ADPKD: Beyond Total Kidney Volume

In the context of autosomal dominant polycystic kidney disease (ADPKD), measurement of the total kidney volume (TKV) is crucial. It acts as a marker for tracking disease progression, and evaluating the effectiveness of treatment strategies. The TKV has also been recognized as an enrichment biomarker and a possible surrogate endpoint in clinical trials. Several imaging modalities and methods are available to calculate the TKV, and the choice depends on the purpose of use. Technological advancements have made it possible to accurately assess the cyst burden, which can be crucial to assessing the disease state and helping to identify rapid progressors. Moreover, the development of automated algorithms has increased the efficiency of total kidney and cyst volume measurements. Beyond these measurements, the quantification and characterization of non-cystic kidney tissue shows potential for stratifying ADPKD patients early on, monitoring disease progression, and possibly predicting renal function loss. A broad spectrum of radiological imaging techniques are available to characterize the kidney tissue, showing promise when it comes to non-invasively picking up the early signs of ADPKD progression. Radiomics have been used to extract textural features from ADPKD images, providing valuable information about the heterogeneity of the cystic and non-cystic components. This review provides an overview of ADPKD imaging biomarkers, focusing on the quantification methods, potential, and necessary steps toward a successful translation to clinical practice.

QSM Throughout the Body

Magnetic materials in tissue, such as iron, calcium, or collagen, can be studied using quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied in the brain, but is increasingly utilized outside the brain. QSM relies on the effect of tissue magnetic susceptibility sources on the MR signal phase obtained with gradient echo sequence. However, in the body, the chemical shift of fat present within the region of interest contributes to the MR signal phase as well. Therefore, correcting for the chemical shift effect by means of water-fat separation is essential for body QSM. By employing techniques to compensate for cardiac and respiratory motion artifacts, body QSM has been applied to study liver iron and fibrosis, heart chamber blood and placenta oxygenation, myocardial hemorrhage, atherosclerotic plaque, cartilage, bone, prostate, breast calcification, and kidney stone.

Acute renal intracystic hemorrhage in patients with autosomal dominant polycystic kidney disease

BACKGROUND

Renal cyst bleeding is a frequent problem in patients with autosomal dominant polycystic kidney disease (ADPKD). However, information is still limited on its frequency, causative factors, and effects on enlargement of polycystic kidneys in ADPKD.

METHODS

We investigated the total volume of acute renal intracystic hemorrhage and its association with total kidney volume (TKV) in a large series of patients with ADPKD on dialysis, referred for renal transcatheter arterial embolization. All patients had undergone CT scan and MRI scan before the procedure. We evaluated factors potentially associated with acute renal intracystic hemorrhage. The association between the  volume of acute renal intracystic hemorrhage and the potential predisposing and associated factors was analysed by univariable and multivariable regressions.  RESULTS: We enrolled 199 patients who underwent renal transcatheter arterial embolization from 2014 to 2018 (107 men, 92 women; mean age 59.1 ± 8.6 years). The median volume of acute renal intracystic hemorrhage was 97.3 ml (interquartile range 36.6-261.7 ml). Multivariable analysis revealed that body weight, kidney stones, systolic blood pressure, and total volume of acute renal intracystic hemorrhage were significantly associated with TKV; age, body mass index, smoking, renal cyst infection, serum alkaline phosphatase, and TKV were significantly associated with the volume of acute renal intracystic hemorrhage ; and sex, age, dialysis vintage, TKV, and total volume of acute renal intracystic hemorrhage were significantly associated with the number of microcoils required to achieve renal  transcatheter arterial embolization. Total volume of acute renal intracystic hemorrhage was significantly associated with TKV (r = 0.15, p = 0.0325) and was greater in younger patients (r= - 0.32, p < 0.0001). Total volume of acute renal intracystic hemorrhage was also correlated with the number of microcoils required for renal transcatheter arterial embolization (r = 0.23, p = 0.0012).

CONCLUSION

Acute renal intracystic hemorrhage is frequent among ADPKD patients on dialysis, and total volume of acute renal intracystic hemorrhage significantly associated with  TKV. Total volume of acute renal intracystic hemorrhage  was greater in younger patients with higher renal artery luminal size. These results suggest that renal cyst bleeding and renal artery blood flow may synergistically accelerate the enlargement of polycystic kidneys in ADPKD patients on dialysis.

Cyst Fraction as a Biomarker in Autosomal Dominant Polycystic Kidney Disease

Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic kidney disease. Patients at high risk of severe disease progression should be identified early in order to intervene with supportive and therapeutic measures. However, the glomerular filtration rate (GFR) may remain within normal limits for decades until decline begins, making it a late indicator of rapid progression. Kidney volumetry is frequently used in clinical practice to allow for an assessment of disease severity. Due to limited prognostic accuracy, additional imaging markers are of high interest to improve outcome prediction in ADPKD, but data from clinical cohorts are still limited. In this study, we examined cyst fraction as one of these parameters in a cohort of 142 ADPKD patients. A subset of 61 patients received MRIs in two consecutive years to assess longitudinal changes. All MRIs were analyzed by segmentation and volumetry of the kidneys followed by determination of cyst fraction. As expected, both total kidney volume (TKV) and cyst fraction correlated with estimated GFR (eGFR), but cyst fraction showed a higher R² in a univariate linear regression. Besides, only cyst fraction remained statistically significant in a multiple linear regression including both htTKV and cyst fraction to predict eGFR. Consequently, this study underlines the potential of cyst fraction in ADPKD and encourages prospective clinical trials examining its predictive value in combination with other biomarkers to predict future eGFR decline.

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

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

A MRI-based radiomics nomogram for evaluation of renal function in ADPKD

OBJECTIVES

This study is aimed to establish a fusion model of radiomics-based nomogram to predict the renal function of autosomal dominant polycystic kidney disease (ADPKD).

METHODS

One hundred patients with ADPKD were randomly divided into training group (n = 69) and test group (n = 31). The radiomics features were extracted from T1-weighted fat suppression images (FS-T1WI) and T2-weighted fat suppression images (FS-T2WI). Decision tree algorithm was employed to build radiomics model to get radiomics signature. Then multivariate logistic regression analysis was used to establish the radiomics nomogram based on independent clinical factors, conventional MR imaging variables and radiomics signature. The receiver operating characteristic (ROC) analysis and Delong test were used to compare the performance of radiomics model and radiomics nomogram model, and the decision curve to evaluate the clinical application value of radiomics nomogram model in the evaluation of renal function in patients with ADPKD.

RESULTS

Fourteen radiomics features were selected to establish radiomics model. Based on FS-T1WI and FS-T2WI sequences, the radiomics model showed good discrimination ability in training group and test group [training group: (AUC) = 0.7542, test group (AUC) = 0.7417]. The performance of radiomics nomogram model was significantly better than that of radiomics model in all data sets [radiomics model (AUC) = 0.7505, radiomics nomogram model (AUC) = 0.8435, p value = 0.005]. The analysis of calibration curve and decision curve showed that radiomics nomogram model had more clinical application value.

CONCLUSION

radiomics analysis of MRI can be used for the preliminary evaluation and prediction of renal function in patients with ADPKD. The radiomics nomogram model shows better prediction effect in renal function evaluation, and can be used as a non-invasive renal function prediction tool to assist clinical decision-making. Trial registration ChiCTR, ChiCTR2100046739. Registered 27 May 2021-retrospectively registered, http://www.ChiCTR.org.cn/showproj.aspx?proj=125955.