Citations

Significance Statement Segmentation of multiple structures in cross-sectional imaging is time-consuming and impractical to perform manually, especially if the end goal is clinical implementation. In this study, we developed, validated, and demonstrated the capability of a deep learning algorithm to segment individual medullary pyramids in a rapid, accurate, and reproducible manner. The results demonstrate that cortex volume, medullary volume, number of pyramids, and mean pyramid volume is associated with patient clinical characteristics and microstructural findings and provide insights into the mechanisms that may lead to CKD. Background The kidney is a lobulated organ, but little is known regarding the clinical importance of the number and size of individual kidney lobes. Methods After applying a previously validated algorithm to segment the cortex and medulla, a deep-learning algorithm was developed and validated to segment and count individual medullary pyramids on contrast-enhanced computed tomography images of living kidney donors before donation. The association of cortex volume, medullary volume, number of pyramids, and mean pyramid volume with concurrent clinical characteristics (kidney function and CKD risk factors), kidney biopsy morphology (nephron number, glomerular volume, and nephrosclerosis), and short- and long-term GFR <60 or <45 ml/min per 1.73 m 2 was assessed. Results Among 2876 living kidney donors, 1132 had short-term follow-up at a median of 3.8 months and 638 had long-term follow-up at a median of 10.0 years. Larger cortex volume was associated with younger age, male sex, larger body size, higher GFR, albuminuria, more nephrons, larger glomeruli, less nephrosclerosis, and lower risk of low GFR at follow-up. Larger pyramids were associated with older age, female sex, larger body size, higher GFR, more nephrons, larger glomerular volume, more nephrosclerosis, and higher risk of low GFR at follow-up. More pyramids were associated with younger age, male sex, greater height, no hypertension, higher GFR, lower uric acid, more nephrons, less nephrosclerosis, and a lower risk of low GFR at follow-up. Conclusions Cortex volume and medullary pyramid volume and count reflect underlying variation in nephron number and nephron size as well as merging of pyramids because of age-related nephrosclerosis, with loss of detectable cortical columns separating pyramids.

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.

ABSTRACT Background Autosomal dominant polycystic kidney disease (ADPKD) presents with variable disease severity and progression. Advanced imaging biomarkers may provide insights into cystic and non-cystic processes leading to kidney failure in different age groups. Methods This pilot study included 39 ADPKD patients with kidney failure, stratified into three age groups (&lt;46, 46–56, &gt;56 years old). Advanced imaging biomarkers were assessed using an automated instance cyst segmentation tool. The biomarkers were compared with an age- and sex-matched ADPKD cohort in early chronic kidney disease (CKD). Results Ht-total parenchymal volume correlated negatively with age at kidney failure. The median Ht-total parenchymal volume was significantly lower in patients older than 56 years. Cystic burden was significantly higher at time of kidney failure, especially in patients who reached it before age 46 years. The cyst index at kidney failure was comparable across age groups and Mayo Imaging Classes. Advanced imaging biomarkers showed higher correlation with Ht-total kidney volume in early CKD than at kidney failure. Cyst index and parenchymal index were relatively stable over 5 years prior to kidney failure, whereas Ht-total cyst volume and cyst parenchymal surface area increased significantly. Conclusion Age-related differences in advanced imaging biomarkers suggest variable pathophysiological mechanisms in ADPKD patients with kidney failure. Further studies are needed to validate the utility of these biomarkers in predicting disease progression and guiding treatment strategies.

Significance Statement Nephron number currently can be estimated only from glomerular density on a kidney biopsy combined with cortical volume from kidney imaging. Because of measurement biases, refinement of this approach and validation across different patient populations have been needed. The prognostic importance of nephron number also has been unclear. The authors present an improved method of estimating nephron number that corrects for several biases, resulting in a 27% higher nephron number estimate for donor kidneys compared with a prior method. After accounting for comorbidities, the new nephron number estimate does not differ between kidney donors and kidney patients with tumor and shows consistent associations with clinical characteristics across these two populations. The findings also indicate that low nephron number predicts CKD independent of biopsy and clinical characteristics in both populations. Background Nephron number can be estimated from glomerular density and cortical volume. However, because of measurement biases, this approach needs refinement, comparison between disparate populations, and evaluation as a predictor of CKD outcomes. Methods We studied 3020 living kidney donors and 1354 patients who underwent radical nephrectomy for tumor. We determined cortex volume of the retained kidney from presurgical imaging and glomerular density by morphometric analysis of needle core biopsy of the donated kidney and wedge sections of the removed kidney. Glomerular density was corrected for missing glomerular tufts, absence of the kidney capsule, and then tissue shrinkage on the basis of analysis of 30 autopsy kidneys. We used logistic regression (in donors) and Cox proportional hazard models (in patients with tumor) to assess the risk of CKD outcomes associated with nephron number. Results Donors had 1.17 million nephrons per kidney; patients with tumor had 0.99 million nephrons per kidney. A lower nephron number was associated with older age, female sex, shorter height, hypertension, family history of ESKD, lower GFR, and proteinuria. After adjusting for these characteristics, nephron number did not differ between donors and patients with tumor. Low nephron number (defined by <5th or <10th percentile by age and sex in a healthy subset) in both populations predicted future risk of CKD outcomes independent of biopsy and clinical characteristics. Conclusions Compared with an older method for estimating nephron number, a new method that addresses several sources of bias results in nephron number estimates that are 27% higher in donors and 1% higher in patients with tumor and shows consistency between two populations. Low nephron number independently predicts CKD in both populations. Podcast This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/JASN/2023_06_22_JASN0000000000000124.mp3

Introduction Maintaining fluid intake sufficient to reduce arginine vasopressin (AVP) secretion has been hypothesised to slow kidney cyst growth in autosomal dominant polycystic kidney disease (ADPKD). However, evidence to support this as a clinical practice recommendation is of poor quality. The aim of the present study is to determine the long-term efficacy and safety of prescribed water intake to prevent the progression of height-adjusted total kidney volume (ht-TKV) in patients with chronic kidney disease (stages 1–3) due to ADPKD. Methods and analysis A multicentre, prospective, parallel-group, open-label, randomised controlled trial will be conducted. Patients with ADPKD (n=180; age ≤65 years, estimated glomerular filtration rate (eGFR) ≥30 mL/min/1.73 m 2 ) will be randomised (1:1) to either the control (standard treatment+usual fluid intake) or intervention (standard treatment+prescribed fluid intake) group. Participants in the intervention arm will be prescribed an individualised daily fluid intake to reduce urine osmolality to ≤270 mOsmol/kg, and supported with structured clinic and telephonic dietetic review, self-monitoring of urine-specific gravity, short message service text reminders and internet-based tools. All participants will have 6-monthly follow-up visits, and ht-TKV will be measured by MRI at 0, 18 and 36 months. The primary end point is the annual rate of change in ht-TKV as determined by serial renal MRI in control vs intervention groups, from baseline to 3 years. The secondary end points are differences between the two groups in systemic AVP activity, renal disease (eGFR, blood pressure, renal pain), patient adherence, acceptability and safety. Ethics and dissemination The trial was approved by the Human Research Ethics Committee, Western Sydney Local Health District. The results will inform clinicians, patients and policy-makers regarding the long-term safety, efficacy and feasibility of prescribed fluid intake as an approach to reduce kidney cyst growth in patients with ADPKD. Trial registration number ANZCTR12614001216606.

Objective: Investigate the performance of multiparametric MRI radiomic features, alone or combined with current standard-of-care methods, for pulmonary nodule classification. Assess the impact of segmentation variability on feature reproducibility and reliability. Methods: Radiomic features were extracted from 74 pulmonary nodules of 68 patients who underwent nodule resection or biopsy after MRI exam. The MRI features were compared with histopathology and conventional quantitative imaging values (maximum standardized uptake value [SUVmax] and mean Hounsfield unit [HU]) to determine whether MRI radiomic features can differentiate types of nodules and associate with SUVmax and HU using Wilcoxon rank sum test and linear regression. Diagnostic performance of features and four machine learning (ML) models were evaluated with area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs). Concordance correlation coefficient (CCC) assessed the segmentation variation impact on feature reproducibility and reliability. Results: Elevn diffusion-weighted features distinguished malignant from benign nodules (adjusted p < 0.05, AUC: 0.73–0.81). No features differentiated cancer types. Sixty-seven multiparametric features associated with mean CT HU and 14 correlated with SUVmax. All significant MRI features outperformed traditional imaging parameters (SUVmax, mean HU, apparent diffusion coefficient [ADC], T1, T2, dynamic contrast-enhanced imaging values) in distinguishing malignant from benign nodules with some achieving statistical significance (p < 0.05). Adding ADC and smoking history improved feature performance. Machine learning models demonstrated strong performance in nodule classification, with extreme gradient boosting (XGBoost) having the highest discrimination (AUC = 0.83, CI=[0.727, 0.932]). We found good to excellent inter- and intrareader feature reproducibility and reliability (CCC≥0.80). Conclusion: Eleven MRI radiomic features differentiated malignant from benign lung nodules, outperforming traditional quantitative methods. MRI radiomic ML models demonstrated good nodule classification performances with XGBoost superior to three others. There was good to excellent inter- and intrareader feature reproducibility and reliability. Advances in knowledge: Our study identified MRI radiomic features that successfully differentiated malignant from benign lung nodules and demonstrated high performance of our MR radiomic feature-based ML models for nodule classification. These new findings could help further establish thoracic MRI as a non-invasive and radiation-free alternative to standard practice for pulmonary nodule assessment.

Key Points Asymptomatic pyuria is associated with kidney failure and faster kidney function decline irrespective of the ADPKD gene and cystic growth. The eGFR decline occurred after detection of asymptomatic pyuria without significant changes in the rate of total kidney volume growth. This study supports the use of asymptomatic pyuria as an enriching prognostic biomarker to predict faster disease progression. Background Autosomal dominant polycystic kidney disease (ADPKD) has phenotypic variability only partially explained by established biomarkers that do not readily assess pathologically important factors of inflammation and kidney fibrosis. We evaluated asymptomatic pyuria (AP), a surrogate marker of inflammation, as a biomarker for disease progression. Methods We performed a retrospective cohort study of adult patients with ADPKD. Patients were divided into AP and no pyuria (NP) groups. We evaluated the effect of pyuria on kidney function and kidney volume. Longitudinal models evaluating kidney function and kidney volume rate of change with respect to incidences of AP were created. Results There were 687 included patients (347 AP, 340 NP). The AP group had more women (65% versus 49%). Median ages at kidney failure were 86 and 80 years in the NP and AP groups (log rank, P =0.49), respectively, for patients in Mayo Imaging Class (MIC) 1A–1B as compared with 59 and 55 years for patients in MIC 1C–1D–1E (log rank, P =0.02), respectively. Compared with the NP group, the rate of kidney function (ml/min per 1.73 m 2 per year) decline shifted significantly after detection of AP in the models, including all patients (−1.48; P <0.001), patients in MIC 1A–1B (−1.79; P <0.001), patients in MIC 1C–1D–1E (−1.18; P <0.001), and patients with PKD1 (−1.04; P <0.001). Models evaluating kidney volume rate of growth showed no change after incidence of AP as compared with the NP group. Conclusions AP is associated with kidney failure and faster kidney function decline irrespective of the ADPKD gene, cystic burden, and cystic growth. These results support AP as an enriching prognostic biomarker for the rate of disease progression.

Background In kidney transplantation, a contrast CT scan is obtained in the donor candidate to detect subclinical pathology in the kidney. Recent work from the Aging Kidney Anatomy study has characterized kidney, cortex, and medulla volumes using a manual image-processing tool. However, this technique is time consuming and impractical for clinical care, and thus, these measurements are not obtained during donor evaluations. This study proposes a fully automated segmentation approach for measuring kidney, cortex, and medulla volumes. Methods A total of 1930 contrast-enhanced CT exams with reference standard manual segmentations from one institution were used to develop the algorithm. A convolutional neural network model was trained ( n =1238) and validated ( n =306), and then evaluated in a hold-out test set of reference standard segmentations ( n =386). After the initial evaluation, the algorithm was further tested on datasets originating from two external sites ( n =1226). Results The automated model was found to perform on par with manual segmentation, with errors similar to interobserver variability with manual segmentation. Compared with the reference standard, the automated approach achieved a Dice similarity metric of 0.94 (right cortex), 0.90 (right medulla), 0.94 (left cortex), and 0.90 (left medulla) in the test set. Similar performance was observed when the algorithm was applied on the two external datasets. Conclusions A fully automated approach for measuring cortex and medullary volumes in CT images of the kidneys has been established. This method may prove useful for a wide range of clinical applications.