Improved predictions of total kidney volume growth rate in ADPKD using two-parameter least squares fitting.

Mayo Imaging Classification (MIC) for predicting future kidney growth in autosomal dominant polycystic kidney disease (ADPKD) patients is calculated from a single MRI/CT scan assuming exponential kidney volume growth and height-adjusted total kidney volume at birth to be 150 mL/m. However, when multiple scans are available, how this information should be combined to improve prediction accuracy is unclear. Herein, we studied ADPKD subjects ( n = 36 ) with 8+ years imaging follow-up (mean = 11 years) to establish ground truth kidney growth trajectory. MIC annual kidney growth rate predictions were compared to ground truth as well as 1- and 2-parameter least squares fitting. The annualized mean absolute error in MIC for predicting total kidney volume growth rate was 2.1 % ± 2 % compared to 1.1 % ± 1 % ( p = 0.002 ) for a 2-parameter fit to the same exponential growth curve used for MIC when 4 measurements were available or 1.4 % ± 1 % ( p = 0.01 ) with 3 measurements averaging together with MIC. On univariate analysis, male sex ( p = 0.05 ) and PKD2 mutation ( p = 0.04 ) were associated with poorer MIC performance. In ADPKD patients with 3 or more CT/MRI scans, 2-parameter least squares fitting predicted kidney volume growth rate better than MIC, especially in males and with PKD2 mutations where MIC was less accurate.

A Primer for Utilizing Deep Learning and Abdominal MRI Imaging Features to Monitor Autosomal Dominant Polycystic Kidney Disease Progression.

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.

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.

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 [...].

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.

Effect of Averaging Measurements From Multiple MRI Pulse Sequences on Kidney Volume Reproducibility in Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND: Total kidney volume (TKV) is an important biomarker for assessing kidney function, especially for autosomal dominant polycystic kidney disease (ADPKD). However, TKV measurements from a single MRI pulse sequence have limited reproducibility, ± ~5%, similar to ADPKD annual kidney growth rates. PURPOSE: To improve TKV measurement reproducibility on MRI by extending artificial intelligence algorithms to automatically segment kidneys on T1-weighted, T2-weighted, and steady state free precession (SSFP) sequences in axial and coronal planes and averaging measurements. STUDY TYPE: Retrospective training, prospective testing. SUBJECTS: Three hundred ninety-seven patients (356 with ADPKD, 41 without), 75% for training and 25% for validation, 40 ADPKD patients for testing and 17 ADPKD patients for assessing reproducibility. FIELD STRENGTH/SEQUENCE: T2-weighted single-shot fast spin echo (T2), SSFP, and T1-weighted 3D spoiled gradient echo (T1) at 1.5 and 3T. ASSESSMENT: 2D U-net segmentation algorithm was trained on images from all sequences. Five observers independently measured each kidney volume manually on axial T2 and using model-assisted segmentations on all sequences and image plane orientations for two MRI exams in two sessions separated by 1-3 weeks to assess reproducibility. Manual and model-assisted segmentation times were recorded. STATISTICAL TESTS: Bland-Altman, Schapiro-Wilk (normality assessment), Pearson's chi-squared (categorical variables); Dice similarity coefficient, interclass correlation coefficient, and concordance correlation coefficient for analyzing TKV reproducibility. P-value < 0.05 was considered statistically significant. RESULTS: In 17 ADPKD subjects, model-assisted segmentations of axial T2 images were significantly faster than manual segmentations (2:49 minute vs. 11:34 minute), with no significant absolute percent difference in TKV (5.9% vs. 5.3%, P = 0.88) between scans 1 and 2. Absolute percent differences between the two scans for model-assisted segmentations on other sequences were 5.5% (axial T1), 4.5% (axial SSFP), 4.1% (coronal SSFP), and 3.2% (coronal T2). Averaging measurements from all five model-assisted segmentations significantly reduced absolute percent difference to 2.5%, further improving to 2.1% after excluding an outlier. DATA CONCLUSION: Measuring TKV on multiple MRI pulse sequences in coronal and axial planes is practical with deep learning model-assisted segmentations and can improve TKV measurement reproducibility more than 2-fold in ADPKD. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Pleural Effusions on MRI in Autosomal Dominant Polycystic Kidney Disease.

Autosomal dominant polycystic kidney disease (ADPKD) has cystic fluid accumulations in the kidneys, liver, pancreas, arachnoid spaces as well as non-cystic fluid accumulations including pericardial effusions, dural ectasia and free fluid in the male pelvis. Here, we investigate the possible association of ADPKD with pleural effusion. ADPKD subjects (n = 268) and age-gender matched controls without ADPKD (n = 268) undergoing body magnetic resonance imaging from mid-thorax down into the pelvis were independently evaluated for pleural effusion by 3 blinded expert observers. Subjects with conditions associated with pleural effusion were excluded from both populations. Clinical and laboratory data as well as kidney, liver and spleen volume, pleural fluid volume, free pelvic fluid and polycystic kidney disease genotype were evaluated. Pleural effusions were observed in 56 of 268 (21%) ADPKD subjects compared with 21 of 268 (8%) in controls (p < 0.0001). In a subpopulation controlling for renal function by matching estimated glomerular filtration rate (eGFR), 28 of 110 (25%) ADPKD subjects had pleural effusions compared to 5 of 110 (5%) controls (p < 0.001). Pleural effusions in ADPKD subjects were more prevalent in females (37/141; 26%) than males (19/127,15%; p = 0.02) and in males were weakly correlated with the presence of free pelvic fluid (r = 0.24, p = 0.02). ADPKD subjects with pleural effusions were younger (48 ± 14 years old vs. 43 ± 14 years old) and weighed less (77 vs. 70 kg; p ≤ 0.02) than those without pleural effusions. For ADPKD subjects with pleural effusions, the mean volume of fluid layering dependently in the posterior−inferior thorax was 19 mL and was not considered to be clinically significant. Pleural effusion is associated with ADPKD, but its role in the pathogenesis of ADPKD requires further evaluation.

A common intronic single nucleotide variant modifies PKD1 expression level.

Autosomal dominant polycystic kidney disease (ADPKD), caused by mutations in PKD1 and PKD2 (PKD1/2), has unexplained phenotypic variability likely affected by environmental and other genetic factors. Approximately 10% of individuals with ADPKD phenotype have no causal mutation detected, possibly due to unrecognized risk variants of PKD1/2. This study was designed to identify risk variants of PKD genes through population genetic analyses. We used Wright's F-statistics (Fst) to evaluate common single nucleotide variants (SNVs) potentially favored by positive natural selection in PKD1 from 1000 Genomes Project (1KG) and genotyped 388 subjects from the Rogosin Institute ADPKD Data Repository. The variants with >90th percentile Fst scores underwent further investigation by in silico analysis and molecular genetics analyses. We identified a deep intronic SNV, rs3874648G> A, located in a conserved binding site of the splicing regulator Tra2-ß in PKD1 intron 30. Reverse-transcription PCR (RT-PCR) of peripheral blood leukocytes (PBL) from an ADPKD patient homozygous for rs3874648-A identified an atypical PKD1 splice form. Functional analyses demonstrated that rs3874648-A allele increased Tra2-ß binding affinity and activated a cryptic acceptor splice-site, causing a frameshift that introduced a premature stop codon in mRNA, thereby decreasing PKD1 full-length transcript level. PKD1 transcript levels were lower in PBL from rs3874648-G/A carriers than in rs3874648-G/G homozygotes in a small cohort of normal individuals and patients with PKD2 inactivating mutations. Our findings indicate that rs3874648G > A is a PKD1 expression modifier attenuating PKD1 expression through Tra2-ß, while the derived G allele advantageously maintains PKD1 expression and is predominant in all subpopulations.

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.

Deployed Deep Learning Kidney Segmentation for Polycystic Kidney Disease MRI.

This study develops, validates, and deploys deep learning for automated total kidney volume (TKV) measurement (a marker of disease severity) on T2-weighted MRI studies of autosomal dominant polycystic kidney disease (ADPKD). The model was based on the U-Net architecture with an EfficientNet encoder, developed using 213 abdominal MRI studies in 129 patients with ADPKD. Patients were randomly divided into 70% training, 15% validation, and 15% test sets for model development. Model performance was assessed using Dice similarity coefficient (DSC) and Bland-Altman analysis. External validation in 20 patients from outside institutions demonstrated a DSC of 0.98 (IQR, 0.97-0.99) and a Bland-Altman difference of 2.6% (95% CI: 1.0%, 4.1%). Prospective validation in 53 patients demonstrated a DSC of 0.97 (IQR, 0.94-0.98) and a Bland-Altman difference of 3.6% (95% CI: 2.0%, 5.2%). Last, the efficiency of model-assisted annotation was evaluated on the first 50% of prospective cases (n = 28), with a 51% mean reduction in contouring time (P < .001), from 1724 seconds (95% CI: 1373, 2075) to 723 seconds (95% CI: 555, 892). In conclusion, our deployed artificial intelligence pipeline accurately performs automated segmentation for TKV estimation of polycystic kidneys and reduces expert contouring time. Keywords: Convolutional Neural Network (CNN), Segmentation, Kidney ClinicalTrials.gov identification no.: NCT00792155 Supplemental material is available for this article. © RSNA, 2022.

Pericardial Effusion on MRI in Autosomal Dominant Polycystic Kidney Disease.

Autosomal dominant polycystic kidney disease (ADPKD) has been associated with cardiac abnormalities including mitral valve prolapse and aneurysmal dilatation of the aortic root. Herein, we investigated the potential association of pericardial effusion with ADPKD. Subjects with ADPKD (n = 117) and control subjects without ADPKD matched for age, gender and renal function (n = 117) undergoing MRI including ECG-gated cine MRI of the aorta and heart were evaluated for pericardial effusion independently by three observers measuring the maximum pericardial effusion thickness in diastole using electronic calipers. Pericardial effusion thickness was larger in ADPKD subjects compared to matched controls (Mann-Whitney p = 0.001) with pericardial effusion thickness >5 mm observed in 24 of 117 (21%) ADPKD subjects compared to 4 of 117 (3%) controls (p = 0.00006). Pericardial effusion thickness in ADPKD was associated with female gender patients (1.2 mm greater than in males, p = 0.03) and pleural effusion thickness (p < 0.001) in multivariate analyses. No subjects exhibited symptoms related to pericardial effusion or required pericardiocentesis. In conclusion, pericardial effusion appears to be more prevalent in ADPKD compared to controls. Although in this retrospective cross-sectional study we did not identify clinical significance, future investigations of pericardial effusion in ADPKD subjects may help to more fully understand its role in this disease.

Detection of PKD1 and PKD2 Somatic Variants in Autosomal Dominant Polycystic Kidney Cyst Epithelial Cells by Whole-Genome Sequencing.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is a genetic disorder characterized by the development of multiple cysts in the kidneys. It is often caused by pathogenic mutations in PKD1 and PKD2 genes that encode polycystin proteins. Although the molecular mechanisms for cystogenesis are not established, concurrent inactivating germline and somatic mutations in PKD1 and PKD2 have been previously observed in renal tubular epithelium (RTE). METHODS: To further investigate the cellular recessive mechanism of cystogenesis in RTE, we conducted whole-genome DNA sequencing analysis to identify germline variants and somatic alterations in RTE of 90 unique kidney cysts obtained during nephrectomy from 24 unrelated participants. RESULTS: Kidney cysts were overall genomically stable, with low burdens of somatic short mutations or large-scale structural alterations. Pathogenic somatic "second hit" alterations disrupting PKD1 or PKD2 were identified in 93% of the cysts. Of these, 77% of cysts acquired short mutations in PKD1 or PKD2 ; specifically, 60% resulted in protein truncations (nonsense, frameshift, or splice site) and 17% caused non-truncating mutations (missense, in-frame insertions, or deletions). Another 18% of cysts acquired somatic chromosomal loss of heterozygosity (LOH) events encompassing PKD1 or PKD2 ranging from 2.6 to 81.3 Mb. 14% of these cysts harbored copy number neutral LOH events, while the other 3% had hemizygous chromosomal deletions. LOH events frequently occurred at chromosomal fragile sites, or in regions comprising chromosome microdeletion diseases/syndromes. Almost all somatic "second hit" alterations occurred at the same germline mutated PKD1/2 gene. CONCLUSIONS: These findings further support a cellular recessive mechanism for cystogenesis in ADPKD primarily caused by inactivating germline and somatic variants of PKD1 or PKD2 genes in kidney cyst epithelium.

Prevalence of Inferior Vena Cava Compression in ADPKD.

INTRODUCTION: Kidney and liver cysts in autosomal dominant polycystic kidney disease (ADPKD) can compress the inferior vena cava (IVC), but IVC compression prevalence and its risk factors are unknown. METHODS: Patients who have ADPKD (n = 216) with abdominal magnetic resonance imaging (MRI) studies and age-/sex-matched controls (n = 216) were evaluated for IVC compression as well as azygous vein diameter (a marker of collateral blood flow) and IVC aspect ratio (left-to-right dimension divided by anterior-to-posterior dimension with a value of 1 corresponding to a circular (high pressure) IVC caudal to compression. RESULTS: Severe IVC compression (≥70%) was observed in 33 (15%) ADPKD subjects and mild compression (≥50% to <70%) was observed in 33 (15%) subjects; whereas controls had no IVC compression (P < 0.001). Severe IVC compression was associated with larger azygous vein (4.0 ± 1.3 mm versus 2.3 ± 0.8 mm without IVC compression; P < 0.001) and a more circular IVC cross-section upstream (mean IVC aspect ratio: 1.16 ± 0.27 vs. 1.69 ± 0.67, P < 0.001), suggesting higher pressure upstream from the compression. IVC compression was associated with older age, lower estimated glomerular filtration rate (eGFR), greater height-adjusted total kidney volumes, greater height-adjusted liver volume (ht-LV), and greater liver and renal cyst fractions (P < 0.001). No subject younger than 30 years had IVC compression, but ADPKD subjects ≥40 years old had 12-fold higher risk of IVC compression (95% confidence interval [CI]: 4.2-42.4), with highest predicted probability for Mayo Clinic classes 1D (59%; 95% CI: 39%-76%) and 1E (74%; 95% CI: 49%-90%) after adjustment (P < 0.001). Women with ht-LV ≥ 2000 ml/m had 83% (95% CI: 59%-95%) prevalence of IVC compression. Complications of IVC compression included deep vein thrombosis (DVT) and symptomatic hypotension. CONCLUSIONS: IVC compression is common in ADPKD patients >40 years old, with Mayo Clinic class 1D/E, and in females with ht-LV > 2000 ml/m.

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

BACKGROUND: Screening for rapidly progressing autosomal dominant polycystic kidney disease (ADPKD) is necessary for assigning and monitoring therapies. Height-adjusted total kidney volume (ht-TKV) is an accepted biomarker for clinical prognostication, but represents only a small fraction of information on abdominal MRI. PURPOSE: To investigate the utility of other MR features of ADPKD to predict progression. STUDY TYPE: Single-center retrospective. POPULATION: Longitudinal data from 186 ADPKD subjects with baseline serum creatinine, PKD gene testing, abdominal MRI measurements, and ≥2 follow-up serum creatinine were reviewed. FIELD STRENGTH/SEQUENCE: 1.5T, T2 -weighted single-shot fast spin echo, T1 -weighted 3D spoiled gradient echo (liver accelerated volume acquisition) and 2D cine velocity encoded gradient echo (phase contrast MRA). ASSESSMENT: Ht-TKV, renal blood flow (RBF), number and fraction of renal and hepatic cysts, bright T1 hemorrhagic renal cysts, and liver and spleen volumes were independently assessed by three observers blinded to estimated glomerular filtration rate (eGFR) data. STATISTICAL TESTS: Linear mixed-effect models were applied to predict eGFR over time using MRI features at baseline adjusted for confounders. Validation was performed in 158 patients who had follow-up MRI using receiver operator characteristic, sensitivity, and specificity. RESULTS: Hemorrhagic cysts, fraction of renal and hepatic cysts, height-adjusted liver and spleen volumes were significant independent predictors of future eGFR (final prediction model R2 = 0.88 P < 0.05). The number of hemorrhagic cysts significantly improved the prediction compared to ht-TKV in predicting future eGFR (area under the curve [AUC] = 0.94, 95% confidence interval [CI]: 0.9-0.94 vs. R2 = 0.9, 95% CI: 0.85-0.9, P = 0.045). For baseline eGFR ≥60 ml/min/1.73m2 , sensitivity for predicting eGFR<45 ml/min/1.73m2 by ht-TKV alone was 29%. Sensitivity increased to 72% with all MRI variables in the model (P < 0.05 = 0.019), whereas specificity was unchanged, 100% vs. 99%. DATA CONCLUSION: Combining multiple MR features including hemorrhagic renal cysts, renal cyst fraction, liver and spleen volume, hepatic cyst fraction, and renal blood flow enhanced sensitivity for predicting eGFR decline in ADPKD compared to the standard model including only ht-TKV. Level of Evidence 2 Technical Efficacy Stage 2 J. MAGN. RESON. IMAGING 2021;53:564-576.

Hypertension: evolving from standardized to individualized care.

: The hypertension paradigm has contributed to a dramatic reduction in CVD mortality. This has been achieved by applying average results of population studies to identify a target population and design a common intervention to achieve a BP goal. Progressive lowering of the BP threshold has expanded the fraction of persons at risk who have access to treatment. Meanwhile, falling risk reduces potential benefit, while treatment-induced adverse events increase - making further expansion of the treatment pool no longer tenable. Still, CVD remains the leading cause of death. Fortunately, new science reveals opportunities to enhance CVD prevention when BP management is based upon individual characteristics. Treatment can be directed at those most likely to benefit, while sparing others the hazards of unnecessary therapy. Treatment can be designed to achieve a variety of physiological objectives that influence cardiovascular outcomes. This new strategy should improve both the efficacy and efficiency of BP-related CVD prevention.

MRI in autosomal dominant polycystic kidney disease.

Magnetic resonance imaging (MRI) is increasingly used in autosomal dominant polycystic kidney disease (ADPKD) for diagnosis, classification, assessment of disease progression and treatment response, and for identifying complications. Herein we review the role of MRI in the management of patients with ADPKD. We show how MRI-derived total kidney volume is a biomarker for assessing ADPKD severity and predicting decline in renal function. We also demonstrate the MR appearances of common complications. Level of Evidence: 3 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2019;50:41-51.

Relationship of Seminal Megavesicles, Prostate Median Cysts, and Genotype in Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) can involve prostate and seminal vesicles but the potential interrelationship of these findings and associations with PKD gene mutation locus and type is unknown. PURPOSE: To determine the interrelationship of seminal megavesicles (seminal vesicles with lumen diameter > 10mm) and prostatic cysts in ADPKD and to determine whether there are associations with PKD gene mutations. STUDY TYPE: Retrospective, case control. POPULATION: Male ADPKD subjects (n = 92) with mutations in PKD1 (n = 71; 77%) or PKD2 (n = 21; 23%), and age/gender-matched controls without ADPKD (n = 92). FIELD STRENGTH/SEQUENCE: 1.5T, axial/coronal T2 -weighted MR images. ASSESSMENT: Reviewers blinded to genotype independently measured seminal vesicle lumen diameter and prevalence of cysts in prostate, kidney, and liver. STATISTICAL TESTS: Nonparametric tests for group comparisons and univariate and multivariable logistic regression analyses to identify associations of megavesicles and prostate median cysts with mutations and renal/hepatic cyst burden. RESULTS: Seminal megavesicles were found in 23 of 92 ADPKD (25%) subjects with PKD1 (22/71, 31%) or PKD2 (n = 1/21, 5%) mutations, but in only two control subjects (P < 0.0001). Prostate median cysts were found in 17/92 (18%) ADPKD subjects, compared with only 6/92 (7%) controls (P = 0.01), and were correlated with seminal vesicle diameters (ρ = 0.24, P = 0.02). Nonmedian prostate cyst prevalence was identical between ADPKD and controls (7/92, 8%). After adjusting for age, estimated glomerular filtration rate, and height-adjusted total kidney volume, ADPKD subjects with megavesicles were 10 times more likely to have a PKD1 than a PKD2 mutation. Among PKD1 subjects, seminal megavesicles occurred more frequently with nontruncating mutations with less severe kidney involvement. DATA CONCLUSION: ADPKD is associated with prostate median cysts near ejaculatory ducts. These cysts correlate with seminal megavesicles (dilated to >10 mm) which predict a 10-fold greater likelihood of PKD1 vs. PKD2 mutation. Cysts elsewhere in the prostate are not related to ADPKD. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:894-903.