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

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.

Autosomal Dominant Polycystic Kidney Disease: Extrarenal Involvement

Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disorder, but kidneys are not the only organs involved in this systemic disorder. Individuals with the condition may display additional manifestations beyond the renal system, involving the liver, pancreas, and brain in the context of cystic manifestations, while involving the vascular system, gastrointestinal tract, bones, and cardiac valves in the context of non-cystic manifestations. Despite kidney involvement remaining the main feature of the disease, thanks to longer survival, early diagnosis, and better management of kidney-related problems, a new wave of complications must be faced by clinicians who treated patients with ADPKD. Involvement of the liver represents the most prevalent extrarenal manifestation and has growing importance in the symptom burden and quality of life. Vascular abnormalities are a key factor for patients' life expectancy and there is still debate whether to screen or not to screen all patients. Arterial hypertension is often the earliest onset symptom among ADPKD patients, leading to frequent cardiovascular complications. Although cardiac valvular abnormalities are a frequent complication, they rarely lead to relevant problems in the clinical history of polycystic patients. One of the newest relevant aspects concerns bone disorders that can exert a considerable influence on the clinical course of these patients. This review aims to provide the "state of the art" among the extrarenal manifestation of ADPKD.

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.

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.

Sperm morphological abnormalities in autosomal dominant polycystic kidney disease are associated with the Hippo signaling pathway via PC1

Background

Autosomal dominant polycystic kidney disease (ADPKD) is a hereditary kidney disorder mostly caused by mutations in PKD1 or PKD2 genes. Here, we report thirteen ADPKD males with infertility and investigated the sperm morphological defects associated with PC1 disruption.

Methods

Targeted next-generation sequencing was performed to detect PKD1 variants in patients. Sperm morphology was observed by immunostaining and transmission electron microscopy, and the sperm motility was assessed using the computer-assisted sperm analysis system. The Hippo signaling pathway was analyzed with by quantitative reverse transcription polymerase chain reaction (qPCR) and western blotting in vitro.

Results

The ADPKD patients were infertile and their sperm tails showed morphological abnormalities, including coiled flagella, absent central microtubules, and irregular peripheral doublets. In addition, the length of sperm flagella was shorter in patients than in controls of in in. In vitro, ciliogenesis was impaired in Pkd1-depleted mouse kidney tubule cells. The absence of PC1 resulted in a reduction of MST1 and LATS1, leading to nuclear accumulation of YAP/TAZ and consequently increased transcription of Aurka. which might promote HDAC6-mediated ciliary disassembly.

Conclusion

Our results suggest the dysregulated Hippo signaling significantly contributes to ciliary abnormalities in and may be associated with flagellar defects in spermatozoa from ADPKD patients.

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.

The Sonographic Appearance of Seminal Megavesicles, an Association With Autosomal Dominant Polycystic Kidney Disease: A Series Review

Megavesicles are an uncommon diagnostic finding during sonography of the bladder, especially when the examination is performed transabdominally. Although megavesicles are more likely seen with transrectal ultrasonography, computed tomogram (CT), vesiculography, or magnetic resonance imaging (MRI), it has been noted during transabdominal sonography for patients suffering from autosomal dominant polycystic kidney disease (ADPKD). When seminal vesicles become dilated, they are often visualized during transabdominal sonography. Two patient cases are provided of seminal megavesicles, associated with ADPKD and have documented sonographic findings. Both patient cases of megavesicles were discovered incidentally during the course of a renal sonogram. The importance of these diagnostic findings and the possible pathogenesis are provided.