Monoallelic IFT140 pathogenic variants are an important cause of the autosomal dominant polycystic kidney-spectrum phenotype.

Autosomal dominant polycystic kidney disease (ADPKD), characterized by progressive cyst formation/expansion, results in enlarged kidneys and often end stage kidney disease. ADPKD is genetically heterogeneous; PKD1 and PKD2 are the common loci (∼78% and ∼15% of families) and GANAB, DNAJB11, and ALG9 are minor genes. PKD is a ciliary-associated disease, a ciliopathy, and many syndromic ciliopathies have a PKD phenotype. In a multi-cohort/-site collaboration, we screened ADPKD-diagnosed families that were naive to genetic testing (n = 834) or for whom no PKD1 and PKD2 pathogenic variants had been identified (n = 381) with a PKD targeted next-generation sequencing panel (tNGS; n = 1,186) or whole-exome sequencing (WES; n = 29). We identified monoallelic IFT140 loss-of-function (LoF) variants in 12 multiplex families and 26 singletons (1.9% of naive families). IFT140 is a core component of the intraflagellar transport-complex A, responsible for retrograde ciliary trafficking and ciliary entry of membrane proteins; bi-allelic IFT140 variants cause the syndromic ciliopathy, short-rib thoracic dysplasia (SRTD9). The distinctive monoallelic phenotype is mild PKD with large cysts, limited kidney insufficiency, and few liver cysts. Analyses of the cystic kidney disease probands of Genomics England 100K showed that 2.1% had IFT140 LoF variants. Analysis of the UK Biobank cystic kidney disease group showed probands with IFT140 LoF variants as the third most common group, after PKD1 and PKD2. The proximity of IFT140 to PKD1 (∼0.5 Mb) in 16p13.3 can cause diagnostic confusion, and PKD1 variants could modify the IFT140 phenotype. Importantly, our studies link a ciliary structural protein to the ADPKD spectrum.

Visceral Adiposity and Progression of ADPKD: A Cohort Study of Patients From the TEMPO 3:4 Trial.

RATIONALE & OBJECTIVE: Body mass index (BMI) is an independent predictor of kidney disease progression in individuals with autosomal dominant polycystic kidney disease (ADPKD). Adipocytes do not simply act as a fat reservoir but are active endocrine organs. We hypothesized that greater visceral abdominal adiposity would associate with more rapid kidney growth in ADPKD and influence the efficacy of tolvaptan. STUDY DESIGN: A retrospective cohort study. SETTING & PARTICIPANTS: 1,053 patients enrolled in the TEMPO 3:4 tolvaptan trial with ADPKD and at high risk of rapid disease progression. PREDICTOR: Estimates of visceral adiposity extracted from coronal plane magnetic resonance imaging (MRI) scans using deep learning. OUTCOME: Annual change in total kidney volume (TKV) and effect of tolvaptan on kidney growth. ANALYTICAL APPROACH: Multinomial logistic regression and linear mixed models. RESULTS: In fully adjusted models, the highest tertile of visceral adiposity was associated with greater odds of annual change in TKV of≥7% versus<5% (odds ratio [OR], 4.78 [95% CI, 3.03-7.47]). The association was stronger in women than men (interaction P<0.01). In linear mixed models with an outcome of percent change in TKV per year, tolvaptan efficacy (% change in TKV) was reduced with higher visceral adiposity (3-way interaction of treatment ∗ time ∗ visceral adiposity, P=0.002). Visceral adiposity significantly improved classification performance of predicting rapid annual percent change in TKV for individuals with a normal BMI (DeLong's test z score: -2.03; P=0.04). Greater visceral adiposity was not associated with estimated glomerular filtration rate (eGFR) slope in the overall cohort; however, visceral adiposity was associated with more rapid decline in eGFR slope (below the median) in women (fully adjusted OR, 1.06 [95% CI, 1.01-1.11] per 10 unit increase in visceral adiposity) but not men (OR, 0.98 [95% CI, 0.95-1.02]). LIMITATIONS: Retrospective; rapid progressors; computational demand of deep learning. CONCLUSIONS: Visceral adiposity that can be quantified by MRI in the coronal plane using a deep learning segmentation model independently associates with more rapid kidney growth and improves classification of rapid progression in individuals with a normal BMI. Tolvaptan efficacy decreases with increasing visceral adiposity. PLAIN-LANGUAGE SUMMARY: We analyzed images from a previous study with the drug tolvaptan conducted in patients with autosomal dominant polycystic kidney disease (ADPKD) to measure the amount of fat tissue surrounding the kidneys (visceral fat). We had previously shown body mass index can predict kidney growth in this population; now we determined whether visceral fat was an important factor associated with kidney growth. Using a machine learning tool to automate measurement of fat in images, we observed that visceral fat was independently associated with kidney growth, that it was a better predictor of faster kidney growth in lean patients than body mass index, and that having more visceral fat made treatment of ADPKD with tolvaptan less effective.

Kidney Energetics and Cyst Burden in Autosomal Dominant Polycystic Kidney Disease: A Pilot Study.

RATIONALE & OBJECTIVE: In this pilot study, we hypothesized that autosomal dominant polycystic kidney disease (ADPKD) is characterized by impaired kidney oxidative metabolism that associates with kidney size and cyst burden. STUDY DESIGN: Cross-sectional study. SETTING & PARTICIPANTS: Twenty adults with ADPKD (age, 31±6 years; 65% women; body mass index [BMI], 26.8 [22.7-30.4] kg/m2; estimated glomerular filtration rate [eGFR, 2021 CKD-EPI creatinine], 103±18mL/min/1.73m2; height-adjusted total kidney volume [HTKV], 731±370mL/m; Mayo classifications 1B [5%], 1C [42%], 1D [21%], and 1E [32%]) and 11 controls in normal weight category (NWC) (age, 25±3 years; 45% women; BMI, 22.5 [21.7-24.2] kg/m2; eGFR, 113±15mL/min/1.73m2; HTKV, 159±31mL/m) at the University of Colorado Anschutz Medical Campus. PREDICTORS: ADPKD status (yes/no) and severity (Mayo classifications). OUTCOME: HTKV and cyst burden by magnetic resonance imaging, kidney oxidative metabolism, and perfusion by 11C-acetate positron emission tomography/computed tomography, insulin sensitivity by hyperinsulinemic-euglycemic clamps (presented as ratio of M-value of steady state insulin concentration [M/I]). ANALYTICAL APPROACH: For categorical variables, χ2/Fisher's exact tests, and for continuous variables t tests/Mann-Whitney U tests. Pearson correlation was used to estimate the relationships between variables. RESULTS: Compared with NWC individuals, the participants with ADPKD exhibited lower mean±SD M/I ratio (0.586±0.205 vs 0.424±0.171 [mg/kg lean/min]/(µIU/mL), P=0.04), lower median cortical perfusion (1.93 [IQR, 1.80-2.09] vs 0.68 [IQR, 0.47-1.04] mL/min/g, P<0.001) and lower median total kidney oxidative metabolism (0.17 [IQR, 0.16-0.19] vs. 0.14 [IQR, 0.12-0.15] min-1, P=0.001) in voxel-wise models excluding cysts. HTKV correlated inversely with cortical perfusion (r: -0.83, P < 0.001), total kidney oxidative metabolism (r: -0.61, P<0.001) and M/I (r: -0.41, P = 0.03). LIMITATIONS: Small sample size and cross-sectional design. CONCLUSIONS: Adults with ADPKD and preserved kidney function exhibited impaired renal perfusion and kidney oxidative metabolism across a wide range of cysts and kidney enlargements. FUNDING: Grants from government (National Institutes of Health, Centers for Disease Control and Prevention) and not-for-profit (JDRF) entities. TRIAL REGISTRATION: Registered at ClinicalTrials.gov with study numbers NCT04407481 and NCT04074668. PLAIN-LANGUAGE SUMMARY: In our study, we explored how a common genetic kidney condition, autosomal dominant polycystic kidney disease (ADPKD), relates to kidney metabolism. ADPKD leads to the growth of numerous cysts in the kidneys, which can impact their ability to work properly. We wanted to understand the kidneys' ability to process oxygen and blood flow in ADPKD. Our approach involved using advanced imaging techniques to observe kidney metabolism and blood flow in people with ADPKD compared with healthy individuals. We discovered that those with ADPKD had significant changes in kidney oxygen metabolism even when their kidney function was still normal. These findings are crucial as they provide deeper insights into ADPKD, potentially guiding future treatments to target these changes.

Artificial intelligence model for enhancing the accuracy of transvaginal ultrasound in detecting endometrial cancer and endometrial atypical hyperplasia.

OBJECTIVES: Transvaginal ultrasound is typically the initial diagnostic approach in patients with postmenopausal bleeding for detecting endometrial atypical hyperplasia/cancer. Although transvaginal ultrasound demonstrates notable sensitivity, its specificity remains limited. The objective of this study was to enhance the diagnostic accuracy of transvaginal ultrasound through the integration of artificial intelligence. By using transvaginal ultrasound images, we aimed to develop an artificial intelligence based automated segmentation model and an artificial intelligence based classifier model. METHODS: Patients with postmenopausal bleeding undergoing transvaginal ultrasound and endometrial sampling at Mayo Clinic between 2016 and 2021 were retrospectively included. Manual segmentation of images was performed by four physicians (readers). Patients were classified into cohort A (atypical hyperplasia/cancer) and cohort B (benign) based on the pathologic report of endometrial sampling. A fully automated segmentation model was developed, and the performance of the model in correctly identifying the endometrium was compared with physician made segmentation using similarity metrics. To develop the classifier model, radiomic features were calculated from the manually segmented regions-of-interest. These features were used to train a wide range of machine learning based classifiers. The top performing machine learning classifier was evaluated using a threefold approach, and diagnostic accuracy was assessed through the F1 score and area under the receiver operating characteristic curve (AUC-ROC). RESULTS: 302 patients were included. Automated segmentation-reader agreement was 0.79±0.21 using the Dice coefficient. For the classification task, 92 radiomic features related to pixel texture/shape/intensity were found to be significantly different between cohort A and B. The threefold evaluation of the top performing classifier model showed an AUC-ROC of 0.90 (range 0.88-0.92) on the validation set and 0.88 (range 0.86-0.91) on the hold-out test set. Sensitivity and specificity were 0.87 (range 0.77-0.94) and 0.86 (range 0.81-0.94), respectively. CONCLUSIONS: We trained an artificial intelligence based algorithm to differentiate endometrial atypical hyperplasia/cancer from benign conditions on transvaginal ultrasound images in a population of patients with postmenopausal bleeding.

The Number and Size of Individual Kidney Medullary Pyramids is Associated with Clinical Characteristics, Kidney Biopsy Findings, and CKD Outcomes among Kidney Donors.

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.

Utility of new image-derived biomarkers for autosomal dominant polycystic kidney disease prognosis using automated instance cyst segmentation.

New image-derived biomarkers for patients affected by autosomal dominant polycystic kidney disease are needed to improve current clinical management. The measurement of total kidney volume (TKV) provides critical information for clinicians to drive care decisions. However, patients with similar TKV may present with very different phenotypes, often requiring subjective decisions based on other factors (e.g., appearance of healthy kidney parenchyma, a few cysts contributing significantly to overall TKV, etc.). In this study, we describe a new technique to individually segment cysts and quantify biometric parameters including cyst volume, cyst number, parenchyma volume, and cyst parenchyma surface area. Using data from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study the utility of these new parameters was explored, both quantitatively as well as visually. Total cyst number and cyst parenchyma surface area showed superior prediction of the slope of estimated glomerular filtration rate decline, kidney failure and chronic kidney disease stages 3A, 3B, and 4, compared to TKV. In addition, presentations such as a few large cysts contributing significantly to overall kidney volume were shown to be much better stratified in terms of outcome predictions. Thus, these new image biomarkers, which can be obtained automatically, will have great utility in future studies and clinical care for patients affected by autosomal dominant polycystic kidney disease.

Automated Segmentation of Kidney Cortex and Medulla in CT Images: A Multisite Evaluation Study.

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.

Effect of Dataset Size and Medical Image Modality on Convolutional Neural Network Model Performance for Automated Segmentation: A CT and MR Renal Tumor Imaging Study.

The aim of this study is to investigate the use of an exponential-plateau model to determine the required training dataset size that yields the maximum medical image segmentation performance. CT and MR images of patients with renal tumors acquired between 1997 and 2017 were retrospectively collected from our nephrectomy registry. Modality-based datasets of 50, 100, 150, 200, 250, and 300 images were assembled to train models with an 80-20 training-validation split evaluated against 50 randomly held out test set images. A third experiment using the KiTS21 dataset was also used to explore the effects of different model architectures. Exponential-plateau models were used to establish the relationship of dataset size to model generalizability performance. For segmenting non-neoplastic kidney regions on CT and MR imaging, our model yielded test Dice score plateaus of [Formula: see text] and [Formula: see text] with the number of training-validation images needed to reach the plateaus of 54 and 122, respectively. For segmenting CT and MR tumor regions, we modeled a test Dice score plateau of [Formula: see text] and [Formula: see text], with 125 and 389 training-validation images needed to reach the plateaus. For the KiTS21 dataset, the best Dice score plateaus for nn-UNet 2D and 3D architectures were [Formula: see text] and [Formula: see text] with number to reach performance plateau of 177 and 440. Our research validates that differing imaging modalities, target structures, and model architectures all affect the amount of training images required to reach a performance plateau. The modeling approach we developed will help future researchers determine for their experiments when additional training-validation images will likely not further improve model performance.

A systematic review on the use of artificial intelligence in gynecologic imaging - Background, state of the art, and future directions.

OBJECTIVE: Machine learning, deep learning, and artificial intelligence (AI) are terms that have made their way into nearly all areas of medicine. In the case of medical imaging, these methods have become the state of the art in nearly all areas from image reconstruction to image processing and automated analysis. In contrast to other areas, such as brain and breast imaging, the impacts of AI have not been as strongly felt in gynecologic imaging. In this review article, we: (i) provide a background of clinically relevant AI concepts, (ii) describe methods and approaches in computer vision, and (iii) highlight prior work related to image classification tasks utilizing AI approaches in gynecologic imaging. DATA SOURCES: A comprehensive search of several databases from each database's inception to March 18th, 2021, English language, was conducted. The databases included Ovid MEDLINE(R) and Epub Ahead of Print, In-Process & Other Non-Indexed Citations, and Daily, Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, and Ovid Cochrane Database of Systematic Reviews and ClinicalTrials.gov. METHODS OF STUDY SELECTION: We performed an extensive literature review with 61 articles curated by three reviewers and subsequent sorting by specialists using specific inclusion and exclusion criteria. TABULATION, INTEGRATION, AND RESULTS: We summarize the literature grouped by each of the three most common gynecologic malignancies: endometrial, cervical, and ovarian. For each, a brief introduction encapsulating the AI methods, imaging modalities, and clinical parameters in the selected articles is presented. We conclude with a discussion of current developments, trends and limitations, and suggest directions for future study. CONCLUSION: This review article should prove useful for collaborative teams performing research studies targeted at the incorporation of radiological imaging and AI methods into gynecological clinical practice.

Early assessment of shear wave elastography parameters foresees the response to neoadjuvant chemotherapy in patients with invasive breast cancer.

BACKGROUND: Early prediction of tumor response to neoadjuvant chemotherapy (NACT) is crucial for optimal treatment and improved outcome in breast cancer patients. The purpose of this study is to investigate the role of shear wave elastography (SWE) for early assessment of response to NACT in patients with invasive breast cancer. METHODS: In a prospective study, 62 patients with biopsy-proven invasive breast cancer were enrolled. Three SWE studies were conducted on each patient: before, at mid-course, and after NACT but before surgery. A new parameter, mass characteristic frequency (fmass), along with SWE measurements and mass size was obtained from each SWE study visit. The clinical biomarkers were acquired from the pre-NACT core-needle biopsy. The efficacy of different models, generated with the leave-one-out cross-validation, in predicting response to NACT was shown by the area under the receiver operating characteristic curve and the corresponding sensitivity and specificity. RESULTS: A significant difference was found for SWE parameters measured before, at mid-course, and after NACT between the responders and non-responders. The combination of Emean2 and mass size (s2) gave an AUC of 0.75 (0.95 CI 0.62-0.88). For the ER+ tumors, the combination of Emean_ratio1, s1, and Ki-67 index gave an improved AUC of 0.84 (0.95 CI 0.65-0.96). For responders, fmass was significantly higher during the third visit. CONCLUSIONS: Our study findings highlight the value of SWE estimation in the mid-course of NACT for the early prediction of treatment response. For ER+ tumors, the addition of Ki-67improves the predictive power of SWE. Moreover, fmass is presented as a new marker in predicting the endpoint of NACT in responders.

Automated total kidney volume measurements in pre-clinical magnetic resonance imaging for resourcing imaging data, annotations, and source code.

The objective of this study was to validate a fully automated total kidney volume measurement method for pre-clinical rodent trials that is fast, accurate, reproducible, and to provide these resources to the research community. Rodent studies that involve imaging are crucial for monitoring treatment efficacy in diseases such as polycystic kidney disease. Previous studies utilize manual or semi-automated segmentations, which are time consuming and potentially biased. To develop our automated system, a total of 150 axial magnetic resonance images (MRI) from a variety of mouse models were manually segmented and used to train/validate an automated algorithm. To test the longitudinal application of the model, four mutant and four wild-type mice were imaged sequentially over three to twelve weeks via MRI. Segmentations of the kidneys (excluding the renal pelvis) were generated by the automated method and two different readers, with one reader repeating the measurements. Similarity metrics and longitudinal analysis were calculated to assess the performance of the automated compared to the manual methods. The automated approach required no user input, besides a final visual quality control step. Similarity metrics of the automated method versus the manual segmentations were on par with inter- and intra-reader comparisons. Thus, our fully automated approach described here can be safely used in longitudinal, pre-clinical trials that involve the segmentation of rodent kidneys in T2-weighted MRIs.

Semantic Instance Segmentation of Kidney Cysts in MR Images: A Fully Automated 3D Approach Developed Through Active Learning.

Total kidney volume (TKV) is the main imaging biomarker used to monitor disease progression and to classify patients affected by autosomal dominant polycystic kidney disease (ADPKD) for clinical trials. However, patients with similar TKVs may have drastically different cystic presentations and phenotypes. In an effort to quantify these cystic differences, we developed the first 3D semantic instance cyst segmentation algorithm for kidneys in MR images. We have reformulated both the object detection/localization task and the instance-based segmentation task into a semantic segmentation task. This allowed us to solve this unique imaging problem efficiently, even for patients with thousands of cysts. To do this, a convolutional neural network (CNN) was trained to learn cyst edges and cyst cores. Images were converted from instance cyst segmentations to semantic edge-core segmentations by applying a 3D erosion morphology operator to up-sampled versions of the images. The reduced cysts were labeled as core; the eroded areas were dilated in 2D and labeled as edge. The network was trained on 30 MR images and validated on 10 MR images using a fourfold cross-validation procedure. The final ensemble model was tested on 20 MR images not seen during the initial training/validation. The results from the test set were compared to segmentations from two readers. The presented model achieved an averaged R2 value of 0.94 for cyst count, 1.00 for total cyst volume, 0.94 for cystic index, and an averaged Dice coefficient of 0.85. These results demonstrate the feasibility of performing cyst segmentations automatically in ADPKD patients.

Shear wave elastography of the median nerve: A mechanical study.

BACKGROUND: Shear wave elastography (SWE) shows promise in peripheral neuropathy evaluation but has potential limitations due to tissue size and heterogeneity. We tested SWE sensitivity to elasticity change and the effect of probe position in a median nerve cadaver model. METHODS: Ten specimens were used to measure median nerve elasticity under increasing loads using SWE and indentation. Measurements were compared using repeated-measures analysis of variance. RESULTS: Indentation and SWE-based longitudinal nerve elasticity increased with tensile loading (P < .01), showing a similar relationship. Acquisition in a transverse plane showed lower values compared with longitudinal measurements, mostly under higher loads (P = .03), as did postdissection elasticity (P = .02). Elasticity did not change when measured proximal to the carpal tunnel. CONCLUSIONS: Longitudinal SWE is sensitive to changes in median nerve elasticity. Measuring elasticity of peripheral nerves noninvasively could elucidate intra-neural pathology related to compression neuropathies, and proof to be of added value as a diagnostic or prognostic tool.

Fully Automated Segmentation of Bladder Sac and Measurement of Detrusor Wall Thickness from Transabdominal Ultrasound Images.

Ultrasound measurements of detrusor muscle thickness have been proposed as a diagnostic biomarker in patients with bladder overactivity and voiding dysfunction. In this study, we present an approach based on deep learning (DL) and dynamic programming (DP) to segment the bladder sac and measure the detrusor muscle thickness from transabdominal 2D B-mode ultrasound images. To assess the performance of our method, we compared the results of automated methods to the manually obtained reference bladder segmentations and wall thickness measurements of 80 images obtained from 11 volunteers. It takes less than a second to segment the bladder from a 2D B-mode image for the DL method. The average Dice index for the bladder segmentation is 0.93 ± 0.04 mm, and the average root-mean-square-error and standard deviation for wall thickness measurement are 0.7 ± 0.2 mm, which is comparable to the manual ground truth. The proposed fully automated and fast method could be a useful tool for segmentation and wall thickness measurement of the bladder from transabdominal B-mode images. The computation speed and accuracy of the proposed method will enable adaptive adjustment of the ultrasound focus point, and continuous assessment of the bladder wall during the filling and voiding process of the bladder.

Ultrasound Radiation Force for the Assessment of Bone Fracture Healing in Children: An In Vivo Pilot Study.

Vibrational characteristics of bone are directly dependent on its physical properties. In this study, a vibrational method for bone evaluation is introduced. We propose a new type of quantitative vibro-acoustic method based on the acoustic radiation force of ultrasound for bone characterization in persons with fracture. Using this method, we excited the clavicle or ulna by an ultrasound radiation force pulse which induces vibrations in the bone, resulting in an acoustic wave that is measured by a hydrophone placed on the skin. The acoustic signals were used for wave velocity estimation based on a cross-correlation technique. To further separate different vibration characteristics, we adopted a variational mode decomposition technique to decompose the received signal into an ensemble of band-limited intrinsic mode functions, allowing analysis of the acoustic signals by their constitutive components. This prospective study included 15 patients: 12 with clavicle fractures and three with ulna fractures. Contralateral intact bones were used as controls. Statistical analysis demonstrated that fractured bones can be differentiated from intact ones with a detection probability of 80%. Additionally, we introduce a "healing factor" to quantify the bone healing progress which successfully tracked the progress of healing in 80% of the clavicle fractures in the study.

Deep learning-based automated kidney and cyst segmentation of autosomal dominant polycystic kidney disease using single vs. multi-institutional data.

PURPOSE: This study aimed to investigate if a deep learning model trained with a single institution's data has comparable accuracy to that trained with multi-institutional data for segmenting kidney and cyst regions in magnetic resonance (MR) images of patients affected by autosomal dominant polycystic kidney disease (ADPKD). METHODS: We used TensorFlow with a Keras custom UNet on 2D slices of 756 MRI images of kidneys with ADPKD obtained from four institutions in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study. The ground truth was determined via a manual plus global thresholding method. Five models were trained with 80 % of all institutional data (n = 604) and each institutional data (n = 232, 172, 148, or 52), respectively, and validated with 10 % and tested on an unseen 10 % of the data. The model's performance was evaluated using the Dice Similarity Coefficient (DSC). RESULTS: The DSCs by the model trained with all institutional data ranged from 0.92 to 0.95 for kidney image segmentation, only 1-2 % higher than those by the models trained with single institutional data (0.90-0.93).In cyst segmentation, however, the DSCs by the model trained with all institutional data ranged from 0.83 to 0.89, which were 2-20 % higher than those by the models trained with single institutional data (0.66-0.86). CONCLUSION: The UNet performance, when trained with a single institutional dataset, exhibited similar accuracy to the model trained on a multi-institutional dataset. Segmentation accuracy increases with models trained on larger sample sizes, especially in more complex cyst segmentation.

AI in the Loop: functionalizing fold performance disagreement to monitor automated medical image segmentation workflows.

Introduction: Methods that automatically flag poor performing predictions are drastically needed to safely implement machine learning workflows into clinical practice as well as to identify difficult cases during model training. Methods: Disagreement between the fivefold cross-validation sub-models was quantified using dice scores between folds and summarized as a surrogate for model confidence. The summarized Interfold Dices were compared with thresholds informed by human interobserver values to determine whether final ensemble model performance should be manually reviewed. Results: The method on all tasks efficiently flagged poor segmented images without consulting a reference standard. Using the median Interfold Dice for comparison, substantial dice score improvements after excluding flagged images was noted for the in-domain CT (0.85 ± 0.20 to 0.91 ± 0.08, 8/50 images flagged) and MR (0.76 ± 0.27 to 0.85 ± 0.09, 8/50 images flagged). Most impressively, there were dramatic dice score improvements in the simulated out-of-distribution task where the model was trained on a radical nephrectomy dataset with different contrast phases predicting a partial nephrectomy all cortico-medullary phase dataset (0.67 ± 0.36 to 0.89 ± 0.10, 122/300 images flagged). Discussion: Comparing interfold sub-model disagreement against human interobserver values is an effective and efficient way to assess automated predictions when a reference standard is not available. This functionality provides a necessary safeguard to patient care important to safely implement automated medical image segmentation workflows.

Evaluation of advanced imaging biomarkers at kidney failure in patients with ADPKD: a pilot study.

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 (<46, 46-56, >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.

Bone health in autosomal dominant polycystic kidney disease (ADPKD) patients after kidney transplantation.

ADPKD is caused by pathogenic variants in PKD1 or PKD2, encoding polycystin-1 and -2 proteins. Polycystins are expressed in osteoblasts and chondrocytes in animal models, and loss of function is associated with low bone mineral density (BMD) and volume. However, it is unclear whether these variants impact bone strength in ADPKD patients. Here, we examined BMD in ADPKD after kidney transplantation (KTx). This retrospective observational study retrieved data from adult patients who received a KTx over the past 15 years. Patients with available dual-energy X-ray absorptiometry (DXA) of the hip and/or lumbar spine (LS) post-transplant were included. ADPKD patients (n = 340) were matched 1:1 by age (±2 years) at KTx and sex with non-diabetic non-ADPKD patients (n = 340). Patients with ADPKD had slightly higher BMD and T-scores at the right total hip (TH) as compared to non-ADPKD patients [BMD: 0.951 vs. 0.897, p < 0.001; T-score: -0.62 vs. -0.99, p < 0.001] and at left TH [BMD: 0.960 vs. 0.893, p < 0.001; T-score: -0.60 vs. -1.08, p < 0.001], respectively. Similar results were found at the right femoral neck (FN) between ADPKD and non-ADPKD [BMD: 0.887 vs. 0.848, p = 0.001; T-score: -1.20 vs. -1.41, p = 0.01] and at left FN [BMD: 0.885 vs. 0.840, p < 0.001; T-score: -1.16 vs. -1.46, p = 0.001]. At the LS level, ADPKD had a similar BMD and lower T-score compared to non-ADPKD [BMD: 1.120 vs. 1.126, p = 0.93; T-score: -0.66 vs. -0.23, p = 0.008]. After adjusting for preemptive KTx, ADPKD patients continued to have higher BMD T-scores in TH and FN. Our findings indicate that BMD by DXA is higher in patients with ADPKD compared to non-ADPKD patients after transplantation in sites where cortical but not trabecular bone is predominant. The clinical benefit of the preserved cortical bone BMD in patients with ADPKD needs to be explored in future studies.

Clinical Implementation of an Artificial Intelligence Algorithm for Magnetic Resonance-Derived Measurement of Total Kidney Volume.

OBJECTIVE: To evaluate the performance of an internally developed and previously validated artificial intelligence (AI) algorithm for magnetic resonance (MR)-derived total kidney volume (TKV) in autosomal dominant polycystic kidney disease (ADPKD) when implemented in clinical practice. PATIENTS AND METHODS: The study included adult patients with ADPKD seen by a nephrologist at our institution between November 2019 and January 2021 and undergoing an MR imaging examination as part of standard clinical care. Thirty-three nephrologists ordered MR imaging, requesting AI-based TKV calculation for 170 cases in these 161 unique patients. We tracked implementation and performance of the algorithm over 1 year. A radiologist and a radiology technologist reviewed all cases (N=170) for quality and accuracy. Manual editing of algorithm output occurred at radiology or radiology technologist discretion. Performance was assessed by comparing AI-based and manually edited segmentations via measures of similarity and dissimilarity to ensure expected performance. We analyzed ADPKD severity class assignment of algorithm-derived vs manually edited TKV to assess impact. RESULTS: Clinical implementation was successful. Artificial intelligence algorithm-based segmentation showed high levels of agreement and was noninferior to interobserver variability and other methods for determining TKV. Of manually edited cases (n=84), the AI-algorithm TKV output showed a small mean volume difference of -3.3%. Agreement for disease class between AI-based and manually edited segmentation was high (five cases differed). CONCLUSION: Performance of an AI algorithm in real-life clinical practice can be preserved if there is careful development and validation and if the implementation environment closely matches the development conditions.