Non-traumatic brachial plexopathy identification from routine MRIs: Retrospective studies with deep learning networks.

PURPOSE: This study aims to seek an optimized deep learning model for differentiating non-traumatic brachial plexopathy from routine MRI scans. MATERIALS AND METHODS: This retrospective study collected patients through the electronic medical records (EMR) or pathological reports at Mayo Clinic and underwent BP MRI from January 2002 to December 2022. Using sagittal T1, fluid-sensitive and post-gadolinium images, a radiology panel selected BP's region of interest (ROI) to form 3 dimensional volumes for this study. We designed six deep learning schemes to conduct BP abnormality differentiation across three MRI sequences. Utilizing five prestigious deep learning networks as the backbone, we trained and validated these models by nested five-fold cross-validation schemes. Furthermore, we defined a 'method score' derived from the radar charts as a quantitative indicator as the guidance of the preference of the best model. RESULTS: This study selected 196 patients from initial 267 candidates. A total of 256 BP MRI series were compiled from them, comprising 123 normal and 133 abnormal series. The abnormal series included 4 sub-categories, et al. breast cancer (22.5 %), lymphoma (27.1 %), inflammatory conditions (33.1 %) and others (17.2 %). The best-performing model was produced by feature merging mode with triple MRI joint strategy (AUC, 92.2 %; accuracy, 89.5 %) exceeding the multiple channel merging mode (AUC, 89.6 %; accuracy, 89.0 %), solo channel volume mode (AUC, 89.2 %; accuracy, 86.7 %) and the remaining. Evaluated by method score (maximum 2.37), the feature merging mode with backbone of VGG16 yielded the highest score of 1.75 under the triple MRI joint strategy. CONCLUSION: Deployment of deep learning models across sagittal T1, fluid-sensitive and post-gadolinium MRI sequences demonstrated great potential for brachial plexopathy diagnosis. Our findings indicate that utilizing feature merging mode and multiple MRI joint strategy may offer satisfied deep learning model for BP abnormalities than solo-sequence analysis.

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.

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.

A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images.

Automated segmentation tools often encounter accuracy and adaptability issues when applied to images of different pathology. The purpose of this study is to explore the feasibility of building a workflow to efficiently route images to specifically trained segmentation models. By implementing a deep learning classifier to automatically classify the images and route them to appropriate segmentation models, we hope that our workflow can segment the images with different pathology accurately. The data we used in this study are 350 CT images from patients affected by polycystic liver disease and 350 CT images from patients presenting with liver metastases from colorectal cancer. All images had the liver manually segmented by trained imaging analysts. Our proposed adaptive segmentation workflow achieved a statistically significant improvement for the task of total liver segmentation compared to the generic single-segmentation model (non-parametric Wilcoxon signed rank test, n = 100, p-value << 0.001). This approach is applicable in a wide range of scenarios and should prove useful in clinical implementations of segmentation pipelines.

Association of Kidney Cysts With Progressive CKD After Radical Nephrectomy.

RATIONALE & OBJECTIVE: Simple kidney cysts, which are common and usually considered of limited clinical relevance, are associated with older age and lower glomerular filtration rate (GFR), but little has been known of their association with progressive chronic kidney disease (CKD). STUDY DESIGN: Observational cohort study. SETTING & PARTICIPANTS: Patients with presurgical computed tomography or magnetic resonance imaging who underwent a radical nephrectomy for a tumor; we reviewed the retained kidney images to characterize parenchymal cysts at least 5mm in diameter according to size and location. EXPOSURE: Parenchymal cysts at least 5mm in diameter in the retained kidney. Cyst characteristics were correlated with microstructural findings on kidney histology. OUTCOME: Progressive CKD defined by dialysis, kidney transplantation, a sustained≥40% decline in eGFR for at least 3 months, or an eGFR<10mL/min/1.73m2 that was at least 5mL/min/1.73m2 below the postnephrectomy baseline for at least 3 months. ANALYTICAL APPROACH: Cox models assessed the risk of progressive CKD. Models adjusted for baseline age, sex, body mass index, hypertension, diabetes, eGFR, proteinuria, and tumor volume. Nonparametric Spearman's correlations were used to examine the association of the number and size of the cysts with clinical characteristics, kidney function, and kidney volumes. RESULTS: There were 1,195 patients with 50 progressive CKD events over a median 4.4 years of follow-up evaluation. On baseline imaging, 38% had at least 1 cyst, 34% had at least 1 cortical cyst, and 8.7% had at least 1 medullary cyst. A higher number of cysts was associated with progressive CKD and was modestly correlated with larger nephrons and more nephrosclerosis on kidney histology. The number of medullary cysts was more strongly associated with progressive CKD than the number of cortical cysts. LIMITATIONS: Patients who undergo a radical nephrectomy may differ from the general population. A radical nephrectomy may accelerate the risk of progressive CKD. Genetic testing was not performed. CONCLUSIONS: Cysts in the kidney, particularly the medulla, should be further examined as a potentially useful imaging biomarker of progressive CKD beyond the current clinical evaluation of kidney function and common CKD risk factors. PLAIN-LANGUAGE SUMMARY: Kidney cysts are common and often are considered of limited clinical relevance despite being associated with lower glomerular filtration rate. We studied a large cohort of patients who had a kidney removed due to a tumor to determine whether cysts in the retained kidney were associated with kidney health in the future. We found that more cysts in the kidney and, in particular, cysts in the deepest tissue of the kidney (the medulla) were associated with progressive kidney disease, including kidney failure where dialysis or a kidney transplantation is needed. Patients with cysts in the kidney medulla may benefit from closer monitoring.

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.

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.

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.

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.

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.

An Improved Method for Estimating Nephron Number and the Association of Resulting Nephron Number Estimates with Chronic Kidney Disease Outcomes.

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.

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.

Magnetic resonance radiomic feature performance in pulmonary nodule classification and impact of segmentation variability on radiomics.

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.

Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis.

BACKGROUND & AIMS: Our purpose was to detect pancreatic ductal adenocarcinoma (PDAC) at the prediagnostic stage (3-36 months before clinical diagnosis) using radiomics-based machine-learning (ML) models, and to compare performance against radiologists in a case-control study. METHODS: Volumetric pancreas segmentation was performed on prediagnostic computed tomography scans (CTs) (median interval between CT and PDAC diagnosis: 398 days) of 155 patients and an age-matched cohort of 265 subjects with normal pancreas. A total of 88 first-order and gray-level radiomic features were extracted and 34 features were selected through the least absolute shrinkage and selection operator-based feature selection method. The dataset was randomly divided into training (292 CTs: 110 prediagnostic and 182 controls) and test subsets (128 CTs: 45 prediagnostic and 83 controls). Four ML classifiers, k-nearest neighbor (KNN), support vector machine (SVM), random forest (RM), and extreme gradient boosting (XGBoost), were evaluated. Specificity of model with highest accuracy was further validated on an independent internal dataset (n = 176) and the public National Institutes of Health dataset (n = 80). Two radiologists (R4 and R5) independently evaluated the pancreas on a 5-point diagnostic scale. RESULTS: Median (range) time between prediagnostic CTs of the test subset and PDAC diagnosis was 386 (97-1092) days. SVM had the highest sensitivity (mean; 95% confidence interval) (95.5; 85.5-100.0), specificity (90.3; 84.3-91.5), F1-score (89.5; 82.3-91.7), area under the curve (AUC) (0.98; 0.94-0.98), and accuracy (92.2%; 86.7-93.7) for classification of CTs into prediagnostic versus normal. All 3 other ML models, KNN, RF, and XGBoost, had comparable AUCs (0.95, 0.95, and 0.96, respectively). The high specificity of SVM was generalizable to both the independent internal (92.6%) and the National Institutes of Health dataset (96.2%). In contrast, interreader radiologist agreement was only fair (Cohen's kappa 0.3) and their mean AUC (0.66; 0.46-0.86) was lower than each of the 4 ML models (AUCs: 0.95-0.98) (P < .001). Radiologists also recorded false positive indirect findings of PDAC in control subjects (n = 83) (7% R4, 18% R5). CONCLUSIONS: Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time before clinical diagnosis. Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility.

Asymptomatic Pyuria as a Prognostic Biomarker in Autosomal Dominant Polycystic Kidney Disease.

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 m2 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.

Weight loss and cystic disease progression in autosomal dominant polycystic kidney disease.

Progression of autosomal dominant polycystic kidney disease (ADPKD) is modified by metabolic defects and obesity. Indeed, reduced food intake slows cyst growth in preclinical rodent studies. Here, we demonstrate the feasibility of daily caloric restriction (DCR) and intermittent fasting (IMF) in a cohort of overweight or obese patients with ADPKD. Clinically significant weight loss occurred with both DCR and IMF; however, weight loss was greater and adherence and tolerability were better with DCR. Further, slowed kidney growth correlated with body weight and visceral adiposity loss independent of dietary regimen. Similarly, we compared the therapeutic efficacy of DCR, IMF, and time restricted feeding (TRF) using an orthologous ADPKD mouse model. Only ADPKD animals on DCR lost significant weight and showed slowed cyst growth compared to ad libitum, IMF, or TRF feeding. Collectively, this supports therapeutic feasibility of caloric restriction in ADPKD, with potential efficacy benefits driven by weight loss.

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.

Prescribed Water Intake in Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND: Arginine vasopressin promotes kidney cyst growth in autosomal dominant polycystic kidney disease (ADPKD). Increased water intake reduces arginine vasopressin and urine osmolality and may slow kidney cyst growth. METHODS: In this randomized controlled 3-year clinical trial, we randomly assigned adults with ADPKD who had a height-corrected total kidney volume in Mayo imaging subclass categories 1B to 1E and an estimated glomerular filtration rate of 30 ml/min/1.73 m2 or greater to (1) water intake prescribed to reduce 24-hour urine osmolality to 270 mOsmol/kg or less or (2) ad libitum water intake irrespective of 24-hour urine osmolality. The primary end point was the percentage annualized rate of change in height-corrected total kidney volume. RESULTS: A total of 184 patients participated in either the ad libitum water intake group (n=92) or the prescribed water intake group (n=92). Over 3 years, there was no difference in the annualized rate of change in height-corrected total kidney volume between the ad libitum (7.8% per year; 95% confidence interval [CI], 6.6 to 9.0) and prescribed (6.8% per year; 95% CI, 5.8 to 7.7) water intake groups (mean difference, −0.97% per year; 95% CI, −2.37 to 0.44; P=0.18). The difference in mean 24-hour urine osmolality between the ad libitum and prescribed water intake groups was −91 mOsmol/kg (95% CI, −127 to −54 mOsmol/kg), with 52.3% of patients achieving adherence to the target 24-hour urine osmolality and no reduction in serum copeptin over 3 years. The frequency of adverse events was similar between groups. CONCLUSIONS: For patients with ADPKD, prescribed water intake was not associated with excess adverse events and achieved the target 24-hour urine osmolality for half of the patients but did not reduce copeptin or slow the growth of total kidney volume over 3 years compared with ad libitum water intake. (Funded by the National Health and Medical Research Council of Australia [grant GNT1138533], Danone Research, PKD Australia, the University of Sydney, and the Westmead Medical Research Foundation; Australian New Zealand Clinical Trials Registry number, ACTRN12614001216606).

Use of 3D Robotic Ultrasound for In Vivo Analysis of Mouse Kidneys.

Common modalities for in vivo imaging of rodents include positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US). Each method has limitations and advantages, including availability, ease of use, cost, size, and the use of ionizing radiation or magnetic fields. This protocol describes the use of 3D robotic US for in vivo imaging of rodent kidneys and heart, subsequent data analysis, and possible research applications. Practical applications of robotic US are the quantification of total kidney volume (TKV), as well as the measurement of cysts, tumors, and vasculature. Although the resolution is not as high as other modalities, robotic US allows for more practical high throughput data collection. Furthermore, using US M-mode imaging, cardiac function may be quantified. Since the kidneys receive 20%-25% of the cardiac output, assessing cardiac function is critical to the understanding of kidney physiology and pathophysiology.

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.