Background/Objectives: Accurate and reproducible spleen volume measurements are essential for assessing treatment response and disease progression in myelofibrosis. This study evaluates techniques for measuring spleen volume on abdominal MRI. Methods: In 20 patients with bone marrow biopsy-proven myelofibrosis, 5 observers independently measured spleen volume on 3 abdominal MRI pulse sequences, 3D-spoiled gradient echo T1, axial single-shot fast spin echo (SSFSE) T2, and coronal SSFSE T2, using ellipsoidal approximation, manual contouring, and 3D nnU-Net model-assisted contouring comparing coefficients of variation. Changes in spleen volume were compared to all information to assess which measurement technique tracked disease progression with the greatest accuracy. Results: The coefficient of variation in spleen volume measurements averaging over 3 sequences was significantly lower for model-assisted contouring, 1.6% and manual contouring, 3.5%, compared to ellipsoidal estimation from 3 dimensions measured on axial and coronal T2 images, 15, p < 0.001. In 4 subjects with divergent treatment response predictions, model-assisted contouring was consistent with all information while ellipsoidal estimation was not. Manual contouring tracked similarly to model-assisted contouring but required more operator time. Conclusions: Model-assisted segmentations provide efficient and more reproducible spleen volume measurements compared to estimates of spleen volume from ellipsoidal approximations and improve objective determinations of clinical trial enrollment eligibility based upon spleen volume as well as assessments of treatment response.

Background/Objectives: Although tolvaptan efficacy in ADPKD has been demonstrated in randomized clinical trials, there is no definitive method for assessing its efficacy in the individual patient in the clinical setting. In this exploratory feasibility study, we report a method to quantify the change in total kidney volume (TKV) growth rate to retrospectively evaluate tolvaptan efficacy for individual patients. Treatment-related changes in estimated glomerular filtration rate (eGFR) are also assessed. Methods: MRI scans covering at least 1 year prior to and during treatment with tolvaptan were performed, with deep learning facilitated kidney segmentation and fitting multiple imaging timepoints to exponential growth in 32 ADPKD patients. Clustering analysis differentiated tolvaptan treatment “responders” and “non-responders” based upon the magnitude of change in TKV growth rate. Differences in rate of eGFR decline, urine osmolality, and other parameters were compared between responders and non-responders. Results: Eighteen (56%) tolvaptan responders (mean age 42 ± 8 years) were identified by k-means clustering, with an absolute reduction in annual TKV growth rate of >2% (mean = −5.1% ± 2.5% per year). Thirteen (44%) non-responders were identified, with <1% absolute reduction in annual TKV growth rate (mean = +2.4% ± 2.7% per year) during tolvaptan treatment. Compared to non-responders, tolvaptan responders had significantly higher mean TKV growth rates prior to tolvaptan treatment (7.1% ± 3.6% per year vs. 3.7% ± 2.4% per year; p = 0.003) and higher median pretreatment spot urine osmolality (Uosm, 393 mOsm/kg vs. 194 mOsm/kg, p = 0.03), confirmed by multivariate analysis. Mean annual rate of eGFR decline was less in responders than in non-responders (−0.25 ± 0.04, CI: [−0.27, −0.23] mL/min/1.73 m2 per year vs. −0.40 ± 0.06, CI: [−0.43, −0.37] mL/min/1.73 m2 per year, p = 0.036). Conclusions: In this feasibility study designed to assess predictors of tolvaptan treatment efficacy in individual patients with ADPKD, we found that high pretreatment levels of annual TKV growth rate and higher pretreatment spot urine osmolality were associated with a responder phenotype.

Background:Autosomal dominant polycystic kidney disease (ADPKD) can lead to polycystic liver disease (PLD), characterized by liver cysts. Although majority of the patients are asymptomatic, massively enlarged liver secondary to PLD can cause discomfort, and compression on adjacent structures requiring cyst aspiration/fenestration, partial liver resection, or liver transplantation. Monitoring PLD by measuring liver volume fails to track the early stages when liver cyst volume is too small to affect liver volume. Purpose:To improve PLD assessment in the early stages by automating detection and segmentation of liver cysts using deep learning (DL) models. Materials and Methods:A self-configured UNet-based platform (nnU-Net) was trained with 40 ADPKD subjects with liver cysts annotated by a radiologist. Internal (n = 7), External (n = 10), and test-retest reproducibility (n = 17) validations included macro- and micro-level performance metrics: patient-level Dice scores (PDice), along with voxel-level true positive rates (VTPR), as well as analysis of time saved in a model-assisted scenario. Additionally, we assessed human-level reliability in liver cyst segmentation and evaluated the model's test-retest reproducibility. We further compared liver volume vs cyst volume for tracking disease in a subject with 16+ years follow-up. Results:The model achieved an 82% ± 11% PDice and a 75% ± 15% VTPR on the internal test sets (n = 7 patients), and 80% ± 12% Dice score and a 91% ± 7% VTPR on the external test sets (n = 10 patients). It excelled particularly in detecting small liver cysts, a challenging task for manual annotation. This efficiency translated to a median of 91% (IQR: 14%) reduction in annotation time compared to manual labeling. Test-retest assessment demonstrated excellent reproducibility, with coefficients of variation of 94% for liver cyst fraction and 92% for cyst count. Conclusion:DL automation of liver cyst segmentations demonstrates potential to improve tracking of liver cyst volume in polycystic liver disease.

Abdominal imaging of autosomal dominant polycystic kidney disease (ADPKD) has historically focused on detecting complications such as cyst rupture, cyst infection, obstructing renal calculi, and pyelonephritis; discriminating complex cysts from renal cell carcinoma; and identifying sources of abdominal pain. Many imaging features of ADPKD are incompletely evaluated or not deemed to be clinically significant, and because of this, treatment options are limited. However, total kidney volume (TKV) measurement has become important for assessing the risk of disease progression (i.e., Mayo Imaging Classification) and predicting tolvaptan treatment’s efficacy. Deep learning for segmenting the kidneys has improved these measurements’ speed, accuracy, and reproducibility. Deep learning models can also segment other organs and tissues, extracting additional biomarkers to characterize the extent to which extrarenal manifestations complicate ADPKD. In this concept paper, we demonstrate how deep learning may be applied to measure the TKV and how it can be extended to measure additional features of this disease.

Key Points Water therapy in autosomal dominant polycystic kidney disease (ADPKD) reduces urine osmolality and serum copeptin level, a marker of vasopressin activity. Water therapy reduces the ADPKD kidney growth rate indicating it is slowing disease progression. Patients with ADPKD are less likely to report pain on water therapy. Background In animal models of autosomal dominant polycystic kidney disease (ADPKD), high water intake (HWI) decreases vasopressin secretion and slows disease progression, but the efficacy of HWI in human ADPKD is uncertain. Methods This exploratory, prospective, cross-over study of patients with ADPKD ( N =7) evaluated the hypothesis that HWI slows the rate of increase in height-adjusted total kidney volume (ht-TKV; a biomarker for ADPKD progression) and reduces pain. Patients at high risk of ADPKD progression ( i.e ., Mayo Imaging Classifications 1C/1D) were evaluated during 6 months of usual water intake (UWI), followed by 12 months of HWI calculated to reduce urine osmolality (Uosm) to <285 mOsm/kg. Measurements of Uosm, serum copeptin (secreted in equimolar amounts with vasopressin), magnetic resonance imaging measurements of ht-TKV, and pain survey responses were compared between HWI and UWI. Results During HWI, mean 24-hour Uosm decreased compared with UWI (428 [398–432] mOsm/kg versus 209 [190–223] mOsm/kg; P = 0.01), indicating adherence to the protocol. Decreases during HWI also occurred in levels of serum copeptin (5.8±2.0 to 4.2±1.6 pmol/L; P = 0.03), annualized rate of increase in ht-TKV (6.8% [5.9–8.5] to 4.4% [3.0–5.0]; P < 0.02), and pain occurrence and pain interference during sleep ( P < 0.01). HWI was well tolerated. Conclusions HWI in patients at risk of rapid progression of ADPKD slowed the rate of ht-TKV growth and reduced pain. This suggests that suppressing vasopressin levels by HWI provides an effective nonpharmacologic treatment of ADPKD.

Background and purpose The objective is to demonstrate feasibility of quantitative susceptibility mapping (QSM) in autosomal dominant polycystic kidney disease (ADPKD) patients and to compare imaging findings with traditional T1/T2w magnetic resonance imaging (MRI). Methods Thirty-three consecutive patients (11 male, 22 female) diagnosed with ADPKD were initially selected. QSM images were reconstructed from the multiecho gradient echo data and compared to co-registered T2w, T1w and CT images. Complex cysts were identified and classified into distinct subclasses based on their imaging features. Prevalence of each subclass was estimated. Results QSM visualized two renal calcifications measuring 9 and 10 mm, and three pelvic phleboliths measuring 2 mm but missed 24 calcifications measuring 1mm or less and 1 larger calcification at the edge of the field of view. A total of 121 complex T1 hyperintense/T2 hypointense renal cysts were detected. 52 (43%) cysts appeared hyperintense on QSM consistent with hemorrhage; 60 (49%) cysts were isointense with respect to simple cysts and normal kidney parenchyma, while the remaining 9 (7%) were hypointense. The presentation of the latter two complex cyst subtypes is likely indicative of proteinaceous composition without hemorrhage. Conclusions Our results indicate that QSM of ADPKD kidneys is possible and uniquely suited to detect large renal calculi without ionizing radiation, and able to identify properties of complex cysts unattainable with traditional approaches.

Total kidney volume measured on MRI is an important biomarker for assessing the progression of autosomal dominant polycystic kidney disease and response to treatment. However, we have noticed that there can be substantial differences in the kidney volume measurements obtained from the various pulse sequences commonly included in an MRI exam. Here we examine kidney volume measurement variability among five commonly acquired MRI pulse sequences in abdominal MRI exams in 105 patients with ADPKD. Right and left kidney volumes were independently measured by three expert observers using model-assisted segmentation for axial T2, coronal T2, axial single-shot fast spin echo (SSFP), coronal SSFP, and axial 3D T1 images obtained on a single MRI from ADPKD patients. Outlier measurements were analyzed for data acquisition errors. Most of the outlier values (88%) were due to breathing during scanning causing slice misregistration with gaps or duplication of imaging slices (n = 35), slice misregistration from using multiple breath holds during acquisition (n = 25), composing of two overlapping acquisitions (n = 17), or kidneys not entirely within the field of view (n = 4). After excluding outlier measurements, the coefficient of variation among the five measurements decreased from 4.6% pre to 3.2%. Compared to the average of all sequences without errors, TKV measured on axial and coronal T2 weighted imaging were 1.2% and 1.8% greater, axial SSFP was 0.4% greater, coronal SSFP was 1.7% lower and axial T1 was 1.5% lower than the mean, indicating intrinsic measurement biases related to the different MRI contrast mechanisms. In conclusion, MRI data acquisition errors are common but can be identified using outlier analysis and excluded to improve organ volume measurement consistency. Bias toward larger volume measurements on T2 sequences and smaller volumes on axial T1 sequences can also be mitigated by averaging data from all error-free sequences acquired.

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

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-retest 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.

Total kidney volume (TKV) is an important biomarker for assessing kidney function, especially for autosomal dominant polycystic kidney disease (ADPKD). However, TKV measurements from a single MRI pulse sequence have limited reproducibility, ± ~5%, similar to ADPKD annual kidney growth rates.

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

We observed multiple fatal intracranial hemorrhages shortly after initiating therapeutic anticoagulation for treatment of venous thromboembolism (VTE) in COVID-19 patients suggesting increased anticoagulation risk associated with COVID-19. The objective of this study is to quantify risk of major hemorrhage in hospitalized COVID-19 patients on therapeutic anticoagulation for deep venous thrombosis (DVT) or pulmonary embolism (PE). Hospitalized patients with COVID-19 receiving therapeutic anticoagulation for DVT, PE or both at four New York City hospitals were evaluated for hemorrhagic complications. These were categorized as major (including fatal) or clinically relevant non-major according to the criteria of the International Society of Thrombosis and Haemostasis. Hemorrhagic complications were correlated with clinical and laboratory data, ICD-10 code diagnoses and type of anticoagulation treatment. Minor hemorrhages were excluded. Major/clinically relevant hemorrhages occurred in 36 of 170 (21%) hospitalized COVID-19 patients being treated with therapeutic anticoagulation for VTE including 4 (2.4%) fatal hemorrhages. Hemorrhage was 3.4 times more likely with unfractionated heparin 27/76 (36%) compared to 8/81 (10%) with low molecular weight heparin (p = 0.002). Multivariate analysis showed that major hemorrhage was associated with intubation (p = 0.04) and elevated serum LDH (p < 0.001) and low fibrinogen (p = 0.05). Increased risk of hemorrhagic complications in treating VTE in hospitalized COVID-19 patients should be considered especially when using unfractionated heparin, in intubated patients, with low fibrinogen and/or elevated LDH. Checking serum fibrinogen and LDH before initiating therapeutic anticoagulation and monitoring coagulation parameters frequently may reduce bleeding complications.

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.

Background Pulmonary embolism (PE) commonly complicates SARS-CoV-2 infection, but incidence and mortality reported in single-center studies, along with risk factors, vary. Purpose To determine the incidence of PE in patients with COVID-19 and its associations with clinical and laboratory parameters. Materials and Methods In this HIPAA-compliant study, electronic medical records were searched retrospectively for demographic, clinical, and laboratory data and outcomes among patients with COVID-19 admitted at four hospitals from March through June 2020. PE found at CT pulmonary angiography and perfusion scintigraphy was correlated with clinical and laboratory parameters. The d-dimer level was used to predict PE, and the obtained threshold was externally validated among 85 hospitalized patients with COVID-19 at a fifth hospital. The association between right-sided heart strain and embolic burden was evaluated in patients with PE undergoing echocardiography. Results A total of 413 patients with COVID-19 (mean age, 60 years ± 16 [standard deviation]; age range, 20–98 years; 230 men) were evaluated. PE was diagnosed in 102 (25%; 95% CI: 21, 29) of 413 hospitalized patients with COVID-19 who underwent CT pulmonary angiography or perfusion scintigraphy. PE was observed in 21 (29%; 95% CI: 19, 41) of 73 patients in the intensive care unit (ICU) versus 81 (24%; 95% CI: 20, 29) of 340 patients who were not in the ICU (P = .37). PE was associated with male sex (odds ratio [OR], 1.74; 95% CI: 1.1, 2.8; P = .02); smoking (OR, 1.86; 95% CI: 1.0, 3.4; P = .04); and increased d-dimer (P < .001), lactate dehydrogenase (P < .001), ferritin (P = .001), and interleukin-6 (P = .02) levels. Mortality in hospitalized patients was similar between patients with PE and those without PE (14% [13 of 102]; 95% CI: 8, 22] vs 13% [40 of 311]; 95% CI: 9, 17; P = .98), suggesting that diagnosis and treatment of PE were not associated with excess mortality. The d-dimer levels greater than 1600 ng/mL [8.761 nmol/L] helped predict PE with 100% sensitivity and 62% specificity in an external validation cohort. Embolic burden was higher in patients with right-sided heart strain among the patients with PE undergoing echocardiography (P = .03). Conclusion Pulmonary embolism (PE) incidence was 25% in patients hospitalized with COVID-19 suspected of having PE. A d-dimer level greater than 1600 ng/mL [8.761 nmol/L] was sensitive for identification of patients who needed CT pulmonary angiography. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Ketai in this issue.

Background Screening for rapidly progressing autosomal dominant polycystic kidney disease (ADPKD) is necessary for assigning and monitoring therapies. Height-adjusted total kidney volume (ht-TKV) is an accepted biomarker for clinical prognostication, but represents only a small fraction of information on abdominal MRI. Purpose To investigate the utility of other MR features of ADPKD to predict progression. Study Type Single-center retrospective. Population Longitudinal data from 186 ADPKD subjects with baseline serum creatinine, PKD gene testing, abdominal MRI measurements, and >= 2 follow-up serum creatinine were reviewed. Field Strength/Sequence 1.5T,T-2-weighted single-shot fast spin echo,T-1-weighted3Dspoiled gradient echo (liver accelerated volume acquisition) and2Dcine velocity encoded gradient echo (phase contrastMRA). Assessment Ht-TKV, renal blood flow (RBF), number and fraction of renal and hepatic cysts, bright T(1)hemorrhagic renal cysts, and liver and spleen volumes were independently assessed by three observers blinded to estimated glomerular filtration rate (eGFR) data. Statistical Tests Linear mixed-effect models were applied to predict eGFR over time using MRI features at baseline adjusted for confounders. Validation was performed in 158 patients who had follow-up MRI using receiver operator characteristic, sensitivity, and specificity. Results Hemorrhagic cysts, fraction of renal and hepatic cysts, height-adjusted liver and spleen volumes were significant independent predictors of future eGFR (final prediction model R-2= 0.88P < 0.05). The number of hemorrhagic cysts significantly improved the prediction compared to ht-TKV in predicting future eGFR (area under the curve [AUC] = 0.94, 95% confidence interval [CI]: 0.9-0.94 vs. R-2= 0.9, 95% CI: 0.85-0.9,P= 0.045). For baseline eGFR >= 60 ml/min/1.73m(2), sensitivity for predicting eGFR<45 ml/min/1.73m(2)by ht-TKV alone was 29%. Sensitivity increased to 72% with all MRI variables in the model (P < 0.05 = 0.019), whereas specificity was unchanged, 100% vs. 99%. Data Conclusion Combining multiple MR features including hemorrhagic renal cysts, renal cyst fraction, liver and spleen volume, hepatic cyst fraction, and renal blood flow enhanced sensitivity for predicting eGFR decline in ADPKD compared to the standard model including only ht-TKV. Level of Evidence 2 Technical Efficacy Stage 2

Purpose To develop and externally validate a multivariate prediction model for the prediction of acute kidney injury (AKI) in COVID-19, based on baseline renal perfusion from contrast-enhanced CT together with clinical and laboratory parameters. Methods In this retrospective IRB-approved study, we identified COVID-19 patients who had a standard-of-care contrast-enhanced abdominal CT scan within 5 days of their COVID-19 diagnosis at our institution (training set; n = 45, mean age 65 years, M/F 23/22) and at a second institution (validation set; n = 41, mean age 61 years, M/F 22/19). The CT renal perfusion parameter, cortex-to-aorta enhancement index (CAEI), was measured in both sets. A multivariate logistic regression model for predicting AKI was constructed from the training set with stepwise feature selection with CAEI together with demographical and baseline laboratory/clinical data used as input variables. Model performance in the training and validation set was evaluated with ROC analysis. Results AKI developed in 16 patients (35.6%) of the training set and in 6 patients (14.6%) of the validation set. Baseline CAEI was significantly lower in the patients that ultimately developed AKI ( P = 0.003). Logistic regression identified a model combining baseline CAEI, blood urea nitrogen, and gender as most significant predictor of AKI. This model showed excellent diagnostic performance for prediction of AKI in the training set (AUC = 0.89, P < 0.001) and good performance in the validation set (AUC 0.78, P = 0.030). Conclusion Our results show diminished renal perfusion preceding AKI and a promising role of CAEI, combined with laboratory and demographic markers, for prediction of AKI in COVID-19.

Total kidney volume (TKV) is a critical biomarker for monitoring disease severity in autosomal dominant polycystic kidney disease (ADPKD). TKV is typically measured by manually contouring kidneys on one sequence from abdominal MRI without any quality control. Here we show that by using a deep learning model to measure kidney volume on 5 routinely acquired abdominal MRI sequences, it is possible to apply outlier analysis to find images with acquisition artifacts and to correct or exclude them from TKV estimation. This improves volume measurement consistency among the 5 sequences from 4.3% to 1.3% after quality control.

Organ volume measurements on MRI are typically performed on a single pulse sequence because of the tedious process of manual contouring. Here we use deep learning to automate kidney segmentations so that kidney volume can be measured on five abdominal MRI sequences. In 17 subjects scanned twice within 3 weeks (when no change in kidney volume was expected), the power of averaging 5 measurements improved reproducibility, achieving 2.5% absolute percent difference compared to 5.9% with manual contouring (p<0.05). Absolute percent error was reduced further to 2.1%, p<0.05 compared to manual segmentations, by excluding outlier measurements.

Motivation: By measuring height-adjusted total kidney volume (ht-TKV), renal MRI monitors autosomal dominant polycystic kidney disease (ADPKD) progression and evaluates therapeutic interventions’ efficacy for slowing cyst growth. Goal(s): Since arginine vasopressin (AVP) stimulates cyst growth, reducing vasopressin with high-water intake (HWI) may slow ADPKD progression. Approach: We test this hypothesis using a case-crossover study with 7 ADPKD subjects, measuring ht-TKV changes on MRI during usual and high-water intake. Results: High-water intake decreased 24-hour urine osmolality (359 vs 202mOsm/kg, p=0.002) and rate of increase in annualized ht-TKV growth rate (7.1% vs 3.5%, p=0.003), suggesting that high-water intake can slow ADPKD progression. Impact: Decreasing the rate of ht-TKV increase, measured by renal MRI during high water intake, suggests this as a potential treatment to slow progression of ADPKD, forestalling end-stage kidney failure requiring dialysis or transplantation.

Motivation: Height-adjusted total kidney volume (htTKV) growth rate measured on MRI or CT is a critical biomarker for monitoring autosomal dominant polycystic kidney disease (ADPKD) progression. Goal(s): This study aims to develop a tool for accurate calculation of the htTKV growth rate based on all available MRI scans. Approach: Accuracy of four MRI methods for calculating htTKV growth rate were assessed as compared to ground truth calculated from 10+ years of imaging follow up. Results: Using 2-parameter least squares fitting with 3+ scans or 5+ years of follow up reduce error 2-fold compared to the current clinical standard, Mayo Imaging Classification. Impact: Accurate estimation of kidney growth rate on abdominal MRI using FIT will enable better prediction of disease progression and response to therapy in patients with ADPKD.