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Wendee M Wechsberg,1–4 Irene A Doherty,1 Felicia A Browne,1,5 Tracy L Kline,1 Monique G Carry,6 Jerris L Raiford,6 Jeffrey H Herbst6 1Substance Abuse Treatment Evaluations and Interventions Research Program, RTI International, Research Triangle Park, 2Gillings Global School of Public Health, University of North Carolina, Chapel Hill, 3Psychology in the Public Interest, North Carolina State University, Raleigh, 4Psychiatry and Behavioral Sciences, School of Medicine, Duke University, Durham, NC, 5Department of Social and Behavioral Sciences, Harvard School of Public Health, Boston, MA, 6Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD and TB Prevention, US Centers for Disease Control and Prevention, Atlanta, GA, USA Abstract: The southeastern US sustains the highest high school dropout rates, and gangs persist in underserved communities. African American female adolescents who drop out of school and are gang members are at substantial risk of exposure to severe violence, physical abuse, and sexual exploitation. In this study of 237 female African American adolescents 16–19 years of age from North Carolina who dropped out or considered dropping out, 11% were current or past gang members. Adolescents who reported gang membership began smoking marijuana at a mean age of 13, whereas those who reported no gang membership began at a mean age of 15 years (P

Alexandra M Minnis,1,2 Irene A Doherty,1,3 Tracy L Kline,1 William A Zule,1 Bronwyn Myers,4,5 Tara Carney,4 Wendee M Wechsberg1,3,6,7 1RTI International, Research Triangle Park, NC, 2School of Public Health, University of California, Berkeley, CA, 3University of North Carolina, Chapel Hill, NC, USA; 4Alcohol, Tobacco and Other Drug Research Unit, South African Medical Research Council, 5Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa; 6North Carolina State University, Raleigh, 7Duke University School of Medicine, Durham, NC, USA Background: Inequitable gender-based power in relationships and intimate partner violence contribute to persistently high rates of HIV infection among South African women. We examined the effects of two group-based HIV prevention interventions that engaged men and their female partners together in a couples intervention (Couples Health CoOp [CHC]) and a gender-separate intervention (Men’s Health CoOp/Women’s Health CoOp [MHC/WHC]) on women’s reports of power, communication, and conflict in relationships. Methods: The cluster-randomized field experiment included heterosexual couples from a high-density South African township in which neighborhoods were randomized to one of the intervention arms or a control arm that received the WHC only. Participants completed in-person study visits at baseline and 6-month follow-up. We examined group differences using one-way analysis of variance and multivariable regression models.Results: Of the 290 couples enrolled, 255 women remained in the same partnership over 6 months. Following the intervention, women in the CHC arm compared with those in the WHC arm were more likely to report an increase in relationship control (ß=0.92, 95% confidence interval [CI]: 0.02, 1.83, P=0.045) and gender norms supporting female autonomy in relationships (ß=0.99, 95% CI: 0.07, 1.91, P=0.035). Women in the MHC/WHC arm were more likely to report increases in relationship equity, relative to those in the CHC arm, and had a higher odds of reporting no victimization during the previous 3 months (MHC/WHC vs WHC: odds ratio =3.05, 95% CI: 1.55, 6.0, P=0.001; CHC vs MHC/WHC: odds ratio =0.38, 95% CI: 0.20, 0.74, P=0.004).Conclusion: Male partner engagement in either the gender-separate or couples-based interventions led to modest improvements in gender power, adoption of more egalitarian gender norms, and reductions in relationship conflict for females. The aspects of relationship power that improved, however, varied between the couples and gender-separate conditions, highlighting the need for further attention to development of both gender-separate and couples interventions. Keywords: gender, partner interventions, empowerment, HIV prevention

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., Competing Interests: Competing interests: None declared., (© IGCS and ESGO 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

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., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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/m 2 ; estimated glomerular filtration rate [eGFR, 2021 CKD-EPI creatinine], 103±18mL/min/1.73m 2 ; 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/1.73m 2 ; 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 11 C-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, χ 2 /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., (Copyright © 2024 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2024 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.)

Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic kidney disorder and the fourth leading cause of kidney failure (KF) in adults. Characterized by a reduction in glomerular filtration rate (GFR) and increased kidney size, ADPKD exhibits significant variability in progression, highlighting the urgent need for reliable and predictive biomarkers to optimize management and treatment approaches. This review explores the roles of diverse biomarkers-including clinical, genetic, molecular, and imaging biomarkers-in evaluating disease progression and customizing treatments for ADPKD. Clinical biomarkers such as biological sex, the predicting renal outcome in polycystic kidney disease ( PROPKD) score, and body mass index are shown to correlate with disease severity and progression. Genetic profiling, particularly distinguishing between truncating and non-truncating pathogenic variants in the PKD1 gene, refines risk assessment and prognostic precision. Advancements in imaging significantly enhance our ability to assess disease severity. Height-adjusted total kidney volume (htTKV) and the Mayo imaging classification (MIC) are foundational, whereas newer imaging biomarkers, including texture analysis, total cyst number (TCN), cyst-parenchyma surface area (CPSA), total cyst volume (TCV), and cystic index, focus on detailed cyst characteristics to offer deeper insights. Molecular biomarkers (including serum and urinary markers) shed light on potential therapeutic targets that could predict disease trajectory. Despite these advancements, there is a pressing need for the development of response biomarkers in both the adult and pediatric populations, which can evaluate the biological efficacy of treatments. The holistic evaluation of these biomarkers not only deepens our understanding of kidney disease progression in ADPKD, but it also paves the way for personalized treatment strategies aiming to significantly improve patient outcomes., (© 2024 International Society of Nephrology. Published by Elsevier Inc.)

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/1.73m 2 that was at least 5mL/min/1.73m 2 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., (Copyright © 2024 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.)

This protocol presents a multilevel cluster randomized study in 24 communities in Cape Town, South Africa. The study comprises four specific aims. Aim 1, conducted during the formative phase, was to modify the original Couples Health CoOp (CHC) intervention to include antiretroviral therapy/pre-exposure prophylaxis (ART/PrEP), called the Couples Health CoOp Plus (CHC+), with review from our Community Collaborative Board and a Peer Advisory Board. Aim 1 has been completed for staging the trial. Aim 2 is to evaluate the impact of a stigma awareness and education workshop on community members' attitudes and behaviors toward young women and men who use AODs and people in their community seeking HIV services (testing/ART/PrEP) and other health services in their local clinics. Aim 3 is to test the efficacy of the CHC+ to increase both partners' PrEP/ART initiation and adherence (at 3 and 6 months) and to reduce alcohol and other drug use, sexual risk and gender-based violence, and to enhance positive gender norms and communication relative to HIV testing services (n = 480 couples). Aim 4 seeks to examine through mixed methods the interaction of the stigma awareness workshop and the CHC+ on increased PrEP and ART initiation, retention, and adherence among young women and their primary partners. Ongoing collaborations with community peer leaders and local outreach staff from these communities are essential for reaching the project's aims. Additionally, a manualized field protocol with regular training, fidelity checks, and quality assurance are critical components of this multilevel community trial for successful ongoing data collection. Trial registration. Clinicaltrials.gov Registration Number: NCT05310773. Pan African Trials: pactr.samrc.ac.za/ Registration Number: PACTR202205640398485., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wechsberg et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: To determine whether body composition derived from medical imaging may be useful for assessing biologic age at the tissue level because people of the same chronologic age may vary with respect to their biologic age., Methods: We identified an age- and sex-stratified cohort of 4900 persons with an abdominal computed tomography scan from January 1, 2010, to December 31, 2020, who were 20 to 89 years old and representative of the general population in Southeast Minnesota and West Central Wisconsin. We constructed a model for estimating tissue age that included 6 body composition biomarkers calculated from abdominal computed tomography using a previously validated deep learning model., Results: Older tissue age associated with intermediate subcutaneous fat area, higher visceral fat area, lower muscle area, lower muscle density, higher bone area, and lower bone density. A tissue age older than chronologic age was associated with chronic conditions that result in reduced physical fitness (including chronic obstructive pulmonary disease, arthritis, cardiovascular disease, and behavioral disorders). Furthermore, a tissue age older than chronologic age was associated with an increased risk of death (hazard ratio, 1.56; 95% CI, 1.33 to 1.84) that was independent of demographic characteristics, county of residency, education, body mass index, and baseline chronic conditions., Conclusion: Imaging-based body composition measures may be useful in understanding the biologic processes underlying accelerated aging., (Copyright © 2023 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

Automatic abnormality identification of brachial plexus (BP) from normal magnetic resonance imaging to localize and identify a neurologic injury in clinical practice (MRI) is still a novel topic in brachial plexopathy. This study developed and evaluated an approach to differentiate abnormal BP with artificial intelligence (AI) over three commonly used MRI sequences, i.e. T1, FLUID sensitive and post-gadolinium sequences. A BP dataset was collected by radiological experts and a semi-supervised artificial intelligence method was used to segment the BP (based on nnU-net). Hereafter, a radiomics method was utilized to extract 107 shape and texture features from these ROIs. From various machine learning methods, we selected six widely recognized classifiers for training our Brachial plexus (BP) models and assessing their efficacy. To optimize these models, we introduced a dynamic feature selection approach aimed at discarding redundant and less informative features. Our experimental findings demonstrated that, in the context of identifying abnormal BP cases, shape features displayed heightened sensitivity compared to texture features. Notably, both the Logistic classifier and Bagging classifier outperformed other methods in our study. These evaluations illuminated the exceptional performance of our model trained on FLUID-sensitive sequences, which notably exceeded the results of both T1 and post-gadolinium sequences. Crucially, our analysis highlighted that both its classification accuracies and AUC score (area under the curve of receiver operating characteristics) over FLUID-sensitive sequence exceeded 90%. This outcome served as a robust experimental validation, affirming the substantial potential and strong feasibility of integrating AI into clinical practice., (Copyright © 2024 IBRO. Published by Elsevier Inc. All rights reserved.)

Background: Body composition can be accurately quantified from abdominal computed tomography (CT) exams and is a predictor for the development of aging-related conditions and for mortality. However, reference ranges for CT-derived body composition measures of obesity, sarcopenia, and bone loss have yet to be defined in the general population., Methods: We identified a population-representative sample of 4 900 persons aged 20 to 89 years who underwent an abdominal CT exam from 2010 to 2020. The sample was constructed using propensity score matching an age and sex stratified sample of persons residing in the 27-county region of Southern Minnesota and Western Wisconsin. The matching included race, ethnicity, education level, region of residence, and the presence of 20 chronic conditions. We used a validated deep learning based algorithm to calculate subcutaneous adipose tissue area, visceral adipose tissue area, skeletal muscle area, skeletal muscle density, vertebral bone area, and vertebral bone density from a CT abdominal section., Results: We report CT-based body composition reference ranges on 4 649 persons representative of our geographic region. Older age was associated with a decrease in skeletal muscle area and density, and an increase in visceral adiposity. All chronic conditions were associated with a statistically significant difference in at least one body composition biomarker. The presence of a chronic condition was generally associated with greater subcutaneous and visceral adiposity, and lower muscle density and vertebrae bone density., Conclusions: We report reference ranges for CT-based body composition biomarkers in a population-representative cohort of 4 649 persons by age, sex, body mass index, and chronic conditions., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Gerontological Society of America.)

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., Competing Interests: Declaration of competing interest M. M. reports grants and consulting fees outside the submitted work from Otsuka Pharmaceuticals, Sanofi, Palladio Biosciences, Reata, Natera, Chinook Therapeutics, Goldilocks Therapeutics, and Carraway Therapeutics., (Copyright © 2023. Published by Elsevier Inc.)

Adolescent girls and young women (AGYW) in South Africa experience contextual barriers to HIV risk reduction including incomplete schooling, unintended pregnancy, substance use, and gender-based violence. A cluster randomised trial in Cape Town allocated 24 Black and Coloured communities to a gender-focused HIV risk-reduction intervention or HIV testing, with 500 AGYW total enrolled. We evaluated intervention efficacy by comparing mean differences overall, by community population group (Black and Coloured) and among those with structural barriers based on neighbourhood, education, and employment (n = 406). Both arms reported reductions in alcohol, cannabis, and condomless sex, with no intervention efficacy overall. Among AGYW with barriers, intervention participants reported fewer days of methamphetamine use at 6 months (t(210) = 2·08, p  = ·04). In population group analysis, we found intervention effects on alcohol and sexual communication. Intervention participants in Black communities had fewer days of alcohol use at 12 months (t(136) = 2·10, p  = ·04). Sexual discussion (t(147) = -2·47, p  = ·02) and condom negotiation (t(146) = -2·51, p  = ·01) increased for intervention participants at 12 months in Coloured communities. Gender-focused interventions must address population group differences and intersecting barriers to decrease substance use and increase education, skills, and sexual health protection.

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., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Society of Nephrology.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Gottlich, Korfiatis, Gregory and Kline.)

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.

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., (© 2023. The Author(s).)

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., (Copyright © 2023 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2023 by the American Society of Nephrology.)

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., Competing Interests: V.E.T. receives grants for preclinical research and clinical trials from Palladio Biosciences, Mironid, Blueprint Medicines, Tribune, Sanofi, and Reata and Regulus. He reports consultancy agreements with uResearch Technology and MFMER for imaging analytics for PCKD and repurposing of probenecid to treat PCKD. He reports royalties for system and method of classifying ADPKD. He is a member of the International Society of Nephrology Kaplan award committee, the American Society of Nephrology editorial board and the PKD Foundation advisory board. P.C.H. reports receiving grants and/or research reagents from Amgen, Inc., Bayer AG, Genzyme Corporation, GlaxoSmithKline, Mitobridge Inc., Otsuka Pharmaceuticals, Palladio Biosciences, Regulus Therapeutics and Vertex Pharmaceuticals, all outside of the submitted work. P.C.H. also reports a position on the Clinical Advisory Board of Mironid, honoraria from Otsuka Pharmaceuticals and Vertex Pharmaceuticals, and other fees from Otsuka Pharmaceuticals. F.T.C. receives research funding from Natera Inc. and Otsuka Pharmaceuticals. He is Chair of the educational advisory panel at the PKD foundation in the USA. All others authors declared no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of the ERA.)

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., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: PCH reports receiving grants and/or research reagents from Amgen, Inc., Bayer AG, Genzyme Corporation, GlaxoSmithKline, Mitobridge Inc., Otsuka Pharmaceuticals, Palladio Biosciences, Regulus Therapeutics, and Vertex Pharmaceuticals, all outside of the submitted work. PCH also reports a position on the Clinical Advisory Board of Mironid, honoraria from Otsuka Pharmaceuticals and Vertex Pharmaceuticals, and other fees from Otsuka Pharmaceuticals. VET reports grants and/or other fees from Blueprint Medicines, Mironid, Otsuka Pharmaceuticals, Palladio Biosciences, Sanofi Genzyme, and Regulus Therapeutics, all outside the submitted work. ZMZ reports grants from Kadmon Inc. and consulting fees from Chronisense Medical, LTD. FTC reports research grant support from Otsuka Pharmaceuticals. All the other authors declared no competing interests., (© 2023 The Authors.)

Previous research shows that educational attainment is a protective factor for substance use and sexual risk among adolescents and young adults. Evidence also shows that this relationship may differ by race/ethnicity and gender. This study aimed to elucidate the relationship between educational attainment, substance use and sexual risk among African American women in emerging adulthood. This study uses cross-sectional data from 646 African American women (aged 18 to 25) enrolled in a randomized trial of a behavioral HIV risk-reduction intervention. At enrollment, participants completed a risk behavior assessment via audio-computer assisted self-interview and provided a urine sample for drug screening. Bivariate logistic regression analyses were conducted to examine substance use and sexual risk factors associated with educational attainment: completing some college or more vs. completing high school or less). Participants who completed some college or more (52%) were more likely to report heavy alcohol use (four or more drinks in one day) in the past 30 days (OR=1.48; p =0.014) and more likely to report alcohol or other drug use just before or during last sex (OR=1.43; p =0.026) compared with participants who completed high school or less. Completing some college or more was protective for having a positive urine screen for cocaine (OR=0.43; p =0.018) and reporting condomless sex at last sex (OR=0.71; p =0.041). Differences in positive marijuana screens, reporting a previous STI, or reporting their partner used alcohol or other drugs at last sex were not statistically significant. The findings reveal notable differences in the magnitude and direction of associations between educational attainment and substance use and sexual risk. Although educational attainment is subject to change because of the frequent pursuit of education during emerging adulthood, the findings may have important implications for tailoring HIV risk-reduction interventions to key populations, such as African American women., Competing Interests: Declaration of Interest The authors report no conflicts of interest.

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.

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., (Copyright © 2022 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Purpose: To determine if pancreas radiomics-based AI model can detect the CT imaging signature of type 2 diabetes (T2D)., Methods: Total 107 radiomic features were extracted from volumetrically segmented normal pancreas in 422 T2D patients and 456 age-matched controls. Dataset was randomly split into training (300 T2D, 300 control CTs) and test subsets (122 T2D, 156 control CTs). An XGBoost model trained on 10 features selected through top-K-based selection method and optimized through threefold cross-validation on training subset was evaluated on test subset., Results: Model correctly classified 73 (60%) T2D patients and 96 (62%) controls yielding F1-score, sensitivity, specificity, precision, and AUC of 0.57, 0.62, 0.61, 0.55, and 0.65, respectively. Model's performance was equivalent across gender, CT slice thicknesses, and CT vendors (p values > 0.05). There was no difference between correctly classified versus misclassified patients in the mean (range) T2D duration [4.5 (0-15.4) versus 4.8 (0-15.7) years, p = 0.8], antidiabetic treatment [insulin (22% versus 18%), oral antidiabetics (10% versus 18%), both (41% versus 39%) (p > 0.05)], and treatment duration [5.4 (0-15) versus 5 (0-13) years, p = 0.4]., Conclusion: Pancreas radiomics-based AI model can detect the imaging signature of T2D. Further refinement and validation are needed to evaluate its potential for opportunistic T2D detection on millions of CTs that are performed annually., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Globally, an urgent need exists to expand access to HIV prevention among adolescent girls and young women (AGYW), but the need is particularly acute in sub-Saharan Africa. Oral pre-exposure prophylaxis (PrEP) offers an effective HIV prevention method. In many countries, however, accessing PrEP necessitates that AGYW visit their local health clinic, where they may face access challenges. Some countries have implemented youth-friendly services to reduce certain challenges in local health clinics, but barriers to access persist, including clinic stigma. However, evidence of clinic stigma toward AGYW, particularly with respect to PrEP service delivery, is still limited. This mixed methods study explores stigma toward AGYW seeking clinic services, in particular PrEP, from the perspective of both clinic staff (clinical and nonclinical) and AGYW who seek services at clinic sites in Tshwane province, South Africa., Methods: Six focus group discussions were conducted with AGYW (43 total participants) and four with clinic staff (42 total participants) and triangulated with survey data with AGYW (n = 449) and clinic staff (n = 130). Thematic analysis was applied to the qualitative data and descriptive statistics were conducted with the survey data., Results: Four common themes emerged across the qualitative and quantitative data and with both AGYW and clinic staff, although with varying degrees of resonance between these two groups. These themes included (1) clinic manifestations of stigma toward AGYW, (2) concerns about providing PrEP services for AGYW, (3) healthcare providers' identity as mothers, and (4) privacy and breaches of confidentiality. An additional theme identified mainly in the AGYW data pertained to stigma and access to healthcare., Conclusion: Evidence is needed to inform strategies for addressing clinic stigma toward AGYW, with the goal of removing barriers to PrEP services for this group. While awareness has increased and progress has been achieved around the provision of comprehensive, youth-friendly sexual and reproductive health services, these programs need to be adapted for the specific concerns of young people seeking PrEP services. Our findings point to the four key areas noted above where programs seeking to address stigma toward AGYW in clinics can tailor their programming., (© 2022. The Author(s).)

Purpose: Total kidney volume (TKV) is the most important imaging biomarker for quantifying the severity of autosomal-dominant polycystic kidney disease (ADPKD). 3D ultrasound (US) can accurately measure kidney volume compared to 2D US; however, manual segmentation is tedious and requires expert annotators. We investigated a deep learning-based approach for automated segmentation of TKV from 3D US in ADPKD patients., Method: We used axially acquired 3D US-kidney images in 22 ADPKD patients where each patient and each kidney were scanned three times, resulting in 132 scans that were manually segmented. We trained a convolutional neural network to segment the whole kidney and measure TKV. All patients were subsequently imaged with MRI for measurement comparison., Results: Our method automatically segmented polycystic kidneys in 3D US images obtaining an average Dice coefficient of 0.80 on the test dataset. The kidney volume measurement compared with linear regression coefficient and bias from human tracing were R 2  = 0.81, and - 4.42%, and between AI and reference standard were R 2  = 0.93, and - 4.12%, respectively. MRI and US measured kidney volumes had R 2  = 0.84 and a bias of 7.47%., Conclusion: This is the first study applying deep learning to 3D US in ADPKD. Our method shows promising performance for auto-segmentation of kidneys using 3D US to measure TKV, close to human tracing and MRI measurement. This imaging and analysis method may be useful in a number of settings, including pediatric imaging, clinical studies, and longitudinal tracking of patient disease progression., (© 2022. The Author(s).)

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., (Copyright © 2022 by the American Society of Nephrology.)

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., Competing Interests: Declaration of interests M.M. reports grants and consulting fees outside the submitted work from Otsuka Pharmaceuticals, Sanofi, Chinook, Goldilocks, Natera, and Palladio. R.D.P. reports clinical trial support from Reata, Kadmon, Sanofi-Genzyme, US Department of Defense; consultant/advisory fees from Otsuka and Sanofi-Genzyme; and is section editor Renal Cystic Disease: UpToDate. J.A.S. has received honorarium from consulting positions from Otsuka Pharmaceuticals, Sanofi, and Takeda. V.E.T. reports grants and/or other fees from Mironid, Blueprint Medicines, Otsuka Pharmaceuticals, Palladio Biosciences, Sanofi Genzyme, Reata, and Regulus Therapeutics, all outside the submitted work., (Copyright © 2021 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

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).

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., Competing Interests: K.H. receives royalties for industry use of the Pkd1RC/RC mouse model in concordance with Mayo Clinic Ventures regulations (Mayo Technology Case #2012–14). B.G. is a member of the PKD Foundation Scientific Advisory Committee., (© 2021 The Author(s).)

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 m 2 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., Competing Interests: C. Hanna reports consultancy agreements with Horizon Therapeutics and honoraria from Horizon Therapeutics. P.C. Harris reports consultancy agreements with Mitobridge, Otsuka, Regulus, and Vertex; research funding from Acceleron, Jemincare, Navitor, Otsuka Pharmaceuticals, and Palladio Biosciences; patents and inventions with Amgen, Bayer, Genzyme, GlaxoSmithKline, Millipore, Mitobridge, and Vertex; honoraria from Otsuka Pharmaceuticals and Vertex Pharmaceuticals; and serving on the clinical advisory board of Mironid. P.J. Schulte reports other interests/relationships with OxThera. S.R. Senum reports ownership interest with Marvell Technology Inc. V.E. Torres reports consultancy agreements with Acceleron Pharma, Blueprint Medicines, Mironid, Otsuka Pharmaceuticals, Palladio, Reata, and Sanofi; research funding from Blueprint Medicines, Mironid, Otsuka Pharmaceuticals, Palladio Biosciences, Reata, and Sanofi Genzyme (all preclinical trials, preclinical research, or clinical trials); honoraria from Otsuka Pharmaceuticals (to the institution); and scientific advisor or membership with Mironid, Otsuka Pharmaceuticals, and Sanofi. Z.M. Zoghby reports research funding from Kadmon Corporation. All remaining authors have nothing to disclose., (Copyright © 2022 by the American Society of Nephrology.)

Women in South Africa living with HIV who use alcohol may not adhere to ART, affecting the country's 90-90-90 targets. The Women's Health CoOp (WHC), a woman-focused HIV intervention, has shown efficacy in numerous trials with key populations of women in South Africa who use alcohol and drugs. In a hybrid implementation effectiveness study, the WHC was implemented in usual care clinics by healthcare providers in a modified stepped-wedge design. We present the outcomes of alcohol use and ART adherence with 480 women, with a 95% 6-month follow-up rate across 4 implementation cycles. Compared with the first cycle, women in the fourth cycle were significantly less likely (OR = 0.10 [95% CI 0.04, 0.24]) to report alcohol use disorder risk and were 4 times more likely (OR = 4.16 [95% CI 1.05, 16.51]) to report ART adherence at 6-month follow-up. Overall, acceptability and satisfaction were extremely high. The WHC intervention was successful in reaching key populations of women to reduce alcohol use and increase ART adherence, which is essential for South Africa to reach the 90-90-90 goals., (© 2021. The Author(s).)

BACKGROUND. Imaging biomarkers of response to neoadjuvant therapy (NAT) for pancreatic ductal adenocarcinoma (PDA) are needed to optimize treatment decisions and long-term outcomes. OBJECTIVE. The purpose of this study was to investigate metrics from PET/MRI and CT to assess pathologic response of PDA to NAT and to predict overall survival (OS). METHODS. This retrospective study included 44 patients with 18 F-FDG-avid borderline resectable or locally advanced PDA on pretreatment PET/MRI who also underwent post-NAT PET/MRI before surgery between August 2016 and February 2019. Carbohydrate antigen 19-9 (CA 19-9) level, metabolic metrics from PET/MRI, and morphologic metrics from CT ( n = 34) were compared between pathologic responders (College of American Pathologists scores 0 and 1) and nonresponders (scores 2 and 3). AUCs were measured for metrics significantly associated with pathologic response. Relation to OS was evaluated with Cox proportional hazards models. RESULTS. Among 44 patients (22 men, 22 women; mean age, 62 ± 11.6 years), 19 (43%) were responders, and 25 (57%) were nonresponders. Median OS was 24 months (range, 6-42 months). Before treatment, responders and nonresponders did not differ in CA 19-9 level, metabolic metrics, or CT metrics ( p > .05). After treatment, responders and nonresponders differed in complete metabolic response (CMR) (responders, 89% [17/19]; nonresponders, 40% [10/25]; p = .04], mean change in SUV max (ΔSUV max ; responders, -70% ± 13%; nonresponders, -37% ± 42%; p < .001), mean change in SUV max corrected to serum glucose level (ΔSUV gluc ) (responders, -74% ± 12%; nonresponders, -30% ± 58%; p < .001), RECIST response on CT (responders, 93% [13/14]; nonresponders, 50% [10/20]; p = .02)], and mean change in tumor volume on CT (ΔTvol) (responders, -85% ± 21%; nonresponders, 57% ± 400%; p < .001). The AUC of CMR for pathologic response was 0.75; ΔSUV max , 0.83; ΔSUV gluc , 0.87; RECIST, 0.71; and ΔTvol 0.86. The AUCs of bivariable PET/MRI and CT models were 0.83 (CMR and ΔSUV max ), 0.87 (CMR and ΔSUV gluc ), and 0.87 (RECIST and ΔTvol). OS was associated with CMR ( p = .03), ΔSUV max ( p = .003), ΔSUV gluc ( p = .003), and RECIST ( p = .046). CONCLUSION. Unlike CA 19-9 level, changes in metabolic metrics from PET/MRI and morphologic metrics from CT after NAT were associated with pathologic response and OS in patients with PDA, warranting prospective validation. CLINICAL IMPACT. Imaging metrics associated with pathologic response and OS in PDA could help guide clinical management and outcomes for patients with PDA who undergo emergency therapeutic interventions.

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.

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 R 2 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., (© 2021. The Author(s).)

This brief report emphasizes the need to focus on women with HIV who are pregnant who use alcohol or other drugs. A recently completed implementation science study tested a gender-focused behavioral intervention, the Women's Health CoOp (WHC), to improve antiretroviral therapy (ART) adherence and reduce alcohol use among women with HIV. The study identified 33 participants who had a positive pregnancy test result at the baseline assessment, of whom five participants remained pregnant during the 6-month duration of the study. Of the 33 pregnant participants at the baseline assessment, 55% reported past-month alcohol use, with 27% reporting a history of physical abuse and 12% reporting a history of sexual abuse. The five women who remained pregnant at 6 months showed improved ART adherence and reduced prenatal alcohol use. The gender-focused WHC intervention shows promise as a cost-effective, sustainable, behavioral intervention to address these intersecting syndemic issues. Future research should focus on identifying the needs of women with HIV who are pregnant who use alcohol or other drugs and developing tailored evidence-based behavioral interventions such as the WHC for preventing FASD in addition to improving ART adherence in this key population of women and reducing the economic burden on society.

Autosomal dominant polycystic kidney disease (ADPKD), primarily due to PKD1 or PKD2 mutations, causes progressive kidney cyst development and kidney failure. There is significant intrafamilial variability likely due to the genetic background and environmental/lifestyle factors; variability that can be modeled in PKD mice. Here, we characterized mice homozygous for the PKD1 hypomorphic allele, p.Arg3277Cys (Pkd1 RC/RC ), inbred into the BALB/cJ (BC) or the 129S6/SvEvTac (129) strains, plus F1 progeny bred with the previously characterized C57BL/6J (B6) model; F1(BC/B6) or F1(129/B6). By one-month cystic disease in both the BC and 129 Pkd1 RC/RC mice was more severe than in B6 and continued with more rapid progression to six to nine months. Thereafter, the expansive disease stage plateaued/declined, coinciding with increased fibrosis and a clear decline in kidney function. Greater severity correlated with more inter-animal and inter-kidney disease variability, especially in the 129-line. Both F1 combinations had intermediate disease severity, more similar to B6 but progressive from one-month of age. Mild biliary dysgenesis, and an early switch from proximal tubule to collecting duct cysts, was seen in all backgrounds. Preclinical testing with a positive control, tolvaptan, employed the F1(129/B6)-Pkd1 RC/RC line, which has moderately progressive disease and limited isogenic variability. Magnetic resonance imaging was utilized to randomize animals and provide total kidney volume endpoints; complementing more traditional data. Thus, we show how genetic background can tailor the Pkd1 RC/RC model to address different aspects of pathogenesis and disease modification, and describe a possible standardized protocol for preclinical testing., (Copyright © 2021 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2020 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.)

Purpose: For patients affected by autosomal-dominant polycystic kidney disease (ADPKD), successful differentiation of cysts is useful for automatic classification of patient phenotypes, clinical decision-making, and disease progression. The objective was to develop and evaluate a fully automated semantic segmentation method to differentiate and analyze renal cysts in patients with ADPKD., Methods: An automated deep learning approach using a convolutional neural network was trained, validated, and tested on a set of 60 MR T2-weighted images. A three-fold cross-validation approach was used to train three models on distinct training and validation sets (n = 40). An ensemble model was then built and tested on the hold out cases (n = 20), with each of the cases compared to manual segmentations performed by two readers. Segmentation agreement between readers and the automated method was assessed., Results: The automated approach was found to perform at the level of interobserver variability. The automated approach had a Dice coefficient (mean ± standard deviation) of 0.86 ± 0.10 vs Reader-1 and 0.84 ± 0.11 vs. Reader-2. Interobserver Dice was 0.86 ± 0.08. In terms of total cyst volume (TCV), the automated approach had a percent difference of 3.9 ± 19.1% vs Reader-1 and 8.0 ± 24.1% vs Reader-2, whereas interobserver variability was - 2.0 ± 16.4%., Conclusion: This study developed and validated a fully automated approach for performing semantic segmentation of kidney cysts in MR images of patients affected by ADPKD. This approach will be useful for exploring additional imaging biomarkers of ADPKD and automatically classifying phenotypes.

Introduction: Cystic expansion damaging the parenchyma is thought to lead to end-stage kidney disease (ESKD) in autosomal dominant polycystic kidney disease (ADPKD). Here we characterized genotypic and phenotypic attributes of ADPKD at time of ESKD., Methods: This is a retrospective cross-sectional study of patients with ADPKD with ESKD evaluated at Mayo Clinic with available abdominal computed tomography (CT) or magnetic resonance imaging (MRI). Kidney volumes were measured (total kidney volume adjusted for height [HtTKV]), Mayo Image Class (MIC) calculated, ADPKD genotype determined, and clinical and laboratory features obtained from medical records., Results: Differences in HtTKV at ESKD were associated with patient age and sex; older patients and women had smaller HtTKV at ESKD. HtTKV at ESKD was observed to be 12.3% smaller with each decade of age ( P  < 0.01); but significant only in women (17.8%, P  < 0.01; men 6.9%, P  = 0.06). Patients with onset of ESKD at <47, 47-61, or >61 years had different characteristics, with a shift from youngest to oldest in male to female enrichment, MIC from 1D/1E to 1B/1C, likely fully penetrant PKD1 mutations from 95% to 42%, and presence of macrovascular disease from 8% to 40%. Macrovascular disease was associated with smaller kidneys in female patients., Conclusion: HtTKV at ESKD was smaller with advancing age in patients with ADPKD, particularly in women. These novel findings provide insight into possible underlying mechanisms leading to ESKD, which differ between younger and older individuals. Cystic growth is the predominant mechanism in younger patients with ESKD, whereas aging-related factors, including vascular disease, becomes potentially important as patients age., (© 2020 International Society of Nephrology. Published by Elsevier Inc.)