Deep learning of renal scans in children with antenatal hydronephrosis.

INTRODUCTION: Antenatal hydronephrosis (ANH) is one of the most common anomalies identified on prenatal ultrasound, found in up to 4.5% of all pregnancies. Children with ANH are surveilled with repeated renal ultrasound and when there is high suspicion for a ureteropelvic junction obstruction on renal ultrasound, a mercaptuacetyltriglycerine (MAG3) Lasix renal scan is performed to evaluate for obstruction. However, the challenging interpretation of MAG3 renal scans places patients at risk of misdiagnosis. OBJECTIVE: Our objective was to analyze MAG3 renal scans using machine learning to predict renal complications. We hypothesized that our deep learning model would extract features from MAG3 renal scans that can predict renal complications in children with ANH. STUDY DESIGN: We performed a case-control study of MAG3 studies drawn from a population of children with ANH concerning for ureteropelvic junction obstruction evaluated at our institution from January 2009 until June of 2021. The outcome was renal complications that occur ≥6 months after an equivocal MAG-3 renal scan. We created two machine learning models: a deep learning model using the radiotracer concentration versus time data from the kidney of interest and a random forest model created using clinical data. The performance of the models was assessed using measures of diagnostic accuracy. RESULTS: We identified 152 eligible patients with available images of which 62 were cases and 90 were controls. The deep learning model predicted future renal complications with an overall accuracy of 73% (95% confidence inteveral [CI] 68-76%) and an AUC of 0.78 (95% CI 0.7, 0.84). The random forest model had an accuracy of 62% (95% CI 60-66%) and an AUC of 0.67 (95% CI. 0 64, 0.72) DISCUSSION: Our deep learning model predicted patients at high risk of developing renal complications following an equivocal renal scan and discriminate those at low risk with moderately high accuracy (73%). The deep learning model outperformed the clinical model built from clinical features classically used by urologists for surgical decision making. CONCLUSION: Our models have the potential to influence clinical decision making by providing supplemental analytical data from MAG3 scans that would not otherwise be available to urologists. Future multi-institutional retrospective and prospective trials are needed to validate our model.

Why are pediatric urologists unable to predict renal deterioration using urodynamics? A focused narrative review of the shortcomings of the literature.

In this focused narrative review we set out to review the current literature addressing the utilization of UDS in patients with spina bifida (SB). We specifically analyzed 6 urodynamic parameters and their roles as predictors of upper tract deterioration in pediatric SB patients. The material available did not allow a systematic analysis or the usage of metanalysis methodology, due to the predominance of small retrospective cohorts, and high heterogeneity. We identified 10 retrospective chart reviews that met our study criteria. The results of each of these papers, as well as other studies deemed relevant to the discussion, are included in our narrative review of the literature. We summarize the current literature, offer explanations for divergences in opinion, and identify future research directions and emerging solutions with a focus on machine learning.

Urologic care and progression to end-stage kidney disease: a Chronic Kidney Disease in Children (CKiD) nested case-control study.

INTRODUCTION: Children with chronic kidney disease (CKD) risk progressing to end-stage kidney disease (ESKD). The majority of CKD causes in children are related to congenital anomalies of the kidney and urinary tract, which may be treated by urologic care. OBJECTIVE: To examine the association of ESKD with urologic care in children with CKD. STUDY DESIGN: This was a nested case-control study within the Chronic Kidney Disease in Children (CKiD) prospective cohort study that included children aged 1-16 years with non-glomerular causes of CKD. The primary exposure was prior urologic referral with or without surgical intervention. Incidence density sampling matched each case of ESKD to up to three controls on duration of time from CKD onset, sex, race, age at baseline visit, and history of low birth weight. Conditional logistic regression analysis was performed to estimate rate ratios (RRs) for the incidence of ESKD. RESULTS: Sixty-six cases of ESKD were matched to 153 controls. Median age at baseline study visit was 12 years; 67% were male, and 7% were black. Median follow-up time from CKD onset was 14.9 years. Seventy percent received urologic care, including 100% of obstructive uropathy and 96% of reflux nephropathy diagnoses. Cases had worse renal function at their baseline visit and were less likely to have received prior urologic care. After adjusting for income, education, and insurance status, urology referral with surgery was associated with 50% lower risk of ESKD (RR 0.50 [95% confidence interval [CI] 0.26-0.997), compared to no prior urologic care (Figure). After excluding obstructive uropathy and reflux nephropathy diagnoses, which were highly correlated with urologic surgery, the association was attenuated (RR 0.72, 95% CI 0.24-2.18). DISCUSSION: In this study, urologic care was commonly but not uniformly provided to children with non-glomerular causes of CKD. Underlying specific diagnoses play an important role in both the risk of ESKD and potential benefits of urologic surgery. CONCLUSION: Within the CKiD cohort, children with non-glomerular causes of CKD often received urologic care. Urology referral with surgery was associated with lower risk of ESKD compared to no prior urologic care but depended on specific underlying diagnoses.

Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features.

INTRODUCTION: Anatomic characteristics of kidneys derived from ultrasound images are potential biomarkers of children with congenital abnormalities of the kidney and urinary tract (CAKUT), but current methods are limited by the lack of automated processes that accurately classify diseased and normal kidneys. OBJECTIVE: The objective of the study was to evaluate the diagnostic performance of deep transfer learning techniques to classify kidneys of normal children and those with CAKUT. STUDY DESIGN: A transfer learning method was developed to extract features of kidneys from ultrasound images obtained during routine clinical care of 50 children with CAKUT and 50 controls. To classify diseased and normal kidneys, support vector machine classifiers were built on the extracted features using (1) transfer learning imaging features from a pretrained deep learning model, (2) conventional imaging features, and (3) their combination. These classifiers were compared, and their diagnosis performance was measured using area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity. RESULTS: The AUC for classifiers built on the combination features were 0.92, 0.88, and 0.92 for discriminating the left, right, and bilateral abnormal kidney scans from controls with classification rates of 84%, 81%, and 87%; specificity of 84%, 74%, and 88%; and sensitivity of 85%, 88%, and 86%, respectively. These classifiers performed better than classifiers built on either the transfer learning features or the conventional features alone (p < 0.001). DISCUSSION: The present study validated transfer learning techniques for imaging feature extraction of ultrasound images to build classifiers for distinguishing children with CAKUT from controls. The experiments have demonstrated that the classifiers built on the transfer learning features and conventional image features could distinguish abnormal kidney images from controls with AUCs greater than 0.88, indicating that classification of ultrasound kidney scans has a great potential to aid kidney disease diagnosis. A limitation of the present study is the moderate number of patients that contributed data to the transfer learning approach. CONCLUSIONS: The combination of transfer learning and conventional imaging features yielded the best classification performance for distinguishing children with CAKUT from controls based on ultrasound images of kidneys.

PSA screening: determinants of primary-care physician practice patterns.

BACKGROUND: The effect of practice guidelines and the European Randomised Screening for Prostate Cancer (ERSPC) and Prostate, Lung, Colorectal and Ovarian (PLCO) trials on PSA screening practices of primary-care physicians (PCPs) is unknown. METHODS: We conducted a national cross-sectional on-line survey of a random sample of 3010 PCPs from July to August 2010. Participants were queried about their knowledge of prostate cancer, PSA screening guidelines, the ERSPC and PLCO trials, and about their PSA screening practices. Factors associated with PSA screening were identified using multivariable linear regression. RESULTS: A total of 152 (5%) participants opened and 89 completed the on-line survey, yielding a response rate of 58% for those that viewed the invitation. Eighty percent of respondents correctly identified prostate cancer risk factors. In all, 51% and 64% reported that they discuss and order PSA screening for men aged 50-75 years, respectively. Fifty-four percent were most influenced by the US Preventative Services Task Force (USPSTF) guidelines. Also, 21% and 28% of respondents stated that their PSA screening practices were influenced by the ERSPC and PLCO trials, respectively. Medical specialty was the only variable associated with propensity to screen, with family medicine physicians more likely to use PSA screening than internists (ß=0.21, P=0.02). CONCLUSIONS: Half of the physicians surveyed did not routinely discuss PSA screening with eligible patients. The impact of the ERSPC and PLCO trials on PSA screening practices was low among US PCPs. USPSTF recommendations for PSA screening continue to be the strongest influence on PCPs' propensity to use PSA screening.