Potretzke, Theodora A.

Key Points Automated cyst segmentation in autosomal dominant polycystic kidney disease could transform care by enabling precise monitoring of cyst progression with minimal manual effort. This technology can facilitate more consistent assessments of cyst burden across patients. Translating these segmentation models from magnetic resonance imaging to computed tomography is crucial for providing comprehensive, modality-independent evaluations. Background Total kidney and liver volumes are key image-based biomarkers to predict the severity of kidney and liver phenotype in autosomal dominant polycystic kidney disease (ADPKD). However, magnetic resonance (MR) imaging-based advanced biomarkers like total cyst number and cyst parenchyma surface area have been shown to more accurately assess cyst burden and improve the prediction of disease progression. The main aim of this study was to extend the calculation of advanced biomarkers to other imaging modalities; thus, we propose a fully automated model to segment kidney and liver cysts in computed tomography (CT) images. Methods Abdominal CTs of patients with ADPKD were gathered retrospectively between 2001 and 2018. A three-dimensional deep learning method using the nnU-Net architecture was trained to learn cyst edges-cores and the noncystic kidney/liver parenchyma. Separate segmentation models were trained for kidney cysts in contrast-enhanced CTs and liver cysts in noncontrast CTs using an active learning approach. Two experienced research fellows manually generated the reference standard segmentation, which were reviewed by an expert radiologist for accuracy. Results Two-hundred CT scans from 148 patients (mean age, 51.2±14.1 years; 48% male) were used for model training (80%) and testing (20%). In the test set, both models showed good agreement with the reference standard segmentations, similar to the agreement between two independent human readers (model versus reader: TCN kidney/liver r =0.96/0.97 and CPSA kidney r =0.98, inter-reader: TCN kidney/liver r =0.96/0.98 and CPSA kidney r =0.99). Conclusions Our study demonstrates that automated models can segment kidney and liver cysts accurately in CT scans of patients with ADPKD.

Radiologists play a key role in the management of renal masses, and it is important to understand the nonsurgical treatment options for solid renal masses, typical imaging findings after treatment, common complications, and methods to avoid pitfalls.

Background National guidelines recommend that waist circumference (WC) be measured in patients with a body mass index (BMI) 27–35 kg/m2. Unfortunately, perhaps due to logistical reasons, WC is seldom measured in clinical settings. Herein, we describe the performance of a self-operated waist measurement device (SOWMD) as a potential means to overcome barriers to measuring WC. Materials and methods Ten volunteers underwent WC measures by professionals and SOWMD on 5 separate days to assess the reproducibility and accuracy. We then compared SOWMD measures with CT-derived fat content by recruiting 81 patients scheduled for a diagnostic abdominal CT scan. Results There was no difference between professionally measured and SOWMD-measured WC; the intraindividual coefficient of variation over the 5 days was between 0.4% and 2.2%. The WC measured manually, by SOWMD and CT scan were highly correlated (r=0.90–0.92, all p<0.001). The minimal WC measured by SOWMD was a better predictor (r=0.81 for all patients, r=0.87 for men, both p<0.001) of CT-measured visceral adipose tissue volume than other approaches. The minimal WC measured by SOWMD was correlated with fasting plasma glucose (r=0.40, p<0.05), triglyceride (r=0.41, p<0.01) and high-density cholesterol (r=−0.49, p<0.001) concentrations. Conclusion SOWMD is a reproducible, accurate and convenient way to measure WC that can provide added value for health care providers when combined with BMI information.