Ramanathan, Sumana

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.

ABSTRACT Background Autosomal dominant polycystic kidney disease (ADPKD) is the most commonly inherited progressive kidney disease. Time-restricted eating (TRE) is a fasting regimen that restricts eating to a particular window (typically 8 hours/day), which could slow cyst growth based on preclinical models. Methods A 12-month, randomized, controlled, behavioral dietary intervention compared TRE with a control group given healthy eating advice without TRE (HE), without caloric restriction. Participants underwent baseline and 12-month measurements, including adherence (percentage of participants adhering to the 8-hour window; primary outcome), and MRI to determine height-adjusted total kidney volume (htTKV) and adiposity. Results Twenty-nine participants (23 females, mean standard ± deviation 48 ± 9 years) with a body mass index of 31.1±5 kg/m2 were randomized to TRE (n = 14) or HE (n = 15). Of the total participants, 71% (n = 10) of TRE and 87% (n = 13) of HE participants completed the intervention. The eating window was 9.6 ± 3.6 hours for TRE (60% achieving the 8-hour window) and 12.0 ± 2.0 for HE groups (P = .07). At month 12, both groups lost modest weight (−2.4 ± 6.4% and −3.6 ± 5.4% in the TRE and HE groups, respectively). Annual change in htTKV was 3.0 ± 8.5% and 4.6 ± 8.8% in the TRE and HE groups, respectively. Both change in weight (r = 0.67, P < .01) and change in visceral adiposity (r = 0.54, P < .01) were positively correlated with change in htTKV. Conclusion Both the TRE and HE group lost modest weight at 12 months. The targeted TRE adherence of ≥75% of participants was not achieved. Weight and adiposity loss may be more important drivers of kidney growth than the timing of eating.