Robotic Ultrasound and Novel Collagen Analyses for Polycystic Kidney Disease Research Using Mice.

Key Points: Robotic ultrasound performed favorably compared with magnetic resonance imaging for evaluating total kidney volume. Collagen evaluation by two novel methods of picrosirius red imaging were more informative than the standard method by brightfield imaging. Findings can improve research by increasing speed and access to total kidney volume determination and sensitivity of collagen assessment.

Semantic Instance Segmentation of Kidney Cysts in MR Images: A Fully Automated 3D Approach Developed Through Active Learning.

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.

Borderline Resectable and Locally Advanced Pancreatic Cancer: FDG PET/MRI and CT Tumor Metrics for Assessment of Pathologic Response to Neoadjuvant Therapy and Prediction of Survival.

Imaging biomarkers of response to neoadjuvant therapy (NAT) for pancreatic ductal adenocarcinoma (PDA) are needed to optimize treatment decisions and long-term outcomes. 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). This retrospective study included 44 patients with 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.

Automatic semantic segmentation of kidney cysts in MR images of patients affected by autosomal-dominant polycystic kidney disease.

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.

Automated total kidney volume measurements in pre-clinical magnetic resonance imaging for resourcing imaging data, annotations, and source code.

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.