Purpose: The aim of this study was a retrospective dosimetric comparison of iridium-192 (Ir) high-dose-rate (HDR) interstitial brachytherapy plans using model-based dose calculation algorithm (MBDCA) following TG-186 recommendations and TG-43 dosimetry protocol for breast, head-and-neck, and lung patient cohorts, with various treatment concepts and prescriptions.
Material And Methods: In this study, 59 interstitial Ir HDR brachytherapy cases treated in our center (22 breast, 22 head and neck, and 15 lung) were retrospectively selected and re-calculated with TG-43 dosimetry protocol as well as with Acuros BV dose calculation algorithm, with dose to medium option based on computed tomography images. Treatment planning dose volume parameter differences were determined and their significance was assessed.
This retrospective, multi-centered study aimed to improve high-quality radiation treatment (RT) planning workflows by training and testing a Convolutional Neural Network (CNN) to perform auto segmentations of organs at risk (OAR) for prostate cancer (PCa) patients, specifically the bladder and rectum. The objective of this project was to develop a clinically applicable and robust artificial intelligence (AI) system to assist radiation oncologists in OAR segmentation. The CNN was trained using manual contours in CT-datasets from diagnostic Ga-PSMA-PET/CTs by a student, then validated (n = 30, PET/CTs) and tested (n = 16, planning CTs).
View Article and Find Full Text PDFLipid membranes are key to the nanoscale compartmentalization of biological systems, but fluorescent visualization of them in intact tissues, with nanoscale precision, is challenging to do with high labeling density. Here, we report ultrastructural membrane expansion microscopy (umExM), which combines a novel membrane label and optimized expansion microscopy protocol, to support dense labeling of membranes in tissues for nanoscale visualization. We validated the high signal-to-background ratio, and uniformity and continuity, of umExM membrane labeling in brain slices, which supported the imaging of membranes and proteins at a resolution of ~60 nm on a confocal microscope.
View Article and Find Full Text PDFPurpose/objectives: Auto-segmentation with artificial intelligence (AI) offers an opportunity to reduce inter- and intra-observer variability in contouring, to improve the quality of contours, as well as to reduce the time taken to conduct this manual task. In this work we benchmark the AI auto-segmentation contours produced by five commercial vendors against a common dataset.
Methods And Materials: The organ at risk (OAR) contours generated by five commercial AI auto-segmentation solutions (Mirada (Mir), MVision (MV), Radformation (Rad), RayStation (Ray) and TheraPanacea (Ther)) were compared to manually-drawn expert contours from 20 breast, 20 head and neck, 20 lung and 20 prostate patients.