Purpose: The manual delineation of organs at risk is a process that requires a great deal of time both for the technician and for the physician. Availability of validated software tools assisted by artificial intelligence would be of great benefit, as it would significantly improve the radiation therapy workflow, reducing the time required for segmentation. The purpose of this article is to validate the deep learning-based autocontouring solution integrated in syngo.
View Article and Find Full Text PDFPurpose: Design and evaluate a knowledge-based model using commercially available artificial intelligence tools for automated treatment planning to efficiently generate clinically acceptable hippocampal avoidance prophylactic cranial irradiation (HA-PCI) plans in patients with small-cell lung cancer.
Materials And Methods: Data from 44 patients with different grades of head flexion (range 45°) were used as the training datasets. A Rapid Plan knowledge-based planning (KB) routine was applied for a prescription of 25 Gy in 10 fractions using two volumetric modulated arc therapy (VMAT) arcs.
Measurements were taken with the Exradin A20 (Standard Imaging) ionisation chamber, and the 'homemade' MARM phantom was made with the 3D Ultimaker 2+ printer using PLA material. The material used for validation was ABS Medical from Smart Materials 3D. The irradiation was undertaken with aIr source by means of Varian's GammaMed Plus iX HDR equipment.
View Article and Find Full Text PDFPurpose: To evaluate the efficacy and safety of lung low-dose radiation therapy (LD-RT) for pneumonia in patients with coronavirus disease 2019 (COVID-19).
Materials And Methods: Inclusion criteria comprised patients with COVID-19-related moderate-severe pneumonia warranting hospitalization with supplemental O and not candidates for admission to the intensive care unit because of comorbidities or general status. All patients received single lung dose of 0.