Clinical Validation of Siemens' Syngo.via Automatic Contouring System.

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.

Machine learning-based automated planning for hippocampal avoidance prophylactic cranial irradiation.

Purpose: 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.

Intraoperative irradiation in breast cancer: preliminary results in 80 patients as partial breast irradiation or anticipated boost prior to hypo-fractionated whole breast irradiation.

Purpose: To present the first results of intraoperative irradiation (IORT) in breast cancer with a low-energy photon system used as partial breast irradiation (PBI) or as an anticipated boost before whole breast hypo-fractionated irradiation (IORT + WBI), concerning tolerance, side effects, quality of life, and patient-reported outcomes.

Materials And Methods: Eighty patients treated with an Intrabeam system of 50 kV X-rays received a 20 Gy dose intraoperatively were included. Moderate daily hypofractionation of 2.

Validation of 3D printing materials for high dose-rate brachytherapy using ionisation chamber and custom phantom.

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.

Determination of the optimal range for virtual monoenergetic images in dual-energy CT based on physical quality parameters.

Purpose: Virtual monoenergetic images (VMI) obtained from Dual-Energy Computed Tomography (DECT) with iodinated contrast are used in radiotherapy of the Head and Neck to improve the delineation of target volumes and organs at-risk (OAR). The energies used to vary from 40 to 70 keV, but noise at low keV and the use of Single Energy CT (SECT) at low kV settings may shrink this interval. There is no guide about how to find out the optimal range where VMI has a significant improvement related to SECT images.