Quality assurance of radiotherapy in the ongoing EORTC 22042–26042 trial for atypical and malignant meningioma: Results from the dummy runs and prospective individual case Reviews

BACKGROUND: The ongoing EORTC 22042–26042 trial evaluates the efficacy of high-dose radiotherapy (RT) in atypical/malignant meningioma. The results of the Dummy Run (DR) and prospective Individual Case Review (ICR) were analyzed in this Quality Assurance (QA) study. MATERIAL/METHODS: Institutions were requested to submit a protocol compliant treatment plan for the DR and ICR, respectively. DR-plans (n=12) and ICR-plans (n=50) were uploaded to the Image-Guided Therapy QA Center of Advanced Technology Consortium server (http://atc.wustl.edu/) and were assessed prospectively. RESULTS: Major deviations were observed in 25% (n=3) of DR-plans while no minor deviations were observed. Major and minor deviations were observed in 22% (n=11) and 10% (n=5) of the ICR-plans, respectively. Eighteen% of ICRs could not be analyzed prospectively, as a result of corrupted or late data submission. CTV to PTV margins were respected in all cases. Deviations were negatively associated with the number of submitted cases per institution (p=0.0013), with a cutoff of 5 patients per institutions. No association (p=0.12) was observed between DR and ICR results, suggesting that DR’s results did not predict for an improved QA process in accrued brain tumor patients. CONCLUSIONS: A substantial number of protocol deviations were observed in this prospective QA study. The number of cases accrued per institution was a significant determinant for protocol deviation. These data suggest that successful DR is not a guarantee for protocol compliance for accrued patients. Prospective ICRs should be performed to prevent protocol deviations

Adaptive Neural Architectures for Intuitive Robot Control

This thesis puts forward a novel way of control for robotic morphologies. Taking inspiration from Behaviour Based robotics and self-organisation principles, we present an interfacing mechanism, capable of adapting both to the user and the robot, while enabling a paradigm of intuitive control for the user. A transparent mechanism is presented, allowing for a seamless integration of control signals and robot behaviours. Instead of the user adapting to the interface and control paradigm, the proposed architecture allows the user to shape the control motifs in their way of preference, moving away from the cases where the user has to read and understand operation manuals or has to learn to operate a specific device. The seminal idea behind the work presented is the coupling of intuitive human behaviours with the dynamics of a machine in order to control and direct the machine dynamics. Starting from a tabula rasa basis, the architectures presented are able to identify control patterns (behaviours) for any given robotic morphology and successfully merge them with control signals from the user, regardless of the input device used. We provide a deep insight in the advantages of behaviour coupling, investigating the proposed system in detail, providing evidence for and quantifying emergent properties of the models proposed. The structural components of the interface are presented and assessed both individually and as a whole, as are inherent properties of the architectures. The proposed system is examined and tested both in vitro and in vivo, and is shown to work even in cases of complicated environments, as well as, complicated robotic morphologies. As a whole, this paradigm of control is found to highlight the potential for a change in the paradigm of robotic control, and a new level in the taxonomy of human in the loop systems

Radiation therapy quality assurance in clinical trials--Global Harmonisation Group

The need for a global forum on harmonisation of RTQA (Radiation Therapy (RT) Quality Assurance (QA)) within clinical trials thus became apparent. After initial discussions in Göteborg during ESTRO 27 in 2008 the Global Clinical Trials RTQA Harmonisation Group (GHG) was formally established in 2010

Effective Behavioural Dynamic Coupling through Echo State Networks

This work presents a novel approach and paradigm for the coupling of human and robot dynamics with respect to control. We present an adaptive system based on Reservoir Computing and Recurrent Neural Networks able to couple control signals and robotic behaviours. A supervised method is utilised for the training of the network together with an unsupervised method for the adaptation of the reservoir. The proposed method is tested and analysed using a public dataset, a set of dynamic gestures and a group of users under a scenario of robot navigation. First, the architecture is benchmarked and placed among the state of the art. Second, based on our dataset we provide an analysis for key properties of the architecture. We test and provide analysis on the variability of the lengths of the trained patterns, propagation of geometrical properties of the input signal, handling of transitions by the architecture and recognition of partial input signals. Based on the user testing scenarios, we test how the architecture responds to real scenarios and users. In conclusion, the synergistic approach that we follow shows a way forward towards human in-the-loop systems and the evidence provided establish its competitiveness with available methods, while the key properties analysed the merits of the approach to the commonly used ones. Finally, reflective remarks on the applicability and usage in other fields are discussed