No abstract

Intensity modulated particle therapy (IMPT) with carbon ions can generate highly conformal treatment plans; however, IMPT is limited in robustness against range and positioning uncertainty. This is particularly true for moving targets, even though all motion states of a 4DCT are considered in 4D-IMPT. Here, we expand 4D-IMPT to include robust non-linear RBE-weighted optimization to explore its potential in improving plan robustness and sparing critical organs. In this study, robust 4D-optimization—based on worst-case optimization on 9 scenarios—was compared to conventional 4D-optimization with PTV margins using 4D dose calculation and robustness analysis for 21 uncertainty scenarios. Slice-by-slice rescanning was used for motion mitigation. Both 4D-optimization strategies were tested on a cohort of 8 multi-lesion lung cancer patients with the goal of prioritizing OAR sparing in a hypofractionated treatment plan. Planning objectives were to keep the OAR volume doses below corresponding limits while simultaneously achieve CTV coverage with D95% ≥ 95 %. For the conventional plans, average D95% was at 98.7% which fulfilled the target objective in 83.2% of scenarios. For the robust plans, average D95% was reduced to 97.6% which still fulfilled the target objective in 80.7% of cases, but led to significantly improved overall OAR sparing: Volume doses were below the limits in 96.2% of cases for the conventional and 99.5% for the robust plans. When considering the particularly critical smaller airways only, fulfillment rates could be increased from 76.2% to 96% for the robust plans. This study has shown that plan robustness of 4D-IMPT could be improved by using robust 4D-optimization, offering greater control over uncertainties in the actual delivered dose. In some cases, this required sacrificing target coverage for the benefit of better OAR sparing.

BackgroundQuality management and safety are integral to modern radiotherapy. New radiotherapy technologies require new consensus guidelines on quality and safety. Established analysis strategies, such as the failure modes and effects analysis (FMEA) and incident learning systems have been developed as tools to assess the safety of several types of radiation therapies. An extensive literature documents the widespread application of risk analysis methods to photon radiation therapy. Relatively little attention has been paid to performing risk analyses of nascent radiation therapy systems to treat moving tumors with scanned heavy ion beams. The purpose of this study was to apply a comprehensive safety analysis strategy to a motion-synchronized dose delivery system (M-DDS) for ion therapy.MethodsWe applied a risk analysis method to new treatment planning and treatment delivery processes with scanned heavy ion beams. The processes utilize a prototype, modular dose delivery system, currently undergoing preclinical testing, that provides new capabilities for treating moving anatomy. Each step in the treatment process was listed in a process map, potential errors for each step were identified and scored using the risk probability number in an FMEA, and the possible causes of each error were described in a fault tree analysis. Solutions were identified to mitigate the risk of these errors, including permanent corrective actions, periodic quality assurance (QA) tests, and patient specific QA (PSQA) tests. Each solution was tested experimentally.ResultsThe analysis revealed 58 potential errors that could compromise beam delivery quality or safety. Each of the 14 binary (pass-or-fail) tests passed. Each of the nine QA and four PSQA tests were within anticipated clinical specifications. The modular M-DDS was modified accordingly, and was found to function at two centers.ConclusionWe have applied a comprehensive risk analysis strategy to the M-DDS and shown that it is a clinically viable motion mitigation strategy. The described strategy can be utilized at any ion therapy center that operates with the modular M-DDS. The approach can also be adapted for use at other facilities and can be combined with existing safety analysis systems.

This article describes the updated GSI radiotherapy research facility (Cave M) located at the GSI Helmholtz Center for Heavy Ion Research in Darmstadt, Germany. This facility was upgraded by modernizing the beamline that supported a pilot project in carbon ion cancer therapy in Europe from 1997 to 2008. Descriptions are provided of the modernized beamline, related hardware components and treatment delivery system. The performance specifications and general characteristics for each major component are described, along with example pre-clinical test results of selected components. These upgrades to Cave M allow for investigating novel therapy methods. The radiotherapy research facility is located on a beamline of the heavy ion synchrotron (Schwer-Ionen-Synchrotron, or SIS-18) accelerator complex, capable of delivering 0.1 to 2 GeV/u charged particle beams, ranging from protons to uranium. This beamline contains components for fast beam gating, aborting, focusing, scanning, monitoring, and shifting the range of the beam. The beam scanning magnets, position detectors, and beam monitors are described, along with tests of functionality and performance. A dose delivery system (DDS) was adapted from a clinical unit at the National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy, and consists of modular real-time hardware and software. The DDS was modified to enable research on adaptively-managed patient motion through the use of libraries of 4D-optimized radiation treatment plans, an unsolved problem of importance for treating moving tumors. The system is modular and is designed to support future research studies, such as high dose rate (Flash) radiotherapy and radioactive ion beams. A series of validation tests confirmed the functionality and performance of various key components and systems. For example, an end-to-end test revealed that dosimetric spatial homogeneity of over 95% was achieved for square treatment fields. More generally, all performance characteristics that were tested satisfied anticipated clinical requirements.

A lot of real-world phenomena are complex and cannot be captured by single task annotations. This causes a need for subsequent annotations, with interdependent questions and answers describing the nature of the subject at hand. Even in the case a phenomenon is easily captured by a single task, the high specialisation of most annotation tools can result in having to switch to another tool if the task only slightly changes. We introduce HU-MAN, a novel web-based annotation tool that addresses the above problems by a) covering a variety of annotation tasks on both textual and image data, and b) the usage of an internal deterministic state machine, allowing the researcher to chain different annotation tasks in an interdependent manner. Further, the modular nature of the tool makes it easy to define new annotation tasks and integrate machine learning algorithms e.g., for active learning. HUMAN comes with an easy-to-use graphical user interface that simplifies the annotation task and management.