A code orange for traffic-light-protocols as a communication mechanism in IGRT.

Introduction: Traffic-light protocols (TLPs) use color codes to standardize image registration and improve interdisciplinary communication in IGRT. Generally, green indicates no relevant anatomical changes, orange signals changes requiring follow-up but does not compromise the current fraction, and red flags unacceptable changes. This study examines the communication aspect, specifically the reporting accuracy for locally advanced non-small-cell lung cancer (LA-NSCLC), and identifies barriers to reporting.

Post-irradiation darkening model for EBT-3 films characterized using a single lot calibration approach.

In this study, we present a model to correct the progressive post-irradiation darkening of EBT3 films. The model allows for a clinical use of EBT3 using application and calibration films scanned with different post-irradiation times.The model is a post-irradiation time- and dose-dependent power-law function, projecting the scanned transmittance of application films to the transmittance matching the same post-irradiation time of calibration films.

Safety and efficiency of a fully automatic workflow for auto-segmentation in radiotherapy using three commercially available deep learning-based applications.

Advancements in radiotherapy auto-segmentation necessitate reliable and efficient workflows. Therefore, a standardized fully automatic workflow was developed for three commercially available deep learning-based auto-segmentation applications and compared to a manual workflow for safety and efficiency. The workflow underwent safety evaluation with failure mode and effects analysis.

Optimizing volumetric modulated arc therapy prostate planning using an automated Fine-Tuning process through dynamic adjustment of optimization parameters.

In radiotherapy treatment planning, optimization is essential for achieving the most favorable plan by adjusting optimization criteria. This study introduced an innovative approach to automatically fine-tune optimization parameters for volumetric modulated arc therapy prostate planning, ensuring all constraints were met. A knowledge-based planning model was invoked, and the fine-tuning process was applied through an in-house developed script.

New optically stimulated luminescence dosimetry film optimized for energy dependence guided by Monte Carlo simulations.

Optically stimulated luminescence (OSL) film dosimeters, based on BaFBr:Euphosphor material, have major dosimetric advantages such as dose linearity, high spatial resolution, film re-usability, and immediate film readout. However, they exhibit an energy-dependent over-response at low photon energies because they are not made of tissue-equivalent materials. In this work, the OSL energy-dependent response was optimized by lowering the phosphor grain size and seeking an optimal choice of phosphor concentration and film thickness to achieve sufficient signal sensitivity.

Are offline ART decisions for NSCLC impacted by the type of dose calculation algorithm?

Introduction: Decisions for plan-adaptations may be impacted by a transitioning from one dose-calculation algorithm to another. This study examines the impact on dosimetric-triggered offline adaptation in LA-NSCLC in the context of a transition from superposition/convolution dose calculation algorithm (Type-B) to linear Boltzmann equation solver dose calculation algorithms (Type-C).

Materials & Methods: Two dosimetric-triggered offline adaptive treatment workflows are compared in a retrospective planning study on 30 LA-NSCLC patients.

Deep learning based MLC aperture and monitor unit prediction as a warm start for breast VMAT optimisation.

. Automated treatment planning today is focussed on non-exact, two-step procedures. Firstly, dose-volume histograms (DVHs) or 3D dose distributions are predicted from the patient anatomy.

The NCS code of practice for the quality assurance of treatment planning systems (NCS-35).

A subcommittee of the Netherlands Commission on Radiation Dosimetry (NCS) was initiated in 2018 with the task to update and extend a previous publication (NCS-15) on the quality assurance of treatment planning systems (TPS) (Bruinvis2005). The field of treatment planning has changed considerably since 2005. Whereas the focus of the previous report was more on the technical aspects of the TPS, the scope of this report is broader with a focus on a department wide implementation of the TPS.

Quality assurance and safety of hippocampal avoidance prophylactic cranial irradiation in the multicenter randomized phase III trial (NCT01780675).

Objective: NCT01780675, a multicenter randomized phase III trial of prophylactic cranial irradiation (PCI) versus PCI with hippocampal sparing in small cell lung cancer (SCLC) investigated neurocognitive decline and safety. As part of quality assurance, we evaluated if hippocampal avoidance (HA)-PCI was performed according to the NCT01780675 trial protocol instructions, and performed a safety analysis to study the incidence and location of brain metastases for patients treated with HA-PCI.

Methods: This retrospective analysis evaluated the quality of the irradiation given in the randomized controlled trial (RCT) comparing SCLC patients receiving PCI with or without hippocampal avoidance, using intensity modulated radiotherapy (IMRT) or volumetric modulated arc therapy (VMAT).

Calibration and time fading characterization of a new optically stimulated luminescence film dosimeter.

Background: Optically stimulated luminescence (OSL) dosimeters produce a signal linear to the dose, which fades with time due to the spontaneous recombination of energetically unstable electron/hole traps. When used for radiotherapy (RT) applications, fading affects the signal-to-dose conversion and causes an error in the final dose measurement. Moreover, the signal fading depends to some extent on treatment-specific irradiation conditions such as irradiation times.

Treatment plan prediction for lung IMRT using deep learning based fluence map generation.

Purpose: Recently, it has been shown that automated treatment planning can be executed by direct fluence prediction from patient anatomy using convolutional neural networks. Proof of principle publications utilise a fixed dose prescription and fixed collimator (0°) and gantry angles. The goal of this work is to further develop these principles for the challenging lung cancer indication with variable dose prescriptions, collimator and gantry angles.

Clinical evaluation of a deep learning model for segmentation of target volumes in breast cancer radiotherapy.

Purpose/objective(s): Precise segmentation of clinical target volumes (CTV) in breast cancer is indispensable for state-of-the art radiotherapy. Despite international guidelines, significant intra- and interobserver variability exists, negatively impacting treatment outcomes. The aim of this study is to evaluate the performance and efficiency of segmentation of CTVs in planning CT images of breast cancer patients using a 3D convolutional neural network (CNN) compared to the manual process.

Experimental investigation of dynamic real-time rotation-including dose reconstruction during prostate tracking radiotherapy.

Background: Hypofractionation in prostate radiotherapy is of increasing interest. Steep dose gradients and a large weight on each individual fraction emphasize the need for motion management. Real-time motion management techniques such as multileaf collimator (MLC) tracking or couch tracking typically adjust for translational motion while rotations remain uncompensated with unknown dosimetric impact.

Automated treatment planning of prostate stereotactic body radiotherapy with focal boosting on a fast-rotating O-ring linac: Plan quality comparison with C-arm linacs.

Purpose: The integration of auto-segmentation and automated treatment planning methods on a fast-rotating O-ring linac may improve the time efficiency of online adaptive radiotherapy workflows. This study investigates whether automated treatment planning of prostate SBRT with focal boosting on the O-ring linac could generate plans that are of similar quality as those obtained through manual planning on clinical C-arm linacs.

Methods: For 20 men with prostate cancer, reference treatment plans were generated on a TrueBeam STx C-arm linac with HD120 MLC and a TrueBeam C-arm linac with Millennium 120 MLC using 6 MV flattened dual arc VMAT.

Expanding the medical physicist curricular and professional programme to include Artificial Intelligence.

Purpose: To provide a guideline curriculum related to Artificial Intelligence (AI), for the education and training of European Medical Physicists (MPs).

Materials And Methods: The proposed curriculum consists of two levels: Basic (introducing MPs to the pillars of knowledge, development and applications of AI, in the context of medical imaging and radiation therapy) and Advanced. Both are common to the subspecialties (diagnostic and interventional radiology, nuclear medicine, and radiation oncology).

Modulating ultrasound contrast generation from injectable nanodroplets for proton range verification by varying the degree of superheat.

Purpose: Despite the physical benefits of protons over conventional photon radiation in cancer treatment, range uncertainties impede the ability to harness the full potential of proton therapy. While monitoring the proton range in vivo could reduce the currently adopted safety margins, a routinely applicable range verification technique is still lacking. Recently, phase-change nanodroplets were proposed for proton range verification, demonstrating a reproducible relationship between the proton range and generated ultrasound contrast after radiation-induced vaporization at 25°C.

Machine learning applications in radiation oncology: Current use and needs to support clinical implementation.

Background And Purpose: The use of artificial intelligence (AI)/ machine learning (ML) applications in radiation oncology is emerging, however no clear guidelines on commissioning of ML-based applications exist. The purpose of this study was therefore to investigate the current use and needs to support implementation of ML-based applications in routine clinical practice.

Materials And Methods: A survey was conducted among medical physicists in radiation oncology, consisting of four parts: clinical applications (1), model training, acceptance and commissioning (2), quality assurance (QA) in clinical practice and General Data Protection Regulation (GDPR) (3), and need for education and guidelines (4).

Six degrees of freedom dynamic motion-including dose reconstruction in a commercial treatment planning system.

Purpose: Intrafractional motion during radiotherapy delivery can deteriorate the delivered dose. Dynamic rotational motion of up to 38 degrees has been reported during prostate cancer radiotherapy, but methods to determine the dosimetric consequences of such rotations are lacking. Here, we create and experimentally validate a dose reconstruction method that accounts for dynamic rotations and translations in a commercial treatment planning system (TPS).

Dosimetric impact of intrafraction prostate rotation and accuracy of gating, multi-leaf collimator tracking and couch tracking to manage rotation: An end-to-end validation using volumetric film measurements.

Background And Purpose: Both gating and tracking can mitigate the deteriorating dosimetric impact of intrafraction translation during prostate stereotactic body radiotherapy (SBRT). However, their ability to manage intrafraction rotation has not yet been thoroughly investigated. The dosimetric accuracy of gating, MLC tracking and couch tracking to manage intrafraction prostate rotation was investigated.

Radiotherapy Treatment plannINg study Guidelines (RATING): A framework for setting up and reporting on scientific treatment planning studies.

Radiotherapy treatment planning studies contribute significantly  to advances and improvements in radiation treatment of cancer patients. They are a pivotal step to support and facilitate the introduction of novel techniques into clinical practice, or as a first step before clinical trials can be carried out. There have been numerous examples published in the literature that demonstrated the feasibility of such techniques as IMRT, VMAT, IMPT, or that compared different treatment methods (e.

Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance.

Artificial Intelligence (AI) is currently being introduced into different domains, including medicine. Specifically in radiation oncology, machine learning models allow automation and optimization of the workflow. A lack of knowledge and interpretation of these AI models can hold back wide-spread and full deployment into clinical practice.

Extended field radiotherapy measurements in a single shot using a BaFBr-based OSL-film.

This work evaluated the use of a class solution specific calibration for an extra-large BaFBr-based optically stimulated luminescence film (OSL; 43  ×  35 cm; Z   =  4.55). The clinical need for such large dosimeters follows from the increased use of extended-field radiation therapy (EFRT).