Electromagnetic tracking for integrated quality assurance in breast interstitial brachytherapy: The impact of registration method.

Purpose: Electromagnetic tracking (EMT) has great potential as a quality assurance tool in interstitial brachytherapy. Since its clinical application in most cases comprises a comparison with brachytherapy plan data, EMT registration and plan data are crucial. Registration uncertainties influence EMT outcomes and further decision-making processes.

Investigating the effects of table movement and sag on optical surrogate-driven respiratory-guided computed tomography.

Purpose: Respiratory-guided computed tomography (CT) typically employs breathing motion surrogates to feed image reconstruction or visual breathing coaching. Our study aimed to assess the impact of table movements and table sag on the breathing curves recorded in four-dimensional (4D) CT and deep-inspiration breath-hold (DIBH) CT.

Methods: For breathing curve measurements, static and dynamic phantom scenarios were used.

The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning.

Background: Promptable foundation auto-segmentation models like Segment Anything (SA, Meta AI, New York, USA) represent a novel class of universal deep learning auto-segmentation models that could be employed for interactive tumor auto-contouring in RT treatment planning.

Methods: Segment Anything was evaluated in an interactive point-to-mask auto-segmentation task for glioma brain tumor auto-contouring in 16,744 transverse slices from 369 MRI datasets (BraTS 2020 dataset). Up to nine interactive point prompts were automatically placed per slice.

Comprehensive multimodal deep learning survival prediction enabled by a transformer architecture: A multicenter study in glioblastoma.

Background: This research aims to improve glioblastoma survival prediction by integrating MR images, clinical, and molecular-pathologic data in a transformer-based deep learning model, addressing data heterogeneity and performance generalizability.

Methods: We propose and evaluate a transformer-based nonlinear and nonproportional survival prediction model. The model employs self-supervised learning techniques to effectively encode the high-dimensional MRI input for integration with nonimaging data using cross-attention.

Principles of artificial intelligence in radiooncology.

Purpose: In the rapidly expanding field of artificial intelligence (AI) there is a wealth of literature detailing the myriad applications of AI, particularly in the realm of deep learning. However, a review that elucidates the technical principles of deep learning as relevant to radiation oncology in an easily understandable manner is still notably lacking. This paper aims to fill this gap by providing a comprehensive guide to the principles of deep learning that is specifically tailored toward radiation oncology.

Improving the safety of radiotherapy treatment processes via incident-driven FMEA feedback loops.

Background: Failure mode and effects analysis (FMEA) is a valuable tool for radiotherapy risk assessment, yet its outputs might be unreliable due to failures not being identified or due to a lack of accurate error rates.

Purpose: A novel incident reporting system (IRS) linked to an FMEA database was tested and evaluated. The study investigated whether the system was suitable for validating a previously performed analysis and whether it could provide accurate error rates to support the expert occurrence ratings of previously identified failure modes.

Initial needle tracking with the first standalone combined infrared camera - CT system for brachytherapy-analysis of tracking accuracy and uncertainties.

Purpose: A prototype infrared camera - cone-beam computed tomography (CBCT) system for tracking in brachytherapy has recently been developed. We evaluated for the first time the corresponding tracking accuracy and uncertainties, and implemented a tracking-based prediction of needles on CBCT scans.

Methods: A marker tool rigidly attached to needles was 3D printed.

Inter-observer effects in needle reconstruction for temporary prostate brachytherapy: Dosimetric implications and adaptive CBCT-TRUS registration solutions.

Purpose: To investigate geometric and dosimetric inter-observer variability in needle reconstruction for temporary prostate brachytherapy. To assess the potential of registrations between transrectal ultrasound (TRUS) and cone-beam computed tomography (CBCT) to support implant reconstructions.

Methods And Materials: The needles implanted in 28 patients were reconstructed on TRUS by three physicists.

Optimized raw data selection for artifact reduction of breathing controlled four-dimensional sequence scanning.

Background And Purpose: Even with most breathing-controlled four-dimensional computed tomography (4DCT) algorithms image artifacts caused by single significant longer breathing still occur, resulting in negative consequences for radiotherapy. Our study presents first phantom examinations of a new optimized raw data selection and binning algorithm, aiming to improve image quality and geometric accuracy without additional dose exposure.

Materials And Methods: To validate the new approach, phantom measurements were performed to assess geometric accuracy (volume fidelity, root mean square error, Dice coefficient of volume overlap) for one- and three-dimensional tumor motion trajectories with and without considering motion hysteresis effects.

First implementation of an innovative infra-red camera system integrated into a mobile CBCT scanner for applicator tracking in brachytherapy-Initial performance characterization.

Purpose: To enable a real-time applicator guidance for brachytherapy, we used for the first time infra-red tracking cameras (OptiTrack, USA) integrated into a mobile cone-beam computed tomography (CBCT) scanner (medPhoton, Austria). We provide the first description of this prototype and its performance evaluation.

Methods: We performed assessments of camera calibration and camera-CBCT registration using a geometric calibration phantom.

"sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy.

Background: Radiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images are used for RT treatment planning whereas magnetic resonance imaging (MRI) images are used for tumor delineation. Therefore, MRI and CT need to be registered, which is an error prone process.

A comprehensive quality assurance protocol for electromagnetic tracking in brachytherapy.

Background: Electromagnetic tracking (EMT) systems have proven to be a valuable source of information regarding the location and geometry of applicators in patients undergoing brachytherapy (BT). As an important element of an enhanced and individualized pre-treatment verification, EMT can play a pivotal role in detecting treatment errors and uncertainties to increase patient safety.

Purpose: The purpose of this study is two-fold: to design, develop and test a dedicated measurement protocol for the use of EMT-enabled afterloaders in BT and to collect and compare the data acquired from three different radiation oncology centers in different clinical environments.

Process failure mode and effects analysis for external beam radiotherapy: Introducing a literature-based template and a novel action priority.

Purpose: The first aim of the study was to create a general template for analyzing potential failures in external beam radiotherapy, EBRT, using the process failure mode and effects analysis (PFMEA). The second aim was to modify the action priority (AP), a novel prioritization method originally introduced by the Automotive Industry Action Group (AIAG), to work with different severity, occurrence, and detection rating systems used in radiation oncology.

Methods And Materials: The AIAG PFMEA approach was employed in combination with an extensive literature survey to develop the EBRT-PFMEA template.

Quality requirements for MRI simulation in cranial stereotactic radiotherapy: a guideline from the German Taskforce "Imaging in Stereotactic Radiotherapy".

Accurate Magnetic Resonance Imaging (MRI) simulation is fundamental for high-precision stereotactic radiosurgery and fractionated stereotactic radiotherapy, collectively referred to as stereotactic radiotherapy (SRT), to deliver doses of high biological effectiveness to well-defined cranial targets. Multiple MRI hardware related factors as well as scanner configuration and sequence protocol parameters can affect the imaging accuracy and need to be optimized for the special purpose of radiotherapy treatment planning. MRI simulation for SRT is possible for different organizational environments including patient referral for imaging as well as dedicated MRI simulation in the radiotherapy department but require radiotherapy-optimized MRI protocols and defined quality standards to ensure geometrically accurate images that form an impeccable foundation for treatment planning.

Toward a deep learning-based magnetic resonance imaging only workflow for postimplant dosimetry in I-125 seed brachytherapy for prostate cancer.

Background And Purpose: The current standard imaging-technique for creating postplans in seed prostate brachytherapy is computed tomography (CT), that is associated with additional radiation exposure and poor soft tissue contrast. To establish a magnetic resonance imaging (MRI) only workflow combining improved tissue contrast and high seed detectability, a deep learning-approach for automatic seed segmentation on MRI-scans was developed.

Material And Methods: Patients treated with I-125 seed brachytherapy received a postplan-CT and a 1.

Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for ai-assisted medical education and decision making in radiation oncology.

Purpose: The potential of large language models in medicine for education and decision-making purposes has been demonstrated as they have achieved decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. This work aims to evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology.

Methods: The 38th American College of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red Journal Gray Zone cases are used to benchmark the performance of ChatGPT-4.

Deep Learning and Registration-Based Mapping for Analyzing the Distribution of Nodal Metastases in Head and Neck Cancer Cohorts: Informing Optimal Radiotherapy Target Volume Design.

We introduce a deep-learning- and a registration-based method for automatically analyzing the spatial distribution of nodal metastases (LNs) in head and neck (H/N) cancer cohorts to inform radiotherapy (RT) target volume design. The two methods are evaluated in a cohort of 193 H/N patients/planning CTs with a total of 449 LNs. In the deep learning method, a previously developed nnU-Net 3D/2D ensemble model is used to autosegment 20 H/N levels, with each LN subsequently being algorithmically assigned to the closest-level autosegmentation.

Plan robustness analysis for threshold determination of SGRT-based intrafraction motion control in 3DCRT breast cancer radiation therapy.

Purpose: The goal of this study was to obtain maximum allowed shift deviations from planning position in six degrees of freedom (DOF), that can serve as threshold values in surface guided radiation therapy (SGRT) of breast cancer patients.

Methods: The robustness of conformal treatment plans of 50 breast cancer patients against 6DOF shifts was investigated. For that, new dose distributions were calculated on shifted computed tomography scans and evaluated with respect to target volume and spinal cord dose.

Investigating the impact of breast positioning control on physical treatment parameters in multi-catheter breast brachytherapy.

Purpose: To assess the effects of a workflow for reproducible patient and breast positioning on implant stability during high-dose-rate multi-catheter breast brachytherapy.

Methods: Thirty patients were treated with our new positioning control workflow. Implant stability was evaluated based on a comparison of planning-CTs to control-CTs acquired halfway through the treatment.

Development and clinical implementation of a digital system for risk assessments for radiation therapy.

Before introducing new treatment techniques, an investigation of hazards due to unintentional radiation exposures is a reasonable activity for proactively increasing patient safety. As dedicated software is scarce, we developed a tool for risk assessment to design a quality management program based on best practice methods, i.e.

Estimating follow-up CTs from geometric deformations of catheter implants in interstitial breast brachytherapy: A feasibility study using electromagnetic tracking.

Background: Electromagnetic tracking (EMT) systems have been shown to provide valuable information on the geometry of catheter implants in breast cancer patients undergoing interstitial brachytherapy (iBT). In the context of an extended patient-specific, pre-treatment verification, EMT can play a key role in determining the potential need and, if applicable, the appropriate time for treatment adaptation. To detect dosimetric shortcomings the relative position between catheters, and target volume and critical structures must be known.